But there was a problem.

It faced a typical chicken-and-egg problem.

To attract travelers, Airbnb needed enough listings.

To convince people to list their homes, it needed enough travelers.

The marketplace needed both sides before either side had a reason to show up.

Building an audience from scratch would take time. But Airbnb didn’t need to create demand if it could find people who were already looking for accommodation.

So it looked elsewhere.

And the answer was Craigslist.

Airbnb used Craigslist to reach people already searching for accommodation, giving the company an initial source of demand while it built up its own marketplace.

It wasn’t a scalable distribution strategy. But Airbnb didn’t need one yet.

It needed enough activity to get the marketplace moving.

Borrowing distribution before building it

The Craigslist strategy gave Airbnb a way to get early demand while it worked on building its own supply.

As more hosts joined, Airbnb had more listings to offer travelers.

More listings attracted more guests.

Successful stays generated reviews, giving future travelers more confidence to book.

And as demand grew, more hosts had a reason to list their properties.

The important decision wasn’t finding a clever way to use Craigslist.

It was recognizing that Airbnb didn’t need to own distribution on day one.

It could temporarily borrow an audience that already existed, use that traction to get both sides of the marketplace moving, and then build distribution of its own.

The Mental Model

When you’re solving an early distribution problem, don’t always try to build an audience from scratch.

Find where your customers already are, borrow that distribution, and use the traction to build something you can eventually own.

In December 2019, Zoom had 10 million daily meeting participants.

By April 2020, that number had reached 300 million.

Yet Zoom kept its free plan.

The company offered paid plans for businesses that needed longer meetings, larger participant limits, cloud recording, and administrative controls.

But it didn’t push every user toward a paid plan.

Why?

Understanding how customers discovered Zoom

Zoom had millions of people using the product who weren’t paying for it.

But many of those people weren’t coming to Zoom on their own.

They were introduced to it through someone else’s meeting.

People came across Zoom in different settings – a colleague sharing a meeting link, a client setting up a call, or a teacher hosting a class.

The experience gave people a reason to use Zoom for their own meetings.

Some became hosts themselves and introduced Zoom to more people.

The product could spread from one meeting to the next, and the free plan made that easier.

People could join without paying, while new hosts could start using Zoom without committing to a paid plan.

Monetization beyond revenue

A paid-only product could have increased revenue from existing users.

But it could also have made it harder for people to experience Zoom through someone else’s meeting.

Some of those people might not go on to become hosts themselves.

So Zoom had to consider not just what it could charge but also how that pricing would affect how the product spread.

Keeping the free plan made that easier, while Zoom could still charge businesses for features they were willing to pay for.

The Mental Model

Successful monetization isn’t only about capturing value.

It also influences how customers discover, adopt, and share a product.

The strongest monetization strategies don’t simply maximise revenue.

They strengthen the system that creates future customers.

By 2012, it had built a successful self-serve business used by individuals and teams across thousands of organisations.

With this success, the company was starting at its next phase of revenue growth: enterprise

Large organisations represented a much bigger opportunity. They had more users, more complex requirements, and bigger budgets. SurveyMonkey believed those customers would be willing to pay more for enterprise features

And because larger organisations had more employees using the product, charging per user seemed like the logical pricing model.

SurveyMonkey launched its enterprise plan, expecting larger organisations to upgrade for the additional capabilities.

Instead, many companies stayed on the lower-priced self-serve plans, even if it meant giving up enterprise features.

That left the team with an important question:

Why weren’t customers willing to pay for features SurveyMonkey believed were valuable?

Instead of tweaking the pricing, SurveyMonkey stepped back to understand how customers were actually adopting the product.

Understanding the adoption journey

Instead of asking, “What enterprise features should we charge for?”, the team started asking different questions:

• Who actually uses the product inside a company?

• How do teams discover it?

• What problems are they trying to solve?

• What makes them willing to pay?

The answers revealed something they hadn’t expected.

Enterprise adoption wasn’t happening through company-wide rollouts. It was spreading team by team.

• Marketing teams used it for customer research.

• HR teams used it for employee surveys.

• Product teams relied on it to gather user feedback.

By the time procurement or IT became involved, SurveyMonkey had often already gained traction across multiple teams.

SurveyMonkey had designed its pricing around the moment a company decided to buy.

But customers started experiencing value long before that decision was made.

The issue wasn’t what customers valued. It was when they valued it.

That insight completely changed how SurveyMonkey thought about pricing.

