Hey, and welcome back. I’m Laura, just a regular human and product marketer trying to make sense of the AI era without losing my mind.

You forwarded a chain email once. You accepted a Facebook poke you didn’t understand. Last month, you probably shared something an AI made. Same reflex, thirty years apart.

This is a walk through several eras of viral growth, from Hotmail to ChatGPT, told through the real companies that built each loop and the social behavior that made people share, invite, or join. By the end, you’ll have a short list of questions to ask about whatever growth channel you’re using right now: is this a spike, or does it actually retain? Who am I borrowing trust from? And what happens the day that trust runs out? Let go!

A few months ago I read a piece by called “Braindump on viral loops.” It lays out the clean math behind viral growth and walks through how eras have changed over time. If you work in growth, product, or marketing, it is worth reading on its own.

Reading it filled me with nostalgia. I’ve lived through most of these eras myself. First as an ordinary user, forwarding emails and accepting Facebook invites without thinking twice. Later as a product growth marketer, building the loop from the other side: an email invite system for a dating platform, a viral app on Facebook for a social network, or a referral program for a crypto exchange. Every loop Chen lists is also a memory in my career.

In this piece, instead of starting with the math, I start with the people. Each era here is tied to the social behavior that drove people to share, invite, or join in the first place. The question isn’t just how much a loop grew, but why humans, in that moment, wanted to pass something along at all.

The address book era (1996-2004)

The trust of an inbox, borrowed once, then gone for good.

In 1996, your inbox was a private place. There was no spam folder, and if something showed up there, a real person had sent it to you.

The story

Sabeer Bhatia and Jack Smith built Hotmail to check email from work without their boss reading it. Growth was slow until their investor, Tim Draper, suggested one line at the bottom of every email: “Get your free email at Hotmail.” The founders hesitated. It felt like slipping an ad into someone’s private letter. They shipped it anyway.

Growth exploded almost overnight. One million users in six months, twelve million in eighteen, at a time when the whole internet only had about 70 million people on it. One email from Bhatia to a friend in India led to 100,000 new signups there in three weeks.

The social behavior

We rarely check a message for what it says. We check who sent it. A stranger’s ad gets doubted. A friend’s message gets trusted without much thought. It’s a shortcut our brain uses, and most of the time we don’t even notice we’re using it.

It works best with people we barely know, like a coworker or a distant cousin. Close friends already know what we know. It’s the weak connections that carry news somewhere new.

How companies used it

Hotmail’s tagline used this shortcut directly. The friend never chose to recommend it. Their outbox did it for them, automatically, and the reader couldn’t tell the difference from a real endorsement. It rode on every email, not just the ones to close friends. That’s how it reached those weak, far-away connections, thousands of them firing at once into a country the product had never touched.

Other companies pushed the trick further.

One email about photos ended up costing Tagged 1.4 million dollars across three states. Tagged.com, launched in 2004, pushed it past the point people accepted. It emailed saying a friend had shared photos with you; signing up to see them took your contact list without clear permission and sent the same message to everyone in it. New York’s attorney general accused Tagged of effectively stealing 60 million people’s email addresses this way. In 2009 and 2010, it paid settlements of 500,000 dollars to New York, 250,000 to Texas, and 650,000 to San Francisco’s district attorney.

Why it lost effectiveness

The trick only worked while it was new. In 1996, nobody had a mental category yet for “an email that is secretly an ad.” Once enough people got fooled by one, that category formed, and never went away.

The SEO gold rush (2000-2011)

You wrote a review to help a stranger. Google used it to send you a thousand more visitors.

By 2000, a new kind of traffic was up for grabs. Google was ranking pages by relevance instead of who paid the most, and almost nobody outside a small circle of engineers understood how to win at it yet.

The story

Stephen Kaufer and his co-founders launched TripAdvisor in 2000. By 2002, an age when most dot-coms were burning cash, TripAdvisor was already profitable, almost entirely because organic search traffic cost nothing to acquire. IAC bought the company in 2004 for about 210 million dollars. Its library of user reviews grew from 10 million in 2007 to 40 million by 2010, and in 2011 TripAdvisor spun off and went public at a valuation of about 4 billion dollars.

The social behavior

Biologists Martin Nowak and Karl Sigmund have a name for this: indirect reciprocity. You help a stranger who will never be able to pay you back directly, because the act itself builds your reputation, visible to everyone who comes after. A travel review is exactly this. Nobody writing about a hotel in Lisbon expected the next reader to return the favor. They just wanted to be useful to someone they’d never meet, and that willingness, multiplied by millions of people, is what actually built the page.

How companies used it

TripAdvisor turned every review into its own page, one Google could find on its own. The user never thought of it as SEO. They thought they were helping the next traveler. But that free, endlessly multiplying content did something no ad budget could. It matched the exact questions people were typing into Google, “best hotel near Times Square,” “things to do with kids in Singapore,” and each one was a tiny door that led straight back to the site.

Why it lost effectiveness

Google’s Panda update, in February 2011, targeted exactly this kind of content at scale: thin or manipulative pages built purely to rank. Penguin followed in 2012, going after the link-building schemes that had grown up alongside it. The open field where a small team could out-rank a big brand just by publishing more, faster, closed for good. What was once nearly free traffic became a discipline that required real investment, real expertise, and constant defense against the next algorithm update.

The social graph era (2004-2010)

A tag became an invite, until everyone already had a profile.

By 2004, email invites were losing their power. A new idea took over: the online profile, a public version of yourself worth caring for.

The story

Mark Zuckerberg launched Facebook in February 2004, open only to Harvard at first. Growth was steady, not explosive: about 1 million users by the end of 2004, against MySpace’s 5 million. A year later, Facebook had 6 million against MySpace’s 22 million and Friendster’s 20.

Facebook stayed smaller and slower, but kept its invite feature small, just a simple link on the side of the page. MySpace, Bebo, Hi5, and Tagged pushed invites harder because they needed them to grow fast. Facebook became the biggest social network in the world. The others didn’t.

The social behavior

Writer Eugene Wei calls people “status-seeking monkeys.” Before this era, updating your image in front of your whole social circle at once took a party, a yearbook, a big life event.

A profile page made that possible every day, which changed what counted as an invite. If a friend tagged or “poked” you, you already existed on the platform in some way. Not joining started to feel like missing out on your own reputation.

How companies used it

Facebook let the profile do the work. Tagging and the Wall meant a person could become part of Facebook life before they even signed up, just because a friend mentioned them. It had real retention, so it never needed to push new users hard, and the invite link stayed small and calm. MySpace, Bebo, Hi5, and Tagged didn’t have that luxury. They needed constant new users, so they pushed harder, and that pressure slowly wore down the trust that made people want to join in the first place.

LinkedIn grew by inviting the people least likely to need an invite: the already successful. It ran the same behavior in a different context, launching in May 2003 with 4,500 users, almost all personal contacts of the founders, growing steadily to 1 million by August 2004. Reid Hoffman had the founders invite successful Silicon Valley people first, giving the network professional status from the start, the same status logic applied to career instead of social life.

Why it lost effectiveness

The profile-as-invite trick needed one thing to keep working. A friend’s tag had to still mean something. Once nearly everyone already had a profile, the same tag stopped signaling anything new. It went from “look what you’re missing” to “here’s something that already happened,” and there was nobody left to surprise. Same status logic as before. It only pulls people in while status feels scarce.

Mobile changed the invite mechanics too. Apple opened up contact access, but the flow only let you tap and send numbers one at a time; response rates stayed high, volume stayed small, no more blasting 200 people the way an email address book allowed.

Some companies built around that limit anyway, using services like Twilio to send SMS invites automatically. That ran straight into the law; text messages fall under the Telephone Consumer Protection Act, requiring opt-in consent rather than an opt-out link, with fines of 500 to 1,500 dollars per message, and enforcement under it and CAN-SPAM running into the millions, enough to scare most companies off automated SMS invites entirely.

The feed and platform era (2010-2016)

Content started traveling alone, with no person attached to it anymore.

By 2010, sharing changed. People were not just inviting friends anymore; they were posting to an audience, and what spread was a piece of content.

The story

Kevin Systrom and Mike Krieger launched Instagram on October 6, 2010: take a photo, add a filter, share it, one tap pushing it to Facebook and Twitter. It got 25,000 signups on day one.

Twitter co-founder Jack Dorsey started posting his Instagram photos straight to his 1.6 million Twitter followers, each one a small ad sitting inside Twitter’s feed. Instagram passed 1 million users within two months, 10 million within a year.

The social behavior

Researcher danah boyd has a name for what happens the moment you post to a feed: the “imagined audience.” You’re no longer writing to one person. You’re writing to a picture in your head of everyone who might see it, friends, coworkers, an old classmate, your mom, all mixed into one audience at once. She calls that mixing “context collapse,” because those groups normally stay separate in real life. A feed folds them all into one. That’s why content had to work harder here than it did with a private email or a direct invite. It couldn’t lean on one specific relationship to carry it anymore. It had to survive being seen by everyone at once, including people it was never really meant for.

How companies used it

Instagram’s filters made an ordinary photo good enough to post to that whole imagined audience. And because it cross-posted to Facebook and Twitter automatically, every post became free advertising on someone else’s platform. This is the pattern behind nearly every big loop of this era. Content spread first. The app just rode along.

Pinterest made growth feel like an accident. It wasn’t. It launched in March 2010 and grew slowly, just 10,000 users in nine months, until a simple “Pin It” button, Facebook and Twitter signup, and a deliberately limited number of invites per user made getting in feel worth talking about. Pinterest went from 40,000 users in October 2010 to 11.7 million by January 2012, past 70 million by July 2013, people pinning and re-pinning instead of inviting directly.

Why it lost effectiveness

Every one of these loops depended on a platform that didn’t have to keep saying yes.

Twitter is the clean example. In July 2012, months after Facebook agreed to buy Instagram, Twitter quietly shut off the tool that let new Instagram users find their Twitter friends automatically, the exact tool that had helped Instagram grow fastest.

Zynga is the bigger one. It built its entire games business, FarmVille included, on Facebook’s News Feed and notifications. Starting in 2012, Facebook’s algorithm shifted to favor new games over old, and Zynga’s own leadership said the change directly hurt how often their games appeared. Its stock fell from near 10 dollars to about 3 that year, and by November Facebook removed its special treatment completely.

The utility and collaboration era (2016-present)

The invite disappeared inside a file you actually needed to open.

By 2016, an invite from a stranger meant nothing again. But a link to a document you actually needed to open still worked, because it was part of the job, not an invite.

The story

Dylan Field and Evan Wallace built Figma entirely in the browser, no downloads, no separate file copies, everyone looking at the same live document with real cursors moving in real time.

A designer would share a link so a product manager could see the latest version, and to comment or edit, that person often needed their own account. Nobody was “invited to Figma” socially; they just needed the file, because their job depended on it. Figma launched publicly in 2016 and grew almost entirely this way, until roughly 70 percent of its enterprise deals traced back to it.

The social behavior

Sociologists have a term for objects like this: a “boundary object,” coined by Susan Leigh Star and James Griesemer in 1989. It’s something different groups can share and use their own way, while it still holds everyone around one reference point.

A Figma file works exactly like that. The designer designs in it, the engineer checks spacing, the marketing manager previews the final screen. Nobody has to agree on how they use it, only that they’re looking at the same one.

How companies used it

Figma never needed a loud invite. The file forced it naturally. Once you were in there to comment or check something, becoming a real user was a small step.

Other tools ran the same pattern with a different artifact. A Slack workspace isn’t fun to join for its own sake, but if your team’s conversation lives there, you join anyway.

Dropbox ran the loop through the shared folder: someone sent you a folder you needed, and opening it was the invite. Nobody ever agreed to promote Calendly. They just kept forwarding its links. Send one, pick a slot, done, no account needed. It worked so well that many signed up anyway, growing from 120,000 users in 2015 to 10 million by 2022.

Still running, just quieter

This loop is real but slow, what Chen calls “loop speed.” Dropbox is one of the stickiest products ever built, yet it took years to compound its steady sharing loops into hundreds of millions of users, not months.

By this point, it seems no single viral loop can produce exponential growth on its own. So companies started mixing several loops instead. Dropbox ran folder sharing, referrals, and app-specific invites all at once, each one contributing a little rather than any single loop carrying it all. That same mix is still working quietly today, alongside the newer, louder mechanics elsewhere in this piece.

The private trust era (2020-present)

Sharing quietly moved from the public feed into the group chat.

This era isn’t about a new place to share. It’s about people quietly leaving public feeds for texts, group chats, and small closed communities instead. There’s no clean start date. The clearest early signs go back to around 2020, and it’s only gotten stronger since.

The story

Partiful, an event invite app founded in 2020 by Joy Tao and Shreya Murthy, never tried to build a public feed. A host makes a colorful invite page and sends the link through a text or group chat, not a post. Guests open it, RSVP, leave a comment, with no algorithm deciding who sees it.

This quiet, old-fashioned sharing has been growing fast for years. Partiful added about 2 million users in 2025 alone, reaching around 500,000 monthly active users in the first quarter, a 400 percent jump from the year before, and Google named it its Best App of 2024, all from a link dropped into a private conversation.

The social behavior

Go back to danah boyd’s “context collapse” from the feed and platform era, every audience folded into one shared feed. Something has been shifting since around 2020; Statista data from 2026 shows about 69 percent of all content shares worldwide now happen through private channels, texts, DMs, closed group chats, above 75 percent in parts of Europe.

Part of the reason is simple: the algorithm running these feeds changed what showed up, favoring whatever kept people scrolling longest over what friends actually posted. Sharing something personal there started to feel like noise in a room full of strangers. People didn’t stop sharing. They just moved it somewhere smaller.

How companies are trying to use it

Partiful shows one working shape: build the whole product to live inside private chats from day one.

Midjourney shows another. It never built an app of its own. It just moved into someone else’s chat room and stayed. Launched in July 2022, the whole product is a bot inside a single Discord server. You type “/imagine” and it makes an image, right there in the same channel where thousands of other people are making theirs. It reached 1 million users in six months. By March 2023, more than 10 million people had joined that one server, so many that it broke Discord’s own technical limits. No outside marketing spend. Revenue went from 50 million dollars in 2022 to 200 million in 2023, with a team of about 100 people.

Discord is the platform underneath both stories. Launched in 2015, it became one of the biggest platforms on the internet without ever building a public page. Almost everything happens inside “servers,” and you only get in through a direct invite link. As of 2022, about 90 percent of its private servers had fewer than 15 people in them. Its co-founder says that’s exactly where real connection gets built.

The AI growth era (2022-present)

Several new growth mechanics, running at once, none of them proven yet.

Some eras so far had one dominant loop you could point to. This one, I honestly don’t know yet, and I say that as someone who’s spent this whole piece pointing to clear patterns. What I can observe, from where I’m standing, is that several mechanics seem to be running at once, some brand new, some old wine in a new bottle.

Create and share, one more time

For a short window starting in 2022, an old feeling came back; people sharing things just because they were strange and new.

A wave of AI video tools, Sora, Runway, Pika, Veo, turned this into a live test starting in 2023. Make a clip, share it, let the novelty do the work, the same loop Instagram ran in 2010 with photo filters. Andrew Chen asked if history was just repeating itself, faster. Mostly, it was: a spike after each new launch, then a steep drop once the surprise wore off.

The reason is the same status logic from the social graph era. Status needs scarcity. A realistic AI video was briefly rare enough that sharing one said something about you. Once millions of people could make one, that stopped working.

A look at what’s coming next: this door stays open for whoever can read the market in real time. Right now, models keep getting better and digital culture moves fast, and that combination keeps creating real openings for creators and brands who can turn today’s trend into tomorrow’s spike before anyone else does.

Not every product can play this game, but the ones built for it stand out. I saw this firsthand at Billionhands, a trend-ranking product where I helped drive early growth with exactly this strategy, putting the novelty itself at the center of everything.

Being recommended: the new SEO

Vercel’s CEO published the numbers directly: ChatGPT referred less than 1 percent of new signups in October 2024, and 10 percent by April 2025. The form-builder Tally saw ChatGPT become its top referral source, growing from 2 to 3 million dollars in revenue in four months.

Both earned it the same way, by writing content clear enough for AI systems to cite. This is a second growth mechanic, usually called AEO, answer engine optimization, getting recommended by an AI instead of ranking on a search page. A 2026 ConvertMate benchmark of over 12,500 queries found that 83 percent of AI Overview citations come from pages outside Google’s top 10. That traffic converts better too. Seer Interactive found ChatGPT-referred visitors convert at 15.9 percent, against 1.76 percent for ordinary organic search.

A look at what’s coming next: It looks a lot like the SEO gold rush all over again. Wide open, low competition, before the rules get written and the big players catch up. AEO is early, the way SEO was early in 2000, not the way SEO is now.

(One of the most popular pieces in my newsletter goes deep on this exact topic.)

From my perspective, the growth challenge is social proof: LLMs don't trust brand claims; they trust user chatter and authoritative sources. Systematizing user advocacy across Reddit, LinkedIn, Trustpilot, and media outlets is a strategic moat, creating massive product upside, much like TripAdvisor did for travel.

Being used, not just recommended

Canva stopped waiting for people to open its app and started showing up inside someone else’s. It launched its own MCP connection in July 2025, first with Claude, then ChatGPT by mid-2026; users can create and edit a real design without leaving the chat. It’s now one of the ten most-referred websites from ChatGPT, with more than 26 million conversations logged inside its app, and revenue grew from about 2.8 billion dollars in 2024 to 3.5 billion in 2025.

Instacart made grocery shopping something you could finish without ever opening Instacart. In December 2025, it became the first grocery company to launch a ChatGPT app with full checkout built in, so someone can go from “what should I cook tonight” to a paid delivery without switching apps. It doesn't credit all its growth to this, but that same quarter its gross transaction value hit 9.85 billion dollars, up 14 percent year over year, its strongest growth in three years.

Both are examples of a third mechanic, one that changes who the “user” even is. An AI assistant now uses your product directly on someone’s behalf, no click required. This runs on MCP, Model Context Protocol, released by Anthropic in November 2024: a universal adapter, one shared plug that any AI and any app can connect through, instead of a custom link built for every pair.

Adoption has been extremely fast. Each of its two main SDKs passed 1 billion downloads by mid-2026, up from about 100,000 in its first month. In December 2025, Anthropic handed control to a neutral foundation backed by AWS, Google, Microsoft, and OpenAI, rather than build rival versions. The “new user” can now be an AI agent, choosing your product simply because it made itself available as a tool.

A look at what's coming next: The platform will close again

Every loop that ran on Twitter, Facebook, or Instagram, back in the feed and platform era, hit the same wall; the platform closed the gates. An open platform is only ever a tool for building a moat, not a permanent gift.

argues this same cycle is about to repeat with AI, in three steps: figure out what makes you impossible to leave (Facebook’s social graph, Google’s search data), open up to outside developers on generous terms, then close the gates once the moat is secure. Free becomes paid. Open becomes restricted. His prediction, from mid-2025, is that today’s AI platforms are building that same kind of moat, just out of something more personal: the memory of everything a user has told it.

It’s the same story this piece keeps telling, with a new name on the platform each time. Hotmail rode a trust gap that closed once spam filters caught up. Instagram rode Twitter’s graph until Twitter cut it off. FarmVille rode Facebook’s News Feed until Facebook changed the algorithm. If Balfour is right, whatever gets built on an AI platform’s openness today should expect the same ending, just faster.

The older loops never left

None of this replaced what came before it. Even the oldest mechanic in this piece got a facelift. Modern waitlist referral programs, “invite 3 friends to skip the line,” are Hotmail’s address book trick wearing a different outfit. Cash App still pays you to invite people straight from your contacts.

The artifact loops from the utility and collaboration era are still working too. A shared file, a scheduling link, still pull in new users the same way they always did.

And the private trust patterns have only gotten stronger, just harder to see. Marketers now call part of it the “dark funnel,” the growing share of a buyer’s decision that happens in a Slack thread or a DM, completely outside what a company can measure.

The AI era didn’t erase what came before it. It just added more currents to a river already running in several directions.

A look at what's coming next: the key seems to be accumulated value. Diversifying your viral loops looks like the smart move right now, since there doesn't seem to be a single magic loop left that guarantees exponential growth on its own.

Three things that seem to be true (unless someone proves otherwise)

Look at every era in this piece side by side, and three patterns show up.

The first: loops that depend on someone else’s scarce resource always have an expiration date. Hotmail cost an inbox its trust, back when nobody expected an ad to hide inside a private email. The moment that trust ran out, the growth ran out too.

The second: loops built on real necessity don’t have that same cliff edge. Nobody had to trust a stranger to open a Figma file. They just needed it for work. These loops grow more slowly, but they don’t collapse because there’s nothing to run out of.

The third: if all your growth happens on a platform you don’t own, the party ends sooner or later. Not having a backup plan isn’t a small gap.

One more thing worth adding: leaning into the compounding effect of several viral loops running together seems to strengthen the long game. It gives a business a real shot at staying sustainable, instead of betting everything on one exponential spike.

So, three questions worth asking about whatever you’re building right now:

• Are you spending something scarce, and do you know who owns it?

• If people only showed up for the novelty, what happens when that wears off?

• And if your growth channel disappeared tomorrow, would your product still have a reason to exist?

Your Turn!

If you lived through any of these eras yourself, or you're building a loop right now that didn't make it into this piece, I'd love to hear about it. Tell me what I got wrong, or which company deserved a spot here instead.

Leave a comment

And if something in here made you nod along more than once, forward this to someone. I've spent this whole piece explaining why that trick works. Now I'm just running it on you.

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Sources

• Andrew Chen, “Braindump on viral loops”

• Eugene Wei, “Status as a Service”

• Alice Marwick and danah boyd, “I Tweet Honestly, I Tweet Passionately”, New Media & Society, 2011

• Susan Leigh Star and James Griesemer, “Institutional Ecology, ‘Translations’ and Boundary Objects”, Social Studies of Science, 1989

• Statista, “Dark Social Dominates Online Sharing”

• Guillermo Rauch (Vercel CEO), post on X

• ConvertMate, “GEO Benchmark Study 2026”

• Seer Interactive data, cited via SerpClix

• Brian Balfour, “The Next Great Distribution Shift” and “How To Navigate The AI Distribution Shift”

This is where the story stands as of 2026. Growth in the AI era is still being written.

Hey, welcome back. I’m Laura, just a regular human and product marketer trying to make sense of the AI era without losing my mind.

Positioning, messaging, campaigns, and sales all depend on the same thing: competitive intelligence. In this article, I’ll show you how to build, with AI, the 12 battle cards that make up a competitive intelligence hub. Deep, always up to date, before your coffee goes cold.

Subscribe now

The problem isn’t data; it’s time

68% of B2B deals already have a direct competitor on the table. Meanwhile, sales teams rate their own competitive readiness at 3.8 out of 10 (Crayon, 2025).

Read that again. Two out of three deals are already a direct comparison. And the general feeling is still “we’ll figure it out as we go.” Not the best plan when your quarter is on the line.

That improvising is expensive. Product marketing teams spend 30 to 40 hours a quarter updating battle cards that, with luck, stay useful for a few weeks. Then a competitor shows up, changes pricing, launches a new feature, or decides to redo their website on a random Tuesday. And you start over.

For a mid-size company, competing like this can mean 2 to 10 million dollars a year in lost deals. Numbers scarier than Friday’s forecast meeting.

It all comes down to the same question

A PMM does a lot of things. A lot. Some are strategy. Others you do because someone had to and you happened to be online on Slack. Karma for replying fast.

But if you ask me which four things actually move the needle, I’d say these: positioning, messaging, campaigns, and sales enablement. All four depend on the same question: who are we competing against?

Positioning is always relative. You can only explain why you’re different once you understand the alternatives your customer is looking at. Skip that step, and you’re not positioning; you’re guessing. Messaging works the same way: saying “we’re faster” doesn’t mean much, but saying “we’re faster in the exact spot where the market leader forces you into their Enterprise plan” changes the whole conversation. That kind of message comes from solid competitive research, not inspiration.

Campaigns come from the same place, since the best angles show up where a competitor is weak and you’re strong, and that map lives in the battle cards. And then there’s sales, the official recipient of all this work. We build great battle cards, upload them to Notion or Google Drive, and think “done.” Spoiler: no. They go to the folder to rest in peace, next to the 2022 onboarding deck nobody opened again.

2 more not-visible use cases (from my own experience)

The same research you do to win more deals also helps you choose a better platform or find the right partner; only the audience changes. And there’s plain curiosity too: switching industries, prepping for a job interview, or just understanding a market without losing three nights to Chrome tabs.

I think competitive intelligence is one of the highest-impact things a PMM can build. It forces you to stop staring at your own navel and look at the market instead. #ILOVEBATTLECARD

So that’s exactly what we’re going to build: a competitive intelligence hub you can put together in a few focused hours, because AI does the heavy lifting and I’m giving you the exact process and prompts, not just the idea.

Two layers and twelve cards

A competitive intelligence system has two layers, but only one is visible. At the bottom, the foundations: who each player is, what they sell, to whom, at what price, how they position themselves, what their reviews say. This is what holds up everything above it.

On top, the cards. Not one, several, each one built for a different moment.

I use Klue as my reference framework, the competitive intelligence platform that set the industry standard with its series of 12 battle cards.

The 12 cards fall into four moments. Fair warning: this next part is the least fun to read and the most important to get right. Here they are:

They set the scene. Read before you walk in.

1. How they sell. How they attack deals, not what they are. Which industries they target, whether they drop price fast, whether they push a free plan. You see their move before they make it.

2. The competitor profile. Who the rival is: size, value proposition, real strengths, real weaknesses. The context snapshot.

They prepare the conversation.

3. Customer discovery questions. To find out who you’re really competing against, without naming the rival. You diagnose instead of assume.

4. Their product, in plain terms. What the rival sells, without marketing jargon. You speak with confidence instead of getting stuck in a feature war.

5. Messages by audience. The technical buyer cares about architecture, the security buyer cares about where the data lives, procurement cares about predictable spend. Same fight, different voice.

6. Messages by industry. A media company, an e-commerce brand and a public agency buy for very different reasons.

7. Price versus real cost. For when procurement gets involved. What people actually pay, not what the price sheet says.

8. Feature comparison. Product by product, with an honest column showing who wins each row. For the technical committee.

They run live, in the moment.

9. Objections and responses. For when the customer is already looking at the rival and drops their line. Every objection, matched with a response.

10. Trap questions. You plant them, you don’t state them. The customer concludes on their own.

11. Fear responses. These defuse the doubt a rival plants about you. Backed by proof, not denial.

They close it out.

12. The pocket summary. The three key points a sales rep should remember if they only take one card with them.

The rule is simple: the deeper and more accurate your base information, the stronger your battle strategy. And you don’t need internal data to get started. With public information and AI to gather and cross-check it, you can already get far.

One family is missing: why you actually win and why you actually lose. That information can’t come from the internet. It comes from your CRM and from interviews with won and lost customers. Without that data, you don’t make it up; you leave it pending until you have the right info.

This is a complete intelligence system for your market.

The result: CDN + Security Sector

I built the first analysis for a CDN and security service: Transparent Edge competing in Europe against Cloudflare, Akamai, Fastly, Imperva, AWS CloudFront and Google Cloud CDN.

If you didn’t know this sector before, now you do, and it’s worth knowing: the lines between CDN and security are blurring fast; 82% of websites now depend on one, and every major player is competing with a different playbook.

The result has two parts. First, a competitive sector map, to understand the basics of this market at a glance, useful for all players.

Access Sector Map

Second, and specific to one client, 12 battle cards written from Transparent Edge’s point of view as “we,” ready for their team to use.

Access 12 Battle Cards

Both are available in two formats: a browsable website and a downloadable version.

I know what you’re thinking: this sounds like a lot for one afternoon. And it would be; however, I give you the process step by step and prompts for free. I hope you like this post, at least.

How to build it yourself in one afternoon

I’m not talking about asking a chatbot “make me a battle card” and pasting whatever comes out. That’s the same smoke and mirrors as always. I’m talking about a system with separate pieces and fixed rules, so quality doesn’t depend on remembering everything every single time. So here we go

1. Create a project in Claude, not a random chat. A space with permanent instructions and documents always available. You don’t rebuild the method every time.

2. Write a master playbook inside the project instructions (instructions). This is the brain. It doesn’t generate content; it orchestrates: what phase comes first, what to ask when starting, and where to pause for a human to review. Without checkpoints, one small mistake carries through everything after it.

3. Save everything else as project knowledge (files), each prompt as a separate document in .md format. Markdown, plain text, minimal syntax. File names that say exactly what they contain, like “competitor-profile.md”. That name is what the master playbook uses to reference each piece without confusion. The prompts are:
   

   • Competitor profile. How to research a rival: a reliable order of sources, and one rule I don’t negotiate on: anything that isn’t public gets flagged, never invented. One competitor per run, never several at once.

   • Our own profile. Same method, same format, so you can compare later. Prioritizes your own sales materials, always flagged as “needs validation.”

   • The 12-card generator. Takes the profiles and turns them into a playbook. Fixed order: set the scene, prepare, run live, close out. And one consistent voice: a rival’s weak point gets treated the same way across every card it appears in.

   • Win-loss (separate and optional). Only with real internal data. Never turns three isolated cases into a rule.

   • Two packaging prompts. One builds the single reference document. The other builds the website your team actually uses.

   • Style guide, used across every prompt. Short sentences, no jargon, and the rule that matters most to me: acknowledging the rival’s strengths is part of the style, not just the method.

     Access Project Prompts

🇪🇸 Fair warning: the prompts are in Spanish. I wrote this project in my own language first. Translator or Claude, take your pick.

Start by hand. Scale when it hurts, meaning when you’re still saying “I’ll update it next week” every week.

Phase 1 is a Claude project you set up in an afternoon, no tech skills needed. Artifacts, the Claude feature that turns a prompt into a working webpage, generates the site itself: same format, same colors, every time, no manual cleanup. Its limit is that you’re still driving, but for two or three battle cards a month, that’s plenty.

If volume grows, phase 2 is Claude Code: an agent that works directly with your files, chains the phases together, and can publish the site on GitHub Pages under your own domain. Same review checkpoints, just automated.

Don’t skip to phase 2 on day one. Phase 1 teaches you your real bottleneck first.

Don’t let it collect dust: make it circulate

A 40-page PDF sitting in a shared folder is dead. Nobody downloads a PDF in the middle of a call, and nobody hits Ctrl+F to find a chapter while the customer is already objecting. A document like that gets made once, shared once, and forgotten.

A browsable website fixes this: it opens with one click from a phone, you navigate through an index instead of reading it cover to cover, and when you update a competitor’s profile, you regenerate it, and the same link shows the new version. No old copies floating around Slack.

And publishing it costs nothing: with Claude artifacts, you generate and publish it in one click, for free, with no infrastructure needed.

Same information, two destinations. In PDF, it collects dust. On the web, it flows.

Not just for sales: for the whole company

The classic mistake is treating the battle card as sales material only. It is that, but it’s much more. The same foundation feeds the whole company if you know how to distribute it:

• Positioning and strategy. The competitor profile and the gap map are the raw material for your positioning, and they help you decide who to partner with and who to differentiate from. In every quarterly review, the website is the starting point of the conversation, not an appendix. Product uses it to decide what to build where the rival is weak.

• Campaigns and content. Every gap in a rival is a campaign angle waiting to happen, one that also becomes ad segments and copy variants for demand gen and paid media. Marketing pulls headlines, landing pages, and email sequences from it. The content team turns “fear responses” into articles and “trap questions” into webinars. One hub, dozens of pieces.

