Lovable, one of the leading vibe coding platforms, recently shared that their users create 1 million new projects on its platform every week. Yes, that is every week, and not including anyone building and launching on Claude, Replit and similar platforms. Most of Lovable’s users are non-technical: founders, designers, or salespeople building tools they previously would have had to hire a developer for.

People who have been in the software industry for a while know this: before building a single line of code, teams would usually do some research on the customer, see whether there is demand, test the feasibility and viability of the business idea. Now, we often tend to ship products and then ask the question “does anyone need this and how do we distribute it” after building.

Over the 17 years of my product and founder career, I’ve been part of over 100+ technology product builds. I’ve seen too many product ideas that sounded incredibly cool, but no one ended up using. I’ve also seen the product ideas that sounded cool, AND people were excited to use, but users weren’t prepared to pay enough to make the business viable.

While the building part has changed, the fundamental truths of having to make the product and the business around it work haven’t changed.

I get it - building a new feature and seeing a first draft live a few minutes later is a huge dopamine rush. Talking to customers, validating the business model, or also hearing the uncomfortable truths from real customers - not so much. Ultimately though, I believe most people are building things because they want customers to use it, and because they want to build a business for themselves. Proper customer discovery is the key to achieving this.

But don’t despair - you can learn the key steps to test and validate your product ideas, and create an actual business around it - and this article will share with you how. The point is to do some of the hard work and learnings upfront, rather than spending a ton of time building, only to then have the big realisations come later. Because they will come.

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The Importance of Customer Testing

”95% of New products fail.”

This is one of my all time favourite quotes and a great reminder whenever we need a reality check in our industry from Clayton Christensen, professor at Harvard Business School. He has been a key influencer over the years on all things business innovation and understanding true customer motivations.

This has always been a hard truth of software development, and I suspect there are many awakenings about to happen for a lot of people who are launching software products for the first time with the help of AI.

For full disclosure: I am excited about the access AI gives people with entrepreneurial ideas without a coding background that previously did not have the resources to build and launch their ideas.

I also run a tech company and we are using AI to speed up our development massively in the business. Especially when pairing experienced developers and technology leaders with AI, the speed to deploy is game changing.

However, we never skip the fundamentals: spending time with our customers. And neither should you if you want to not only launch software, but create a business with customers who love your product.

My first role in tech was actually in a customer testing lab or “User Experience Testing Lab” as they called it back then. Big clients like Adidas would come and test their newly developed websites or applications with customers in our purpose built testing setup with mouse and eye tracking cameras.

This experience was truly an eye opener for me and something that stuck with me until today: users would rarely ever use the website or application the way we expected them to. Not fun when you just proudly shipped the latest features.

Fast forward to spending the next 17 years in software and product leadership roles, as the market got more and more crowded, the challenges became not only designing user friendly software, but also making the products truly desirable for customers. This meant ensuring customers would actually pay for the software and we could create a growing business around the product.

Today, apart from running a FinTech myself, I run workshops with leading startup accelerators in New Zealand and mentor product teams around the globe on the topic of customer insight and product strategy. In this article I will break down the key fundamentals we always go through on customer research and discovery that I truly believe will also help everyone launching their own software with AI right now.

Key Steps to Test and Validate your Product Idea

In the over 100+ software projects I’ve been involved in, absolutely every single product followed these key steps to test and validate the product idea:

1. Market Research (“Desk Research”)

Because you can do this from your computer and without necessarily talking to customers, we’d often also call this part “Desk Research”. It’s the first step of broader research to see if there is an opportunity in the market where you look at the broader market and customer trends.

Say you have an idea, whether it’s an idea you came up for yourself, or something that has been sparked by talking to people in your network or work already - the first step is usually to go online, and get an understanding of the broader market and existing products.

This is not a very structured process (and shouldn’t be) - but typically you go online and research:

1.1. Find general market trends
Look for general consumer trends, regulatory changes that may drive new product needs, or economic changes that may impact customer behaviour. Don’t look for data that just validates your thinking - your job here is to test, not confirm your idea, so look for any data that could either help or challenge your idea.

1.2. Research existing tools & competitors in your space
Look for tools that could be direct competitors in your area that you want to launch in, but don’t forget to also look for global players - these can be great inspiration. And most overlooked: what are the other real alternatives to using your product? It’s a running joke in tech that the most common competitor is often an Excel spreadsheet.

1.3. Understand customer sentiment trends
Let’s say you are looking to build an app for improving healthy eating habits - join some Facebook groups or Reddit threads and understand what customers already use, what annoys them, or what they get excited about. This can be done by reading through manually, or if there is tons of publicly available data you can use your AI tool of choice to analyse common themes.

2. Customer Research

Now that you found what we call a general “Opportunity Space”, it’s time to spend time and build empathy with your potential customers. Customer research is all about understanding the current pain points of customers, and whether there is a gap and an opportunity to solve this problem better for them.

Typically, we’d start this with:

2.1. Drafting a Persona
Write down what your ideal customer looks like - include demographics, but also most importantly their drivers, their current pain points, and what they struggle with. If you have a real person in mind, even better.