Pricing around customer value

Once SurveyMonkey understood the adoption journey, it redesigned its pricing to reflect it.

Instead of charging purely on a per-user basis, it introduced a flat enterprise fee that allowed unlimited users across the organisation, alongside an additional fee for power users who collected more than 100 survey responses each month.

The new pricing reflected how customers experienced value:

• Organisations paid for enterprise capabilities.

• Teams could adopt the product without worrying about seat limits.

• Heavy users paid more because they generated significantly more usage.

Instead of pricing around access, SurveyMonkey started pricing around value.

The Mental Model

Successful pricing reflects how customers experience value, not the assumptions a business makes about it.

Every pricing model is a reflection of how well a company understands how customers discover, adopt, and realise value from its product.

When they launched, they entered one of the most settled, stable, and competitive markets in tech. How often does a company change their task management tool anyway?

Jira had already become the default for enterprises. Trello made Kanban mainstream. Asana, Monday, and ClickUp had each carved out their own corner of the market.

Together, the category is worth $15–30B and is still growing at roughly 12% annually.

That’s what makes Linear interesting.

This wasn’t a broken market waiting to be reinvented. It was a mature one, where most of the obvious innovation and market share had already been captured.

Today, AI makes building another project management tool easier than ever.

Giving people a reason to switch is still the hard part.

Yet somehow, Linear managed to do exactly that.

Over the last few years, it has become one of the most recommended products among developers and product teams.

That raises a much more interesting question.

Why did people care enough to keep talking about an issue tracker?

The answer wasn’t a clever launch or a massive marketing budget. It was a series of product decisions that quietly turned the product into the company’s growth engine.

#1: Become irreplaceable to someone.

Linear never tried to become the next Jira.

Instead, it focused on becoming the default issue tracker for one type of customer: fast-moving startups.

That decision quietly influenced everything that followed. The interface stayed opinionated, the roadmap stayed focused, and even the features the team deliberately chose not to build were filtered through one simple question:

Does this make us better for our ideal customer?

Most software companies become more flexible as they grow.

Linear did the opposite.

It became more opinionated.

The smaller the audience became, the easier every product decision became.

It wasn’t just a positioning decision.

It was an operating decision.

The Insight

#2: Treat product quality like a marketing budget.

One idea appears repeatedly throughout Linear’s product philosophy: Paper cuts.

Not major bugs or missing features, but the tiny moments of friction that interrupt your work.

A slow search, an unnecessary click, or a missing keyboard shortcut may seem insignificant on their own until you experience them hundreds of times every week.

Linear treated those moments as seriously as most companies treat marketing campaigns.

Small teams, constant dogfooding, and obsessing over tiny details.

And the objective? It’s the same today too: “Build a product people naturally want to recommend”

Instead of buying more attention, Linear invested in creating better experiences.

Those experiences became habits.

Habits became recommendations.

And recommendations became growth.

The interesting part is that none of it looked like marketing.

It looked like product development.

The Insight

#3 Give people something to believe in.

Linear wasn’t just building an issue tracker.

It was promoting a philosophy of how software should be built.

You could see that philosophy in almost every product decision.

Linear consistently chose the opinionated path over the configurable one. It removed decisions instead of adding them, simplified instead of expanding, and optimized for clarity over flexibility.

Those weren’t marketing messages.

They were product decisions.

Over time, those decisions became part of the brand.

People didn’t recommend Linear because it was another project management tool.

They recommended it because it represented a different way of thinking about software.

Competitors can copy features.

Taste is much harder to copy because it isn’t one decision. It’s hundreds of small decisions that feels consistent when experienced together.

The Insight

Final Thoughts

Linear didn’t eliminate marketing; it simply moved it upstream.

First, it became indispensable to a specific audience.

Then it built a product those users genuinely wanted to recommend.

Finally, it gave those users a philosophy they wanted to associate with.

Put together, those decisions created a flywheel that looked like this.

Most companies think marketing begins after the product is built.

Linear treated marketing as something designed into the product from day one.

Emergent Labs, a platform serving both business users (companies building apps for their operations) and individual creators (non-technical founders building apps independently), exemplifies this perfectly.

In just two months, they reached $10M ARR, not simply through AI, but by deeply understanding buyer psychology, designing experiences that inspire confidence, and executing creative, data-driven growth strategies. Here’s what I discovered from their journey, and what I personally took away as lessons I could apply to other B2B growth strategies.