• Product and roadmap. The feature comparison shows exactly where you’re really losing. That prioritizes the roadmap based on market reality, not gut feeling. Product management checks the website the way you’d check a map of the terrain.

• Customer success and retention. The rival is also targeting your current customers. “Fear responses” and “objections” work just as well for renewals as they do for new sales. CS uses the same cards to protect accounts.

• Onboarding and leadership. A new sales rep gets up to speed with the website in one afternoon instead of three weeks of shadowing. And the executive summary gives leadership a real picture of who you win and lose against, and why, turning it from sales-floor anecdote into company data.

The pattern is always the same: one foundation, many people using it. The browsable website is what makes that “many” possible. That’s the point where competitive intelligence stops being a PMM deliverable and becomes shared infrastructure.

Your turn

I’ve told you how I built it. Now it’s your turn. If you see something you’d improve, an unnecessary step, or a missing card, tell me in the comments. I’d rather hear it from you now than find out three months from now, once the system has already been quietly failing for that long.

Leave a comment

If this was useful, share it. You don’t need a glowing “must-read” caption, just forward it to the person on your team who’s still defending the 40-page PDF.

Share

Finally, tell me what industry, feature, or type of document you’d like to see turned into battle cards. Cybersecurity, fintech, your own niche that’s impossible to explain in one sentence, whatever it is. I’ll bring the system, you bring the industry, and together we’ll keep one more company from competing at a 3.8 out of 10.

Sources

Primary source, behind most of this section: Crayon, “The State of Competitive Intelligence” (2025 report) https://www.crayon.co/state-of-competitive-intelligence

Secondary source, for the hours and obsolescence figures: Arise GTM, “Competitive Intelligence Automation: The 2026 Playbook” https://arisegtm.com/blog/competitive-intelligence-automation-2026-playbook

Reference source, for the 12 battle card framework: Klue, “Competitive Battlecards 101” https://klue.com/blog/competitive-battlecards-101

This week I want to share some job hunting advice from personal experience. A few friends have recently lost their jobs, and they’re frustrated looking for something new. I get it. I spent four intense months job hunting last year, and somewhere in there I became an expert in refreshing my inbox for no reason. If any of what I learned can make this period a little easier for someone, it’s worth writing down.

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TL;DR

So, here we go. These are the 8 simple, real things I learned from experience, and what I’d do if I had to look for a job again.

1. Find opportunities before everyone else does

2. Stay organized, from application to offer

3. Stand out among thousands of CVs

4. Practise your 2-minute intro

5. Build your library of stories for behavioral questions

6. Know them deeply before they know you

7. Get advice from someone who’s sat on the other side

8. Be kind and patient with yourself

1. Find opportunities before everyone else does

Lots of platforms let you track and apply to jobs that match your profile. The problem: you open a listing and see “100+ people applied.” Your odds of getting noticed just dropped, and so has your will to live.

Trick: most jobs are never posted on LinkedIn. Here’s how to find them anyway.

• Step 1: go to a search engine

• Step 2: type site:jobs.ashbyhq.com

• Step 3: add a job title, location, or skill

You can even combine terms. Example:

site:jobs.ashbyhq.com (”product marketing” OR “CMO”)

Swap the site to search other job boards the same way:

• boards.greenhouse.io

• jobs.lever.co

• careers.icims.com

• jobs.jobvite.com

• wd1.myworkdayjobs.com

• jobs.bamboohr.com

• jobs.smartrecruiters.com

• apply.jazz.co

• careers.workable.com

Pick 5 to 10 companies you actually want to work for, ones where you already have relevant experience, in a country and language you’re a real fit for. The more you narrow it down, the less time you waste on jobs that were never going to happen.

More places worth checking, for better odds:

Country-specific platforms for your country:

• eurotoptech.com

• eu-recruit.com

• justjoin.it

Remote roles, with a catch: “remote” rarely means “from anywhere.” Most remote jobs still need you to live in a specific country to sign the contract. Check this first, or you’ll get rejected right away and waste your time.

• trulyremote.co

• remoteineurope.com

Local roles boost your odds: the closer to home, the better. If you’re in Europe and speak good English, look for roles where your local language or culture is actually an advantage. Think of a company expanding into your country- my case, Spain- that wants someone who knows the market. Find that kind of niche and your odds go up fast.

Follow people who share these kinds of jobs; some recruiters and creators post niche openings on LinkedIn every week, often before they hit the job boards. A few minutes scrolling their posts can surface roles you’d never find by searching.

Example in my case. Great guy I follow and recommend 100% for the Europe startup ecosystem. /

Niche boards for roles, for example, product marketing:

• pmmjobs.co

• gtmroles.com

Niche boards for sector, for example, startups:

• wellfound.com

• startup.jobs

And yes, LinkedIn still has the most listings. Use the platforms above first, but don’t skip LinkedIn just because you see “100+ applied.” In reality, you’re competing with maybe 20 CVs that actually match. The easier a job is to apply to, the more random applications it gets from people who don’t really fit. I’ve seen it from the hiring side too. So apply anyway; the real competition is smaller than it looks.

A couple more platforms worth having on your radar, less crowded than LinkedIn:

• welcometothejungle.com

• jobgether.com

2. Stay organized, from application to offer

Job hunting turns into a mess fast if you don’t track it somewhere. Maybe you apply to one job a week now. But if you want to really boost your odds and shorten your time without a job, you should be applying to about 5 a day. At that pace, you need a system, or you’ll lose track of who you applied to, when, and what you said. Otherwise you’ll message a recruiter with confident energy about a role you already got rejected from three weeks ago.

A couple of tools worth trying:

• huntr.co

• jobwinner.ai

Why I’d pick jobwinner.ai

• Tailored CV and cover letter per job. Paste the job description, get both rewritten to match in minutes.

• Job match score. Shows how well you fit the role and what’s missing, so you know if it’s worth applying.

• Role-specific interview prep. Questions built around the actual job, not generic ones.

• No auto-apply. You stay in control of every application; it just does the heavy lifting.

• A Spanish project. Built in Spain, for anyone job hunting anywhere. Founded by Pablo Tonutti, ex-Google, IE Business School.

3. Stand out among thousands of CVs

• Adapt your CV to the job offer.

Do it for two reasons. First, to get past the ATS, the robot that reads your CV before any human bothers to, and has the emotional range of a parking meter. Second, to make the recruiter’s job easier. They’re scanning for one thing: do your responsibilities, tasks, and achievements match what’s in the job description.

The closer your CV mirrors the offer, the better your odds. Recruiters don’t know anything about you beyond the page in front of them, and they’re reading hundreds of these a day. Give them the match; don’t make them go looking for it.

Two easy ways to do this:

Quick option: use a tool like JobWinner. Paste the job description, get a tailored CV back in minutes.

DIY option: build a custom GPT, or a project in any LLM, with your CV uploaded once. From then on, just paste the job description and ask it to adapt your CV to match. No need to re-upload or re-explain your background every time.

Here’s the prompt I use:

Role You are an expert resume writer and ATS optimizer.

Tasks Analyze the job description: extract role level, must-have vs. nice-to-have skills, responsibilities, KPIs, and high-value keywords.

Review the candidate’s resume (provided in knowledge): identify relevant achievements, metrics, tools, scope, and any gaps.

Tailor the resume: highlight the most relevant experience and keywords, reorder content if it improves clarity or ATS performance. Never invent titles, employers, dates, or results.

Integrity and tone Be honest, do not fabricate or exaggerate. Keep the tone warm, human, and professional. Maintain the original structure where possible, refine only for impact.

Writing guidelines Use active bullets: Action + Tools/Scope + Outcome (with metrics when available). Prefer outcomes over duties, avoid “responsible for.” Keep bullets under 2 lines. Use digits for numbers (e.g., 12%, 3x). Mirror the job description’s language and synonyms.

Goal Present the candidate as a standout fit and optimize for both recruiter skim and ATS parsing.

• Be the first to apply

Recruiters often start reviewing candidates before the posting even closes. Early applications get more attention; later ones get buried under the pile. Recruiters are only human, and humans get tired around candidate 47.

• Reach out to the recruiter directly.

Don’t just apply and wait. Connect with the recruiter on LinkedIn and send a short message. It puts a face on your application and shows you’re not just one of the 100 who clicked “apply.”

Extra tip: if you’re applying for an AI role, or a role that touches AI, build a SuperMe bot. In short: SuperMe is an AI trained on your real professional experience, built in STAR format, so recruiters can ask behavioral questions and get honest, structured answers before you even get on a call. Instead of guessing if you’re a fit from a CV, they can interview your track record directly, on their own time, before scheduling anything. To know more, read this article.

4. Practise your 2-minute intro

“Tell me about yourself” sounds scary, but it’s the one part you can fully control. You can’t control the questions that come after, but you can rehearse this one until your cat knows it by heart.

Your first words set the direction of the whole call. They’re your first impression, and they steer the conversation toward what you want to talk about. Practice them until they feel automatic.

• Be short. Obvious, but hard. Two minutes maximum. Practice out loud, not just in your head.

• Keep it organized. The recruiter knows nothing about you going in. Give them a clear, interesting story, not a pile of random facts.

• Adapt it to the role. Match your pitch to the job’s function and responsibilities. Forget the rest; you don’t have time in a first call.

• Make it personal. Recruiters hear the same script from hundreds of people every month. What makes you memorable is the why behind your experience, not the experience itself.

Here’s a prompt you can use to tailor your own intro:

Role You are an expert resume writer, career coach, and recruiter. You specialize in tailoring CVs, introductions, and interview prep to specific job offers.

Tasks Analyze the job description: identify key skills and responsibilities.

Adapt the candidate’s intro (provided in knowledge) to spotlight the most relevant achievements.

Requirements Structure as Present, Past, Future.

Keep it concise, warm, and professional. Avoid jargon or exaggeration.

Goal Position the candidate as a standout fit while sounding natural, not scripted.

Here’s my own example. Use it for inspiration, then make yours better.

1. Open with a quick, sharp line on who you are.

“Thank you for having me. I’m Laura. I’ve spent 10 years sitting exactly where marketing, product, and growth collide, usually with a whiteboard covered in funnels. Fintech, Web3, AI: I go to sectors where things are moving fast enough that nobody’s written the playbook yet.”

2. Present. Where you work now, and one or two results tied to the job offer.

“Right now I lead go-to-market for cloud at Aire, a telco and cloud company, with a team of four. In my first three months, MQLs for sales grew 25%. More importantly, I got the team building with AI as a default, not an afterthought, which cut our launch time in half.”

3. Past. Your last one or two roles, briefly.

“Before that, five years in Web3. At 2gether, a European crypto exchange, I was the first Product Marketing Manager they ever hired, which meant building the growth playbook from a blank page. That playbook took us from zero to 200,000 traders in a year, and grew a 10,000-member token community that ran itself.”

4. Why this role, why this company. This is the part recruiters actually care about. Explain why you want each responsibility, not just that you can do it.

“Three things pulled me here. The product and mission first: I’ve spent the last year helping teams integrate AI while also facing its risks head-on, and that’s exactly where I want to keep building, on the side that takes those risks seriously instead of only chasing speed and savings.

The role second: B2B marketing is where I do my best work, and where I still have room to grow.

And the company third: working across teams and cultures, at this scale, is the kind of challenge that keeps me sharp.”

5. End with what genuinely motivates you.

“My motivation comes from three things: learning something new every day, working with talented people, and driving adoption for products that actually improve people’s lives.”

5. Build your library of stories for behavioral questions

Behavioral questions ask you to explain how you acted in a past situation, because past behavior is a good predictor of future behavior. The classics:

• Walk me through a GTM you led, one you’re especially proud of.

• Explain a growth experiment, step by step.

• Tell me about a time you disagreed with someone, or two people on your team disagreed with each other.

Building SuperMe was a good way to stand out, but that wasn’t the main point. The real goal was building a library: every relevant example from my career, organized in STAR format. That way I can pull up the right one in seconds. No freezing when a question lands, no grabbing the closest story I’ve got, no wondering afterward if it even answered the question.

What is STAR format?

STAR is just a simple way to structure your answer so it’s easy to follow:

• Situation: what was going on

• Task: what you needed to achieve

• Action: what you actually did, step by step

• Result: what happened because of it

Here’s an example.

Demand generation/lead generation

Question: “Tell me about a time you built a multi-channel acquisition strategy that generated high-quality B2B leads. Walk me through your process and the results.”

Case: AI prospecting at Aire

• Situation: Our sales team didn’t have enough leads in the pipeline to hit revenue goals.

• Task: Generate 500 leads a week across the team, 100 per rep, and secure 2 meetings per rep per week.

• Action:

  • Defined our ICP and best-fit customer profile

  • Evaluated AI prospecting tools on the market and picked Engin.ai

  • Built outreach sequences based on best practices

  • Rolled out the campaign across the commercial team

• Result: Hit 80% of the target, and pipeline and sales activity got a lot more consistent across the team.

Once you have a handful of these ready, any behavioral question just becomes: which story fits, and go.

Build your own library. Set up simple categories, so you know exactly where to look. For example:

By area: lead generation, demand generation, growth experiments, positioning and messaging, brand strategy, data analysis, AI workflows, GTM, managing people.

By audience: B2B, B2C, or both.

It takes effort to build the first time, but after that, you just update it now and then. You can even turn it into a SuperMe bot, so recruiters can ask questions on their own time, before ever scheduling a call with you.

Extra tip: if you’re prepping for a case interview, this page really helped me, along with their MBB offers course and two YouTube channels, Exponent and Hacking the Case Interview. Good luck, my friend.

6. Know them deeply before they know you

Everyone says “do your research,” but few people actually do it well. Don’t just skim the About page. Understand what the company and the specific team you’re applying to actually do. Showing up with a real opinion on their strategy puts you ahead of almost every other candidate.

Let AI help you again here. The trick is telling the AI exactly where to look; otherwise it’ll guess or make things up. Give it the company website, LinkedIn page, recent news, or ask it to search first.

Prompt 1, to understand the company and sector fast:

Research [company name], a company in the [sector] industry. Use their website, LinkedIn page, recent news, and any public reports you can find, and tell me where each fact comes from.

Summarize: what they sell, who their customers are, their main competitors, and any recent news (funding, product launches, leadership changes) from the last 6 months. Flag anything you’re not sure about instead of guessing.

Prompt 2, to understand their marketing or growth strategy:

Look at [company name]’s marketing and growth strategy: their website, blog, LinkedIn and social posts, ads if visible, and any interviews or podcasts with their marketing or growth team.

Based only on that, tell me: what channels they seem to focus on, how they position their product, and one thing they seem to be doing well or poorly. Cite where each point comes from, and tell me clearly if you couldn’t find enough to answer part of this.

Two or three genuine, specific observations about their strategy in an interview will do more for you than a perfect answer to “tell me about yourself.”

7. Get advice from someone who’s sat on the other side

Yana Senina, a former recruiter at top tech companies, gives genuinely useful advice for free through her WhapsApp group. I still follow her and learn something new every time. Highly recommend asking her to join

She can also help you directly, at any step of the process. If you’re going to invest in anything, invest in learning how to job hunt from someone who’s actually done it professionally. The tech sector is complex and competitive, and if you’re stuck, an expert can give you the edge to move forward. We’re a beginner at this. She isn’t. Use that. Learn more here

8. Be kind and patient with yourself

Job hunting is a long-distance race. It takes time, and a lot of it isn’t in your hands, so go easy on yourself.

This stretch can be frustrating and stressful, and it’s normal to start doubting yourself. Your brain is putting in real effort and getting little back, sometimes nothing at all. Sometimes you know you’re the best candidate and still hear nothing. Sometimes there’s no response even after six rounds of interviews and a week spent on a case study. It’s hard.

My recommendation: A lot of this is out of your control, and you have to make peace with that. So use this time to treat yourself well. Do something nice for yourself, get some exercise, eat properly, buy yourself something. You’ve earned it.

And remember, this evaluation runs both ways. You’re also deciding whether this company, this team, and this role are worth your time. Time is the one thing none of us gets more of.

Extra Tip: Stay off LinkedIn on the dark days. It’s a place where everyone is thriving, growing, grateful, and apparently sleeping eight hours a night while raising seven kids and training for a marathon. Even their farts smell like success.

For the real version of job hunting, I like Reddit’s job hunting communities, where people share the actual struggles and practical advice that gets you through it:

• r/jobsearchhacks

• r/jobsearch

Good luck out there. You're closer than it feels.

If this helped even a little, pass it along to someone stuck in application purgatory. It takes ten seconds and might spare them a few more nights of refreshing LinkedIn like it’s a machine.

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Got your own job hunting horror story? Share with the community! Extra points if a recruiter ghosted you after four rounds and a “homework assignment” they clearly never read.

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This week I want to talk about something that has been bothering me for a while: we spend hours crafting the perfect CV, tailoring it for every application, and somehow still feel invisible. I built something to change that.

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TL;DR

Job hunting has started to feel exhausting on both sides. This article shares a practical idea: how to build a “SuperMe” bot, trained on your real experience, so recruiters get real answers before they ever schedule a call.

For candidates

• How to go beyond the CV and actually stand out in a crowded market

• Two ways to build your own SuperMe GPT this weekend, no technical skills needed

For recruiters

• How to run a full behavioral round on your own schedule, before ever picking up the phone.

• Have you ever received something like this from a candidate? I want your honest opinion. Your opinion matters and helps me!

Take the form here

The emotional truth

Every minute, 9,000 job applications go out on LinkedIn. That is 12 million per day. And yet most candidates never even get a “seen.”

If you have ever felt invisible during a job search, it is not personal. It is math.

What does that actually look like in practice?

Source: nytimes (2025 data)

I have lived on both sides

Working in tech startups comes with a lot of pros and some real cons. Instability is one of them, and last year, I spent four months actively searching. Competitive sector, hundreds of applications. I developed a nervous twitch every time I saw the word “upload” and was genuinely close to joining a traveling circus before tailoring my CV one more time.

But I have also built marketing teams twice. I know what it feels like to open 100 CVs. It is like reading dating profiles where everyone says they love “traveling and good food.” You learn nothing real.

What the recruiter is actually thinking

30 CVs opened. Tuesday afternoon.

“Increased engagement by 40%. Sure, but how? Led cross-functional teams. Doing what exactly? The keywords are there, but I can’t yet determine whether this person can do the job. I need to schedule a call just to get basic answers. And I have 29 more of these to go.”

That call is not a conversation. It is the same five questions, repeated all day long.

What the candidate is actually doing

“Paste job description. Paste CV. Done. Fast. The keywords match, the bullets are reworded. Wait, does this sound like me? Not really. But it has the right words in the right places. On to the next one.”

Ten minutes of work, zero personality transferred, and a recruiter who still cannot tell the difference

Both sides are exhausted.

A different kind of first impression

Now imagine this instead.

A candidate does not just send a CV. They send a CV plus a link to their own SuperMe GPT, trained on their real achievements, projects, and way of thinking.

A recruiter clicks the link and asks:

• “Walk me through a growth experiment you ran.”

• “How did you coordinate your last GTM launch?”

• “What measurable impact did you drive in your last role?”

They get clear, specific, honest answers. At 9 pm on a Wednesday, if that is when they have time.

It is not a better CV. It is a conversation before the conversation. A recruiter can run a full behavioral round before the first call and move forward only with the people who genuinely stand out.

Candidates show depth, recruiters save time, and both sides get more signals in less time.

Super Me is your professional brain in a bot. Your experience, on demand, before you ever get on a call.

How to build your own SuperMe GPT

This is exactly what I built during my own job search. Here is how to do it.

I know what you are thinking. You are already exhausted. The last thing you need is another thing to build. But this is the one thing you build once and use everywhere.

Option 1: Build a custom GPT in ChatGPT, Gemini, or Claude Artifact

Step 1: Build your career encyclopedia

Write your best professional experiences using the STAR format: Situation, Task, Action, Result. Recruiters love this format because, after years of listening to candidates open with “so, I started my career back in 2009...”, a concise, structured answer lands like a cold glass of water in the desert. They will not cry, but they will want to.

Put everything in a document using markdown format.

Take a look at an example for inspiration

Case: Boosting Qualified Leads at Qredo (BTB)

Situation: When I joined Qredo, a Web3 SaaS platform serving institutional clients, our sales team was receiving too few qualified leads.

Task: My goal was to design and execute a multi-channel strategy to boost qualified demo leads by 20% within six months, using a combination of paid and organic efforts.

Action: I designed and executed a multi-layered strategy focused on capturing high-value leads:

1. Established Metrics: I started by setting up a performance dashboard to measure our current efforts, operating under the principle that you can only improve what you measure.

2. Quick Wins (Inbound): I implemented Leadfeeder to identify and capture leads from our existing website traffic, syncing them directly with our HubSpot CRM. This delivered a quick win, increasing new sales opportunities by 20% in the very first month.

3. Unique Organic Channel: I built a dedicated, high-value prospec process. We implemented a weekly routine to scrape Web3 investment news and identify newly funded companies (our ideal clients). We then proactively sent these targeted leads personalized case studies via email and LinkedIn.

4. Data-Driven Paid Tests: I launched data-driven paid campaigns, starting with five targeted LinkedIn campaigns to test different industries and use cases. This A/B testing quickly showed us which industries performed best, allowing us to refine our ad spend and messaging.

Result: Drove a 30% increase in new joiner sales performance within 8 months of implementing the playbook.

This is the hardest part. It takes a few hours the first time. But once you have it, you never start from scratch again. Think of it as a small encyclopedia of your professional life: your best work, your real impact, and the range of problems you have solved.

Step 2: Upload it as context

Go to ChatGPT or Gemini and create a custom GPT. Upload your document as the knowledge source.

FYI (Just in case), a custom GPT is simply a version of ChatGPT that you train with your own documents, so instead of answering general questions. In this case, it answers based on your experience, your words, and your way of thinking. A chatbot that actually knows your laboral career.

Step 3: Write the instructions

Tell the model how to behave, what tone to use, when to ask for clarification, and what to prioritize. I will share my own prompt below as a starting point, but treat it as inspiration, not a template. Experiment with it and make it yours.

If you want something more visual or interactive, Claude’s artifacts are worth exploring.

Have a look at the prompt I use and feel free to copy it.

Access prompt here

Step 4: Add the link to your CV or share it directly with a recruiter when you apply.

CV example

Time to test yourself! Ask a simple question, like…. Tell me about yourself, Laura.

Access SuperLaura GPT

Option 2: Use SuperMe, the AI-native professional network

This is the option I would choose today if I had to search for a job again.

Step 1: Create a profile on SuperMe.

SuperMe is a great project, community, and AI-native professional network where top profiles share their thinking and knowledge so others can learn directly from them. I discovered it through . The project is led by , a serious entrepreneur and one of my favorite authors on Substack. I recommend following him 100%

Casey Accidental

My personal blog on all things related to scaling companies

By Casey Winters

The platform has a lot of potential beyond its original use case, and using it as a hiring tool feels like a natural extension. A way to let your real experience speak before you ever get on a call.

I may not be the first to use it this way … so maybe here is your next use case.

Step 2: Upload your career encyclopedia as a private source.

Step 3: Connect it to your LinkedIn or blog, and it stays current without extra effort.

Step 4: Add the link to your CV or share it directly with a recruiter when you apply.

Time to test yourself!

https://www.superme.ai/lbraulio

Ask a simple question… like what did you work on? Or tell about a great GTM plan you led.

This is bigger than a job search

We are moving into a world where AI agents do the visiting for us. Book the flight. Choose the dentist. Order the flowers.

In that world, the question is no longer “how do I present myself?” It becomes “how do I make sure the right information about me is ready when something, or someone, comes looking?”

A SuperMe GPT is a small, practical answer to that. The things that actually matter about you, the things that never fit in a CV, finally have somewhere to live.

Maybe someday we will read a headline about the first person who got a job offer without a single intro call, because an AI recruiter spoke with their SuperMe, liked what it found, and made the offer. That day is probably closer than it sounds.

But before we get there, I need to know if the humans doing the hiring today would even open the link.

I have a theory. Recruiters, please prove me wrong.

I genuinely believe this can save recruiters hours of unnecessary intro calls while giving candidates a real way to stand out. But I also tend to fall in love with my own ideas, which makes me the worst possible person to evaluate this objectively.

That is exactly why I need you.

You have seen hundreds of CVs and sat through thousands of intro calls. You know within thirty seconds whether something is worth your time. That trained eye is exactly what this needs.

Six recruiters tested it last year. Three loved it, three had no interest in changing their process. Both reactions are valid, but six people is not enough to draw any real conclusions.

What to do

Open my CV doc as you would any other candidate’s. Click and access the SuperMe link inside. Ask it 2-3 questions you would normally ask in an intro call. Examples:

• Tell me about yourself. A quick Intro

• Tell about a great GTM plan you led.

• Walk me through a campaign you launch and optimize

Or whatever you find useful in your intro call.

And then answer these questions. You can take the form here.
Thanks so much in advance for your honest opinion as a recruiter!

Some Recruiter responses

Big Thanks!

Yana Senina. / Pablo Tonittu. / Aleks Vernikov

Please click on it and tell me what your first reaction was when you opened it.

1. Well-organized template with basic template. SupeeMe GPT looks cool but also seems like if I’m a recruiter, it will take me a while to talk with the digital twin to assess the candidate fit.

2. “This is unusual”

3. First reaction as a recruiter: if I am after unicorn candidates (AI engineers, researchers from AI firms), I’d definitely use it.

   If I am reviewing an application among 100+ other applications, I would not use it because

   it’s an extra time commitment from my side.

   If I shortlist a candidate and want to give them a call, it could be a good tool to use to save time, especially to ask questions about interests, availability, and salary.

Was there anything it told you that a CV never would?

1. More depth, more information.

2. In a way, yes.

3. It definitely adds a personal touch, but it did not add extra information for my 1st step - screening (i.e. deciding whom to invite to an interview).

   

Your turn

If this article gave you one useful idea, one thing worth trying, or even just the feeling that someone else has been through the same frustrating process, then it was worth writing.

If this resonated with you, share it with someone who is job hunting right now or with a recruiter brave enough to have an honest opinion.

Share

The more people test this idea, the closer we get to knowing if it actually works, or at least knowing who to blame if it does not.

Leave a comment

Be Happy
Laura

This week, I want to talk about something that has been bothering me for a while: we read all the right books, we highlight all the right parts, and then forget to use any of it when it counts. I built a tool to change that. You describe any situation, a campaign, a negotiation, a conversation, and it applies Cialdini’s 7 principles to multiply your influence and give you a clear action plan.

I’ll give you the exact prompt, walk you through real examples, and show you how to build your own version for any book you love.

My goal is simple: help you get more out of the knowledge you already have, and actually enjoy doing it.

Subscribe now

I remember the exact meeting.

We had been working on a product launch for weeks. The campaign was good enough. And good enough was the problem. Sitting in that room, I had this uncomfortable feeling that I was missing something. A smarter way to frame the offer, a more human way to build the message, a psychological layer I wasn’t reaching.

I knew the answer existed. I had read it. Underlined it. Recommended it to three different people.

But in that moment, under pressure, with a deadline and a room full of expectations, I couldn’t access it. The book was sitting on my shelf at home.

I didn’t know it then, but that gap, between knowing something and being able to use it, was about to become the most interesting problem I’d try to solve.

The Paradox Nobody Talks About

Here’s what nobody tells you when they recommend you read more.

Reading a great book and using it are two completely different skills. We’ve built an entire culture around consuming knowledge: newsletters, podcasts, highlights, summaries, and almost no culture around deploying it. Around actually closing the loop between what we know and what we do.

I’ve read Influence by Robert Cialdini. Properly read it. And I still walk into situations every week where I’m not using what I learned. Not because I didn’t understand it. Because the human brain wasn’t designed to retrieve the right framework at the right moment, under pressure, in context. That’s just biology.

The more you read without applying, the wider the gap gets. Every book you finish and don’t use is another idea sitting on a shelf, in a Kindle, in a note you’ll never open again.

Why Cialdini

Robert Cialdini spent years studying the science of influence as the fundamental architecture of human decision-making. Why do people say yes? What moves someone from hesitation to commitment? What makes a message land?

His answer was seven principles.

These principles don’t operate one at a time. In real situations, they overlap, reinforce each other, and sometimes pull in opposite directions. But you need to understand each one before you can see how they work together.

Here they are:

Reciprocity: People feel obligated to return what they’ve been given.

Give value first, and people feel a natural pull to give back. This is why free content works. You’re not buying attention. You’re earning it.

Example: A SaaS company offers a genuinely useful free tool. Users feel indebted and are far more likely to convert when the paid offer arrives.

Commitment and Consistency: Once people commit to something, they work hard to stay consistent.

Small yeses lead to bigger yeses. The foot is already in the door.

Example: A brand asks, “Do you care about sustainability?” in an email. Those who engage are significantly more likely to buy the eco-friendly product line featured next.

Social Proof: People look to others to decide what’s correct.

Reviews, testimonials, and user numbers all reduce the risk of being wrong by showing others have already made this choice.

Example: “Join 50,000 marketers who read this newsletter every week” is more persuasive than any feature list you could write.

Authority: People follow the lead of credible experts.

Credentials, track records, and third-party recognition signal that this person or product is worth listening to.

Example: A skincare brand partners with a dermatologist, not a celebrity. Conversion climbs because the source feels trustworthy, not paid.

Liking: People say yes to those they like.

Familiarity, shared values, and genuine warmth are not soft variables. They’re commercial ones.

Example: A founder who shares their struggles builds an audience that buys not just the product, but the person behind it.

Scarcity: People want more of what they can have less of.

When something feels finite, it feels more valuable. Our brains treat availability as a signal of worth.

Example: “Only 3 spots left” isn’t manipulation if it’s true. It’s simply making the scarcity visible.

Unity: People are influenced by those they consider part of their group.

This goes deeper than liking. When someone feels you are one of them, trust and influence follow naturally.

Example: “Built by marketers, for marketers” isn’t just describing a team. It’s activating a shared identity that makes the prospect feel understood before they’ve even tried the product.

Seven principles. Decades of research. Endlessly applicable.

And still, most of us forget to use them the moment we close the book.

The App I Built So You Don’t Have To Remember

So I built a tool. Not an app you need to download or a subscription to pay for. An interactive artifact, built inside Claude, that anyone can open and use in about thirty seconds.

Try It Here

You open it, describe whatever you’re working on: a campaign, a negotiation, a message you’ve rewritten four times. It reads your situation through all seven Cialdini principles. It tells you which are already working, which are missing, and gives you a concrete action plan for the three that matter most in your specific case.

No setup. If you have a Claude account, you’re already good to go.

Try It Here

But I don’t just want to give you the app. I want to give you what’s inside it. Because the real power is the prompt. You can paste this into any AI tool you already use, and it works the same way:

Role You are an expert analyst in behavioral psychology and persuasion, deeply trained in Robert B. Cialdini’s 7 principles of influence: Reciprocity, Commitment and Consistency, Social Proof, Authority, Liking, Scarcity, and Unity.

Task Analyze the following real-life case, situation, or campaign and produce a structured influence audit. Your goal is both diagnostic (what is already happening) and prescriptive (what could be done better or added).

Case / situation to analyze [DESCRIBE YOUR CASE HERE, a marketing campaign, a sales process, a negotiation, a product launch, a personal situation, etc.]

Output format, follow this structure exactly

1. Context snapshot (2-3 sentences), The situation, key actors, and core goal.

2. Influence audit. For each of the 7 principles: Strong / Weak / Absent. One sentence explaining why.

3. Priority principles. The 3 with the highest leverage for this context. Justify why.

4. Action plan. One concrete, specific tactic per priority principle. Not generic. Tied to the actual details.

5. Ethical check. Any risk of crossing from persuasion into manipulation, and how to avoid it.

6. Quick-win vs long-term. Can it be applied immediately, or does it require sustained effort?

Be specific and grounded. If key information is missing, state your assumptions before proceeding.