Looking Beyond the Product: Why Trust Matters

When I first explored Emergent Labs, I was fascinated by their live coding platform. Users can describe what they want in plain English, and AI agents generate production-ready software in real time. For developers, this speeds up workflows. For business users and creators, it turns app-building from e-commerce stores to AI tools into a matter of days instead of months, at a fraction of the cost.

What struck me, though, was that the product alone couldn’t explain their growth. Emergent Labs intentionally designed experiences that built trust, confidence, and emotional connection.

That’s when I realized: rapid adoption in B2B/B2B2C isn’t just about solving a technical problem, it’s about understanding human decision-making.

Emotional Connections Drive Adoption

From studying their campaigns, I noticed that Emergent effectively tapped into emotions early. Tutorials, demos, and case studies showcased real users’ success, non-technical users creating production-grade apps that immediately reduced perceived risk. They also invested in storytelling campaigns, ad films, and podcasts that didn’t just explain the product but told a story about ambition, reliability, and what’s possible.

Influencer marketing amplified this effect, reaching both technical and business personas. The lesson I personally took away? People buy confidence and reassurance first, features second. Buyers who form an emotional connection early are 8x more likely to book a demo, showing that these campaigns directly impact engagement and conversion (6sense).

Trust Isn’t Optional, It’s the Currency

Emergent made trust visible at every step. They experimented with transparent pricing, starting at $10/month, then testing $25 and $50 tiers, signaling confidence in the product’s value.

Every app built on the platform was production-grade from the outset, demonstrating reliability. Their culture of rapid iteration and data-driven decision-making reinforced credibility externally, showing buyers that the company could deliver on its promises.

From my perspective, this was one of the clearest demonstrations that in B2B/B2B2C, trust drives adoption more than technical excellence alone. 76% of decision-makers consider trust the most critical factor (Edelman 2025), and 55% value trust more than features (G2 2025). Emergent made trust tangible, removing hesitation for new buyers.

Social Proof and Peer Influence Amplify Growth

One thing I found particularly insightful was Emergent’s use of social proof and influencer marketing. Success stories were shared across Slack, Discord, and social channels, highlighting wins for both technical and non-technical users. Influencers in tech, no-code, and startup communities reinforced credibility, signaling ambition and competence. Educational content, tutorials, and community initiatives created a ripple effect of trust, showing real results in multiple contexts.

Seeing this in action made me realize how peer influence and storytelling can convert early adopters into advocates, a principle that 86% of B2B buyers rely on (DemandGen 2025).

Experimentation as a Growth Engine

Emergent approached growth like an engineering problem, which I found fascinating. Over 200 micro-experiments tested content formats, creator engagement, channels, and messaging before launch. Launch day saw nearly 20,000 sign-ups, a number that only makes sense when you understand the careful preparation and testing behind it. Aligning the team around a single visible metric at any time ensured that even in chaos, everyone pulled in the same direction.

The key takeaway: Experimentation isn’t just about improving the product; it signals competence and builds buyer confidence.

Simplifying Complex Workflows

Another thing that stood out was Emergent’s focus on usability. They made it possible for non-technical business users to build apps without months of coding, while giving developers the tools to accelerate advanced workflows. Onboarding, tutorials, and visible progress indicators gave users early “wins,” quickly building confidence.

Effect of Personalization on Pipeline Metrics, Personalized onboarding and tailored experiences led to measurable improvements: 35% faster pipeline velocity, 28% higher deal values, and 21% greater buyer satisfaction (B2B Commerce Report 2025).

Observing this, I learned that psychology + UX = trust, and making early successes visible is critical in driving adoption and loyalty.

Educating Before Selling

Finally, studying Emergent’s journey reinforced something I had seen elsewhere: Buyers consume content long before interacting with sales. Buyers review an average of 13 pieces of content before making a decision, and content-first organizations see 2.8x higher lead-to-close rates (Gartner 2025),

Emergent’s tutorials, webinars, and case studies didn’t just explain features; they translated technical capabilities into tangible business outcomes.

From my experience as well, creating educational touchpoints builds trust, shortens sales cycles, and improves conversion, a lesson I’m personally applying to my work now.

Key Takeaways

Here are the key takeaways from studying Emergent Labs’ growth:

• Emotions drive decisions → even in enterprise B2B, buyers need to feel understood and supported.

• Trust is the currency → transparent pricing, visible delivery, and data-driven iteration create confidence.