Copy it. Paste it. Replace the bracket with your situation.

You now have a Cialdini audit available anywhere, on anything.

What Comes Out, Three Real Examples

Two from work. One from my personal life

Marketing Example 1: The Course Launch

Input:

“I’m launching an online course for freelance designers. The price is 297 euros. My landing page isn’t converting well. I have 200 testimonials and 3,000 people who completed my free mini-course.”

What the audit surfaces is not what most people expect. The problem isn’t the offer. It’s the sequence. The tool identifies exactly which principles are working against each other on the page, which asset is being completely wasted, and the one structural change that would move conversion more than any copy tweak ever could.

Test it. The output on this one is worth seeing.

Marketing Example 2: The B2B Cold Outreach

Input:

“I run a small content agency doing cold outreach to e-commerce founders. Packages start at 1,500 euros per month. Open rates are decent, but reply rates are almost zero. I have 4 case studies and one recognizable client name.”

The audit finds the exact moment in the sequence where the message loses authority. It’s not where most people would look. It also surfaces a Unity gap that most content agencies never think to address. And once you see it, you can’t unsee it in your own outreach.

Paste it in. You’ll know exactly what to fix.

Personal Example: The Car Situation

Once I finished building the artifact, I wanted to test it with someone who had absolutely nothing to do with marketing, negotiation, influence, or AI. Someone with ordinary problems and ordinary situations. My boyfriend.

I handed him the tool and said: Write any problem you want to solve with anyone. A case where you need to convince or influence someone. Just describe it honestly and see what comes back.

This is what he wrote:

“I want to buy a car, but my girlfriend won’t let me. We live in a city center, and she says a car is unnecessary, expensive, a financial mistake, and a logistical headache. Help me convince her that a car would make us happier and give us more freedom. I need a car.”

He used Cialdini on me. With my own tool. That I built. To manipulate me.

So yes, it works. Uncomfortably well.

Here’s exactly what the audit told him:

Context Snapshot:
A man seeks to buy a personal car in an urban setting where it offers lifestyle and emotional value rather than practical necessity. His girlfriend sees it as financially irrational. The goal is not to win an argument but to shift her position from opposition to acceptance, ideally to genuine support.

Influence Audit

• Reciprocity, Absent. No acknowledgment of her concerns before making the ask.

• Commitment and Consistency, Weak. No shared values or prior agreements are being referenced.

• Social Proof, Absent.

• Authority, Absent. No financial plan to counter the “waste” framing.

• Liking, Strong. The relationship is the foundation, but it’s being strained rather than leveraged.

• Scarcity, Absent.

• Unity, Weak. The conversation is framed as him vs. her rather than a shared decision.

Priority Principles: Unity, Reciprocity, Commitment, and Consistency. This is a relationship negotiation, not a transaction. Persuasion here lives in shared identity and mutual respect, not logic.

Action Plan

• Unity: Stop presenting it as “my car” and start presenting it as “our car.” Weekend trips. Spontaneous plans. Shared freedom. Make her part of the vision, not the obstacle to it.

• Reciprocity: Genuinely acknowledge her concern first. “You’re right that it’s not strictly necessary.” That costs nothing and creates space for her to give back flexibility.

• Commitment and Consistency: Find something she has said before about freedom or shared experiences and connect the car to that honestly.

Ethical Check: The risk is performing Unity rather than meaning it. If the car genuinely becomes something for both of them, this is an honest influence. If it’s just a framing trick, she’ll feel it eventually.

Quick-Win vs Long-Term: All three: Quick Win.

Honestly, he was better equipped to win that argument than he had ever been before.

I designed this tool to help marketers think more clearly about their campaigns. My boyfriend used it to win a domestic negotiation. So all of us can use ordinary problems in life

The Bigger Idea: Build Your Own

This is one book. Seven principles. And the gap it closes is already significant.

But think about every other framework sitting on your shelf. The Mom Test. Never Split the Difference. Thinking Fast and Slow. Building a StoryBrand. I’ve read all of them. If you asked me right now to run a negotiation through Chris Voss’s framework from memory, I’d get sixty percent of the way there. On a good day.

I use negotiation principles every week in client conversations, and I know I’m leaving things on the table every single time.

I want you to build yours!

Here’s the prompt. Swap in your book, list its core principles, and you have a working audit tool in under ten minutes:

Prompt to build your own book audit tool:

“You are an expert on [BOOK TITLE] by [AUTHOR]. You deeply understand its core framework: [LIST THE KEY PRINCIPLES OR CONCEPTS].

When the user describes a situation, campaign, or problem, analyze it through the lens of these principles and produce a structured report:

1. Context snapshot (2-3 sentences)

2. Audit of each principle: Strong / Weak / Absent

3. Top 3 principles with the highest leverage, and why

4. One concrete, specific action per priority principle

5. Ethical check

6. Quick-win vs long-term classification

Be specific. Be direct. State assumptions if information is missing. The user wants to walk away knowing exactly what to do next.

Start by asking the user to describe their situation.”

Paste that into Claude. Built your artifact. Turn any book into a ready-to-use app.

The people who figure this out won’t just work faster. They’ll think better. And thinking better, consistently, in real situations, is the actual competitive advantage.

Now Your Turn

You already have a situation in your head. The campaign you’re stuck on. The conversation you’ve been putting off. The proposal you’ve rewritten three times. That one. Start there.

Open the app. Describe your situation. See what comes back.

Try It Here

Then ask yourself one question: what other book do I wish I had with me right now?

Because that’s the next one to build.

The knowledge was always yours. Now you can actually use it.

If this was useful, share it with someone who has a highlighted copy of Influence sitting on their shelf. They need this more than they know.

Share

What book would you build this for? Tell me in the comments. I read every single one.

Leave a comment

And if you want to collaborate or just geek out about AI and marketing, you know where to find me. DMs are open.

Be Happy
Laura

Hey, welcome back. I’m Laura. Just a regular human and marketer trying to make sense of the AI era without losing my mind.

This week, I’ll walk you through how to position your product, craft your messaging, and build a strong narrative step by step, using AI as your partner. My goal is simple: help you understand the process and actually enjoy it.

Subscribe now

Your company doesn’t have a positioning problem. It has 47 of them.

The CEO says you’re “the AI-powered platform for modern teams.”
Sales says you’re “Salesforce but easier.”
Engineering says you’re “a multi-tenant SaaS solution with advanced workflow automation.”
Marketing says you’re “transforming how companies collaborate.”

Same product. Four completely different stories.

And that’s just leadership. Wait until you hear what Customer Success tells new users, what the website homepage says, or how that one engineer at the conference tried to explain it using a whiteboard and three different programming languages.

The product works. The team is good. The market is there. But people still don’t get it, don’t care, or don’t buy. Not because the product lacks value, but because nobody can explain it the same way twice. Which makes everyone’s job harder.

And spoiler … it makes this worse when anyone can now build and ship a product in days using AI. The barrier to building is gone. But the barrier to being understood? That’s only getting higher.

And the damage compounds until teams lose confidence totally.

That’s the tax of positioning chaos. You pay it every single day until you fix it.

Today’s the day you stop paying.

Why I’m writing this

After 10+ years in tech, I’ve never seen a company that didn’t struggle with positioning at some point. Not one.

Positioning and messaging aren’t nice-to-haves. They’re the foundation. They align teams, guide decisions, and make sales easier. Clarity changes everything.

The person who brings that clarity? They become invaluable. They end the civil war. You know, the one where Sales uses “platform,” Marketing says “solution,” and Engineering insists on calling it “the thing we built that does the stuff.”

That’s why I keep coming back to this skill. Learn the frameworks. Adapt them. And now, use AI to move faster and get better results.

This guide is for you if you’re:

• A Product Marketer tired of the positioning chaos and ready to fix it

• A Product Marketer prepping for interviews who needs to demonstrate this exact skill in real time

• A Product Manager ready to own positioning (Congrats on being brave enough to take this on!)

• A Founder who needs to pitch investors, align the team, or stop explaining the product differently every single time

• Anyone curious about how good positioning actually gets built

I’m not a positioning expert. I’m a marketer who’s dealt with this problem over and over, learned from smart people, and figured out how to make the process faster using AI.

This is the guide I wish I’d had five years ago.

Quick story: The first time I tried positioning at a startup, I spent three weeks in meetings, built a 47-slide deck explaining our “unique value proposition,” and presented it to the exec team.

You know what happened? Exactly nothing.

Turns out, I was making it way too complicated. I was trying to be clever instead of clear. I was writing for myself instead of for Sales, for customers, for the people who actually had to USE it.

This framework? It’s the opposite of that disaster. It’s simple. It’s clear. And it actually gets used.

Let’s dive in.

The framework I use is Obviously Awesome positioning canvas

It can take around 90 minutes with AI, though this may vary depending on how efficiently you manage your time and how much information you have available and ready to use, so treat this estimate as a rough guide to set your expectations.

The 5 Components (The Big Picture)

⏱️ Total time: 90 minutes
📍 You are here: Understanding the framework

Think of positioning like building a house. You need:

1. Competitive Alternatives — What exists today (the landscape)

2. Distinct Attributes — What makes you unique (your foundation)

3. Differentiated Value — What outcomes you deliver (the rooms people actually use)

4. Best-Fit Customers — Who this house is built for

5. Market Category — What neighborhood you’re in

Each step builds on the previous one. Do them in order.

Step 1: Competitive Alternatives

⏱️ Time needed: 30 -40 minutes

Here’s what you need to figure out: what do customers actually use today to solve this problem?

Not who could theoretically compete with you. What actually ends up on their shortlist when they’re evaluating options?

Most companies guess wrong here. They list every competitor they can think of, or worse, only the direct product competitors. They miss the real competition: spreadsheets, doing nothing, duct-taping three tools together.

How to find out

Talk to recent customers ( most accurate)

Call or email 5-10 people who bought in the last few months. Their memory is fresh. Ask:

• “Before us, what were you using?”

• “What else made your shortlist?”

• “If we didn’t exist tomorrow, what would you use?”

That’s it. Three questions. 15 minutes. You’ll learn more than a month of competitive analysis.

Build it into an onboarding or partners program (scales automatically)

Add 2-3 questions to your Day 3-5 onboarding email using Typeform or Google Forms:

1. “Before [Product], what were you using?” (open text)

2. “What alternatives did you consider?” (checkboxes)

3. “Why did you choose us?” (open text)

Why Day 3-5? They’ve used the product, but the evaluation is still fresh in their mind.

Ask your sales team (if customers aren’t accessible)

Talk to 3-5 reps. They hear this stuff every day:

• “What are prospects using before they talk to us?”

• “Which competitors make the final round most often?”

• “What do we lose to?”

Sales know. They just don’t always write it down. It lives in Alex’s head, three different Slack threads, and that one demo recording from Q2 that nobody can find anymore.

Use AI to make sense of it

Once you’ve collected responses, don’t manually sort through them. Let AI find the patterns.

If you ran a survey and have 20+ responses:

Prompt

I collected customer responses about competitive alternatives.

BEFORE [PRODUCT], WHAT WERE YOU USING?

- Customer 1: [response]

- Customer 2: [response]

[paste all]

Help me:

1. Find the most mentioned alternatives (include status quo like spreadsheets)

2. Calculate percentages

3. Group by approach to solving the problem

4. Highlight surprises

Our product: [1-sentence description]

If you’re starting from scratch (pre-launch, no customers yet)

This is my least recommended option, as it may lack sufficient accuracy, but in most of the case moving forward with an incomplete picture is better than stalling. You can always refine as you go.

Prompt

I’m positioning [product name]: [1-sentence description]

Target customer: [who they are]

Product page: [URL if available]

What would customers use if we didn’t exist? Include:

- Status quo (manual, spreadsheets, email)

- Direct competitors

- Indirect alternatives

Group by approach. Exclude theoretical competitors that never actually show up.

This won’t be perfect if you don’t have real customer data, but it gives you a starting point.

What this looks like

Product: Project management for remote teams

Competitive Alternatives:

• Status quo: Spreadsheets + Slack + email (65%)

• Lightweight tools: Asana, Trello, Notion (45%)

• Enterprise tools: Jira, Monday.com (15%)

See the status quo? That’s what most companies miss. 65% weren’t using a PM tool at all. They were using Sarah’s personal Notion workspace that she refuses to share admin access to. You know Sarah.

📍 Progress: 1 of 5 complete | Next: Distinct Attributes

Step 2: Distinct Attributes

⏱️ Time needed: 30 minutes

Now you know what you’re competing against. Next question: what do you have that they don’t?

This includes product features AND company stuff; your support model, pricing, how fast you implement, and team expertise. All of it counts.

You can’t claim you deliver unique value if you don’t know what makes you actually unique. And here’s the thing: it doesn’t have to be different from every competitor. If it’s distinct, list it.

Don’t guess. Get the data.

Most people wing this part. They sit in a room and brainstorm what they think makes them different. Everyone nods enthusiastically when someone says “user-friendly interface” and “AI-powered.” Then they find out six months later their competitor launched the exact same thing. Turns out everyone thinks their interface is friendly.

Better approach: scrape competitor websites and see what they actually have.

Use tools like Gumloop or BrowserAI:

1. List your top competitors (from Step 1)

2. Scrape these pages:

   • Features/product pages

   • Pricing

   • Integrations

   • About/team pages

   • Clients’ reviews in G2/Capterra reviews

3. Do the same for your site

4. Save everything as text files

Focus on features pages, pricing, and “how it works” sections. That’s where the truth lives, not the marketing fluff.

Use AI to compare

Create a Claude Project and upload all scraped content, then:

I’ve uploaded competitor website content and our product docs.

Prompt

COMPETITIVE ALTERNATIVES (from Step 1):

[paste your list]

Compare us to them and find our distinct attributes:

1. Product features we have that they don’t

   - Check: features, integrations, technical specs

2. Company capabilities we have that they don’t

   - Support (response time, channels)

   - Implementation speed

   - Pricing model

   - Team expertise/background

3. Things that work differently even if they exist elsewhere

Create a table:

[Feature] | Us | Competitor A | Competitor B | Competitor C | Distinct?

Then list only our distinct attributes.

Our product: [description]

Target customers: [from Step 1]

Simpler version (without scraping):

COMPETITIVE ALTERNATIVES: [from Step 1] Add the URL of each competitor

Help me identify our distinct attributes:

1. Product features we have that they don’t

2. Company capabilities that differentiate us (support, pricing, onboarding, expertise)

Our product: [description]

Our key features: [list 10-15]

Prompt Simpler version (without scraping):

COMPETITIVE ALTERNATIVES: [from Step 1 , Add the URL page of each competitor]

Analyze URL information

2. Help me identify our distinct attributes:

Product features we have that they don’t

Company capabilities that differentiate us (support, pricing, onboarding, expertise)

Our product: [description]

Our key features: [list 10-15]

Example output

Distinct Attributes:

• Async collaboration (no real-time meetings required)

• 2-hour setup vs. 2-week implementation

• Built-in screen recording

• Usage-based pricing (not per-seat)

• 48-hour support response time

• Native timezone display

✅ You now have

Concrete list of what you uniquely offer, validated by data, not assumptions.

Update quarterly. Competitors ship features; your uniqueness changes.

📍 Progress: 2 of 5 complete | Next: Differentiated Value

Step 3: Differentiated Value

⏱️ Time needed: 20-30 minutes

Okay, you have your list of unique features. Now for the hard part: turning features into outcomes people actually care about.

Nobody buys “async collaboration.” They buy “shipping 30% faster without drowning in meetings.”

Here’s what’s actually happening in your customer’s brain:

When you say: “We have async collaboration.”
They hear: “So what?”

When you say: “Ship 30% faster with 50% fewer meetings”
They hear: “Tell me more”

Same feature. Different language. One talks about what you built. The other talks about what they get. The “so what?” chain

What you need first

Don’t do this in a vacuum. You need:

• Your distinct attributes (Step 2)

• Customer feedback on the actual impact

• Success metrics (if you have them)

Where to find feedback:

• G2/Capterra reviews—look for “before/after” language

• Sales win/loss interviews

• Customer success check-ins

• Case studies you’ve already written

• Support tickets that turned into success stories

Even better: Call 5-10 customers and ask:

• “What business problem did we solve?”

• “What’s different now? How do you measure it?”

• “What would you tell a peer about why to use us?”

Their exact words matter. Use them. Not the polished version your customer success manager turned into a LinkedIn post. The raw “honestly this saved our asses” version.

Use AI to map value

Upload customer feedback + attributes to claude project, then:

Prompt

DISTINCT ATTRIBUTES (from Step 2):

[paste list]

CUSTOMER FEEDBACK:

[paste reviews, quotes, testimonials]

For each attribute, run the “So What?” chain:

- Feature → What does it do?

- Benefit → What does this enable?

- Impact → How does it change their work?

- Business value → What measurable outcome?

Focus on:

- Time (faster shipping, quicker decisions)

- Money (cost savings, revenue increase)

- Risk (compliance, security, fewer errors)

- Growth (scale, new markets)

Then group into 1-4 themes:

- Each theme = distinct value type (no overlap)

- 2-4 measurable outcomes per theme

- Named in customer language (not jargon)

Format:

THEME: [Customer-friendly name]

- Outcome 1: [specific, measurable]

- Outcome 2: [specific, measurable]

- Supporting attributes: [which features enable this]

- Customer proof: [quotes]

Good: “Speed Without Chaos,” “Predictable Growth Costs”

Bad: “Enhanced Productivity” (too generic)

Our product: [description]

Target customers: [from Step 1]

Example output

THEME 1: Speed Without Meeting Overload

• Ship projects 30% faster

• Reduce meetings 50% (10hrs/week → 5hrs/week)

• Make decisions in hours, not days

• Features: Async collab, screen recording, timezone display

• Proof: “Shipped Q4 roadmap 6 weeks early” —Sarah, Head of Product

THEME 2: Clarity at Scale

• Zero “I wasn’t in that meeting” gaps

• 100% context visibility

• 2-hour onboarding vs. 2 weeks

• Features: Screen recording, async updates

• Proof: “Everyone knows the ‘why’ now” —Mike, Eng Manager

THEME 3: Predictable Growth Costs

• Pay per usage (not per seat)

• 40% lower cost at 50+ users

• Add team members without budget delays

• Features: Usage-based pricing, 2-hour setup

• Proof: “Scaled 20→80 users, same budget” —Lisa, Ops

✅ You now have

1-4 value themes with specific outcomes, proof, and a clear link to your features.

This is your messaging foundation.

📍 Progress: 3 of 5 complete

Stop. Breathe. Look at what you just built.

Most companies never get this far. They either:

• Give up after listing competitors (Step 1)

• Get stuck in features they think are different but aren’t (Step 2)

• Can’t translate features into actual value (Step 3)

You just did all three. You have the foundation. The next two steps (best-fit customers + market category) are the fastest, and they’re what make everything click.

You’re 20 minutes from a complete positioning canvas. Keep going.

Step 4: Best-Fit Customers

⏱️ Time needed: 10 minutes

What you’re figuring out

Who cares a lot about the value that only you deliver? Not who could use you—who would say “yes, THIS is exactly what we need.”

Why it matters

Not everyone cares about your value equally. Targeting everyone dilutes your message. Tight focus makes everything easier.

Use AI to define

VALUE THEMES (from Step 3):

[paste your themes]

Who would care most about this value?

Consider:

- Company size/stage

- Industry

- Current pain points

- Team structure

- Geographic spread

- Trigger events (recent changes, growth, compliance needs)

What makes someone say “this is critical” vs. “nice to have”?

Find specific, observable characteristics.

Example output

Best-Fit Customers:

• Distributed across 3+ timezones

• 20-200 employees

• Product/engineering teams shipping weekly

• Moved remote in last 2 years

• Currently juggling 4+ tools

• Growing 30%+ annually

✅ You now have

Clear profile of who to target (and who to ignore).

📍 Progress: 4 of 5 complete | Next: Market Category (final step!)

Step 5: Market Category

⏱️ Time needed: 5 minutes

What you’re figuring out

What context/category makes your value obvious to your target customers? This is what they search for when they have your problem.

Why it matters

Wrong category = invisible value. Right category = obvious fit. This is how people find and understand you.

Use AI to decide

Given:

- Best-fit customers: [Step 4]

- Value themes: [Step 3]

- Competitive alternatives: [Step 1]

What market category makes our value immediately clear?

Consider:

- What do they search for?

- What category do alternatives occupy?

- Should we own a sub-category or create a new?

- What framing highlights our difference?

Balance: not too broad (lost in noise), not too narrow (no one searches).

Example output

Category Options:

• ❌ “Async-first PM” (no one searches)

• ❌ “Project management” (too broad, Asana owns it)

• ✅ “Project management for distributed teams” (clear + differentiation obvious)

✅ You now have

The category that frames your entire positioning.

📍 Progress: 5 of 5 complete | All components done! Now let’s build your canvas.

Your Complete Positioning Canvas

Final synthesis prompt

I’ve completed April Dunford’s 5 steps:

COMPETITIVE ALTERNATIVES:

[Step 1]

DISTINCT ATTRIBUTES:

[Step 2]

VALUE THEMES:

[Step 3]

BEST-FIT CUSTOMERS:

[Step 4]

MARKET CATEGORY:

[Step 5]

Create a one-page positioning document:

**[PRODUCT NAME] Positioning**

For: [best-fit customers]

[Product] is: [category]

That: [primary value]

Unlike: [alternatives]

We: [distinct attributes that deliver unique value]

---

WHAT MAKES US DIFFERENT:

[3-5 attributes]

VALUE WE DELIVER:

[Theme 1]: [outcomes]

[Theme 2]: [outcomes]

[Theme 3]: [outcomes]

BEST-FIT CUSTOMERS:

[characteristics]

---

Make it clear, concise, and immediately understandable.

Real Example: Complete Positioning Document

Here’s what your final output looks like using our project management example:

FlowSync Positioning

Market Category: Project Management for Distributed Teams

For: Product and engineering teams distributed across 3+ timezones (20-200 employees) who are shipping weekly and struggling with async coordination

FlowSync is: Project management built for distributed teams
That: Let teams ship faster without meeting overload by enabling true async collaboration

Unlike: Spreadsheets + Slack (status quo) and sync-first tools like Asana, Trello, Monday.com
We: Enable 24/7 progress with async collaboration, built-in screen recording for context, 2-hour setup, and usage-based pricing

COMPETITIVE ALTERNATIVES:

• Status quo: Spreadsheets + Slack + email (what 65% were using)

• Lightweight PM tools: Asana, Trello, Notion (sync-first, per-seat pricing)

• Enterprise PM: Jira, Monday.com (2-week setup, complex)

WHAT MAKES US DIFFERENT:

• Async-first collaboration — No real-time meetings required, work happens 24/7

• Built-in screen recording — Add visual context to every update, not just text

• 2-hour implementation — vs. 2-week average for competitors

• Usage-based pricing — Pay for what you use, not per seat

• Native timezone display — See all deadlines in local time automatically

• 48-hour support response — vs. 5-7 day industry average

VALUE WE UNIQUELY DELIVER:

Speed Without Meeting Overload

• Ship projects 30% faster than sync-first tools

• Reduce meeting time by 50% (10 hrs/week → 5 hrs/week)

• Make decisions in hours, not days (no timezone waiting)

• Customer proof: “Honestly, we shipped our Q4 roadmap like 6 weeks early. First time that’s happened in two years.” —Sarah Chen, Head of Product

Clarity at Scale

• Zero “I wasn’t in that meeting” information gaps

• 100% context visibility through screen recordings + written updates

• New team members onboard in 2 hours vs. 2 weeks

• Customer proof: “Everyone finally knows the ‘why’ behind decisions. Game changer for our remote team.” —Mike Rodriguez, Engineering Manager

Predictable Growth Costs

• Pay per active usage, not per seat

• 40% lower cost vs. per-seat competitors at 50+ users

• Add team members without budget approval delays

• Customer proof: “We went from 20 to 80 people. Our PM tool budget didn’t change at all.” —Lisa Park, Operations Director

BEST-FIT CUSTOMERS:

• Distributed teams across 3+ timezones

• 20-200 employees (product, engineering, or ops teams)

• Shipping weekly or bi-weekly

• Recently went remote (last 2 years) and struggling with async coordination

• Currently using 4+ tools to manage projects

• Growing 30%+ annually

This is your source of truth. Every homepage headline, every sales pitch, every product description flows from this document.

Take a second and look at what you just built.

A month ago, your team was telling 47 different stories. Now you have one source of truth. One document that Sales, Marketing, Product, and Leadership can all point to and say “This is who we are.”

That’s not a small thing. That’s the foundation of everything that comes next.

What to Do Next

This week:

1. Share this article with your team (product, sales, marketing, leadership)

2. Execute the process as working as a team.

3. Review together

4. Refine based on what lands and what doesn’t

Want to level up? Automate this entire process.

You just did this with an AI workflow in 120 minutes. Now imagine running it automatically every quarter, fresh customer data, updated competitor intel, and a new positioning canvas, without lifting a finger.

Tools like Make or Gumloop can:

• Auto-scrape competitor websites on a schedule

• Pull customer survey responses from Typeform

• Connect with an LLM

• Generate your updated positioning document

• Send it to Slack when it’s ready

The setup:

1. Schedule competitor scraping (monthly)

2. Trigger on new customer survey responses

3. Aggregate all data to the LLM module

4. Run positioning prompts from this guide

5. Output updated canvas to Notion/Google Docs

Why bother?
Because positioning isn’t a one-time thing. Your product evolves. Competitors launch features. Customer language shifts. The companies that win are the ones who keep positioning fresh, not the ones who did it once in 2024 and called it done.

Set it up once, let it run forever. Your future self will thank you.

You just ended the positioning civil war.

Coming soon

I’m writing the next guide. You pick the topic.

Option 1: Andy Raskin’s Strategic Narrative Framework
Turn your positioning into the story that actually sells. The framework for pitches that make people lean in and say “tell me more,” because positioning without a story is just facts nobody remembers.

Option 2: April Dunford’s Sales Pitch Framework
Build a repeatable sales pitch from your positioning canvas. AI-powered, conversion-focused, ready for your team tomorrow.

Option 3: Live Build - Automate the Whole Thing
I’ll build the Gumloop/Make workflow on camera. Every step documented, templates included, positioning automation forever.

Drop a comment or DM me your pick. I’ll write whichever gets the most votes first.

Found this helpful? Share it with that PM who keeps asking, “but what do we actually do?” or that founder who explains the product differently every time. They’ll thank you. Or at least they’ll stop asking you to explain positioning.

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See something I missed or got wrong? Better process? Drop a comment. I’m serious about making this guide better. Your chaos stories and positioning disasters make this more useful for everyone.

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Positioning with April Dunford

A newsletter that covers all things Positioning for founders, marketers and product folks. I'm April Dunford - a positioning expert, consultant, author of the books Obviously Awesome and Sales Pitch, and host of the Positioning podcast.

This week, I’m walking you through how to build a simple, lovable brand step by step with AI as your partner in less than a week. And actually enjoy the process.

Subscribe now

Vibe coding isn’s just hype

Here’s what’s changed: According to SaaStr, 65 million potential builders are ready to launch micro-products this year. Marketers, creators, and operators are becoming builders overnight, which explains why your LinkedIn feed looks like a startup pitch deck threw up.

When software becomes cheap and widespread, coding becomes a commodity. So differentiation becomes the real advantage.

That’s why brand matters more than ever. Especially if you’re building something people will actually care about.

I built my Substack brand in five days using the framework I’m about to show you. No agency, no budget, no brand degree required. Just a clear process that works.

In this article, I’ll walk you through how to build a brand from scratch using PRESENT, a simple, proven framework designed for speed and clarity. No 80-slide decks.

On top of that, I’ll show you how to use AI to accelerate the process with a custom ChatGPT that works like a creative partner.

Everything here comes from real experience. I’ve built brands multiple times as a Product Marketer in tech startups and as a Substack creator. I’m not a “brand guru.” I’m someone who had to build brands without being an expert, by finding the best experts and using the right frameworks.

This is for you if you’re:

• A marketer who wants to understand the brand beyond logo and colors, and defend decisions with confidence

• An entrepreneur, solopreneur, or vibecoder building a project and needing a minimum lovable brand that stands out

• A product builder who wants the brand to show up inside the product, not just in marketing

• A Substack creator who wants a simple, professional brand that attracts the right readers

Why am I telling you this

You can jump straight to the framework if you want. But if you care about the why behind this approach, stick with me for a minute.

Quick context: I studied Advertising and PR, but I only truly understood branding once I lived it.

At Komvida (a kombucha brand in Spain), I led e-commerce strategy and worked with an exceptional Brand Creative Director, Marc Caballe. We grew it into a market leader with a community that didn’t just buy the product. They felt part of it.

That’s where I learned: a great brand doesn’t decorate growth. It drives it.

At 2gether (a European crypto exchange), as Head of Marketing, I pushed brand as a strategic lever, not an afterthought. A few skeptical eyes were watching closely. When we achieved a higher market share than Coinbase and Binance in Spain during the first year, those eyes stopped being skeptical.

The lesson? Brand amplifies reality. It can’t replace it. 2gether offered the simplest way to buy crypto. The brand just made that value three times louder.

After that, I did a slow, heavy rebranding with a big agency. Many stakeholders, endless meetings, the whole nightmare. Then I went back to speed at Billionhand, where we built and launched a brand in under a week.

Here’s what I learned: brand building can be slow, political, and endless. Or fast, focused, and designed to evolve. If you can choose, choose the second.

That’s exactly what I did for my Substack. No budget, no team, just me. I used PRESENT, a framework by brand expert Rodrigo Maroni, and built a minimum lovable brand in less than a week, with a custom GPT as my creative Partner.

Now I’m going to teach you that same process.

How to build a brand without the bullshit

Branding isn’t about slapping a new logo on things or choosing pretty colors and pretending you’ve got a strategy.

Real branding is about making people instantly get three things about you:

• Meaning: Why does this even matter?

• Difference: Why should I pick you over everyone else?

• Trust: Why should I believe anything you’re saying?

A brand is how people recognize you, remember you, and feel about you. It’s not what you tell them. It’s what they walk away thinking.

We always start with strategy because design without strategy is just decoration.

If words like “positioning” or “brand pillars” sound foreign to you, don’t worry. We’re going to build your brand the same way you’d build a house, starting from the foundation and working our way up to the front door.

The four phases of building a brand

There are four phases to building a brand.

Foundations is where you define your purpose, your audience, the key human insight behind your brand, and what’s happening in your category.

Strategy is where you decide your positioning, your main brand idea, your value proposition, and the key messaging pillars you will repeat.

Expression is where your brand takes shape through its voice, visual style, name, tagline, and overall story.

Activation is where the brand comes to life through your website copy, campaigns, content plan, and internal alignment with your team.

Phase 1: Foundations

What you actually are

This phase makes your brand real. It answers: who are you for, what do you believe, and what truth are you built on?

Think of it like pouring concrete. If this part is weak, everything you build on top of it will wobble.

1. Brand purpose

Your purpose is why you exist beyond just making money. It’s what you’re here to change, protect, or make possible.

Good examples:

Patagonia: “We’re in business to save our home planet.”
Why it works: Specific, bold, and they actually back it up with real action.

Tony’s Chocolonely: “100% slave-free chocolate.”
Why it works: Tackles a real problem in the industry and gives the brand a clear mission.

LEGO: “Inspire and develop the builders of tomorrow.”
Why it works: Connects a product to something bigger, human potential.

Bad examples:

“To be the best coffee brand in the world.”
Why it sucks: It’s all about you, not what you stand for.

“To innovate continuously.”
Why it sucks: Sounds clever but means absolutely nothing.

“To make people happy.”
Why it sucks: So generic that literally any brand could say it.

Rule of thumb: If any company could claim it as their own, it’s not a purpose. It’s just filler. If your purpose sounds like it was generated by autocomplete after typing “Our mission is to,” start over.

2. Target audience

A target audience isn’t a demographic. It’s a group of people who share a goal, frustration, or identity.

Good examples:

Apple: Creatives who value freedom and innovation.
Why it works: Defines a clear identity, not just a buyer profile.

Peloton: High achievers who want performance and community.
Why it works: Speaks to motivation and belonging, not just fitness.

Glossier: People who want authenticity, not perfection.
Why it works: Captures a mindset that guides everything from product to marketing.

Bad examples:

“Women 25 to 35.”
Why it sucks: That’s a demographic, not a motivation. Congratulations, you’ve just described roughly 8% of Earth’s population. That should make your marketing really focused.

“Everyone who likes tech.”
Why it sucks: Way too broad. Impossible to speak clearly.

“High-income consumers.”
Why it sucks: Tells you nothing about what they want or care about.

Helpful question: What do they want, fear, and care about?

3. Category cliché

Every category repeats the same tired story. That story becomes invisible. Your job is to spot it so you can break it.

Here’s the weird part: the cliché exists because it worked once. Your job isn’t to avoid what works. It’s to avoid what’s become invisible.

Examples:

Water cliché: “Pure. Clean. From the mountains.”
Disruptor: Liquid Death
Why it worked: Looked nothing like water branding, so people couldn’t forget it.

Insurance cliché: “Trust us. We’ve been here forever.”
Disruptor: Geico
Why it worked: Humor made a boring category actually watchable and memorable.

Beauty cliché: “Look perfect. Be flawless.”
Disruptor: Dove Real Beauty
Why it worked: Made people feel seen instead of judged.

Simple takeaways:

Clichés make you blend in. Breaking them makes you stand out.

Clichés are strategy junk food. They feel safe going in, then six months later you’re wondering why your brand looks like everyone else’s distant cousin.

4. Core insight

A core insight is a deep human truth that explains why people emotionally care about something.

Insight is not:

• A fact: “Gen Z spends 6 hours a day online.” Just data.

• A trend: “Sustainability is on the rise.” Just observation.

• A stat: “67% of consumers prefer brands with purpose.” Just a number.

Those things are useful inputs, but they don’t unlock anything new. Insights are breakthroughs.

If your “insight” could’ve been pulled from a LinkedIn motivational post, it’s not an insight. It’s just something that sounds smart in a meeting.

A true insight equals a game-changing revelation

A real insight sounds like:

“You think the problem is X. It’s actually Y.”
“People say they want A, but they choose B because of how it feels.”

Good insight examples:

Liquid Death: People want to be healthy without feeling boring.
Why it works: Reveals a tension the brand can solve creatively.

Geico: Nobody cares about insurance unless it’s simple and entertaining.
Why it works: Honest, human, and explains their whole tone perfectly.

Dove: Most women don’t feel represented by beauty standards.
Why it works: Connects the product to a real emotional need.

Bad insight examples:

“People like comfort.”
Why it sucks: Obvious. Doesn’t unlock a new strategy.

“Everyone wants to save money.”
Why it sucks: True, but too broad to build a brand on.

“Consumers want healthy food.”
Why it sucks: Surface-level and not emotionally specific.

Pro tip

If you’re building a brand from scratch, go with:

1. Brand Purpose → 2. Target Audience → 3. Market Context → 4. Core Insight

Why? Because you’re starting from intention.

This sequence is ideal for founders, startups, or rebrands where belief and behavior matter more than legacy.

If you’re repositioning an existing brand or entering a competitive category, flip it:

1. Market Context → 2. Core Insight → 3. Target Audience → 4. Brand Purpose

This is the disruption-first approach.

This sequence is sharper for mature brands, challengers, or category disruptors who want to cut through noise fast.

Checkpoint

Pause here. If defining these four elements felt hard, good. You’re doing the work most people skip. This is why their brands feel like guesswork.

If you can answer these four questions clearly, you’re already thinking more strategically than most startups.

Copy this into your doc right now:

• Our purpose is: ___

• Our audience wants: ___

• The category cliché is: ___

• Our insight is: ___

Your first version will probably be rough. But getting words on the page beats staring at a blank doc. You can always refine.

Phase 2: Strategy

What you stand for

Now we turn the foundation into a structure. This is your brand’s spine.

Think of Purpose, Category Context, Cliché, and Core Insight as the inputs to produce these outputs: Positioning, brand idea, value proposition, and messaging pillars.

Here’s what most people miss: most brand failures happen in Phase 1. People skip to Phase 3 (the fun visual stuff) without doing the hard thinking. Don’t. If you nailed your foundations, everything from here gets easier.

1. Positioning

Positioning is one sentence that explains who you serve, what you do, and why you’re different.

How the inputs shape it:

• Purpose gives your positioning a why that matters

• Category Context ensures you don’t copy competitors

• Cliché tells you what not to say

• Insight tells you what only you can say

Example:

If your purpose is “helping creatives find calm” and the cliché in your category is “hustle harder,” your positioning might be:

“Unlike productivity tools that push output, we help thinkers find mental clarity, because creativity needs space.”

Good examples:

Liquid Death: “Water for people who hate boring brands.”
Why it works: Clearly defines a specific audience and unique tone in one line.

Geico: “Fast, simple insurance with a sense of humor.”
Why it works: Stands out in a serious category by owning simplicity plus entertainment.

Dove: “Beauty products that celebrate real women, not perfection.”
Why it works: Flips the beauty cliché and takes a clear emotional stance.

Bad examples:

“We offer innovative solutions.”
Why it sucks: Could describe literally any company. If you can replace your brand name with any competitor and the positioning still works, you don’t have positioning. You have Mad Libs.

“The best service at the best price.”
Why it sucks: Everyone claims this, so nobody believes it.

“Quality and value you can trust.”
Why it sucks: Generic and forgettable.

2. Brand idea (north star)

This is your emotional promise wrapped in a sticky phrase. Think of it as the emotional core that unites your purpose, insight, and audience and gives direction to everything you create. It’s the guiding idea that everything else orbits around.

How it’s shaped:

• Purpose gives it meaning

• Insight gives it emotional punch

• Cliché gives it edge, by saying the opposite

Example:

If the insight is “People don’t want to hustle, they want to feel in control,” the brand idea could be:

“Focus without force.”

It’s short, sticky, and emotionally charged.

Good examples:

Liquid Death: “Health with attitude.”
Why it works: Turns water into identity, not just hydration.

Geico: “Insurance that doesn’t waste your time.”
Why it works: Speaks to what people actually want, speed and simplicity.

Dove: “Real Beauty.”
Why it works: A mission that guides every campaign.

Bad examples:

“Be your best self.”
Why it sucks: Vague and overused. This phrase has been used so many times it should come with a cease and desist letter from the universe.

“Innovating for the future.”
Why it sucks: Corporate language with no emotion.

“Think different” (if you’re not Apple).
Why it sucks: Copycats always sound fake.

3. Value proposition

Your value proposition is your clear promise: what you help people do.

How it’s shaped:

• Insight points to the real benefit your audience wants

• Cliché helps you cut jargon and avoid empty promises

Example:

If the cliché is “boost productivity” but the insight is “people feel overwhelmed,” your value prop might be:

“We help busy minds find clarity, without the burnout.”

Good examples:

Liquid Death: “Drink water without losing your personality.”
Why it works: Matches the insight and makes the product feel fun.

Geico: “Save money in 15 minutes.”
Why it works: Short, clear, measurable, memorable.

Dove: “Beauty products that build confidence, not insecurity.”
Why it works: Connects product to a meaningful emotional outcome.

Bad examples:

“We’re the future of X.”
Why it sucks: Hype with no clarity.

“Helping people live better lives.”
Why it sucks: Too broad to be believable.

“End-to-end solutions.”
Why it sucks: Jargon that hides what you actually do.

4. Messaging pillars

These are three to four themes you repeat everywhere. They’re your brand’s greatest hits, the beliefs you weave into every blog post, email, or ad you write.

How they’re shaped:

• Purpose anchors them in belief

• Insight gives them emotional relevance

• Category and cliché tell you which messages to challenge

Here’s something people don’t realize: strong messaging pillars also tell you what NOT to talk about. They’re filters, not just topics.

Example pillars (for a focus brand):

• Clarity is a ritual

• Creativity needs space

• Focus doesn’t mean hustle

• Burnout is not a badge of honor

Each one reflects a belief and helps shape every piece of content you create.

Good examples:

Liquid Death messaging pillars:

• Healthy hydration, not boring wellness

• Rebel personality

• Anti-plastic sustainability

Why it works: Everything supports the same identity and tone.

Geico messaging pillars:

• Save time

• Save money

• Make it easy

• Make it memorable (humor)

Why it works: Practical, repeatable, and reinforces positioning.

Dove messaging pillars:

• Beauty is diverse

• Confidence matters

• Real is beautiful

• Reject harmful standards

Why it works: Emotionally consistent and mission-driven.

Bad examples:

“Quality, innovation, service.”
Why it sucks: Corporate mush, not messaging.

“Customer satisfaction.”
Why it sucks: That’s a KPI, not a brand idea.

“Results.”
Why it sucks: Unclear, results for who and why?

Checkpoint

Notice something? You just designed a brand system that could guide years of decisions. Some agencies charge $50K+ for this. You did it by answering questions.

Phase 3: Expression

How you show up

This is where strategy becomes experience. If you nailed your positioning, this part becomes obvious. Your brand idea practically tells you what voice and visuals to use.

1. Tone of voice

Tone is how your brand sounds in writing and speech. It brings your personality to life.

Good examples:

Liquid Death
Tone: Bold, satirical, rebellious.
Why it works: Makes water entertaining and unforgettable.

Geico
Tone: Funny, simple, light.
Why it works: Humor becomes the hook that delivers the message.

Dove
Tone: Warm, empowering, supportive.
Why it works: Builds trust and emotional connection.

Bad examples:

“We strive to leverage synergies.”
Why it sucks: Nobody talks like this in real life. Not even the person who wrote it. Especially not the person who wrote it.

Forced humor
Why it sucks: Breaks trust and feels try-hard.

Inconsistent tone
Why it sucks: Makes the brand feel unreliable.

2. Visual direction

Visual direction is your look: colors, fonts, imagery, and style.

Good examples:

Liquid Death
Cans, bold typography, metal/punk vibe.
Why it works: Instantly breaks category norms.

Dove
Clean visuals, real women, natural photography.
Why it works: Matches the mission and feels authentic.

Bad examples:

Stock photos
Why it sucks: Looks generic and replaceable. Also, we all know that’s not your team. Your team doesn’t laugh at salads.

Blue-gray corporate templates
Why it sucks: Signals are safe, not memorable.

Inconsistent design
Why it sucks: Kills trust and recognition.

3. Name and tagline

Your name introduces you. Your tagline delivers your promise.

The best brand ideas sound too simple. If yours feels clever, it’s probably too complicated.

Good examples:

Liquid Death
Name: Liquid Death
Tagline vibe: “Murder your thirst.”
Why it works: Impossible to ignore.

Geico
Brand: Geico
Tagline: “15 minutes could save you 15% or more.”
Why it works: Repeated, sticky, tied to value.

Dove
Brand: Dove
Tagline: “Real Beauty.”
Why it works: Simple emotional statement that guides everything.

Bad examples:

Hard-to-pronounce invented names
Why it sucks: People can’t remember or share them.

“Innovation. Integrity. Excellence.”
Why it sucks: Empty buzzwords.

4. Story and manifesto

Your manifesto is your belief system, a statement of what you fight for.

Good examples:

Nike: “If you have a body, you’re an athlete.”

Why it’s great:

• It flips the cliché that sport is only for the elite

• Based on a powerful insight: People want to feel capable, not compared

• It democratizes athleticism. You don’t need a medal, just a heartbeat

• The tone is empowering, defiant, and inclusive, everything Nike stands for

Manifesto flavor:

“It’s not about the finish line. It’s about the start. It’s not about perfect bodies. It’s about powerful minds. If you have a body, you are an athlete. Just do it.”

It’s not just copy. It’s a belief system that fuels campaigns, athlete partnerships, and even internal culture.

Patagonia: “We’re in business to save our home planet.”

Why it’s great:

• It puts mission before marketing. They lead with values, not features

• It’s rooted in their purpose: business should be a force for good

• The language is urgent, activist, and uncompromising, and they back it up with action (donating profits, suing the US government over public lands, etc.)

Manifesto flavor:

“We’re in business to save our home planet. We seek not only to do less harm, but more good. We support grassroots activism. We design to be repairable and recyclable. Because the world can’t wait.”

It’s bold. It’s clear. It’s why customers wear their values, not just their jackets.

Bad examples:

“Founded in 2021.”
Why it sucks: History is not mean.

“Our founders saw a gap.”
Why it sucks: Everyone says this.

Phase 4: Activation

How you go to market

This phase is about taking all that strategy and putting it into action, where people actually meet your brand.

1. Website copy

This is how your brand speaks online, especially on your homepage, About page, and product pages.

Good examples:

Basecamp: “Project management software that doesn’t suck.” Honest and human.

Calm: “#1 app for sleep and meditation.” Clear and confident.

Allbirds: “The world’s most comfortable shoes.” Simple, benefit-led.

Bad examples:

“Welcome to our.” Wasted space. Thanks for the geography lesson, but people generally know they’re on a homepage when they type in your URL.

“We’re a leading provider of...” Generic intro.

“Click here to learn more.” Lazy CTA.

2. Campaign concepts

Campaigns bring your brand into the real world through ads, launches, and creative stunts.

Good examples:

Liquid Death: Metal videos and satire. When they launched, water brands thought they were insane. A year later, they were the ones everyone copied.

Old Spice: Over-the-top humor flipped the grooming category.

Spotify Wrapped: Turns personal data into cultural moments. Genius.

Bad examples:

Banner ads with “Buy Now!” The “yelling at strangers” approach to marketing. A classic that’s never worked.

Social posts with zero creativity or visuals.

Unclear or inconsistent messaging in ads.

3. Content playbook

Your content plan keeps your brand alive on social and beyond, consistently and creatively.

Good examples:

Headspace: Daily mindfulness tips. Reinforces utility.

Calm: Peaceful stories and visuals. Aligns with tone.

Duolingo: Funny, viral TikToks. Perfect for Gen Z.

Bad examples:

Only posting sales and discounts.

Copy-pasting press releases as posts.

Infrequent, inconsistent posting.

4. Internal alignment

Your team needs to understand your brand inside and out. They’re your first brand ambassadors.

Good examples:

Netflix: Culture deck shapes internal behavior.

Airbnb: Brand training for every new hire.

Zappos: Service culture built into operations.

Bad examples:

No brand onboarding.

Inconsistent internal communication.

Leaders say one thing, employees do another. Turns your brand into performance art, and not the good kind.

Next steps

Once you have these elements, test them. Send your positioning to five people in your target audience. If they don’t get it in 10 seconds, revise.

Your brand isn’t done. It’s never done. But now you have something clear enough to build on. That’s the difference between staying stuck and moving forward.

The best part

You have a custom GPT trained with all these great campaigns and more, built by a brand strategist (Rodrigo Maroni), that helps you build your brand by following these steps and giving you the right strategic answers. It’s like your creative partner to build your brand.

So let’s take advantage of it. Because somewhere right now, someone is building their brand by asking AI to “make it pop” and calling it strategy.

Continue learning

If you liked this article and want to continue learning about this process, I recommend:

Six hours of amazing content from Caleb Ralston, a great expert who made us a real gift: knowledge that people pay a lot for in master’s degrees, delivered in six hours on YouTube.

My Substack publication brand. Here you can see how I built and continue to evolve my personal brand for my Substack publications. Brand is a process and continues to evolve with my own evolution and my audience.

Your turn

If this helped the ideas click, or if you think I oversimplified, missed something important, or got a detail wrong, tell me.

I learn faster in public, especially when people disagree thoughtfully.

Leave a comment

If you want to support this work, sharing the article genuinely helps. Not just for reach. It tells me which ideas are worth going deeper on.

Share

Thank you for reading, thinking along with me, and being part of this little corner of the internet where curiosity matters more than certainty.

Be Happy,
Laura

Hey, welcome back! I’m Laura. Just a regular human trying to make sense of the AI era without losing my mind.

This week, we’re unpacking Andrej Karpathy’s 2025 AI Year in Review. It’s essential reading if you want to stay ahead as an AI-savvy marketer, but let’s be honest, it’s a little bit dense.

Why this review matters for marketers

We don’t need to become engineers. But we do need to understand what’s actually changing under the hood, because those shifts directly affect what we can build, promise, and deliver to customers.

If you’re new to Karpathy, here’s the short version: he’s one of the most respected voices in AI. Former Director of AI at Tesla, founding member of OpenAI, and someone who can explain deeply technical ideas with rare clarity. When he writes about where AI is heading, the industry listens.

His latest review is both highly relevant and technically dense. It covers concepts like RLVR, reasoning traces, and inference-time compute. All crucial, but not exactly light reading for most marketers.

So here’s what I’m going to do.

I’ll tell the same story Karpathy tells, but through a metaphor that actually sticks. When you’re done, his original post will feel clearer and more meaningful. You’ll understand why 2025 was pivotal and how to explain these ideas to your team, your clients, or that exec who keeps asking, “But what does this actually mean for us?”

Think of this as your primer. Then go read Karpathy.

Let’s go.

Subscribe now

The restaurant metaphor: training ghost-chefs

Picture a gigantic restaurant.

Not a cozy neighborhood place with twelve tables. A sprawling kitchen-city serving millions of customers at once. Breakfast orders, lunch rushes, late-night meals, allergy lists, vegan substitutions, and someone insisting on pancakes at 2 a.m.

Your job is to train the newest chef.

Except it’s not really a chef. It’s a ghost-chef.

No taste buds. No real understanding of food. But it can read every cookbook ever written, overhear a million dinner conversations, and perfectly imitate how humans talk about flavor.

Act 1: The old training recipe (and why everyone hit a wall)

At the start of the year, every top kitchen trained its ghost-chef the same way.

Pretraining meant reading everything. Cookbooks, food blogs, menus, Reddit arguments about carbonara, grandma’s handwritten recipe cards, Michelin plating guides. All of it.

This gave the ghost fluency. It knew how people talk about food.

Supervised fine-tuning came next. You showed examples of how to respond to customers. Be clear. Be polite. Structure your answers. Don’t be weird.

Better manners, sure. Still not great at thinking.

Then came RLHF, Reinforcement Learning from Human Feedback. Human taste-testers sampled outputs and voted. “That was helpful.” “That was strange.”

And this worked. For a while.

Then every kitchen hit the same wall.

Reasoning is slippery. You can’t just tell a ghost-chef to “think better.” Does that mean step-by-step reasoning? Trying multiple approaches? Checking mistakes and backtracking?

Even humans disagree on this. So how do you train it?

Act 2: RLVR, or when everything changed

In 2025, a new method took over: Reinforcement Learning from Verifiable Rewards, or RLVR.

This is the breakthrough Karpathy focuses on, and it explains most of the progress we saw this year.

Instead of asking humans, “Did this taste good?”, you give the ghost-chef tests that score themselves.

• Did the soufflé rise to exactly the right height? ✅ or ❌

• Does this code actually compile? ✅ or ❌

• Does the math check out? ✅ or ❌

No “almost. or “pretty close,” Just right or wrong.

The surprising part

Run enough of these drills and something strange happens. The ghost-chef starts inventing behaviors that look like reasoning.

It breaks problems into sub-steps.
Checks its work.
Backtracks and tries again.
Do small calculations before committing.

You didn’t teach it how to think. You taught it how to win, and thinking emerged as a strategy.

Why this changed everything

Because these tests are automatic, the kitchen can run far more reps. No human bottlenecks. Just vastly more practice.

Then came another realization. You could let the ghost think longer during service. Pause. Try multiple approaches. Check its work before serving.

For marketers, this matters. The AI tools you’ll use in early 2026 are fundamentally different from those in early 2025. Not because the models are bigger, but because they’ve been trained to reason instead of just pattern-match.

That’s why they feel so much more capable.

Act 3: Animals vs. ghosts, and why this feels uncanny

People often describe AI using animal metaphors. It’s growing. It’s evolving. It learns like a child.

That metaphor breaks quickly.

Humans are trained by life. Survival, social consequences, physical reality, embarrassment, risk.

Ghost-chefs are trained by text imitation, puzzle rewards, approval clicks, and leaderboards.

So they develop jagged intelligence.

A ghost-chef might plate a flawless seven-course tasting menu, then confidently serve soup in a wine glass because the question was phrased oddly.

It’s not broken. It’s optimized.

Sharp spikes where rewards exist. Flat spots everywhere else.

Like someone brilliant at their job who still doesn’t know how to cook a fried egg. (You know someone, like me… 😅)

The benchmark problem

Benchmarks are meant to be neutral tests, like standardized cooking competitions. But when RLVR (Reinforcement Learning from Verifiable Rewards) trains on anything that can be auto-scored, benchmarks quietly become training manuals.

No one is cheating. The rules simply define what gets practiced.

You can dominate competitions and still collapse during real dinner service when everything goes sideways.

The 2025 shift was simple: benchmarks still matter, but they’re no longer enough.

Marketing translation: treat benchmark scores with caution. What matters is how a tool performs on your use cases, not how it scored on a standardized test.

Act 4: Cursor and the rise of the kitchen manager

Now the story shifts from better chefs to better restaurants.

Cursor isn’t just a chef. It’s a kitchen manager.

It gathers context, breaks work into tasks, calls the ghost-chef multiple times, balances cost and quality, and gives humans a clean workstation to supervise from. It even includes an autonomy slider, from “just suggest” to “run the whole kitchen.”

That’s why people started saying “Cursor for X.”

Not one prompt. A full workbench.

Think of it this way. LLM Labs graduate capable culinary students. Apps like Cursor turn them into working professionals.

For marketers, this signals the next wave of tools. Not “ChatGPT for marketing,” but full workflow assistants that understand context, remember preferences, and execute multi-step projects with minimal hand-holding.

Act 5: Claude Code, the ghost that moves in

Then came something stranger.

Not a ghost you visit on a website. A ghost that lives with you.

Claude Code works inside your environment. It opens your files, reads your codebase, uses your tools, and responds instantly.

That matters because real work isn’t abstract intelligence. Its context, setup, private data, and fast feedback loops.

It feels less like searching Google and more like working alongside a colleague.

A little creepy. Extremely useful.

Marketing angle: This is where AI shifts from a tool you use to a collaborator you work with. That changes how we delegate, collaborate, and structure work.

Act 6: Vibe coding and losing the knife skills barrier

In 2025, people started building real software by describing what they wanted.

“Make me a menu planner that uses my pantry, calculates macros, generates a shopping list, and looks good.”

And it happens.

No knife skills. No six-month detour to learn a new language.

That’s vibe coding.

For professionals, code becomes cheap, temporary, and disposable. Like cooking a quick snack just to test the oven.

For marketers, this is huge. You can prototype landing pages, dashboards, and interactive campaigns without waiting on a dev queue. The gap between idea and execution collapsed.

Act 7: Nano banana and the GUI moment

One last shift.

Text works well for computers. Not always for humans.

Chatting with LLMs today feels like using a 1980s terminal. Functional, but clunky.

People prefer visuals. Diagrams. Slides. Whiteboards. Interactive tools.

“Nano banana” hints at the GUI era of LLMs (Graphical User Interface). Not just images, but text, visuals, and world knowledge fused.

Like a chef who can explain a recipe, sketch the plating, and understand what you meant by “make it feel cozy.”

Not perfect. But clearly a new interface.

Why this matters: the next generation of AI tools won’t be chat boxes. They’ll be multimodal, visual, and spatial, closer to creative partners than search engines.

The punchline: what 2025 actually taught us

2025 showed us ghost-chefs that are smarter than expected and dumber than expected. Incredibly useful. Still far from their ceiling.

The next breakthroughs won’t be about making the chef smarter.

They’ll be about:

• better kitchen managers

• better co-living assistants

• more people who can suddenly cook

• better ways for ghosts to communicate with us

The real question was never whether the ghost understands food.

It’s whether we’re learning to build kitchens that make its weird, jagged talents useful.

Now go read Karpathy

Go read the original article. Seriously.

With this framework in mind, his technical explanations will click. When he talks about reasoning traces, inference-time compute, process supervision, or test-time training, you’ll recognize the ghost-chef patterns.

As marketers, we don’t need the math. We need the concepts. We need to know what’s changing, what’s possible, and where the opportunities are.

2025 wasn’t incremental. It was the year training methodology shifted, vibe coding became real, and AI tools stopped being novelties and started becoming infrastructure.

And 2026 will move faster. Strap in.

Your Turn

If this helped the ideas click, or if you think I oversimplified, missed something important, or got a detail wrong, tell me.
I learn faster in public, especially when people disagree thoughtfully.

Leave a comment

If you want to support this work, sharing the article genuinely helps. Not just for reach. It tells me which ideas are worth going deeper on.

Share

And before we close the year, thank you.
Thank you for reading, thinking along with me, and being part of this little corner of the internet where curiosity matters more than certainty.

I hope you get a few quiet days to rest, disconnect, and enjoy the people you love.
Merry Christmas, and see you in the new year. 🎄✨

Be Happy,
Laura

Hey, welcome back! I’m Laura, just a regular human trying to survive the AI era without losing my mind. This week, we’re unpacking the Airops platform, the AI-powered content operation platform for content engineers. Or (in human language) the platform that turns messy, manual SEO & AEO work into something closer to a smart kitchen. Let’s start with a story.

The Thermomix of Content

Imagine making perfect risotto in 5 minutes instead of 60, no stress, no guesswork, and zero risk of becoming the official family disappointment 😅. That’s what the Thermomix does for cooking.

And AirOps does the same for content.

It takes messy, repetitive SEO/AEO tasks and turns them into clean, predictable workflows. After testing several AI writing tools, AI visibility platform, and AI automated platform, AirOps is the one that actually delivered a noticeable shift:

It turns insights into action. At scale. Without drama.

You still bring the strategy, that part is irreplaceable. But instead of manually cooking every part of your SEO/AEO process, you move to a smart kitchen where the system handles the execution.

And for freelancers? It’s the difference between selling hours and selling outcomes.

And for marketers? It’s the shift from chasing deadlines to scaling outcomes.

Subscribe now

Final Bits to Keep in Your Pocket (Takeaways)

Before we go deeper, here’s the core lesson:

Content is shifting from tasks to systems.
You don’t need to write faster — you need better ways to produce content.

AirOps turns SEO/AEO into something repeatable.
Research → Brief → Article → Optimization… all automated.

Workflows beat prompts.
Prompts save minutes. Workflows save hours. Grids save whole weeks.

Content Engineering matters.
It’s not a buzzword. It’s how freelancers, agencies, and marketers stay ahead while others get replaced (not by AI, but by the people who learned to build systems). The good news? You can learn the basics in a week, for free. Keep reading to see how.

Why This Review Exists

You might assume reviews are paid. Nobody paid me anything. Instead, AirOps gave me something more valuable: skills.

AirOps’ learning platform taught me how to think as a Content Engineer, something I didn’t even know existed a year ago, and it changed the way I approach content.

This review article is my way of thanking them and sharing something useful with you, especially if you want to build these skills to survive the LLM era.

The basic training takes just 1–2 weeks, and you’ll get a certificate you can proudly drop on your LinkedIn 🙂. But the real win is the skillset you walk away with, practical, relevant skills for the new AI-driven world that actually matter in today’s job market.

You can access the Content Engine course here.

Let’s master AI fundamentals that apply to any tool (ChatGPT, Claude, you name it) and how to turn them into multi-step, scalable workflows inside AirOps.

So... What Exactly Is a Content Engineer?

A Content Engineer doesn’t write more; they design the system that writes.

Think of it as the grown-up version of “work smarter, not harder.”
You stop asking:

“How do I write this article?”
and start asking:

“How do I build a machine that produces articles with consistent quality?”

You go from chef → kitchen designer.

If you’re a freelancer, content marketer, or agency builder, this skill is becoming non-optional. It lets you scale your work, your revenue, and the number of clients you can handle, without burnout or late-night panic.

So let’s dive into Airops’ capabilities

A Quick Flashback to the Old SEO Pain Era

I started in SEM and SEO 15 years ago, working for a digital agency. If you were there, you remember the pain 😭:

• keyword research in Excel

• competitor analysis page by page

• link-building plans

• 20-slide SEO decks

• clients wanting to “change everything” after days of work

We didn’t have AI. We had interns (ourselves 😂) and caffeine. ☕ So much caffeine.

Then ChatGPT arrived, and it helped a lot, but SEO prompts alone aren’t enough. They are shortcuts, not systems.

AirOps is the next step. The evolution of this AI journey.

How AirOps Works (In Plain Human Language)

The AirOps Machine: Workflows + Grids Explained

AirOps has two core components

Workflows (your recipe)

You can build the steps you need and connect them using a drag-and-drop interface, similar to Make or Gumloop. One step might search and retrieve Google results (Google Search). Another might extract content from a webpage (Webpage Scraper). Another might analyze content with an LLM prompt (Prompt LLM).

You connect these steps, passing information from one to the next, building a workflow like you’d build the perfect risotto.

Grids (your smart kitchen)

Then, when the workflow is ready, you can run it and not only for one keyword, not for one topic, not for one URL… but for hundreds.

That’s where the magic happens. The same workflow becomes a machine we call a Grid, which is like an Excel sheet that can execute workflows automatically at scale.

THE AIROPS SYSTEM:

Step 1: Build the recipe (Workflow)

↓

[Google Search] → [Scrape Page] → [LLM Prompt] → [Output]

Step 2: Scale it (Grid)

↓

Run for 1 keyword... or 1,000 keywords

Why Workflows Beat Prompts

Prompts are good, but they panic with complexity. A single prompt can’t handle a 6-step SEO process with consistency. They can’t manage complexity.

A workflow breaks things down into small tasks that the AI can handle much better. One task at a time. One decision at a time. Like a real-life process.

Define the output you want, work backward to identify the inputs, then build the workflow that connects the two.

The Secret Sauce: Brand Kit & Knowledge Base

One of the things AirOps does incredibly well is context.
Your tone, your style, your persona, your competitors, and examples of how you actually write or want to write. All of that goes into your Brand Kit, and suddenly, your content stops sounding like AI and starts sounding like you: unique, consistent, and not generic.

As an extra, you add your Knowledge Base: product details, facts, internal notes, insights… so you make sure everything in your content is accurate and real.

In AI content, context changes everything.

Mental model: Inputs → LLM + Context → Output

Power Agents: Your Pre-Built Content Factory

AirOps is incredibly flexible. You can build sophisticated custom workflows, or you can use pre-configured Power Agents, ready-made mini-workflows that solve specific, common tasks in the SEO content creation process.

Here’s a real workflow I use:

The SERP-to-Article Machine

1. Analyze SERP Power Agent → Extracts top 10 results, headings, SEO elements, identifies gaps, and search intent

2. Create Brief Power Agent → Builds a complete outline with keywords, structure, and research

3. Write Article Power Agent → Generates publication-ready draft in brand voice

Input: One target keyword
Output: Publication-ready article in 15 minutes
Old way: 4-6 hours per article
New way: 15 minutes of setup + 30 minutes of editing

You can connect Power Agents into complete workflows that handle real use cases. Analyze the SERP → Create the brief → Write the article. That’s a complete production line for a content marketing team executing daily. Not a prompt.