• Social proof and influencers amplify adoption → stories from peers and credible advocates accelerate growth.

• Experimentation fuels both learning and credibility → micro-tests refine product, messaging, and channels.

• Simplify workflows and create visible wins → early success reduces friction and accelerates adoption.

• Educate before selling → teaching first builds trust and enables informed decisions.

• Culture underpins growth → alignment, rapid iteration, and a bias toward shipping drive sustainable scale.

Emergent Labs’ story showed that in B2B/B2B2C, you aren’t just selling a product. You’re selling trust, clarity, and confidence, and if you get that right, revenue follows faster than features ever could.

Studying their journey not only reinforced what I had seen in other B2B adoption patterns, but also gave me concrete, actionable insights I apply with companies I work with.

If you also wish to learn more about Emergent’s journey or as a source for all the numbers I quoted, here’s the Link to the full podcast with the Founder:

And with that, I will see you in the next post, probably diving deep into the B2B Distribution..

Ciao!

I’ve always been curious and drawn to building things. Originally, I studied marketing and finance, but my true passion was in tech, products, and experiences.

I started as a frontend developer, but soon discovered I loved solving problems through strategy, design thinking, and user research far more than debugging code.

For the past 4–5 years, that’s exactly what I’ve been doing: solving complex problems, shipping products, and collaborating with teams across the globe.

My Global Journey

My first real experience was in Japan, working on a B2B SaaS for osteopathy clinics at Threeaspro (Threesides Corporation). Designing for such a niche market taught me how culture shapes product adoption.

Alongside, I consulted with early-stage startups in the US and UK, learning how different regions approach growth, product design, and customer behavior.

Next came Velents, a pioneering AI HR tech product in the Middle East. I led product design and later product strategy. Initially, we thought a great product alone would drive growth, launching on Product Hunt, experimenting with PLG, and testing pricing across regions. But reality hit hard:

You can build the best product in the world and still fail without the right GTM and positioning.

The breakthrough came when we focused on the Middle East and doubled down on a single high-leverage outcome: custom automation for government HR processes. Growth accelerated. Influencer-led campaigns, content marketing, and a single viral founder post created more traction than months of outbound outreach.

People Buy From People

The biggest lesson I’ve learned:

“People buy from people, not businesses.”

We thought features were our moat. But what moved the needle was trust, partnerships, and reputation.

• Warm introductions closed faster than 100 cold emails.

• A viral post brought more inbound than paid ads.

• Once our brand resonated, sales cycles shortened dramatically.

B2B growth isn’t about quick hacks, it’s about building systems that compound:

• Partnerships, communities, and reputation endure.

• A sales cycle that feels impossible suddenly accelerates when a lead has already heard of you 5–7 times.

• Brand isn’t vanity, it’s your moat. It turns customers into advocates, your audience into a distribution engine, and your reputation into your single biggest sales enabler.

In a world where AI makes features easy to copy, your story, relationships, and reputation are what can’t be cloned.

Where I Am Today

Currently, I work with a US-based AI SaaS startup (Microsoft partner). I started as Product & UX Lead and now also lead GTM for our Multi AI-Agents framework, building the growth engine alongside Microsoft co-sellers and enterprises.

This role sits at the intersection of product, design, sales, and partnerships, and it has reinforced one lesson: growth doesn’t happen in silos. It compounds when product, brand, and trust work together.

My global experience from Japan, the UK, and the Middle East helps me see these connections clearly and apply them effectively.

Why This Newsletter Exists

I hesitated for months to start this newsletter, overthinking, consumed with work. But I’ve realized it’s time to share not just polished stories, but the raw lessons, failures, and experiments that actually shaped my thinking.

Breaking B2B will unpack everything I’ve learned about:

• AI B2B growth & GTM

• Brand building & distribution

• Advocacy (aka Influencer) led B2B marketing

• AI’s impact on customer behavior

If you’re a founder, builder, marketer, designer, or just a curious learner, I’d love for you to tag along.

Here’s the promise:

I’ll share everything I’ve learned about building, designing, scaling, and solving growth challenges for B2B and AI products, taking them from 0 to 1 to millions, along with my insights on empathizing with and selling to B2B consumers.

And hopefully, help you build your own compounding growth system along the way.

And remember, “Quick wins fade. Compounding systems last. That’s what How to B2B Growth is all about”.

See you soon for the first deep dive.

Cheers,

Anjali ✨