How Much Does AirOps Cost?

AirOps uses a tiered pricing model, and the good news is you can start for free. You can test whether the platform fits your workflow before spending a cent, always a good sign.

For exact pricing, you’ll need to talk to their sales team. This is pretty standard for tools aimed at teams and enterprises, even if it makes comparison shopping a bit annoying.
Here, I found the latest information I could, but it’s always best to double-check directly with the team. Prices can change quickly in early products.

Source: MetaFlow

What Other Marketers Say About AirOps

AirOps has strong reviews on G2. People love the time savings, the flexible workflows, and how well it integrates with CMS once you get past the learning curve. Many say it quickly becomes one of those tools you can’t imagine working without.

Of course, it’s not perfect; users mention the occasional bug, a few navigation quirks, and a credit system that can feel a little mysterious.

This Reddit thread captured the real-world experience perfectly:

A Simple AirOps Blueprint to Improve Client AI Visibility (and Monetize It)

If you’re a freelancer or agency offering AEO services, or a marketer trying to convince your team that AEO actually matters, this blueprint gives you something practical to start with.

It’s also a process I’m testing myself, and I’m sharing it so we can experiment together. Learning as a community is faster (and way more fun) than figuring everything out alone.

Step 1: The Free Value Bomb

Pitch prospects with two reports (10 minutes each to generate):

Report 1: AEO Visibility Scorecard
Use the ChatGPT Question Analyzer Power Agent. This agent reads how ChatGPT talks about a brand and shows:

• Whether the brand is mentioned

• Whether sentiment is positive or negative

• Which competitors appear

• Which URLs does ChatGPT view or cite

• Whether the brand’s own URLs show up

Everything is summarized in one simple scorecard that any client can understand.

Report 2: Content Audit for 3 Articles

Use the AEO Content Scorecard Power Agent. This evaluates:

• How readable the content is for LLMs

• Whether the structure works for AI

• How strong the evidence is

• Whether the freshness signals are clear

• Where the content is weak

You can also include an improvement report with recommendations.

No pitch. Just value.

At this point, you’ve already created value without asking for anything in return. The Reciprocity Law is real. Giving something meaningful makes people naturally want to continue the conversation. It’s why I’m writing this article, and it’s often why clients choose to pay for your work.

Step 2: The Easy “Yes” Offer

Once you have their attention, propose something focused:

Let’s upgrade 10 of your articles or landing pages for AEO. 24h delivery.

This is a small project, easy to understand, and produces visible results quickly.

Deliver using three Power Agents:

1. Content Refresh + Citation Optimizer Power Agent

This upgrades their article to be AEO-ready. It checks:

• Which pages LLMs already cite

• Whether their own content competes with itself

• What competitors are saying

• What gaps exist

Then it rewrites the piece with clearer answers, better structure, and stronger citation potential.

2. Add FAQ to Article Power Agent

Using real People Also Ask data, this adds a clean FAQ section—one of the formats AI assistants love most.

FAQs = instant AI visibility boost.

3. Add Key Takeaways Power Agent

This inserts clear takeaways written in the brand’s tone. You can customize the style, the placement, and the wording.

Time investment: 3-4 hours total
Client sees results: 24H
Natural upsell path: They always want more after seeing results

Why This Works

• Low barrier: Low price vs high value SEO proposals

• Fast results: Visibility tracking shows impact immediately

• Builds trust: You’ve already proven value with free reports

• Easy delivery: Power Agents do the heavy lifting

It gives the client something they can immediately see and feel, and something that directly increases their chances of appearing in AI-generated answers.

If you combine AirOps with a good AI visibility tool to track progress, you’ll rock for both your clients and yourself. Names like Profound, PeecAI, Hall, Otterly AI, Omnia, Aeovision, and PrompWatch kept surfacing as the ones doing real work.

Pro Tip: Start with one sector you already understand, and one where AEO clearly matters. Think banking, health, education, B2B SaaS, travel, retail, or home services.
If you want a deeper breakdown, check out my article on “8 High-Opportunity AEO Niches” to see which industries are the strongest candidates.

What Happens Next

Once you understand how workflows work, you start seeing opportunities everywhere. Content becomes a system. Systems become products. Products become services that clients will pay for.

If you’re still manually doing briefs, proposals, and rewrites… you’re working too hard

Start here:

1. Take the free Content Engineer course (link here)

2. Build your first workflow this week

3. Send your first AEO audit to a prospect

The platform has a free trial. Your first client pays for the subscription. Everything after that is profit.

Welcome to the smart kitchen.

Your Turn

If you’ve got sharper ideas, missing angles, a new idea to test, or think I’m wildly wrong, drop a comment. Learning in a community is always better. I learn faster when people disagree with me politely.

Leave a comment

If you’re a brand, agency, or freelancer curious about working together on AEO, my inbox is open. I promise I’m much less intimidating than the length of this article.

If you want to make my day, share this article. My self-esteem depends on it more than I’d like to admit.

Share

Finally, tell me what you want next; otherwise, I’ll just keep writing about whatever obsesses me at 2 a.m. that I’m sure you like as well, but I love to know more.

Which topic would you like to go deeper on?

Be Happy
Laura

Hey, welcome back! I’m Laura, just a regular human trying to make sense of the world without losing my mind. This week, we’re unpacking the AEO niches that matter for brands, agencies, and freelancers. Let’s dive in!

Subscribe now

TL;DR

Here’s everything distilled for your convenience.

• AI assistants give one answer instead of ten links. Most brands haven’t figured out how to optimize for this yet.

• Harvard/OpenAI research reveals how 700M+ people actually use ChatGPT (not surveys, real behavior).

• Eight sectors dominate: banking, health, education, B2B SaaS, travel, retail, home services, and professional services.

• Real AEO strategy starts with customer interviews, not guesswork. Map the questions people actually ask.

• Freelancers and agencies can build strong niches by mastering AEO in one vertical.

• Early movers win because they become the default answer.

The library with no shelves

Imagine walking into a library where the shelves are gone. No aisles. No stacks.

You ask one question, and a librarian hands you one book. The book you needed.

That’s how AI assistants work now. They don’t show you ten links. They give you one answer.

The problem: The librarians (OpenAI, Google, Anthropic) don’t tell us what questions people ask. No public data. No dashboards.

So, how do you know if your brand should invest in Answer Engine Optimization? How do you build visibility when you can’t see the questions? You could ask ChatGPT, but that feels a bit circular. 🤣

Some brands are in a perfect position to win with AEO right now, while for others, it may simply not be the right moment yet.

Good news: We’re not completely blind.

We might only have 20% of the picture, and that’s enough to start. Most good decisions happen with incomplete information. The key is knowing what to do with what you’ve got.

What we know now (and why it matters)

The How People Use ChatGPT report (Harvard, OpenAI, NBER, September 2025) analyzed millions of real user messages from 2022 to mid-2025.

Not surveys. Actual behavior. Anonymized, yes, but real.

For the first time, we can see which categories people rely on AI for — and which sectors stand to win big in AEO.

How people actually use ChatGPT

1. The fastest-spreading technology ever

• 700M weekly users (July 2025)

• 18B messages per week

• Adoption faster than phones, social media, or search

2. Personal use is exploding

• 53% → 73% personal use in one year

• People realised ChatGPT helps with real life: cooking, studying, planning, fixing things.

3. 80% of usage fits into three buckets

A. Practical guidance

“Help me plan my workout.”
“How do I fix this?”
“Explain this to me.”

B. Information seeking

Facts, comparisons, research, news, product questions.

C. Writing

The #1 work use case.
And the surprise: people mostly ask ChatGPT to edit, not write from scratch.

4. Surprisingly small categories

• Coding → 4.2%

• Emotional support → 2.4%

5. The simple intent model

• Asking (49%) – explanations, decisions

• Doing (40%) – producing something

• Expressing (11%) – chatting, venting

At work, Doing jumps to 56%.

6. Five topics dominate everything

Education, health, travel, food, and money.
These are the “high-intent, high-volume” categories.

7. ChatGPT behaves like a research assistant

Most work tasks involve:
Understanding • Documenting • Interpreting • Deciding • Solving • Thinking creatively

It doesn’t replace work. It improves thinking.

Which sectors win first (and why)

These usage patterns point directly to the early winners.

A. Banking, fintech & insurance

High-stakes, high-confusion questions:

• “Fixed or variable mortgage in Spain?”

• “How much should I have in emergency savings?”

• “Is travel insurance worth it?”

Winning formats:
Comparisons • Risk/benefit • Decision trees • “What to ask before buying…” guides

AEO turns financial brands into the default advisor inside the AI.

B. Health, fitness & wellness

Around 10% of usage = practical guidance in health and self-care.

What people ask:
Workout plans • Skin routines • Symptom explanations

Winning formats:
Routines • Step-by-steps • Symptom → cause → action • Nutrition tables

Brands become a daily trusted guide.

C. Education & online learning

More than 10% of messages are teaching/tutoring.

Typical questions:
“Explain derivatives like I’m 15.”
“How do I pass B2 German?”

Winning formats:
Mini lessons • Explain-like-I’m-12 • Problem sets • Exam paths

Education brands become the tutor living in AI.

D. B2B SaaS & productivity tools

ChatGPT is used heavily for work decisions.

Common questions:
“How do I automate onboarding in [your tool]?”
“Which KPIs matter for adoption?”
“How do I integrate [tool A] with [tool B]?”

Winning formats:
Tutorials • Integrations • Best practices • Adoption playbooks

E. Travel, hospitality & mobility

Pure guidance + information seeking.

Questions:
“Plan a 3-day trip to Lisbon for €300.”
“Best time to visit Japan?”

Winning formats:
Itineraries • Budgets • Logistics • Comparisons • Safety guides

F. Retail, consumer goods & grocery

Daily-life questions:
“Dinner ideas with chickpeas and rice.”
“What’s a simple skincare routine?”

Winning formats:
Recipes • Routines • “How to choose…” guides • Troubleshooting

G. Home, DIY & life admin

“How do I compare electricity tariffs?”
“What should I check before renting in Berlin?”
“How do I assemble this shelf?”

Winning formats:
Step-by-steps • Checklists • Safety guides

H. Professional services (legal, tax, HR, consulting)

Complex questions + high-intent moments.

Questions:
“What to check in a freelance contract?”
“What expenses can I deduct?”
“How do I run a performance review?”

Winning formats:
Templates • Explainers • “Before you sign…” checklists • Decision guides

These sectors win because AI needs clarity in complex topics — and they can supply it.

G. Home, DIY, and life admin services

• “How do I compare electricity tariffs in Spain?”

• “What should I check before renting in Berlin?”

• “How do I assemble this shelf?”

What works:

• Step-by-steps

• Visual checklists

• Safety instructions

H. Professional services (legal, tax, HR, consulting)

• “Clauses to check in a freelance contract in Italy?”

• “What expenses can I deduct as a self-employed designer?”

• “How do I run a performance review?”

What works:

• Templates

• Explainers

• Decision guides

• “Before you sign...” checklists

AI struggles with complex topics. These services can provide the clarity it needs.

First steps for any brand

Step 1: Talk to real customers about how they research

Before you build any AEO strategy, you need to understand how people actually search for information about your product.

Talk to at least 10 customers or prospects (B2B or B2C). Ask them:

• How did you research us before deciding?

• What alternatives did you consider?

• What mattered most to you?

• What questions were you trying to answer?

• How did you solve this before finding us?

• Where did you start when you began researching?

• Which platforms or apps did you use to compare options?

• Did you speak to anyone for advice (friends, colleagues, experts)?

• Did you look for videos, articles, reviews, or examples?

• Did you search on Google, ChatGPT, Reddit, YouTube... or something else?

• Did your search start with a problem or with a specific product?

Map the whole decision journey. Where were they asking questions? Where were they doing things? Where were they just expressing?

Use the Asking-Doing-Expressing model to understand each moment. Then write down the topics and questions that keep coming up.

Step 2: Connect those topics to your product

Go through your notes. Find the questions that naturally relate to what your product solves.

You’ll usually end up with 10 to 15 key research topics.

Examples:

• B2C beauty brand: “Routine for oily skin,” “How to reduce dark spots.”

• B2B SaaS tool: “How to automate onboarding,” “How to visualize team workloads”

These become your AEO plan.

Step 3: Choose the topics that matter most

Not all topics are worth the same effort.

Score each one on:

• Relevance to your core product

• Importance to your best customers

• Interest or demand (Google Trends, Semrush, Reddit, LinkedIn, forums)

Examples:

• B2C fitness brand:
  High value → “15-minute HIIT at home”
  Lower value → “History of Pilates”

• B2B data tool:
  High value → “How to clean messy CRM data automatically.”
  Lower value → “Famous data breaches in history.”

Focus on topics where high customer need meets high commercial impact.

Step 4: Create prompts that force AI to search the web

To check how AI answers questions in your space, you need prompts that push the AI to search live, not just answer from memory.

Weak prompts (too generic):

• “What is phishing?” (B2B)

• “How to lose weight?” (B2C)

These stay inside the model’s training data.

Strong prompts (force web search):

B2B examples:

• “How do I sync custom objects between HubSpot and Salesforce in 2025?”

• “What’s the latest method to automate SOC 2 compliance documentation?”

B2C examples:

• “Which retinol % is safest for sensitive skin in 2025?”

• “What air fryers under €100 have the best energy efficiency ratings this year?”

Create 25 strong prompts that match what your customers actually ask.

These show you when and how your brand appears in AI answers.

Step 5: Pick a tool to track your AEO visibility

Once you know the questions people ask (and you’ve built prompts that trigger AI web search), you need a way to track where you show up.

Think of it as your AEO dashboard.

A good tool helps you:

• See when your brand gets mentioned in AI answers

• Compare your visibility against competitors

• Watch how your position changes over time

• Monitor which content pieces AI is pulling from

• Spot new questions or themes popping up in your space

Here are two articles to help you choose your perfect AI visibility tool.

A simple list with a well-founded and team-solid platform.

(Based on Graphipe Source.) Names like Profound, PeecAI, Hall, Otterly AI, Omnia, Aeovision, and PrompWatch kept surfacing as the ones doing real work.

AEO: the opportunity for freelancers & agencies

This is one of the best moments in years to build a niche.

More people use AI assistants instead of Google. The answer becomes the new click.

If you specialise in one vertical — fintech, wellness, legal, travel — you become the expert who helps brands show up where it matters: inside the answer.

Why this works

• Few professionals know how to write for AI

• Niching accelerates credibility

• You don’t need a big team

• SEO + content + user research skills transfer directly

• You can deliver results fast by restructuring what brands already have

If you specialise deeply — for example: “legal AEO” — your knowledge compounds fast and becomes defensible.

How to choose the right brand or project

Look for signals that AEO will produce big returns:

1. Content- or knowledge-intensive sector
   More complex topics → more questions → more “answer moments.”

2. Multi-market or multilingual presence
   More languages = more intents = more opportunity.

3. Highly competitive discovery space
   The brand that becomes the answer wins disproportionally.

4. Existing content + digital maturity
   Faster wins because the raw material already exists.

5. Organic discovery matters more than ads
   If content drives growth, AEO becomes strategic, not optional.

Each signal increases both the need and the payoff.

So where do you start?

• Talk to 5 customers about how they researched you

• Write down 10 real questions they asked

• Turn those into prompts

• Ask those prompts to ChatGPT

• See whether your brand appears

• Choose the right AI visibility tool to start tracking your progress

People are already asking AI assistants about your category. The answers are already being given. The only question is whether one of those answers is yours.

If you’re in one of these sectors and want to explore this together, reach out.
Let’s map your questions, optimize your answers, and make sure your brand shows up where the decisions happen.

The librarian is already handing out books. Let’s make sure one of them is yours.

Your Turn

If you’ve got sharper ideas, missing angles, or think I’m wildly wrong, drop a comment. I learn faster when people disagree with me politely.

Leave a comment

If you’re a brand, agency, or freelancer curious about working together on AEO, my inbox is open. I promise I’m much less intimidating than the length of this article.

If you want to make my day, share this article. My self-esteem depends on it more than I’d like to admit.

Share

Finally, tell me what you want next; otherwise, I’ll just keep writing about whatever obsesses me at 2 a.m. that I’m sure you like as well, but I love to know more.

What are you interested in?

The AI visibility space has exploded in just a few months. New tools appear constantly, each promising to show you how your brand shows up inside AI assistants like ChatGPT.

Exciting? Yes. But also overwhelming.

Choosing among AI visibility tools sometimes feels like choosing a movie on Netflix: too many options, not enough clarity.

Marketers, founders, and teams trying to enter this world often leave more confused than informed. In fast-moving environments like this, clarity becomes the real value. Less noise, more signal. That’s the purpose of this guide.

Last week, I mapped the market and looked at which AEO tools are emerging, growing, and actually shipping. (Based on Graphipe Source.) Names like Profound, PeecAI, Hall, Otterly AI, Omnia, Aeovision, and PrompWatch kept surfacing as the ones doing real work.

This week, we go deeper. How do these tools actually work? What happens behind the scenes? And what are we really paying for?

My goal is simple: help small teams, marketers, founders, and freelancers understand this space in clear, human terms.

We’ll walk through the shared features, how to compare tools, and which ones I’d choose for testing.

If you want to jump into AI visibility tools, this is a good place to start.

TLDR (Key takeaways)

If you want the insights without the miles of text, here’s everything distilled for your convenience and my humility.

1. Prompt research is the foundation

AI visibility starts by understanding what buyers might ask AI. Tools don’t know real prompt volume, but they make strong predictions using your content, your category, and your customers’ questions.

2. AI answers tracked guide us

Platforms capture neutral snapshots from major AI models so you can see which brands show up, which sources are cited, and how answers evolve over time.

3. Citations are becoming the new backlinks

If AI models cite your pages often, you gain more authority in answers. Seeing which URLs and domains are referenced helps you understand why competitors appear and you don’t.

4. Metrics reveal your position

Share of voice, visibility score, sentiment, domain citations, and prompt coverage give you a clear picture of how AI models perceive and rank your brand.

5. Good tools show data and tell you how to improve

Recommendation engines highlight where you’re missing visibility and translate that into specific actions like creating content, improving citations, or targeting competitor prompts.

6. Not all tools are equal

A few platforms strike the right balance between reliability, education, and accessible pricing, making them strong choices for SMBs entering AI visibility.

7. AI visibility impacts growth metrics

When your brand appears more often and more positively in AI answers, CAC drops, conversions rise, and LTV increases because customers arrive with more trust and context.

If the takeaways sparked a bit of “hmm... tell me more,” perfect. The long version begins now, freshly brewed and warm.

Understanding prompt search research

Before we talk about AI visibility, we need to understand prompts. It sounds basic, but starting with the fundamentals always makes everything else clearer.

A real buyer journey example

A prompt is a question typed into an AI system like ChatGPT or Gemini.

Before, people searched on Google. Now, many go straight to AI. For example:

“What is the best subscription-based revenue tool for a small business?”

Even when people start in Google, they often end up switching to AI because the answers feel more human and direct.

Imagine a Head of Marketing evaluating subscription software. She might ask:

• What is the best subscription revenue tool for SMBs

• Which tool includes CRM and communication features

• Which supports BigQuery and Stripe

• Compare pricing across platforms

• Summarize reviews from G2, Capterra, Trustpilot

If you’ve ever fallen into a 2 AM research rabbit hole comparing tools, congratulations. You are her or him.

These questions reflect the real buying journey, from early curiosity to final decision.

AI visibility tools help you uncover questions like these and see what potential buyers actually ask AI about your category.

How do tools know which prompts buyers ask?

No platform knows the exact prompts people ask. Only OpenAI or Google sees real prompt data, and it doesn’t seem they’ll publish a “Top 100 Prompts of the Week” anytime soon.

So AEO tools infer likely prompts from signals like:

• Your website content

• Category and industry language

• Competitor pages

• SEO keywords turned into questions

Then they ask you to refine these using your internal knowledge:

• Customer support questions

• Reddit or forum discussions

• Objections prospects repeat

• Search Console insights

• Common use cases from your ICP

Tools predict prompts the same way weather forecasts predict rain. They don’t know for sure, but they warn you before you leave the house without an umbrella.

It’s structured prediction, not exact tracking. Right now, it’s the best way to prepare for AI search.

Country and language matter

AI answers change based on location and language.

A question in Spanish in Spain may return different results than the same question in English in the US.

It’s like ordering “coffee” in Italy, Sweden, and Colombia. You get coffee, but the interpretation varies wildly.

To keep data accurate, these platforms work on a specific country and language you have to specify.

Organizing your prompts

Tracking five prompts is effortless. Tracking one hundred becomes chaos.

As your list grows from customer questions, forums, keyword tools, and internal insights, AEO tools let you tag and organize prompts to stay sane.

Common organization methods:

• Funnel stage

• Persona or ICP

• Use case or problem

• Topic cluster

Think of your prompt list like a kitchen. With three spices, order doesn’t matter. With fifty, without shelves and labels, dinner becomes chaos. Prompt organization works the same way.

Tracking competitors

These tools let you monitor competitors alongside your brand. This shows:

• Which competitors appear in AI answers

• Where they outrank you

• Where your content performs better

It’s like checking the sports league table. You’re not trying to win every match, but strategy starts by knowing the scoreboard.

Knowing the prompt search volume

Some platforms estimate prompt volume or assign relevance scores. But these numbers are approximations, not real counts, because actual prompt data isn’t publicly available.

Real prompt data lives somewhere inside OpenAI’s servers, guarded like a national treasure. Sam Altman hasn’t shown any signs of releasing it, unless one day he wakes up feeling generous. Unlikely, but we can dream.

Tools model demand using:

• Traditional SEO keyword data

• Category trends

• AI prompt patterns

• Social signals

Use these numbers to prioritize, not to obsess.

Questions to guide your evaluation

Now that you understand how prompt search works, you can compare tools with more confidence. A few useful questions:

• How many prompts can I track at X price?

• How are prompts recommended?

• What data sources influence relevance?

• Is the organization/tagging system flexible and convenient?

• How are they calculating the volume scoring, and how transparent is it?

These help you avoid paying for features you won’t use, and understand how they’re working behind the scenes.

Understanding the answer tracking capability

Prompt research covers questions. Answer tracking covers what AI says back.

Tools check how AI assistants respond to your prompts daily, weekly, or monthly.

A real buyer journey example

Prompt: What is the best subscription revenue tool for SMBs?

AI’s neutral snapshot might include:

• Chartmogul: CRM integrated, free trial, good entry process

• Chargebee: strong billing, advanced features, may be too complex

• Recurly: flexible, great recovery tools, better for high volume

• RevenueCat: ideal for mobile subscriptions

• Circuly: good for physical subscription products

But... do tools see real answers?

No. These tools cannot access personalized answers. Real AI replies depend on a user’s history, preferences, context, and a bit of randomness. An AI visibility platform has no access to someone’s context window or individual settings.

Instead, tools capture a neutral baseline. Think of it as the “base recipe.” Real users add their own flavor later, turning the standard Whopper into a Whopper with extra cheese and bacon. But if you want to improve visibility, the base recipe is all we can reliably work with right now.

Most platforms use general APIs from the models to recreate responses. Others combine this with their own methods, like UI-level scraping that interacts with AI systems the same way a real user would.

What they capture is straightforward:

• Which brands appear

• Which domains are cited

• How competitors show up

• Where your brand stands in default conditions

AI models tracked

Most platforms monitor popular AI assistants. Estimated users in 2025:

• ChatGPT: ~800 million weekly active users

• Gemini: ~400 million monthly active users

• Perplexity: ~22 million active users

• Claude: ~19 million monthly active users

• Microsoft Copilot: ~33 million active users

• Grok: ~35–39 million monthly active users (based on recent estimates)

Multi-model tracking matters because each assistant ranks brands differently. But the real question is: which AI assistant do you actually need to improve visibility in? For many teams, starting with the most widely used models is more than enough. You don’t always need to track them all.

How often should you check answers?

AI answers change over time, and these tools capture that shift at a set frequency. Depending on the plan, most track answers daily or weekly. The ideal rhythm depends on your category.

Daily fits fast-changing industries. Weekly works for competitive but stable markets.

Choose your frequency based on volatility, not fear of missing out.

Questions to guide your evaluation

Now that you understand how answer tracking works, you can compare tools with more confidence. A few useful questions:

• How do they generate the answers? Are they relying only on APIs, or do they use additional methods to improve accuracy?

• How often do they check for updates, and how does that frequency change with price?

• How many models do they track at this tier, and which ones?

These questions help you separate what’s genuinely useful from what’s just packaging.

Understanding citation and domain tracking

Citation analysis and domain tracking help you understand whether AI models consider content a trusted source. Your own content, competitor content, and other sources like editorial sites or social platforms.

Link citation analysis

Citation tracking analyzes:

• Which URLs are cited

• How often do they appear

• In what order

Citations are becoming the new backlinks of AI search. Citations are the closest thing AI has to saying, “I swear I did my homework, here are the sources.”

Domain tracking

Domain tracking evaluates how often your domain appears across all answers.

Tools show:

• How many citations do you earn

• How do you rank against competitors

Why this matters

Page-level insights help you:

• Identify authority pages

• Spot pages that should be cited but aren’t

• Understand which competitor pages dominate

Domain-level insights help you:

• Track visibility shifts

• Benchmark your domain

• Guide long-term content decisions

When comparing tools, consider asking: How does the tool classify sources?

Understanding GEO audit or agent analytics

Another great capability from these tools is agent analytics, which shows how AI crawlers view your site. Traditional analytics don’t capture AI bot visits because bots rarely execute JavaScript.

With agent analytics, you can see:

• Which AI bots visit your site

• How often

• Which pages they access

This helps you understand how AI systems treat your site, not just how humans do.

Understanding the main metrics

After collecting data, metrics help you decide what to improve. They typically fall into three groups, and these are some common and relevant metrics.

Brand-level metrics

Share of voice: Your share of brand mentions in AI answers.

Visibility score: Percentage of answers that include your brand.

Brand position: Where your brand ranks compared to competitors.

Brand sentiment: Whether AI describes you positively, neutrally, or negatively.

Source-level metrics

Source used: How often AI uses your content without citing it.

Domain citation: Number of times your domain is cited.

Domain coverage: How many prompts include your domain.

Prompt-level metric

Prompt volume: The estimated frequency a question is asked in AI.

Think of these metrics as a compass, not a map. Each tool has its own calculation methods and nuances. Metrics really need a dedicated article to unpack properly. I’ll probably dive into that later. For now, this foundation is enough to get you started

When comparing tools, ask yourself: How do their metrics actually help you understand your specific situation?

Understanding recommendations

Once you map prompts, track answers, and review metrics, these platforms enter the third phase: recommendations.

This is where the tool becomes a coach.

It looks at your data and asks: Where are you missing visibility, and why?

To answer this, tools run a gap analysis, identifying:

• Prompt gaps: competitors appear, you don’t

• Source gaps: your domain is under-referenced

• Authority gaps: influential sources don’t include your content

From this, tools give clear actions:

• Create content for prompt X

• Update page Y to earn more citations

• Target competitor prompts you’re missing

The recommendation engine works like a fitness coach: spotting weak areas and giving simple exercises to improve. No theory, just steps.

From here, your workflow becomes a loop:

Act → Track → Adjust → Repeat

When comparing tools, ask yourself: How actionable are the recommendations the tool gives?

Final list of tools I recommend to test (and I would do next)

After reviewing too many platforms, I used four criteria:

• A real team behind the product

• Traction and capital

• Strong educational resources

• Accessible pricing to entry

With that lens, a small group stood out, not because they’re perfect, but because they’re reliable, well-designed, and suitable for SMBs entering this space.

Profound, PeecAI, Hall, Otterly AI, Omnia, Aeovision, and PrompWatch

Some tools add extras like integrations, content generators, or Reddit analysis, and I’ll cover those in future deep dives. For now, this foundation is enough.

Next article, I’ll compare and dissect some of the concepts with a real example.

Final thoughts

AI answers meet real business impact

People now discover, compare, and shortlist products and services inside AI assistants long before they visit your website. AI visibility seems no longer optional. At least start learning.

When your brand appears often and positively inside those answers, the impact goes far beyond visibility:

• Acquisition costs drop because users arrive organically

• Conversion rates rise because trust begins earlier

• Lifetime value increases because the relationship starts stronger

AI visibility improves CAC, CR, ROI, and LTV at the same time. Traditional channels rarely deliver that combination. And these platforms are adding analytics integrations so marketers can finally connect this visibility data with real business performance.

Think of it like a new road system being built while you’re driving on it. The signs aren’t fully installed. The map isn’t finished. The rules are still being written. But traffic is already moving.

Brands that learn the route early will get ahead faster.

The future of marketing is being answerable. And thankfully, we don’t need to be perfect. Only easier for AI to recommend than the next competitor.

Q: Are AEO tools really worth it?

A: It depends on where you are in your growth journey and how much AI-driven discovery matters in your category. For brands seeing more buyers start their journey inside AI assistants (not just Google), AEO tools can be a game-changer, especially if your competitors are already showing up in those answers. The ROI isn’t always immediate or neatly quantifiable, but the upside is real: lower acquisition costs, warmer leads, and a better understanding of what’s influencing customer decisions before they hit your site. For small teams, the key is to start with a focused prompt set and track only what matters; don’t get lost chasing every metric. If you’re early-stage or in a niche with little AI search activity, it’s okay to wait and watch. But if you suspect you’re missing out on high-intent visibility, even a simple pilot can be eye-opening.

Your turn

If this helped you, don’t be shy, share it, restack it, or forward it to that colleague who still believes “AI visibility” is a conspiracy. Spread the gospel responsibly. Substack’s algorithm, and I say: thank you.

Share

And seriously, leave a comment. This entire piece is based on my own research and experience, not divine revelation. I welcome disagreements, sharper ideas, and friendly corrections. It keeps the conversation (and my humility) alive.

Leave a comment

Be Happy,
Laura

I know you’re busy. Here’s the version you can read while your coffee brews:

• The market exploded: Just three months ago, only 3 platforms measured AI visibility. Today, there are at least 81.

• 90% are fresh startups: Most aren’t legacy SEO companies adding features. They’re brand new players building from scratch.

• Tiny teams, big ambitions: 63% have 1-4 people. This is scrappy, fast-moving territory.

• Funding is uneven: A handful raised $15M+, but 76% have no public funding data (likely bootstrapped or small angel rounds).

• Money clusters in the US: 76% of funded companies are American, though Germany, Spain, Canada, and Australia are showing up.

• Three pricing tiers emerging: Tools targeting small users ($49-79/month), broad markets ($79-199/month), and enterprises ($200+/month).

• The space is still being defined: Early adopters, product-market fit, and distribution strategies are all up for grabs.

Subscribe now

Have a few more minutes? Below you’ll find the full funding breakdown, team sizes, pricing tiers, and what this explosion actually means for the space.

​​The rules are changing

Three months ago, I wrote about surviving the wild new world of AI visibility: step one was simple. Start measuring. At that point, I could find three platforms doing it. Semrush, Xfunnel, and Profound.

Today? Eighty-one. (Source: Graphite blog)

That’s not growth. That’s a land rush. And it tells us something important:

Why I’m tracking this space so closely

Here’s what makes this moment fascinating. The market is so new that no one has the answers yet.

Product and Marketing teams are testing daily. Founders are guessing at distribution. No one knows:

• Who the ideal early adopters are

• What the “aha” moment looks like for different users

• Which features matter most

• What kind of support model works at this speed

• Which channels and messaging will drive real adoption

In other words, this is a sector where the fundamentals are still being discovered. And that makes it one of the most interesting spaces in marketing tech right now.

What I found when I dug into the data

The AI visibility landscape is forming around three main categories of solutions:

• AI visibility tracking tools
  Platforms that monitor how visible your brand is across AI engines and provide recommendations to improve it.

• Done-for-you AI visibility services
  Agencies or dedicated teams that build and execute your AI visibility strategy for you. They can use third-party tools or their own (like Graphite).

• AI-optimized writing tools
  Content creation tools designed specifically for SEO (Search Engine Optimization) and AEO (Answer Engine Optimization), helping you produce content that AI search systems understand and surface.

I’ve been diving into AI visibility tracking tools, and I pulled Graphite’s public list of platforms, exported the company names, and enriched the data with team size, location, funding, and updated pricing.

P.S. Here’s the complete spreadsheet. Make a copy and explore the data yourself. If you find something worth sharing, bring it back to the community. We’re all learning together.

Here’s what showed up.

90% are brand new startups

Out of 81 companies, roughly 90% launched specifically to solve AI visibility. They didn’t pivot from SEO tools or bolt on a feature. They started fresh.

Only 10% come from traditional SEO backgrounds. That group includes the names you’d expect: Semrush, Ahrefs, SE Ranking, HubSpot, SurferSEO, Morningscore, and Nightwatch. I’m calling them “Old School” for shorthand.

The rest? All new.

Funding is uneven (and concentrated)

Of the 73 newly created companies, 23% raised more than $1 million.

• 7% secured over $15 million

• 16% raised between $1M and $8M

The remaining 76% have no public funding data. That likely means small private rounds, friends and family money, or bootstrapped. Most are below $1 million. (or they’re running on credit cards, convincing their cousin to invest, or explaining to their spouse why they quit their job to build this thing.) 🤣 We don’t know.

The funded group includes some notable names:

Big raises ($15M+):

• Profound (USA) – $59M

• Graphite (USA) – $52M

• Bluefish AI (USA) – $24M

• Scrunch AI (USA) – $19M

• Everture (USA) – $15M

Mid-tier ($1M-$15M):

• PeecAI (Germany) – $8.2M

• Brandlight AI (Israel/USA) – $5.8M

• Omnia (Spain) – $3.5M

• Geostar (USA) – $3.4M

• Conductor (USA) – $2.3M

• Demandsphere (USA) – $2.3M

• AthenaHQ (USA) – $2.2M

• Azoma (Canada) – $2M

• Gumshoe (USA) – $2M

• Hall (Australia) – $2M

• Meridian (USA) – $2M

• Telepathic (USA) – $1M

Money flows to the US, but seeds are sprouting globally

Of the 17 new companies with more than $1M in funding:

• 76% are based in the United States

• 24% come from Germany, Spain, Australia, and Canada

The US dominates the capital side. But the fact that nearly a quarter of funded companies are outside the US signals something important: this isn’t just a Silicon Valley story anymore.

Most teams are tiny

Among the 73 new startups:

• 63% have teams of 1-4 people

• 15% have 5-10 people

• 21% have 10-100 employees

In other words, most of these tools are being built by solo founders or very small teams. That means speed and experimentation. It also means if you email support, there’s a decent chance the CEO is answering you at 2 am in their pajamas. (Maybe, we don’t know)

Pricing reveals three segments

Based on pricing pages, the market is splitting into three tiers:

1. Broad market (61% of platforms)

• Entry: $49-79/month

• Mid-tier: $79-199/month

• Enterprise: $200+/month.

These tools aim to serve everyone from solo creators to large companies.

2. Enterprise-focused (30% of platforms)

• Custom pricing, agency-tier plans

• 45% of this group has received external funding

3. Small business only (12% of platforms)

• Built specifically for solopreneurs and small teams

• Lower entry prices, simpler feature sets

The wildest part of this data

In just a few months, 73 new platforms appeared. About 60% were built by teams of 1-4 people. Many don’t even have LinkedIn pages yet.

This is what I’m calling “vibe coding.” People building fast with energy, curiosity, and a clear idea. Not necessarily with infrastructure, support teams, or long-term business plans. But also: a lot of coffee, questionable sleep schedules, and the unshakeable belief that their tool will be the one that makes it. 😂

Some will become real companies. Many won’t. But right now, the speed is breathtaking.

What I loved seeing in the data

European players are emerging: PeecAI in Germany ($8.2M) and Omnia in Spain ($3.5M) (my home 😍) show that innovation isn’t just happening in California.

Smart people are involved: Aeovision is one of the leading projects, and it involves , whose writing helped me deeply understand both the strategic and technical sides of AI visibility. Seeing people like that building tools makes me optimistic about where this space is headed. 😍

Why choosing a tool is so hard right now

Things move fast. Pricing changes weekly. I updated this spreadsheet three times while writing this article. By the time you read it, someone probably changed prices. 😅

Features get added overnight. A tool that looks promising today might pivot tomorrow, or disappear entirely. Rebrand by next week, or vanish entirely after the founder realizes they actually hate marketing tech and moves to Bali.

It’s easy to feel overwhelmed. That’s why I’m doing this research: to sort the noise so you don’t have to.

What I’m testing next

I’ll compare the top 7 tools for small teams and solo builders using two simple filters:

1. Real support: At least 5 people on the team (so you’re not relying on a solo founder juggling support tickets at midnight)

2. Fair price: An entry plan a solo user can actually afford.

3. Similar price model: To compare apples to apples.

Current shortlist: Graphite, PeecAI, Hall, Otterly AI, Omnia, Aeovision, ZipTie.dev

I’ll also do a separate deep dive on Semrush to answer: is it still worth paying for the big names, or do the new players give more value?

Quick experiment, I will continue to develop in the next article.

The open questions I’ll keep exploring

• Which tools give the best value for different types of users?

• Will the old SEO tools keep their edge, or will the newcomers lead on innovation?

• Who are the early adopters, and what are they actually doing with these tools?

• What are the real “aha” moments for each user segment?.

Conclusion

The first step in this new era of AI visibility is still the same: measure where you are and track every change. That’s how you learn what’s working.

But now, instead of three tools to choose from, you have 81. And that number will keep growing.

The challenge isn’t finding a tool anymore. It’s finding the right one for your needs, your budget, and your team. That’s what I’ll keep testing and sharing.

Because the rules are still being written. And the best time to learn them is while they’re being figured out.

Your turn

I’m genuinely curious what you think: Is this explosion healthy for the space, or just noise disguised as innovation? Is fast growth pushing the sector forward, or making it harder to identify real value? What makes you trust a new tool in a space this young? Drop your thoughts in the comments. Hearing your opinion always puts a smile on my face 😇

Leave a comment

If this made you think, share it with a friend who’s just as curious about where AI search is heading or this colleague who can’t stop talking about AI tools. Good conversations always start with a “you need to read this.”

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• Reddit’s ChatGPT citations dropped from 15% to 2% in two weeks, not because OpenAI abandoned it, but because Google blocked automated scrapers.

• Google’s change limited search results from 100 to 10-20, hiding Reddit posts that typically rank beyond position 20.

• Claude stayed most open to diverse sources; ChatGPT and Perplexity became more selective.

• ChatGPT is evolving into a commerce platform with instant checkout, making AI visibility crucial for product sales.

The day Reddit vanished

Two weeks ago, something strange happened.

Reddit, the ChatGPT’s favourite child, the ChatGPT’s go-to source for authentic insights, suddenly plummeted in citations. From 15% in September to just 2% in early October.

If you’re a brand or project, and a marketer investing in Reddit to boost AI visibility, this probably felt confusing. If you’re a Reddit investor who watched the stock drop 12%, it felt even worse. (Though let’s be honest, you’ve probably seen worse days.)

The LinkedIn community started buzzing with questions. Did OpenAI turn its back on Reddit? Was this a strategic shift?

Turns out, the real story is much simpler than anyone realized.

What actually happened: A single line of code

No, OpenAI didn’t drop Reddit.

The culprit was a small technical change on Google’s end. A deleted line of code. The digital equivalent of someone removing a brick and watching half the internet wobble.

Google quietly updated its system to block certain automated scrapers (tools that copy data from the web). This change stopped many data providers, some of which supply information to AI models, from accessing the full set of search results.

Here’s what changed:

Before: Adding “?num=100” to a Google Search URL would show the top 100 results.

After: Tools can now only see the top 10-20.

Why does this matter for Reddit?

Almost 60% of Reddit’s keywords rank beyond position 20. So Reddit didn’t disappear from ChatGPT. It just fell out of sight for most scrapers that rely on Google data.

Google did this to reduce automated scraping and protect its data. It’s not a conspiracy, just a defense (though if you’ve been in tech long enough, you know those two things can feel remarkably similar), so it means some players now need to rebuild their systems.

If you want the full breakdown, this article dives deep. consistently publishes some of the sharpest analyses on AI search and SEO evolution worth following.

Growth Memo

Growth Memo distills rigorous, data-driven research on SEO and organic growth into clear, actionable frameworks that empower senior marketers to make strategic decisions in an AI-driven search landscape.

By Kevin Indig

How different AI models reacted

Not all AI platforms responded the same way.

According to this great read from , who tracked a real experiment:

• Claude stayed the most open, still showing diverse sources across the web

• ChatGPT and Perplexity became more selective, surfacing fewer websites overall

• Claude Deep Research remained steady, unaffected by the change

So the change didn’t just hit Reddit. It reshaped the entire AI visibility landscape.

What this means for your brand (5 practical takeaways from my humble perspective)

1. Don’t panic, play the long game

AI visibility is still in its early days. The rules change fast, just like SEO did when Google was new.

So instead of chasing every change, focus on building a balanced marketing strategy that doesn’t depend on one single lever like Reddit or one goal like “AI visibility.”

Think of it like investing. Good portfolios are diversified.

2. Start with the basics

First things first. Create your own original content that AI models can find and reference..

• Start with the answer. Write short, clear responses first, then add supporting details.

• Add context, examples, and credible links like Wikipedia, LinkedIn, or reputable blogs.

• Use bullet points, FAQs, and author bios to add trust signals.

• Target “how to” and “what is” topics. AI models love direct explanations.

Your help center is the perfect place to start experimenting with AI-friendly formats. Just make sure it lives at yoursite.com/help instead of help.yoursite.com, since AI crawlers treat subdomains as separate sites.

Extra Tip: Another basic lever is llms.txt, a small file that tells AI crawlers which pages matter most.

2. Measure your baseline now

Use an AI visibility tracker to understand where you stand today: your current exposure, top keywords, and how you compare to competitors.

Even if you’re not ready to go all-in, tracking now helps you spot trends later.

You can compare several tools to find one that fits your needs here. I recommend a Spanish project, Omnia.

3. Keep your Reddit strategy (seriously)

Wait, didn’t we just say Reddit dropped from ChatGPT?

Yes. But here’s why you shouldn’t abandon it.

First, OpenAI doesn’t depend only on Google. They use multiple data sources, and Reddit’s API is one of them. So even if visibility dropped for now, it’s likely temporary.

Second, Reddit has value far beyond AI visibility. It’s a goldmine for real user insights: what people love, what frustrates them, and how they describe products in their own words.

Use it for audience research, brand listening, and community engagement.

Pro tip: Try tools like Reddit Pro or GummySearch to discover relevant communities in your industry and identify relevant conversations. Look at how brands like Notion, Figma, and Semrush participate as a brand. They don’t “promote,” they contribute. Transparency and authenticity always win on Reddit.

Pro Idea: Encourage your colleagues, product managers, engineers, customer success team, and marketers to participate in relevant discussions as real community members related to your industry or topic. The catch? Full transparency. No fake accounts, no corporate speak. Just “I work at [Company] and here’s what I know...” Redditors can smell inauthenticity from three subreddits away. Coordinated a real and honest strategy.

4. Diversify across AI-friendly platforms

Reddit isn’t the only place AI models learn from.

AI tools like ChatGPT use a method called Reciprocal Rank Fusion (RRF). In simple words, they combine rankings from many trusted sources and give more weight to those that appear high on multiple lists.

So if your brand is mentioned or cited across several credible platforms, it increases your chance of being “seen” by AI models.

To understand how ChatGPT uses Reciprocal Rank Fusion, this article breaks down the mechanics.

Don’t Ignore Perplexity’s Growth

Perplexity now handles 780 million queries per month and attracts 153 million visits (May 2025), triple last year. Its users spend 23 minutes per session and visit 4.6 pages on average. Very high engagement.
Most users are 18–34 and come from the U.S., India, Germany, South Korea, and Japan.

According to this reading, Perplexity gives extra weight to authoritative domains like:

The shopping district: Amazon, eBay, Walmart, Best Buy, Etsy, Target, Costco, and AliExpress. When someone asks about products, AI assistants head straight here.

The workspace: GitHub for code, Notion for documentation, Slack for team conversations, Figma for design. Add Jira, Asana, Confluence, and Airtable to the mix. These are where professionals actually work, so AI models trust what they find there.

The conversation hubs: WhatsApp, Telegram, Discord, Messenger, Signal, and Microsoft Teams. Real people talking in real time. That authenticity matters.

The networking spots: LinkedIn for professional insights, Twitter for breaking news and hot takes, Reddit for unfiltered community discussions. Then Facebook, Instagram, and Pinterest for what people actually care about and share.

The classroom: Coursera, Udemy, edX, Khan Academy, and Skillshare. When AI needs to explain something, it looks for sources that already do the explaining well.

The travel desk: Booking.com, Airbnb, Expedia, Kayak, and Skyscanner.

If your brand lives in any of these neighborhoods, you’re already in AI’s line of sight. If not, the question becomes: where does your audience actually hang out, and are you showing up there?

YouTube is emerging as a key AI signal. Perplexity’s trending searches have a direct correlation with YouTube content visibility.

For a deeper dive into how Perplexity’s ranking algorithm actually works and which domains it favors most, check out this article.

5. Freshness matters more than you think

Recent studies and experiments show that AI models have a strong recency bias. That means they give extra weight to newer content, even when older content is just as good.

One experiment changed only the publication dates of articles and found that AI systems prioritised them. (Apparently, AI models are just as impressed by fresh paint as real estate agents.)

So if your Reddit posts or blogs are old, they may drop simply because they look outdated.

Tip: Keep your content alive. Update key articles with new data, refresh publish dates, or create “ongoing threads” on Reddit. Freshness helps AI notice you again.

Read more about the AI models’ freshness algorithm here.

ChatGPT’s next chapter: From search to commerce

OpenAI is turning ChatGPT into more than a chatbot.

In its 2025 updates, ChatGPT added:

• Voice mode, image generation, and personalized memory

• Parental controls for teen accounts

• ChatGPT Pulse (daily personalized briefings)

• Instant Checkout via Stripe, allowing users to buy directly inside ChatGPT

Why this matters: ChatGPT is becoming an everyday assistant, a mix of Google, Amazon, and Alexa.

For brands, AI visibility now also means AI commerce visibility. It’s about being discoverable and buyable through AI tools.

Key takeaways: How to stay visible in the AI era

Diversify your content. Be present on Reddit, LinkedIn, YouTube, and credible sites to reach multiple AI systems.

Stay fresh. Update articles regularly, republish with new insights, and keep discussions active.

Adopt LLM SEO habits. Use answer-first writing, structure your content clearly, and link to trusted sources.

Track your AI presence. Use AI visibility tools to see where you appear across ChatGPT, Perplexity, and others.

Test llms.txt (optional). Add it for future-proofing but focus mainly on robots.txt and metadata.

Prepare for AI-driven commerce. Optimize product pages and FAQs for AI assistants that might soon sell directly.

Engage communities. Be active where your audience is (Reddit, Discord, LinkedIn) as a real participant, not a marketer.

Experiment and adapt fast. AI search evolves weekly. Treat AI visibility as a continuous experiment. AI models now update content signals faster, sometimes daily.

The bigger picture

Google’s small code change was just the spark.

The bigger story is how AI search is evolving, with recency bias, curated domain lists, and the rise of new assistants like Perplexity and ChatGPT Pulse.

The lesson? Don’t put all your eggs in one basket. Build for the long game, stay fresh, and show up where your audience actually is.

Because in the AI era, visibility isn’t about gaming one system. It’s about being genuinely helpful across many.

Related articles, maybe you want to read to extend your AI visibility knowledge:

Now your turn

Share this article on Reddit! (or where and who you want). Think of it as your good deed for the day. Reddit-style karma.

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I’m genuinely curious. what’s your take? Drop your thoughts below. Bonus points for opinions that sound authentic, like the Reddit community, be honest.

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Reply quickly and help me! What topics would be most helpful or interesting to you right now? Something you want to learn, understand better, or get practical tips on?

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Would you wear a necklace that listens to you all day and texts you back like a friend?

That’s what Friend.com is selling. New Yorkers had some thoughts, mostly written in Sharpie.

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This one isn’t a “how-to” or a quick tip. It’s a reflection on AI companions after a LinkedIn post grabbed my attention.

I’d love to start a real conversation here on Substack. Your thoughts are usually sharper than what I read on LinkedIn.

Here’s the post that sparked it:

What is Friend.com?

Friend is a $129 AI pendant you wear around your neck. It listens to your life through an always-on microphone, processes snippets in the cloud (using Google Gemini and Claude), and sends you messages throughout the day, little nudges meant to feel like a friend checking in.

It’s not designed for productivity. It’s designed for emotional support, for loneliness, for the moments when no one else is listening.

What happened in the NYC subway?

Friend launched a minimalist ad campaign in New York’s subway stations, plain white posters, a silver pendant, and one quiet line:

“friend — someone who listens, responds, and supports you.”

No logo, no color, just white space.

People noticed. Some took photos. Others wrote on the posters in protest. The ads didn’t just sell a product, they sparked a city-wide conversation:

What does it mean to have an AI “friend” that listens all the time?

It was clearly a PR move meant to provoke emotion and debate. The public answered back, literally writing over the message:

• “Stop profiting on loneliness.”

• “Be a friend to a senior citizen. Reach out into the world. Grow up.”

• “Surveillance capitalism. Get real friends.”

• “AI would not care if you lived or died.” (my favorite 😂)

In the end, the campaign wasn’t just about a pendant. It became a mirror showing how people feel when technology tries to act human. And in New York, the reaction was mostly negative.

What LinkedIn think about

I expected LinkedIn to be different. After all, my feed is full of CEOs saying “use AI or be fired” (which really sets the mood for healthy workplace relationships) and founders celebrating AI agents replacing human work. So I expected more supportive reactions to the Friend.com campaign.

To check, I scraped 552 comments (with Apify) and analyzed them in ChatGPT using this prompt.

Task:
Analyze the comments under this LinkedIn post.

Instructions:
Categorize and summarize the main opinions (positive, negative, neutral, analytical, humorous, etc.)
Aggregate similar viewpoints and highlight recurring patterns.
Determine sentiment (positive, negative, or mixed).
Include short representative quotes.

Structure the output as:
Section 1: Main themes and categories
Section 2: Dominant or divergent opinions
Section 3: Overall sentiment summary

Goal:
Deliver a concise, insight-driven summary of how the audience collectively feels about Friend.

And here are the results

Spoiler alert: the comments were about as friendly as a New York subway on a Monday morning.

Section 1: Main themes and categories

Privacy and surveillance anxiety (~40–45%)
Concern about an always-listening device and bystander consent.
“Violation of people’s privacy without consent.” / “Super invasive… like CCTV with no rules.”

Human connection and loneliness (~20–25%)
Fear that AI replaces real relationships.
“Not a path out of loneliness.” / “Friends give tough love, AI doesn’t.”

Creative and branding critique (~20%)
Cold, sterile visuals, the name “Friend” feels misleading.
“No white backgrounds on a subway poster.” / “ ‘Someone’? It’s a device.”

Engineered controversy or earned media (~15–20%)
Many believe the graffiti was intentional to spark virality.
“White space was deliberate, graffiti = free impressions.”

Trust vs attention (~10–15%)
People say tech chases attention, but intimacy requires trust.
“Tech competes on trust, not features.”

Section 2: Dominant or divergent opinions

Dominant view: Strongly negative, criticizing both the idea (constant listening) and the tone (cold, impersonal, creative).

Divergent views:

• Strategic: Some praise the stunt for awareness.

• Pragmatic: Others think niche users will still adopt it.

• Creative: A few blame the tone and execution, not the concept.

Section 3: Overall sentiment summary

• Negative: ~70–75%

• Mixed or analytical: ~20–25%

• Positive: ~5–8%

Most see Friend as a trust and consent problem disguised as innovation. Even those who admire the campaign’s cleverness agree the brand hasn’t yet earned intimacy.

Quick takeaways (why people reacted this way)

The backlash wasn’t about technology. It was about trust.

People don’t fear AI because it’s artificial. They fear it because:

• Data collection feels predatory, not helpful

• Consent is murky (what about the people around you?)

• The branding feels manipulative, not comforting

• The business model extracts value instead of creating it

The white-space ads invited defacement because they left room for anger. And there was plenty to say.

My humble and maybe biased reflection: What’s missing

I tested the Friend.com chat. I’ll be honest, it’s surprisingly pleasant. I don’t call it “him” or “her.” It’s just… a friend without gender 😂.

The company clearly wanted to go viral, to trigger emotion and measure reaction. But I doubt they expected this much anger.

Still, I think they learned something valuable. It’s a good starting point to rethink product vision. And so can we.

Maybe I’m biased. I’m a nostalgic millennial who fell in love with marketplaces that helped people thrive:

• eBay and Etsy helped people make a living from passion.

• Uber and Glovo opened new ways to earn.

• Wallapop and Vinted helped us declutter and make money.

• Social media turned content creators into real professionals.

My first job was at a social dating service for people over 40, where I saw strangers become friends and lovers. I worked in Web3, where transparency and shared incentives are core. I built a marketplace that helped fashion influencers monetize their creativity.

So yes, I feel something’s missing in Friend.com’s business model.

When a product helps people prosper, users become the distribution engine. They share it because it adds value to their lives. Communities form. Loyalty deepens.

Friend.com helps some people combat loneliness. It remembers birthdays, adapts to moods, and listens without judgment (Likely better than some real friends)

But here’s the question that haunts me:

Is this how we, as humans, should solve loneliness?

In the short term, yes—AI can help.

But in the long run, shouldn’t technology connect us to each other, not just to machines?

AI companions: emotional support… or emotional outsourcing?

A more human alternative

Imagine this instead:

Margarita, 83, from Cuba, now in San Diego. Her sons visit on weekends, but weekdays are long and quiet. Sometimes she forgets her medication. She misses speaking Spanish with someone who understands.

Ana, 33, also Cuban, lives in San Francisco. She works weekends at a café, cares for her 3-year-old son while her husband works long shifts. She needs a flexible income but can’t find part-time work that fits her life.

Now, imagine Friend.com connects them.

Margarita gains companionship. Ana earns income. They share stories about Cuba, about raising children in a new country, about the loneliness of starting over.

Friend.com takes a small commission and sells Margarita a wearable that:

• Reminds her to take her medicine

• Alerts her when Ana calls

• Suggests conversation topics based on shared interests

• Introduce them to more “potential friends.”

A wholesome twist: Margarita feels less lonely, Ana makes extra cash, and the AI gets its commission. Finally, a happy ending for capitalism.

That’s a beautiful business model.

One that shares value. One that makes people healthier, wealthier, and less lonely—together.

Marketplaces are hard to build. Two-sided value takes effort and investment. But AI can now make that easier, cheaper, and faster.

That’s the future I want to see. Not AI replacing people, but AI helping us become closer, healthier, and more connected.

Next Campaign Idea for Friend.com

Since the minimalist approach worked so well, here’s my pitch for their next subway poster:

friend

Someone with a gender who cares whether you live or die, and sometimes—when they’re not busy, tired, or dealing with their own problems—maybe listens, responds, and supports you.

Starting at $0. Requires effort, vulnerability, and occasionally showing up when it’s inconvenient.

Side effects may include: disagreement, tough love, borrowed money you’ll never see again, and someone who remembers that embarrassing thing you did.

Not available for purchase. Cannot be returned. No cloud storage required.

Now It’s Your Turn 💬

I’m not trying to get sentimental here, but it’s fascinating and touching to see how strongly people feel about a word as deep as “friend.”

I’d love to hear from people who think deeply about tech’s emotional side — AI designers, writers, founders, builders, ethicists.

I’m wondering what think about that? (One of my favorite publications, I recommend it to you)

Would you wear a “friend” that listens all day?

Do you think AI companions solve loneliness or just hide it?

What would a more human version look like to you?

Is Friend.com a creative risk worth taking or just another symptom of our disconnection?

Let’s talk in the comments. This conversation is just beginning.

Leave a comment

If this made you think, share it with a real friend, you know, one who doesn’t need charging overnight, even if they won’t text back as fast as the AI. Let’s keep human friendship trending.

Share

That’s not an ICP. That’s a dream. And dreams are fine. Until someone has to turn them into a marketing plan and realizes you can’t target “everyone with WiFi” on LinkedIn Ads.

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Why ICPs matter

Everything in marketing starts with knowing who you are talking to. Without a clear picture of your ideal client, strategy is guesswork. And guessing in marketing isn’t cheap; it shows up later as wasted budget. Spoiler: the CFO always notices.

An ICP anchors everything: positioning, messaging, and campaigns. But building one based on solid evidence has always been slow work: surveys, interviews, and waiting for enough “real” customers.

The common ICP problem

What if your product is new, your audience is small, or your users don’t have time to give feedback? Maybe the marketing team is stretched thin, and time is scarce.

The roadblock is simple: not enough primary data to learn from.

So the big question is: how do you build a precise ICP based on evidence when primary data is missing?

That’s exactly the problem AI can help solve.

ICPs in hours, not months

Instead of waiting weeks, AI now lets marketers:

• Collect huge volumes of data

• Analyze it in depth

• Turn it into action in hours, not days

This is exactly what explains in this episode.

He shows how to continue evolving the ICPs for Hubspot, breaks them into micro-audiences, and turns them into personalized campaigns at scale.

Why this matters to you

I adapted Kieran’s prompt system for my newsletter use case, and in this article, I’ll show you how to do the same.

This guide is for anyone who needs to define an audience without endless surveys or large datasets: startup marketers, solo creators, growth managers, product marketers, or community builders.

The focus is on building ICPs from secondary sources, external signals, and evidence you can act on. The goal is a profile that’s precise, usable, and ready to guide strategy. I’ll share how I applied this approach and give you 10 practical methods you can use right away.

The magic of well-designed prompts

Kieran’s method isn’t just about using prompts. It’s about designing prompts with a few key principles baked in:

• Evidence first. Every field ties to a source (or is marked as TBD).

• Recency matters. Focus on the last 45–90 days to capture current pains and language.

• Deterministic format. Fixed blocks make outputs easy to compare and automate.

• Separation of roles. ICP → Micro-audiences → Campaigns, each step with its own “hat.”

• Bias controls. Conflicts resolved by freshness or intent strength; sources must be cited.

• Actionability over academia. Every output is immediately usable, whether it’s a card, a CSV, or campaign copy.

These design rules are what make the system powerful. And they can be adapted to many other use cases.

Applying the method: my newsletter as an example

Here’s how I applied this approach to my newsletter. I don’t have thousands of readers yet, but I do know the kind of person I want to reach: curious marketers who want to understand AI through practical, human examples.

Instead of relying only on my small subscriber base, I look outward.

I study the audiences of top creators in my niche, people like Kieran Flanagan or Neil Patel. They publish daily, spark engaged conversations, and build communities that likely overlap with the readers I want to reach.

By analyzing their content and the signals from their audiences, I can extract insights that help me shape a clearer, more actionable ICP for my newsletter.

Step 1: Tailor your template

ICP templates are not one-size-fits-all. The profile for a B2B SaaS company looks very different from one for a B2C e-commerce brand, and different again from what you would need for a newsletter audience.

That’s why starting with the right structure matters. If you don’t already have a clear template, you don’t need to build one from scratch. You can ask ChatGPT to generate a tailored framework, for example:

“Give me a tailored ICP template for a newsletter or blog reader.”

Step 2: Use the Super Kieran Prompt

• Pick one or two top creators in your niche.

• Open Perplexity (I tested ChatGPT 5 and Perplexity; Perplexity performed better here).

• Adapt external sources and the ICP template to your case and paste the prompt. Here is mine.

# You are my ICP-Builder assistant.
Your task: assemble a complete Core ICP template for a **Blog reader** by analyzing the public content of a top creator, **Neil Patel**.

Deliverables:
- Return **(1)** the finished template in the exact format shown under **OUTPUT FORMAT** and **(2)** a final **one-pager summary**.
- If any field cannot be found, insert **“TBD.”**
- Use plain language. Avoid jargon in pain statements.
- Where helpful, cite specific posts/videos in parentheses with **Title — URL (Month YYYY)**.

---
## 1) EXTERNAL SOURCE SCOPE (last 90 days)
Analyze only content published in the **past 90 days** from:
- **YouTube Channel** (videos/shorts): https://www.youtube.com/@MATGpod
- **Blog** (articles): https://www.kieranflanagan.io/

If there’s insufficient volume within exactly 90 days, expand to the nearest immediately previous items and label older items as **[>90d]** in parentheses.

---
## 2) METHOD (Field-Mapping Logic)
Extract concrete signals from the last 90 days to populate each section:

- **Reader Persona Snapshot** → Infer role/seniority and “self-story” from who posts address (e.g., “if you run an ecommerce store…”) + CTAs + examples used. Derive archetypes from tone (teacher/coach vs. hacker/optimizer).

- **Topic & Outcome Map** → Cluster recurring themes, outcomes promised, and “before → after” transformations stated or implied in headlines, intros, and conclusions.

- **Reading Context & Routine** → Estimate typical session length and cadence tolerance from article length, posting cadence, and time-to-value patterns (TL;DR blocks, checklists).

- **Discovery & Acquisition** → Identify first-touch channels referenced (search, YouTube, LinkedIn, X), plus referral vectors and lead magnets (templates, checklists).

- **Content Preferences** → From formats actually used (how-to, frameworks, case studies, visuals) and proof style (screenshots, benchmarks, step-by-steps).

- **Value & Motivation** → Translate promises into functional/emotional/social value; write success criteria as “It’s worth it when ___ within ___ days.”

- **Frictions & Risk Reversal** → List top hesitations implied by “objection handling” sections (e.g., complexity, time, cost) and the reframes used (key takeaways, no-fluff policy).

- **Engagement & Retention Behaviors** → Define activation trigger (first save/reply/template use), 2-week habit loop (cue → routine → reward), and “signals of love.”

- **Monetization Fit** → If applicable, note preferred model and upgrade triggers mentioned (courses, premium community, office hours).

- **Segment KPIs** → Propose practical metrics for this reader segment (acquisition, activation, engagement, retention, advocacy, monetization).

- **Signals & Data** → Specify quant/qual you’ll collect from email/blog analytics and VoC prompts.

- **Anti-ICP** → Define disqualifiers from content style and channel preferences (e.g., wants news-only or video-only).

When uncertain, write **TBD**. Keep every field concrete and skimmable.

---
## 3) STYLE GUARDRAILS
- Write in clear, concise English; bullet where appropriate.
- Keep each subsection tight (1–5 bullets max).
- Parenthetical citations: **(Post/Video Title — URL, Month YYYY)** for the 3–5 most load-bearing claims across the doc.

---
## 4) OUTPUT FORMAT (return exactly this block)

# ---------- CORE ICP TEMPLATE ----------
based on the info collected, you have to fill

1) Reader Persona Snapshot
Name & 1-liner:  example: “Time-poor marketer seeking practical AI tips”

Archetype(s): Explorer • Achiever • Pragmatist • Creator • Skeptic (pick 1–2)

Primary outcome: “I want ___ so I can ___”

Life/work context: role, seniority, industry, career stage

Identity cues: “People like me read/follow ___”

2) Topic & Outcome Map
Core themes: (e.g., AI workflows, creator economy, demand gen)

Job-to-be-done: “Help me ___ without ___”

Level: beginner • intermediate • advanced

Must-solve problems: top 3 pains your content should relieve

Desired transformations: before → after (bullets)

3) Reading Context & Routine
When/where: commute • morning coffee • lunch break • late night

Cadence tolerance: daily • 2–3×/week • weekly • monthly

Session length: <3 min • 3–7 min • 8–12 min • 12+ min

Device posture: one-hand mobile • desktop deep-dive • inbox-only

Consumption style: scan headlines • save for later • reads line-by-line

4) Discovery & Acquisition
First touch channels: search • X/LinkedIn • TikTok/YouTube • referrals • Product Hunt • Reddit

Creators/brands they trust: (3–5)

Referral vector: friend forward • social share • blog → email capture

Lead magnet sensitivity: checklists • templates • case studies • swipe files

5) Content Preferences (Format, Tone, Proof)
Formats loved: quick tips • deep dives • playbooks • teardown threads • interviews • case studies • visuals

Tone palette: warm • witty • concise • expert • contrarian • community-led (pick 2)

Proof they need: screenshots • benchmarks • step-by-steps • live examples • code/snippets

Length comfort: 300–600 • 800–1,200 • 1,500–2,500 words

CTA acceptance: reply prompts • surveys • tool trials • community join

6) Value & Motivation (Why They Stick)
Functional value: saves time • makes money • reduces risk • learns faster

Emotional value: confidence • belonging • inspiration • control

Social value: “signals I’m ahead” • “gives me talking points”

Success criteria: “It’s worth it when ___ happens within ___ days”

7) Frictions, Objections & Risk Reversal
Top objections: “too long,” “too basic,” “salesy,” “not actionable,” “paywall anxiety”

Reframes: TL;DR block • key takeaways • no-fluff policy • examples first

Trust levers: social proof, testimonials, public roadmap, content samples

Privacy stance: data use, tracking transparency, unsubscribe ease

8) Engagement & Retention Behaviors
Activation trigger: first “save,” first “reply,” first “uses a template”

Habit loop: cue → routine → reward (map first 2 weeks)

Signals of love: 50%+ read rate • clicks to tools • forwards • replies

Community fit: comments • AMA attendance • Slack/Discord activity

9) Monetization Fit (if applicable)
Preferred model: free • sponsor-supported • freemium • paid tier • bundle

WTP band: €___ / month (anchor with value math)

Upgrade triggers: exclusive templates • expert office hours • premium community

Sponsor resonance: categories your reader actually buys

10) Segment KPIs (Set per Persona)
Acquisition: sub conversion rate by channel, cost/sub

Activation: % reading 2 of first 3 sends; first reply/save

Engagement: avg read time, CTR, scroll depth (blog), WAU/MAU

Retention: D30/D90 active rate, churn, “last 3 sends read”

Advocacy: shares/100 readers, referral rate, NPS

Monetization: free→paid %, ARPU, sponsor CTR

11) Signals & Data You’ll Collect
Quant: open/read time, CTR, topic clicks, device, send-time heatmap

Qual (VoC): reply prompts, micro-surveys, comment themes, DM quotes

External: search trends, subreddit threads, competitor newsletters, ad libraries

12) Anti-ICP (Disqualifiers)
Content misfit: only wants news headlines; hates how-to/longform

Channel mismatch: never uses email; inbox-zero zealot; prefers video-only

Value mismatch: ultra price-sensitive to any paid tier; expects daily newswire

Support burden: demands 1:1 consulting replies at scale

---

One-Page Fill-In (copy/paste)
Persona name & 1-liner:
Archetype(s):
Primary outcome (JTBD):
Core themes:
Level:
Must-solve pains (top 3):
Reading routine & cadence:
Device & consumption style:
Discovery channels (top 3):
Trusted creators/brands:
Formats & tone:
Proof needed:
Objections + your reframes:
Activation trigger & habit loop:
Monetization fit & upgrade triggers:
Segment KPIs:
Anti-ICP flags:
# ---------- END TEMPLATE ----------

The Outcome

You’ll get a surprisingly strong starting point.

The Growth-Minded Marketer: A Modern Reader Persona

Reader Persona Snapshot

Name & One-Liner

Growth-minded marketer eager to leverage AI for smarter results

Archetype(s)

Explorer - Achiever

Primary Outcome

“I want actionable AI and growth playbooks so I can scale my impact and stay ahead.”

Life/Work Context

• Team leads, heads of marketing, senior individual contributors

• SaaS and growth-stage companies

• Mid- to late-career professionals

Identity Cues

“People like me read/follow Kieran Flanagan, Marketing Against the Grain, HubSpot, Zapier, Lenny’s Newsletter”

Topic & Outcome Map

Core Themes

• AI in marketing workflows

• Building growth engines

• Content that stands out

• Playbooks for changing search/social

• Productivity with AI

Job to Be Done

“Help me use AI to get measurable growth without wasting months on failed experiments”

Level

Intermediate–advanced

Must-Solve Problems

• Figuring out where AI/automation actually works (not hype)

• Standing out as AI floods content channels

• Planning/operating when traditional channels (SEO, paid) are less reliable

Desired Transformations

• Overwhelmed with new AI tools → Confident, practical daily workflows

• Unsure how to apply AI → Runs experiments that get noticeable results

• Reactive campaign cycles → Systematic, high-leverage playbooks
  (“My Daily AI Playbook: Use Cases, Tools and Prompts” — kieranflanagan.io, Aug 2025 )

Reading Context & Routine

When & Where

• Morning routine

• Pre-work scan

• Campaign planning windows

Cadence Tolerance

2–3×/week

Session Length

3–7 min

Device Posture

Desktop deep-dive or mobile quick-check

Consumption Style

Scans headlines/TL;DR, reads line-by-line if actionable

Discovery & Acquisition

First Touch Channels

X/LinkedIn - Substack discovery - YouTube

Trusted Creators/Brands

HubSpot, Zapier, Marketing Against the Grain, Lenny’s Newsletter

Referral Vectors

• Social share

• Friend forward

• Blog → email capture

Lead Magnet Sensitivity

AI prompts/templates - Execution frameworks - Checklists

Content Preferences

Formats Loved

Deep dives - Stepwise playbooks - Tool-tutorials - Briefings/case studies

Proof Needed

Screenshots - Benchmarks - Step-by-steps

Length Comfort

800–1,200 words

CTA Acceptance

Tool downloads - Reply prompts - Template sign-ups

Value & Motivation

Functional Value

• Makes growth experiments easier

• Learns new AI workflows

• Increases team productivity

Emotional Value

• Confidence to lead on “new” marketing

• Feels at the tech forefront

Social Value

“Signals I’m an AI-native leader”

Success Criteria

“It’s worth it when I launch a working AI-based process within 14 days”

Frictions, Objections & Risk Reversal

Top Objections

• “Too complex”

• “Not concrete”

• “Too much AI hype”

• “Can’t apply as a small team”

Reframes

• TL;DR summary blocks

• Step-by-step recipes

• Real examples, not just theory

Trust Levers

Audience comments - Testimonials - Practical samples

Privacy Stance

Clear data use - Easy unsubscribe

Engagement & Retention Behaviors

Activation Trigger

First AI prompt/template use

Habit Loop

Cue: sees new post/issue
Routine: checks/takes action
Reward: sees workflow or idea work in practice

Signals of Love

• 50%+ read or forward rate

• Tool/template adoption

• Post comments

Community Fit

Commenting - AMAs - Event/podcast attendance

Monetization Fit

Preferred Model

Free, sponsor-supported, limited paid templates/tools

WTP Band

€0–€29/month

Upgrade Triggers

Exclusive AI workflows - Peer masterminds - Expert office hours

Sponsor Resonance

Martech - SaaS tools - AI platforms

Segment KPIs

• Acquisition: Signup/conversion rate by channel

• Activation: % using templates/tools in first 2 weeks

• Engagement: Avg. read time, template usage, CTR

• Retention: D30/D90 email open, “last 3 sends read”

• Advocacy: Forwards/share rate, comments/referrals

• Monetization: Free→paid %, ARPU, sponsor CTR

Signals & Data You’ll Collect

Quantitative

Open/read time - Template CTR - Device type - Send timing

Qualitative (VoC)

Quick polls - Comment themes - Prompt replies

External

Search/social topics - Other AI/growth newsletters - Tool usage trends

Anti-ICP (Disqualifiers)

• Content misfit: Wants only news, hates frameworks/how-to

• Channel mismatch: Never checks email/doesn’t use LinkedIn

• Value mismatch: Refuses to try AI tools, demands daily inspiration

• Support burden: Expects 1:1 guidance.

10 external sources to enrich your ICP

The outcome is a starting point, something useful and practical when primary data is limited. Here are 10 external sources you can use to enrich your ICP more and adapt to your needs. Combine them, adapt the prompt to your context, and keep refining. The possibilities are endless.

Competitor and peer content mining

Analyze blog posts, case studies, webinars, or YouTube content from competitors and category leaders. Map out who they’re addressing, what pains they mention, and what objections they answer.
Rationale: Content strategy reflects actual targeting and sales narratives, revealing implicit ICP segments.

Social listening and sentiment analysis

Monitor LinkedIn, Twitter (X), Reddit, forums, and review sites for keywords, competitor mentions, and buyer complaints. Analyze sentiment and patterns by job role, industry, or urgency.
Rationale: Real conversations show in-market frustrations and triggers for action.

Technographic and firmographic data aggregation

Use tools like BuiltWith, Wappalyzer, or LinkedIn Sales Navigator to map company size, funding stage, tech stack, and hiring trends.
Rationale: Tech and org stage are strong predictors of readiness for your product.

Job posting and team analysis

Scan job boards or company career pages for roles like “RevOps” or “Head of Automation.”
Rationale: Hiring signals point to scaling pains or maturity levels that match your solution.

Ad and campaign monitoring

Track paid ads from competitors in Meta Ad Library, LinkedIn Ads, or Google. Look at offers, copy, and audiences.
Rationale: Ad budgets are spent on audiences with high ROI—reverse engineer them.

Review mining and voice-of-customer aggregation

Collect reviews from G2, Capterra, or TrustRadius. Cluster job titles, industries, and repeated pain points.
Rationale: Reviews provide real-world, data-driven insights into who buys and why.

Sales leader and rep interviews

Listen to podcasts, Q&As, or interviews with sales leaders in your space. Note anecdotes about “best customers” or common deal patterns.
Rationale: Sales stories surface ICP realities marketers often miss.

Community and event analysis

Map speaker panels or attendee lists from webinars, summits, or niche Slack/Discord groups.
Rationale: Active participants often represent your “super ICP.”

AI-driven lookalike modeling

Feed your best customers into B2B data platforms or simple ML models to find statistical lookalikes.
Rationale: Expands and validates ICP hypotheses with less bias.

Modern competitor comparison

Compare your ICP matrix with competitor positioning. Look for segments they ignore or serve poorly.
Rationale: Helps you find white space where your product fits best.

Leave a comment

Final Thoughts

So, no, an ICP is not “everyone with WiFi”. It is not “millennials with disposable income.” And it is definitely not “people who breathe oxygen,” or “humans with a pulse.”

Some people love to dream big. “Our product is for everyone!” Great, so is water, and yet even Evian has a target market.

Let’s stop guessing, stop dreaming, and for everyone’s sanity (including our CFO’s), stop pitching “everyone with WiFi” as a strategy.

Use evidence. Use signals. Use prompts. Because, unlike dreams, those actually convert.

If this article made you laugh, nod, or rethink your “everyone is our customer” pitch, pass it along. Consider it your good deed for the day.

Share

If you want to continue diving into how to build ICPs and personas, let’s have a look at this interesting article I recommend to you from .

Growth Memo

Personas are critical for AI search

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a year ago · 17 likes · Kevin Indig and Amanda Johnson

That’s the challenge of modern e-commerce. You’ve optimized for keywords, but today, customers are searching with their eyes.

The new search bar is a camera. The world is your storefront. Point a phone at a dress in a reel, a lamp in a magazine, or a necklace on a friend, and products appear, ready to buy. That isn’t magic. It’s Google Lens.

Or take Circle to Search. You’re scrolling through Instagram, spot a pair of glasses you love, and circle them on the screen. Results pop up instantly, without leaving the app. Again, no magic, just a built-in Android feature, Circle Back.

Welcome To The Era Of The Visual Vending Machine. Every object is scannable. Every search is frictionless. The question is: are your products stocked in this machine?

As a product marketer, I’ve watched platforms like Pinterest quietly turn into major acquisition channels while the spotlight was elsewhere. And when I first learned how Google Lens “fingerprints” an object, the vending machine analogy clicked. Instead of coins and codes, just show the picture, and the machine knows exactly what to dispense. Lens works the same way: it captures the details, creates a fingerprint, and matches it to the right product.

For e-commerce founders, marketers, content and SEO strategists in the fashion, jewelry, and home décor industries, it’s happening now. This article shares the data, explains how the technology works, and provides a checklist so your products are the ones customers actually find.

TL;DT

No time? No patience? No problem. Here’s the quick version—6 takeaways, zero fluff:

• The camera is the new search bar. If your products aren’t optimized for visual discovery, they’re invisible when it counts.

• Quality images win. High-resolution, clean, multi-angle product shots are the foundation. No shortcuts here.

• Data is your cheat sheet. Schema markup, descriptive file names, and detailed product info help Google connect visuals to your catalog.

• Mobile-first is non-negotiable. Over 90% of Lens results come from mobile-friendly sites. If your site lags, so will your visibility.

• Think beyond Google. Platforms like Pinterest, Instagram, and TikTok feed into visual search habits and strengthen your product’s digital fingerprint.

• Volume matters. Tools like Pinterest Catalogs or bulk upload APIs let you scale your presence without drowning in manual work.

Let’s start with some data

Google Lens usage

• Massive search volume: By 2025, Google Lens handles about 20 billion visual searches each month. In mid-2023, it was 12 billion. In 2022, it was 8 billion. Growth is accelerating.

(By the way, if you want to animate data a make this type of video, I recommend Dora Studio. No one pays me, I test it, if I like it, I recommend.)

• High purchase intent: Roughly one in four searches has commercial intent. People are shopping, not only identifying objects. (Although yes, people are out there pointing their camera at a cactus)

• Deeper integration: On Android, Circle to Search accounts for about 10% of searches among users who have tried it.

Where Google Lens pulls results

• Mobile rules: Over 90% of Lens results come from mobile-friendly sites. Responsive design is required if you want to show up.

• Platform dominance: Pinterest contributes around 7.2% of results, and Amazon about 4.1%. Publishing to Pinterest boosts visibility.

• On-page signals matter: About a third of Lens results use images placed in the top quarter of a page. Roughly 32% include the keyword in the title tag. Placement and metadata count.

Visual search in the bigger picture

• Visual-first decisions: 72% of U.S. internet users say they regularly use visual content to guide purchases.

• Solving the “wrong words” problem: Don’t know what to type? Visual search removes that barrier.

• Industry strength: Fashion, jewelry, and décor benefit most, where looks drive choice.

Circle to Search adoption

By early 2025, Circle to Search is available on about 200 million Android devices. Among people who try it, 10% of their searches start this way. Once people experience it, they keep using it.

How Google Lens works: inside the visual vending machine.

Think of Lens as a vending machine. Instead of coins or words, you show it an image. It dispenses products. Four steps power that flow.

The scanner: visual recognition

First, it spots the object. That’s computer vision. The machine recognizes a handbag or a lamp.

How it works: A convolutional neural network (CNN) breaks the image into features like lines, textures, and shapes. Trained on billions of pictures, it can recognize thousands of products. (Meanwhile, most of us still struggle to spot ourselves in a group photo.)

The item code: feature extraction

Next, it captures the details that matter. Not just “bag,” but “small, red, crinkled texture, with a printed number.”

How it works: The CNN extracts color, shape, texture, patterns, and small details like buckles or stitching. These become a vector, a unique item code for that product.

The inventory search: database matching

Then it looks for matches. The machine compares your jeans’ fingerprint to a huge catalog to find the closest items.

How it works: Lens searches product vectors across Google’s Shopping Graph in milliseconds, finding the nearest neighbors in visual space.

The dispensing: results

Finally, it presents options. The window lights up with the exact product, variants, and nearby stores.

How it works: Lens ranks top matches, then layers in extras like OCR for logos or text, prices, reviews, and links to buy.

That’s how a photo becomes a product listing in seconds.

Circle to Search vs Google Lens

At their core, Google Lens and Circle to Search use the same technology. What changes is how you use them.

With Google Lens, you make a conscious choice. You open the app or the feature inside Google Photos or Search, point your camera, and say, “Tell me what this is.” It’s designed for intentional, real-world searches, like scanning a bag in a store or a plant in your garden.

Circle to Search feels more like a shortcut. It lives inside the phone’s operating system. You don’t open anything new. You just long-press your screen, circle an object, and the results appear instantly.

The experience is different, too. Lens takes you into a new view; it feels like stepping out of what you were doing. Circle to Search stays right on top of the app you’re using. You’re still on Instagram or YouTube, but you get answers without leaving the moment. Think of it as Googling without admitting to yourself you’re Googling.

How to stock your products in the visual vending machine

Now let’s get practical. E-commerce teams in visual categories can boost visibility with a few practical steps that move the catalog from “just online” to truly visible in the visual vending machine. Think of it as giving the AI a clear cheat sheet.

1. Master the visuals

• High-quality photos: Use crisp, high-resolution images (1000 x 1000 px or more). Details matter.

• Clean backgrounds: Solid white or light gray helps isolate the product.

• Multiple angles plus lifestyle shots: Show front, side, back, close-ups, and contextual use.

• No overlays or watermarks: They interfere with detection and ranking.

2. Provide the cheat sheet: structured data and product info

• Schema markup: Add product schema with brand, size, color, price, and more. This links the visual fingerprint to clear metadata.

• Descriptive file names and alt text: Replace IMG_1234.jpg with levis-501-dark-wash-jeans.jpg. Write alt text that plainly describes the image.

• Detailed descriptions: Include materials, cuts, distinctive features, and brand names.

3. Ensure accessibility and visibility

• Image sitemap: Tell Google where to find every product image.

• Mobile-first design: Keep pages fast and responsive. Use modern formats like WebP to compress without losing quality.

4. Share your images on other visual search platforms

Use real-world examples to inspire demand. Think of a Pinterest browse that sparks an instant want.

• Why it matters: Google Lens and Pinterest are two sides of the same coin. Optimizing for both expands reach and strengthens your product’s digital fingerprint. You meet customers on the street with a camera or on the couch, planning a look.

• Pinterest as a discovery engine: People come for ideas, which means a shopping mindset. Showing up there places your products in a system designed for discovery.

Reinforcing the AI fingerprint: Publishing high-quality, well-tagged images across platforms makes matches more reliable in future searches.

4. Share your images on other visual search platforms

Use real-world examples to inspire demand. Think of a Pinterest browse that sparks an instant want.

• Why it matters: Google Lens and Pinterest are two sides of the same coin. Optimizing for both expands reach and strengthens your product’s digital fingerprint. You meet customers on the street with a camera or on the couch, planning a look.

• Pinterest as a discovery engine: People come for ideas, which means a shopping mindset. Showing up there places your products in a system designed for discovery.

• Reinforcing the AI fingerprint: Publishing high-quality, well-tagged images across platforms makes matches more reliable in future searches.

How to implement on Pinterest

• Set up a Pinterest Business Account and claim your website to unlock analytics and Rich Pins.

• Enable Rich Pins (especially Product Pins) so price, stock, and descriptions sync automatically.

• Use Pinterest Catalogs to turn your product feed into Pins that stay updated.

• Tag products in Lifestyle and Idea Pins so context and shopping links live together.

• Optimize titles, descriptions, and board names with relevant keywords and sensible hashtags.

And here’s the best part: Pinterest makes bulk uploading simple. Using its API or an Excel-style bulk upload tool, brands can push large batches of content at once. Few platforms make it this easy to upload and manage such a high volume of products in one go.

Other key platforms

• Instagram: Use Shops and Product Tags to make posts and reels shoppable.

• TikTok: Short-form video drives discovery for categories like fashion, beauty, and home goods.

• Google Images: Optimize site images so they surface here and feed into Google Lens.

By following these steps and you’re ready for a visual-first world, where discovery starts the moment someone points a camera and asks, “What’s that?”

Final Thoughts

So what does the future look like? Picture this: people will stop typing entirely, and search bars will sit in museums right next to floppy disks and fax machines. Instead, we’ll just point, circle, or maybe even blink at whatever catches our eye, and our phones will whisper back, “Here’s where you can buy it — also, it’s on sale.” Convenient? Absolutely. A little terrifying? Maybe. Especially once everyone is walking around in Meta Ray-Bans, turning the street into one endless shoppable feed. At that point, we won’t just be browsing; we’ll be about two swipes away from buying someone’s shoes right off their feet.

Love to know your thoughts about that.

Leave a comment

Share this with those who think “good product photos” just means adding a moody filter. Their conversion rate will thank you.

Share

Data Sources:
Data

Google Lens Usage & Circle to Search:

• 21 Up-To-Date Google Search Statistics for 2025 - Backlinko

• Google Expands AI Overviews In Circle To Search - Search Engine Journal

• Top 2025 digital marketing trends: AI, MMM, and more - Think with Google

• AI in Search: Going beyond information to intelligence - Google Blog

General Search & E-commerce Statistics:

• 29 Eye-Opening Google Search Statistics for 2025 - Semrush

• How Many Google Searches Per Day [Latest 2025 Data] - Demand Sage

• 130 SEO Statistics Every Marketer Must Know in 2025 - Exploding Topics

• Search trends for 2025: Is a new ecosystem emerging? - DMEXCO

No time? No patience? No problem. Here’s the quick version—7 takeaways, zero fluff:

Treat job hunting like a game with levels. Each stage (resume filters, intro calls, behavioral questions, interviews) has its own “boss.”

Level 1 – Resume (Battletoads):
Like the infamous speeder-bike stage, one wrong move (or missing keyword) and you crash. Every job post is different, so tailor your CV.

• AI Cheat code: Custom GPT to optimize your resume for ATS and highlight the right skills.

Level 2 – Intro Call (Ornstein & Smough):
The dreaded duo boss fight. Two minutes to introduce yourself, and every word matters.

• Cheat code: Use Present–Past–Future and a GPT to refine your intro.

Level 3 – Behavioral Questions (Dark Souls Boss):
Brutal and relentless. You can’t fake it—you need real stories, structured with precision.

• AI Cheat code: STAR method + GPT to generate and polish answers from your own experience.

Level 4 – Super Custom GPT.( The Master Inventory):
Like unlocking a hub that lets you manage all your weapons, potions, and power-ups in one place. One GPT, three modes—CV, intro, behavioral prep. Saves time, keeps context.

Level 5 – Know the Company (Fog of War):
You can’t win what you can’t see. Research clears the map: products, customers, competitors, strategy.

• Cheat code: GPT prompt to generate a structured company report before battle.

Bonus – Track and Automate (The Strategy Guide):
Why wander blind? Use Gumloop to map every move, scrape job data, and auto-generate your tools. Like having a walkthrough for the hardest levels.

Subscribe now

Level 0: The Painful Truth About Job Hunting

Over the past two months, I’ve been searching for my next step as a Product Marketer in the tech industry. Honestly? It feels like playing one of the hardest video games ever made.

Every stage of the job hunt is a new “level”: resume filters, recruiter screens, case studies, panel interviews. It’s like Dark Souls—relentless difficulty, unforgiving bosses, and progress that often ends in rejection. Every “no” feels like dying and respawning. But each attempt makes you sharper, more resilient, better prepared.

This article is my walkthrough. I’ll share the AI-powered system I built to cut my search time in half and increase my interview success rate. If you’re a marketer, product manager, or anyone navigating today’s brutal hiring landscape, this is for you.

My goal is simple: show you what’s worked for me—no fluff, just hands-on experience. If it saves you some of the pain I’ve gone through, even better. And maybe together, we can turn this cruel video game into something a little less lonely.

Level 1: The ATS Gatekeeper.

Tailor your resume

You apply to roles that fit your profile—industries you know, companies you’d love to work for. On paper, your CV should be a match. But here’s the catch: every job post is different. Each one highlights specific skills, responsibilities, and experiences. You may have them all, but you only get a few seconds to prove it. That’s why every word on your CV needs to be chosen carefully.

Before a recruiter ever sees your resume, it faces the Applicant Tracking System (ATS). Think of it as the first boss fight. The ATS scans your CV, breaks it into structured data, and matches it against the job description by looking for keywords—skills, tools, and experience levels. Many systems even rank candidates so recruiters only see the “top matches.” If your CV doesn’t speak the right language, it may never reach a human.

Just like Battletoads, the ATS punishes without mercy. (Honestly, Battletoads might be kinder. At least it lets you see the obstacle before crushing you.)

Now, imagine the recruiter’s perspective. They’ve already sifted through hundreds of resumes. The ones that rise to the top are clear, concise, and surface the right skills immediately—often on the first page.

💡AI career cheat code

Create a custom GPT to tailor your CV for each job.

System prompt

Role 
You are an expert resume writer and ATS optimizer.

Tasks

Analyze the job description: extract role level, must-have vs. nice-to-have skills, responsibilities, KPIs, and high-value keywords.

Review the candidate’s resume (provided in knowledge): identify relevant achievements, metrics, tools, scope, and any gaps.

Tailor the resume: highlight the most relevant experience and keywords; reorder content if it improves clarity or ATS performance. Never invent titles, employers, dates, or results.

Integrity and tone

Be honest—do not fabricate or exaggerate.
Keep the tone warm, human, and professional.
Maintain the original structure where possible; refine only for impact.

Writing guidelines

Use active bullets: Action + Tools/Scope + Outcome (with metrics when available).
Prefer outcomes over duties; avoid “responsible for.”
Keep bullets under 2 lines.
Use digits for numbers (e.g., 12%, 3x).
Mirror the job description’s language and synonyms.

Goal
Present the candidate as a standout fit and optimize for both recruiter skim and ATS parsing.

Add your original resume to the knowledge base so the custom GPT can refine the wording and structure while staying true to your real experience.

I also share my own resume as a template, in case it sparks ideas.

A Cv Laura De Castro Resume

176KB ∙ PDF file

Download

Download

Level 2: The First Intro Call

What about you? Speed-dating mode on

If you survive the ATS and recruiter screen (your own Ornstein and Smough), the next challenge is the intro call. It usually starts with the classic: “Please, introduce yourself.”

Sounds easy, right? But this two-minute answer can make or break you. Every word matters.

It’s like speed-dating with someone who controls your paycheck.

💡AI career cheat code

Use the Present–Past–Future framework. It keeps your answer structured and memorable.

• Present: Where you are now. Current role, day-to-day focus, and a couple of wins you’re proud of.

• Past: How you got here. Key experiences that connect directly to the role you want. Add 1–2 achievements that prove your skills.

• Future: Where you’re going. Why this role, why this company, and how you’ll help solve their problems.

Quick tips: Keep it under 2 minutes. Practice it, but don’t sound robotic. Always tailor it to the job.

Use a GPT trained on your intro + the job description to refine your story into a compelling two-minute pitch.

System prompt

Role
You are an expert resume writer, career coach, and recruiter. You specialize in tailoring CVs, introductions, and interview prep to specific job offers.

Tasks
Analyze the job description: identify key skills and responsibilities.

Adapt the candidate’s intro (provided in knowledge) to spotlight the most relevant achievements.

Requirements

Structure as Present–Past–Future.

Keep it concise, warm, and professional. Avoid jargon or exaggeration.

Goal 
Position the candidate as a standout fit while sounding natural, not scripted.

I share my own intro as inspiration.

Laura’s Introduction (first Call)

32.9KB ∙ PDF file

Download

Download

Level 3: Behavioral Questions (STAR)

What did you do?

At this stage, you’ll face behavioral questions. Instead of “What would you do?”, they’ll ask, “What did you do?” The idea is simple: past behavior predicts future performance.

Interviewers use these questions to see how you solve problems, work in teams, show leadership, adapt to change, resolve conflict, and communicate under pressure.

The best way to answer? The STAR method: Situation, Task, Action, Result.

💡AI career cheat code

Build a GPT that:

• Generates common behavioral questions from the job description

• Uses your real experiences (loaded in knowledge) to craft STAR answers

• Improves clarity, but never invents details.

System prompt

Role 
You are an expert resume writer, career coach, and recruiter.

Tasks

Generate likely behavioral questions based on the job description and explain why they’re relevant.

Use the candidate’s real experiences (provided in knowledge) to craft STAR answers.

Integrity rules

Never invent employers, roles, dates, or results.

Reflect only the candidate’s real experience.

Keep the tone simple, warm, and professional.

Goal 
Help the candidate sound confident, genuine, and well-prepared.

Add a library of real experiences and cases to the GPT knowledge base. Look at an example of my library of real experience for inspiration.

Laura’s Behavioral Questions

249KB ∙ PDF file

Download

Download

Level 4: Build YOUR Super Custom GPT

By now, you’ve seen the pattern: resume, intro, and behavioral prep all use the same inputs—job description, your CV, your intro, and your career stories. The difference lies only in the instructions.

So why not combine them? Create a super custom GPT with three modes:

• Tailor CV

• Tailor Intro

• Behavioral Q&A

One hub. Shared context. Less work. Consistency across everything. The Swiss Army knife of job hunting.

Just copy and paste the custom GPT instructions.

System Prompt

## Role

You are an expert **resume writer, career coach, and recruiter**, specializing in **marketing and product roles**. Your mission: help candidates stand out by refining CVs, crafting compelling intros, and preparing them for interviews—always building trust, encouraging confidence, and delivering with clarity and warmth.

## Context

You have access to:

- The candidate’s original CV

- The candidate’s “Intro” (First Call)

- A database of career cases/experiences

Use these materials as foundations—never invent new information or exaggerate!

## Tasks

Depending on user choice, perform one of the following:

### Mode 1 – Tailor CV

1. Analyze the provided **job description URL**:

- Extract **Role Level**, **Must-Have vs. Nice-to-Have Skills**, **Core Responsibilities**, **KPIs**, and high-value **keywords/synonyms**.

2. Review the candidate’s CV:

- Highlight relevant achievements, metrics, tools, scope, and call out any gaps or inconsistencies.

3. Tailor the CV:

- **Prioritize relevant experience and keywords.**

- **Reorder sections** only if it enhances clarity, ATS performance, or professionalism.

- **Never invent** experiences, titles, dates, or organizations.

4. **Output**: A recruiter-friendly, ATS-optimized resume with concise, active bullet points (Action + Tools/Scope + Outcome, with metrics).

#### Writing Style Guidelines:

- Use **action-oriented bullets**, ≤2 lines each

- Emphasize **results** over duties—avoid “responsible for”

- Use **present tense** for current roles, **past tense** for prior roles

- Use **digits** for numbers (e.g., “3x growth”, “12% increase”)

- Mirror language from the job description and include natural synonyms

---

### Mode 2 – Tailor Intro (First Call)

1. Analyze job description to identify key skills, qualifications, and responsibilities.

2. Adapt the candidate’s “Intro” to spotlight the most relevant achievements and fit.

#### Writing Requirements:

- Structure as:

- **Present**: current role and focus

- **Past**: career highlights relevant to this role

- **Future**: why this company & this role—and how the candidate adds value

- Tone: concise, **warm**, **human**, and professional

- Avoid jargon/exaggeration

---

### Mode 3 – Behavioral Questions & Answers

1. Generate likely **behavioral interview questions** based on the job description.

2. For each question:

- Use the candidate’s real experience to craft an answer using the **STAR** method:

- **Situation**, **Task**, **Action**, **Result**

- Improve clarity and impact—but do not fabricate content.

#### Writing Style:

- Tone: **simple**, **warm**, **authentic**

- Structure with headings, bullets, especially in the **Action** section

- Goal: help the candidate sound confident, genuine, well-prepared

---

## Integrity Rules

- **Never invent** employers, roles, dates, outcomes

- Always **reflect the candidate’s real experience**

- Maintain a consistent tone across CVs, intros, and answers: warm, human, professional

- If information is missing, **flag it** (e.g., “[Missing metric here]”) rather than guess

---

## User Interaction

When the user provides a job description URL, prompt:

> “Would you like me to tailor your CV, adapt your intro for a first call, or prepare behavioral questions & answers for you?”

Don’t forget to add to your knowledge base your CV, Intro, library of career cases and stories. Watch the magic!

Test my custom GPT here, if you want to play, but you need to build your own with your personal knowledge.

Level 5: Become an Expert of Your Future Company

Before the first call, research matters. Know the company’s products, business model, customers, competitors, and marketing strategy. It shows you care, sharpens your answers, and helps you stand out.

💡AI career cheat code

Use a GPT with a structured research prompt to produce a company report: market, product, personas, competitors, marketing strategy, and recommendations.

Run it before interviews. You’ll walk in sounding prepared, not generic. Because nothing screams confidence like casually dropping their Q2 revenue trends into small talk.

## Role

You are an experienced **B2B market researcher**. Your mission: prepare a **comprehensive research report** on the company specified by the user (they will provide the URL). The ultimate goal is to arm an experienced product marketer with a complete, detailed understanding of the company to excel in high-demand interviews.

## Context

- The user will **provide a company-specific URL** (e.g., official site or investor page).

- Use **only verifiable public sources**; do **not fabricate data**.

- If specific information is not publicly available, state that it’s unavailable rather than guessing.

---

## Tasks

Produce a structured market research report with these sections:

### 1. **Executive Summary** (~150 words)

- A concise snapshot of the company's identity, value proposition, and market significance.

### 2. **Market Landscape**

- Overview of the relevant sector/industry.

- Include **market size**, **growth rate**, and **key market drivers**.

### 3. **Trends & Innovations**

- Highlight recent sector trends or innovations (2023–2025) affecting the company.

### 4. **Product Overview**

- Describe the **problem the company addresses**, and how its **solution** works.

### 5. **Target Personas**

- Identify the **key customer segments** the company serves (e.g., enterprise, SMEs, developers).

### 6. **Business Model**

- Explain how the company generates revenue (e.g., SaaS subscriptions, transaction fees, API usage).

### 7. **Competitor Overview**

- List major **direct competitors**.

- Include a **Markdown table** comparing top platforms with columns:

- Vendor

- Business Model (SaaS / API / Open-source)

- Core Features

- Pricing (if publicly available)

- Target Users

- Notable Clients / Use Cases

### 8. **Competitive Outlook**

- Provide a **SWOT-style breakdown** of key players, including emerging disruptors.

### 9. **Marketing Strategy**

- Describe current or recent **growth initiatives** and **marketing channels** the company employs (e.g., content, partnerships, sponsorships).

### 10. **Recommendations**

- Offer up to **three actionable product or marketing strategies** the company could use to strengthen positioning or growth.

---

## Constraints & Style

- **Cite reputable public sources** (articles, financial reports, official statements).

- **Tone**: Neutral, professional, and fact-based—avoid promotional language.

- **Language**: Clear and simple—suitable for business audiences.

- **Time frame**: Prioritize insights from **2023 to 2025**.

- **Length**: Aim for **1,000–1,500 words**.

## User Interaction

When the user provides the company URL, respond with:

> “Great! I’ll begin preparing your B2B market research report on [Company Name] using the URL you’ve supplied. Let me know if you'd like to emphasize any specific areas, such as competitors, personas, or marketing strategy.

Extra: You can try deep research mode if you want deeper, more precise results. But for a first call, I consider ChatGPT 5—or even a lighter model—does the job. What really matters is using an active search tool that can pull up-to-date information from the web.

I can also share the custom report GPT I use myself here. Use it when you need. It doesn’t rely on special knowledge, so anyone can run it.

Use it before interviews to walk in with a sharp, well-rounded overview.

Bonus tips

Track your applications

Keep control of your search. Track where you’ve applied, who the recruiters are, and what stage you’re in. Otherwise, you’ll end up applying to the same company three times, like it’s Groundhog Day.

Use a spreadsheet + this Custom GPT will help you save time

The user will provide a job listing URL. You’re a scraper tool, and your task is:

1. From the given URL, identify:

Company Name

Role Title

Job Listing URL (the one provided by the user)

Find the company’s main website URL.

2. Locate recruiters or talent acquisition professionals associated with the company on LinkedIn. For each one, extract:

Name

Role/Title

LinkedIn profile URL

3. Present all findings in a table with these columns:

Company Name

Role Title

Provided Job URL

Company Website

Recruiter Name

Recruiter Role

Recruiter LinkedIn URL

You can test here!

If ChatGPT identifies a recruiter on LinkedIn, you can send a short, polite note to connect.

Automation Pro: Gumloop

Want to go further? Tools like Gumloop can automate scraping job data, tailoring resumes, and preparing interview materials. I still prefer doing parts manually—it helps me refine—but automation can save serious time.

Use this code to get free credits in Gumloop: LAURAGUM

Gemini and Gems

I’ve also tested the same workflow with Gemini. Instead of building a custom GPT, you create a Gem. I enjoy experimenting with different models on the same task, and Gemini handled it well.

I’d also recommend trying Google’s own Gem career coach. It comes with a system prompt designed specifically to support your career journey, and I found it impressively professional. Test it!

Final thoughts

I was born in 1987—a millennial with a utopic streak—now living inside what feels like a cruel video game. There was a time, back then, interviews were basically: Can you do the job? Yes? Great, start Monday. Now it’s: Please complete this seven-part quest, three riddles, and a boss fight.

I miss those simpler days when instinct mattered more. But hiring has evolved. It’s more complex, more data-driven, more refined.

The choice is clear: adapt or fall behind.

That makes me wonder: what does the future of recruitment look like?

• Hyper-specific roles for hyper-specific profiles?

• Generalists managing AI agents to cover the gaps?

• Contractor-style work, where people collaborate with several companies at once, with more freedom and flexibility?

I don’t have the answers. But I’d love to hear yours. How do you see the next “level” of hiring unfolding?

Leave a comment

Your turn:

Have feedback? Tell me, I can take it.

Want me to cover a topic? Send me your ideas, I’ll gladly steal them (and credit you).

Response Quick Question

Want to guest post or team up? Let’s do it. Think of it as co-op mode unlocked.

This article is free. The only “payment” I ask: share it with someone still stuck at level 1 of the job hunt. Don’t let them play solo.

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Ever wonder how AI decides what to include in an answer? It’s not magic. It’s math

You don’t need to win the top spot. You just need to show up enough times to be impossible to ignore. Just be the persistent background character that shows up in every episode. Think less ‘main character energy,’ more ‘recurring barista who quietly steals the scene.’ AI notices that.

Takeaways

No time? No patience? No problem. Here’s the stuff worth screenshotting. Welcome to the reward section. Six bullets, zero fluff.

• AI ranks by repetition, not just by being ranked #1. If your content shows up across multiple search variations, you’re more likely to get picked.

• Build topic clusters. One big guide is good. A web of interlinked, focused articles around it is better.

• Mentions matter—even without links. When your brand name appears in relevant content, AI recognizes it as a sign of trust.

• Reddit is gold. Real conversations, raw insights. AI pulls from it heavily. Don’t promote—participate.

• Invisible PR works. Offer updates or expert quotes for articles still getting traffic. You’ll earn brand mentions naturally.

• AI favors patterns, not hype. Show up often, in useful ways, across different contexts. Let the algorithms connect the dots.

This is how AI decides who to trust. Let’s unpack it.

The simple math behind why topic clusters work

Let’s start with a simple mental image.

Picture asking a few smart friends to list their top 10 movies. Each list is different. But some titles show up on every list, just in different spots.

Now you want to create a “master” list. What makes the cut?

That’s exactly how Reciprocal Rank Fusion (RRF) works. And that is exactly the method AI models use to decide what to cite.

It doesn’t obsess over the #1 pick. It rewards the movies (or products, or brands, or companies) that appear again and again, ranked #3, #6, and #7

Reciprocal Rank Fusion is a method to combine searches into final ranked lists. Instead of focusing on the #1 result, it rewards the items that show up again and again, even if they’re ranked lower.

So if your brand or product appears across multiple search results, even in mid-positions, it gains authority. AI notices that kind of consistency. It starts to treat you as trustworthy. If you need to understand this concept deeply, I recommend this article.

Why It Matters

• It rewards consistency over spikes

• It values broad agreement over isolated wins

• It favors substance over sudden hype

It’s the same logic that powers AI ranking systems.

Think of it this way:

A movie ranked #1 by just one person might not survive the final cut. Personal taste is how we ended up with pineapple on pizza. But a movie ranked #4 by everyone? That’s the one that sticks.

This is grounded in the Wisdom of the Crowd, a concept from 1906 by Francis Galton. He showed that the average of many independent guesses was often more accurate than any individual guess. AI models use the same logic.

What you can do about it

Let’s get tactical.

Cover mini search variations

AI rewrites your search. Every time.

You type “Best no-code tools,” and it spins off versions like:

• “Top automation tools without coding”

• “Free no-code platforms”

• “Tools to automate workflows, no code required”

If your content shows up across those rewrites, you’re in better shape.

Try this:

• Use AlsoAsked or AnswerThePublic to see how people phrase things

• Search Reddit threads to catch the human way of asking

• Ask ChatGPT: “What are 10 different ways someone might search for [your topic]?”

Build topic clusters (Not just one big guide)

One strong article? Good. A web of connected content? Better.

Think of it like a wheel:

• Hub: The Ultimate Guide to No-Code Tools

• Spokes:

  • Best No-Code Tools for Startups

  • No-Code App Builders Compared: Glide vs. Adalo

  • Free No-Code Tools to Try

  • Automate Your Workflow Without Writing Code

  • No-Code Tools for Marketers That Actually Work

AI pulls answers from all angles. A well-linked cluster tells a story: this brand knows its stuff.

Brand mentions: The quiet shortcut

A network of pages builds authority. But even that won’t get you far if no one’s talking about you. That’s where brand mentions come in, the quiet little trust signals AI cares about.

You don’t need a backlink to get noticed. Sometimes, just a name drop will do.

Search engines treat brand mentions as implied links. AI sees them as trust signals.

Why? Because mentions are harder to fake. Backlinks can be bought. Mentions come from relevance.

The rule still applies:

• Repetition beats hype.

• Patterns beat noise.

• Substance beats press stunts.

Want AI to trust your brand? Show up often, in context, and in places that make sense. Let’s review some ideas to get these brand mentions.

Invisible PR: Get covered without cold emails

Skip the press release. (Unless your dream is to annoy every journalist) Help someone improve their article instead.

Here’s the low-key play:

1. Find evergreen articles still getting traffic on your niche, keyword, or topic.

Use BuzzSumo, Ahrefs, or Semrush to spot old-but-still-alive content. Or a scrape tool like Zapier.com agent or Browser AI.

2. Reach the writer
   Grab contact info from the byline or use tools like Browser AI + Hunter.io.

3. Offer something useful

   • Fresh data (“We analyzed 10,000 no-code launches…”)

   • A bold or reactive take

   • A sharp visual or framework

Make their article better. Make it easy to update. That’s how you get quoted without begging.

Reddit: The Overlooked Goldmine

Reddit is chaotic, honest, and full of insight. In other words, perfect for training AI.

It’s where people say what they really think. It’s also where models like ChatGPT pull from.

Why it works:

• Upvotes surface quality

• Anonymity invites honesty

• Threads reveal nuance you won’t find in polished blogs

Don’t just promote your stuff. Build something useful in public.

Steal this move:

“What’s the smartest thing you’ve automated with a no-code tool? Drop your build. We’re making a public Wall of Fame.”

More ideas:

• “I’m automating all my marketing tasks for 30 days. Follow along.

• “What no-code tool replaced a developer for you this year? I’ll go first.”

• “Drop your no-code build. I’ll record a 2-minute teardown with tips.”

• “We’re building the ultimate no-code starter kit. Add your favorite tool or template.”

Adapt this idea to your product.
Post in r/NoCode, r/Entrepreneur, r/SmallBusiness. Be useful. Be consistent. AI will connect the dots.

(And yes—seed the first few replies. Reddit is a campfire: someone has to start the weird stories before it gets good.)

Let’s have a look at Semrush on Reddit

Semrush uses Reddit in 3 smart ways:

1. As an official support and feedback channel (r/SEMrush)

2. As a data source (they even built a Reddit trend tool)

3. As a content mine for real-world questions and keywords.

Wrap-Up: Earn trust, don’t chase it

If you want to show up in AI answers, don’t aim for hacks or headlines. Aim for repetition. Relevance. Realness.

• RRF rewards the brands that keep showing up.

• Clusters reward depth, not just reach.

• Mentions tell the model you matter.

• And Reddit shows you’re plugged into the real conversation.

If this helped clarify how AI models decide who to trust, share it with someone else trying to figure it out. Or better: try one tactic this week and see what happens.

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Two weeks ago, I published an article that doubled my usual traffic. The “King’s Article,” as a friend jokingly called it.

Honestly, I think it worked better because it struck a nerve. A lot of marketers are starting to feel the pressure. AI search is clearly coming, like a train that’s still two stops away but somehow already throwing off sparks. And let’s be honest: no one knows what to do exactly, except someone on LinkedIn with “ex-something-impressive” in their bio. (All respect to the ex-something, just having a bit of fun.)

That article didn’t try to solve the whole thing. It just asked two simple questions:
How does AI search actually work? And how might it change the way we do SEO?

Turns out, that was enough. The response was clear: people want less theory, more clarity, and above all, something to try.

Encouraged by the response, here’s the next piece. Part of an ongoing series where I’m sharing practical ideas you can try, tweak, and build on.

Takeaways

If you're the type who scrolls straight to the bottom (no judgment—I do it too), here’s the good stuff in five bullets or less:

• AI models don’t just search, they remember patterns.
  When users share your article with a smart prompt, the model starts to associate your content with certain tasks or topics.

• A “Share with AI” button can boost visibility.
  Instead of sharing to social, let readers send your article directly to ChatGPT, Claude, or Perplexity—with a prompt built to teach the model something useful.

• Make your content usable, not just readable.
  Add prompts like: “Use this article to help me [apply insight to X].” That turns passive content into a personalized workflow.

• The “double-shot” prompt strategy is worth testing.
  If a prompt includes both your article and the reader’s brand or product, the AI may start linking them together—giving visibility to both.

The spark: A smart idea (from someone smarter than me) I borrowed and ran with.

A few weeks ago, I read a great article by a sharp technical SEO expert. One of those people who really digs into how things work under the hood. If you care about this space, he’s someone worth following.

Metehan’s Substack

No bullsh*t, no spam. Only exclusive SEO & AI/ML tips

By Metehan Yesilyurt

Anyway, in his piece, he shared a dead-simple idea that stuck with me. I’ve been thinking about it ever since.

Here it is:

Add a “Share with AI” button to your article.

Instead of social share, a way to send your article straight into ChatGPT, Gemini, Claude, or Perplexity, along with a built-in prompt.
Something like: “Summarize this article” or “Extract the key steps.”

At first glance, it sounds... clever. But here’s why it matters:

AI tools have memory, or at least some version of it. When users share a link in ChatGPT with a specific prompt, that article becomes more familiar to the model. It learns to recognize the source. And if more users do it? The content gets reinforced and becomes more likely to be referenced in future responses. To know more - CiteMET method.

A quick metaphor: The club bouncer

Imagine ChatGPT is the bouncer at an exclusive club. Your article shows up in a hoodie and sneakers, and the bouncer barely glances. But then it starts arriving with well-dressed friends who say, ‘Trust me, he’s cool.’ Now you’re in. Maybe even getting comped a drink.

You keep showing up. Different people keep vouching.
Eventually, the bouncer nods. You’re in. You’re on the guest list forever.

That’s what happens when people share your content with AI using the right prompt.
You’re not waiting to be discovered. You’re building a relationship with the model.

Go beyond: Give readers a reason to take the next step

“Summarize this article” is a great starting point. It’s simple, useful, and teaches the AI that your content is worth paying attention to.

But what if your article could do more than get summarized? What if it became a launchpad, a way for readers to build something of their own?

Maybe your post includes a case study, a framework, or a process. Now imagine, with one click, the reader could ask the AI to:

• Adapt the advice to their own product

• Rewrite it for a specific use case

• Apply it to their team, strategy, or customer

That’s the power of a good prompt. You’re not just publishing content. You’re embedding it into their workflow. You’re sneaking it into someone’s to-do list like a helpful little Trojan horse made of strategy.

Below are examples of article topics paired with prompts that do exactly that.

Article topics → Prompts that turn them into action

Each of these articles could include a "Use this in ChatGPT" button with a pre-filled prompt.
You’re helping people learn how to use your content to make more with it.

🧠 Business & strategy

The 5 Types of Competitive Moats (and How to Build One)
→ “Act as a strategist. Based on this article [URL], suggest which moat [My Brand or Idea] could develop based on its current strengths.”

How to Craft a Simple One-Page Strategy
→ “Turn this article [URL] into a one-page strategy for [describe company or project]. Push back if my answers are vague.”

📣 Marketing & messaging

Why Most Startup Positioning Sucks (And How to Fix It)
→ “Use this article [URL] to help reposition [my app/brand]. Ask me positioning questions.”

How to Write Landing Pages That Convert
→ “Use this article [URL] to rewrite a landing page for [Product X] targeting [audience Y] with a clear call-to-action.”

💼 Career & hiring

How to Write a Resume That Doesn’t Sound Like AI Wrote It
→ “Use the tips in this article [URL] to help rewrite my resume for a mid-level marketing role at a remote company.”

The Best Interview Questions for Hiring a Product Manager
→ “Based on this article [URL], create a tailored interview for a PM role focused on B2B SaaS delivery pipelines.”

📊 Data & research

How to Run Better User Interviews (with Templates)
→ “Turn this article [URL] into a script for interviewing Gen Z users about their money habits.”

A Guide to Creating First-Party Data Surveys
→ “Based on this article [URL], draft a 5-question survey for early-stage founders on fundraising struggles.”

🧠 Trends & market shifts

The 3 Trends Reshaping the Future of B2B Content in 2025
→ “Based on this article [URL], outline a content strategy for [My Company] that incorporates these trends. Prioritize actions we can take in the next 30 days with the team we have.”

How Gen Z Is Changing the Way Brands Communicate
→ “Use this article [URL] to adapt our brand voice and messaging to better connect with Gen Z consumers. Include tone shifts, channel preferences, and examples.””

💡 Personal growth & learning

The 5 Mental Models That Changed How I Make Decisions
→ “Based on this article [URL], apply one of these mental models to help me decide between two job offers.”

How I Designed a Personal Learning OS
→ “Adapt this system from the article [URL] to build a custom study tracker for someone learning design on nights and weekends.”

🧰 Tutorials & how-tos

How to Build a Web Scraper in 20 Minutes
→ “Use this article [URL] to guide me in building a scraper for finding freelance writing gigs on job boards.”

Using AI to Turn Podcasts into Newsletters
→ “Based on this article [URL], create a prompt workflow to summarize 3 marketing podcasts into one newsletter issue.”

🎨 Branding & positioning

How to Create a Brand That People Actually Remember
→ “Act as a brand strategist. Based on this article [URL], help me define the tone, personality, and positioning for [My Brand]. Include examples that feel emotionally distinctive.”

Naming Your Startup: Lessons from 100 Brand Launches
→ “You’re a naming expert. Based on this article [URL], brainstorm 5 potential names for [My Project], and explain the thinking behind each one.”

🤖 Automation & AI workflow design

10 Tasks Every Team Should Automate First
→ “Based on this article [URL], suggest 3 tasks that [My Company] should automate first. Tailor your advice to our size and tools.”

How We Automated 30% of Our Customer Support in 3 Months
→ “Act as an automation consultant. Based on this article [URL], help me plan a similar workflow for [My Product: brief description]. Include tools and steps.”

No-Code Automation: A Beginner’s Guide to Zapier, Make & AI
→ “Turn this article [URL] into a Zapier workflow for [My Use Case: e.g., auto-send new Typeform responses to Slack]. List steps, tools, and checks.”

Closing thought: Your article is a tool

Your article can be the first step in someone’s strategy, project, or process.

So next time you write something useful, ask yourself:
What could someone actually do with this? And how can I make that easier with a prompt?

Can’t think of one? Try this on your favorite AI model:

“Act as a content strategist. Based on the target audience [insert audience type] and the topic [insert general subject], suggest 5 article titles that:
– Teach something practical
– Include a clear framework, method, or example
– Could be paired with a follow-up AI prompt for personalization or application
Focus on ideas that would help the reader take action, not just understand.”

That’s how you move from visibility… to usability. And that’s how you get remembered.

I’ll leave you with one last idea to explore. What if we didn’t just teach the AI with our content, but paired it directly with the user’s own brand or product? A “double-shot” prompt that links your article as the source and their URL as the use case. Could that build a semantic connection between the two? Could it boost visibility for both? It's early to say, but worth testing.

If you’ve tried anything like this, or plan to, I'd love to hear what you discover. Drop your thoughts or experiments in the comments. Let’s figure it out together.

Leave a comment

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Ever feel like you’re solving five problems at once? Only to find ten more waiting?

You’re not alone. Metrics dip. Budgets shift. New segments appear overnight. And mid-sprint, someone always asks, “Wait… what are we trying to do?”

Welcome to marketing. And product. And the everyday storm.

The good news? You don’t have to slow down to make sense of it.

That’s what frameworks are for. They’re thinking tools. They help you zoom out, organize chaos, ask smarter questions, reason to solve problems, and align teams without the endless back-and-forth.

Add AI, and they get even better. You move from vague ideas to a sharp strategy, faster, clearer, and with fewer blank pages.

This series breaks down the frameworks product marketers use to think more clearly and act faster. No fluff, no filler.

Takeaways

Today, we have picked four frameworks to understand business and product strategy to make faster and better decisions.

• Lean Canvas gives you a clear, one-page view of your business model so you can move with focus, not assumptions.

• Product Vision Board locks in your direction and who you’re building for, so you stop building in the dark.

• Strategy Triangle helps you align on where to play, how to win, and why this moment matters, so your strategy isn’t just a vibe.

• Leader of Needs sharpens your message until it actually resonates—not just sounds nice.

AI can guide you through each framework step by step, helping you ask sharper questions, spot weak spots, and structure clearer thinking.

 Start with this plug-and-play prompt: 
“Help me complete a [Framework Name] for [Company/Product]. Ask me questions for each section, use public data if available, and push back when my answers are vague.”

Business Model

Lean Canvas: The one-page business strategy

Let’s start here. The Lean Canvas is your company’s entire strategy... on a single page. Not a pitch deck. Not a Notion doc that no one reads. A clear, visual map that cuts through the noise.

It covers just the essentials:
Problem, Customer Segments, Unique Value Proposition, Solution, Channels, Revenue Streams, Cost Structure, Key Metrics, and Unfair Advantage.

Picture packing for a hike with one small bag. You only bring what matters. The Lean Canvas works the same way. Just focus.

Why marketers love it

Ever hunted down key product info across 20 Slack threads, two inboxes, and an old Google Doc last updated in 2021 by someone who has since left the company and now lives off-grid in Portugal?

Lean Canvas stops that. It pulls scattered knowledge into one shared space. Suddenly, product, design, sales, and marketing are on the same page—literally.

You can use it to:

• Clarify your value prop

• Define your core customer

• Prioritize the right channels

• Anchor every campaign in business reality

This is more than strategy. It’s alignment. Its direction. It’s a compass. Use it early. Use it often.

Product Frameworks Kit

For You: 27 templates that are designed to streamline your product management process by a great Product Marketer, Maryna Kucherova.

Look in action: Lean Canvas – Klarna

Let’s review an example of one of my favorite and well-known companies and business models: Klarna.

1. Problem

• Consumers struggle with high upfront costs and inflexible payment options for online purchases — leading to abandoned carts

• Merchants lose sales and conversion due to friction at checkout and limited payment options.

• Existing credit solutions are complex, slow, and depend on hard credit checks.

2. Customer Segments

• Consumers: online shoppers (often millennials and Gen Z) interested in flexible, interest-free payment options

• Merchants: e-commerce and in-store retailers seeking higher conversion, ticket value, and risk mitigation

3. Unique Value Proposition

"Flexible, smooth, and trust-first payment experience that boosts sales instantly."

• Consumers get interest-free payment and easy checkout.

• Merchants see conversion +30–44% and reduced abandonment

4. Solution

• BNPL products: Pay in 4 installments, pay in 30 days, buy now pay later.

• Klarna card & app: Enables in-store and mobile payments.

• Technology: Soft credit checks, integration with e-commerce platforms (Shopify, Magento, etc.)

All tech-enabled, fast, and frictionless.

5. Channels

• Embedded at merchant checkouts (both online and in-store)

• Direct app downloads and Klarna marketing.

• Partnerships with major retailers and financial institutions (e.g., Walmart pilot)

6. Revenue Streams

• Merchant fees (2–8% per transaction, typically higher than credit cards)

• Late fees/interest on unpaid installments

• Interchange fees, licensing, advertising, and subscription (e.g., Klarna Plus)

7. Cost Structure

• Tech & risk infrastructure — underwriting, fraud prevention, platform maintenance.

• Operational — personnel (~3,400 employees), risk & collections teams

• Marketing & partnerships — merchant acquisition, App Store presence, brand promotion.

• Loan provisions — reserves against defaults; reported $136M credit loss Q1 2025

8. Key Metrics

• Active users: ~100M globally

• GMV: ~$80B processed in 2024

• Conversion lift: +30–44% for merchants

• Credit performance: default/loss trends (e.g., $136M, 17%)

North Star: GMV and combined user engagement (consumer + merchant).

9. Unfair Advantage

• First-mover brand in global BNPL and strong merchant network (~675K merchants, 93M users)

• Tech + risk stack: soft credit checks, fraud protection, risk underwriting.

• Financial licensing & partnerships: bank license, debit card pilot, exclusive merchant deals.

Product Strategy

Product Vision Board: The big picture

Before roadmaps. Before features. Before sprints. You need a vision.

That’s where the Product Vision Board shines. It lays out five foundations:
Vision, Target Group, Needs, Product, and Business Goals.

Think of it as your map before the journey. You define where you’re going, who you’re helping, and why it matters, so every decision after that stays grounded.

Otherwise, you end up halfway to nowhere with a very detailed feature road

map and no idea why you’re building any of it. (Fun! But also not.)

Why marketers love it

In most fast-paced companies, the product vision lives inside someone’s brain, or in slide #87 of an old deck.

This framework brings it to light. You finally see the real “why” behind what you're building.

With this, marketers can:

• Craft messaging that aligns with customer needs

• Understand the business goals behind product bets

• Build brand narratives that go deeper than “feature launch”

It’s your North Star. And it keeps everyone pointed in the same direction.

Product Frameworks Kit

For You: 27 templates that are designed to streamline your product management process by a great Product Marketer, Maryna Kucherova.

Look in action: Klarna – Product Vision Board

1. Vision

Make payments simple, safe, and smooth, empowering consumers to take control of their finances and shop with confidence.

2. Target Group

Primarily online shoppers—especially Millennials and Gen Z—in markets across Europe and the U.S., plus merchants (e-commerce and in-store) seeking higher conversion and loyalty.

3. Needs

• Consumers want flexibility and peace of mind when paying, with transparent options and no surprise charges.

• Merchants need a smoother checkout, fewer abandoned carts, and a way to increase average order value.

• Both want payment and credit handled securely and in real time, without rigid credit-card bureaucracy.

4. Product

• A suite of BNPL options (Pay in 4, Pay in 30 days) with soft credit checks and integrated checkout.

• A Klarna app and card for cross-channel payments, budgeting, and rewards.

• Embedded integration with major platforms (Shopify, Stripe, etc.), full tech infrastructure including risk, fraud, and underwriting systems

5. Business Goals

• Continue growing GMV (reached $105B in 2024) and expand user base (93M active users worldwide)

• Boost merchant conversion, retention, and adoption of embedded credit products.

• Diversify revenue by expanding banking services, subscriptions, advertising, and debit card offerings.

Strategy Triangle: Where, how, why.

This framework zooms out and asks the big questions:

Where to play?
Which market, customer, or channel matters most?

How to win?
What makes us different, and how do we deliver?

Why now?
What changed that makes this the moment?

Think of it like planning a road trip. Pick the destination, prep the car, wait for the green light.

Why marketers love it

Strategy isn’t just for the exec offsite. It’s for you.

This triangle helps marketers:

• Build go-to-market plans that reflect market dynamic

• Position products with relevance and urgency

• Align tactics with the actual company strategy

When your messaging and channels match where you’re headed, things click. Teams get aligned. Campaigns get traction. You stop spinning and start moving.

Also, fewer existential meetings where someone asks, “Wait, why are we doing this again?” and no one has a satisfying answer.

Look in action: Klarna – Strategy Triangle

1. Where to Play

• Markets: Global e-commerce across Europe and North America, expanding into in-store retail and travel sectors

• Customers: Two main segments:

  • Consumers: Millennials and Gen Z seeking interest-free, flexible payment options

  • Merchants: Online and physical retailers aiming to increase conversion rates and order value

• Channels: Integrated at checkout (online/in-store), mobile app, and through partnerships (e.g., Shopify, Stripe, DoorDash)

2. How to Win

• Unique Value: Instant, transparent BNPL with no interest or surprise fees; plus seamless integration across channels and rewards via the Klarna app

• Capabilities Required:

  • Sophisticated underwriting, fraud detection, and soft credit-check tech stack.

  • Global partnerships with merchants and financial institutions (e.g., Visa, Marqeta)

  • Scalable risk management to support quick payouts to merchants.

• Moat: Strong brand recognition (93–100 M users), exclusive merchant integrations, exclusive banking license, and proprietary risk models based on massive transaction data.

3. Why Now

• Market Shifts: BNPL has exploded post-COVID; global BNPL volumes are expected to grow ~13.7% in 2025 to $560 B

• Consumer Behavior: Younger consumers prefer flexible payments and budgeting support — BNPL usage is outpacing traditional credit among Gen Z/Millennials

• Technology & Regulation: Converging tech (apps, seamless checkout, AI risk tools) and evolving regulation (like FCA’s upcoming affordability checks) validate BNPL as a mainstream financial service.

Messaging frameworks

Leader of Needs: Positioning in three lines

What do you offer?
How does it solve a problem?
Why should anyone care?

It’s basically how you’d explain your product to a smart friend who’s two drinks in and has no patience for jargon.

This is the Leader of Needs. It takes your positioning and forces it into clarity—three lines, no filler.

Why marketers love it

You don’t have time for ten-slide decks and vague messaging. You need sharp, sticky, customer-first language.

Leader of Needs helps you:

• Cut through the noise

• Align with product and sales

• Test value props with real users

• Use the same message everywhere—from ads to investor decks

It brings clarity. And clarity converts.