💡 Avoid this common mistake:
Most people describe personas only in customer demographics - e.g. female, 45, has a good paying job. This is not enough. You’re here to build empathy with your customers, so look beyond the surface and ask yourself what is going on in their life or work that may trigger them to look for your solution. Once you identify this, you might actually find this trigger can also appear in customers at different ages or demographics, meaning demographics are not always the best way to categorise your customers.

2.2. Run Empathy Interviews:
Now, you gotta talk to people. While the desk research can be greatly supported by AI to synthesise or find data, this is not a step I recommend to outsource to AI. Remember the goal is to build empathy with your customers, and truly understand their deepest frustrations, motivations and goals.

”Customer discovery is not about asking people what they want. It’s about understanding their world and the problems they face.”

- Rob Fitzpatrick (The Mom Test - a great read!)​

The key is now to find 4-5 of your target customers, and schedule 30-45min interviews with them for research purposes. Find people in your network, or ask people to introduce you to people who would fit the persona. Incentivise with a little thank you or voucher if needed.

Come prepared with a script of questions. I’ve run what must be over a thousand customer interviews over the years, and the best scripts are simple and follow a structure like this:

1. Detailed Profiling: ask them more details about their current life or work, or any key data points relevant for your product.
2. Real Scenarios: ask them to recall a couple examples where they had to perform x task before (the one you want to solve), and how they went about it.
3. Dig deeper here and ask follow up questions - what tools did they use, how did they feel during it. That’s it. The key is to truly listen.

We always found more inspiration in past behaviour than “future hypothetical scenarios”. People are inherently bad at predicting future behaviour. Do not talk about your app or solutions you have already created in your mind that might bias their answers, but focus on understanding their pain points, motivations, and what they currently do as a workaround.

💡 Avoid this common mistake:
The golden rule is to do 4-5 of the same target customer, so you get to see actual trends and commonalities per persona. In other words, do not pick 5 completely different target personas and do an interview with each. You’ll end up with incredibly biased feedback as you only have a sample size of one per persona.

While there are exceptions e.g. marketplaces with several types of users simultaneously, most successful product launches start with one key persona most of the time and then expand over time. However if you decide you have let’s say 3 personas that are key to your project - you need to 3x 4-5 interviews.

3. Test Your Idea against the 4 Product Risks

This is the time to get honest and test your product and business idea against 4 major categories.

You now either 1) have a good overview of the market and a clear understanding of the common pain points of your customers - which means it’s time to figure out how exactly you might be able to solve their pain points through your software, and how the business model could work.

Or 2) you might realise you are not as confident as you’d like yet in understanding the customers and the market, which means you might need to do more research before moving to the solution and building phase. This is totally normal, and happens a LOT more than you typically hear about.

In either case, before starting to write any code, we’d typically go through the “4 Big Product Risks”. Those 4 risks come from a very popular and in my opinion timeless Product Management framework introduced by Marty Cagan (one of the OG Product coaches). I’ve slightly adjusted the questions over the years to what resonated with me:

You’re basically reviewing your product idea from 4 key angles:
- Value: will customers buy it and choose to use it?
- Usability: can customers figure out how to use it?
- Feasibility: can we build & scale this in a way it meets customer expectations?
- Business Viability: does this work for our business? Review your business idea from a commercial (will people pay for it?), regulatory and also ethical lens.

How to reflect and test for each:

• Value: hopefully you got a sense for that in your initial empathy interviews. If you are not convinced you found a problem that customers are literally screaming out for, there is no shame and it is smart to go back to the drawing board, refine your idea and run another set of interviews.

• Usability: can you find a way to design an application that is user friendly, and that people will be able to understand and enjoy using? This is not to be overlooked - for decades customers have chosen products they find easier to use over competitor products that do the same on a functional level, but are clunky or “too technical” to use. The iPhone must have been one of the most famous examples of this.

• Feasibility: we used to call this “tech discovery”. If it’s a simple interface app, this might not be an issue. But if you’re building something that uses a lot of data, calculations, integrations or even some hardware - you might want to try a small functioning prototype next. This does not have to be polished or ready for any user to see - the uglier the better - the goal here is to test whether you can technically build something that will work and be scalable, e.g. meeting a certain level of data accuracy.

• Business Viability: you want to do some basic business planning here. How much will you charge customers for your product, and are they willing to pay for it? Is the revenue going to be enough to run and grow the business? You also want to check whether you’re infringing on any legal constraints such as giving health or financial advice. Lastly, often overlooked is the ethical lens: does your product genuinely make your customers life better? Apart from the moral side, there is risk of not having a very sustainable business that may fall under scrutiny or regulatory shifts at some point (see what happened to a lot of Buy-Now-Pay-Later companies that struggled in the long term).

The goal here is to review whether you’re ready to build. From my experience it’s always been impossible to be certain on all 4 areas, but you do want to spend some time researching and testing each category, and get to a level of “confident enough” to move to the building phase. Once you launch and get more feedback in market, it is completely normal (and in fact extremely alarming if this doesn’t happen) to revisit all 4 questions again, and continue to iterate on the product and business model.

4. Analyse Product Usage & Feedback Post Launch

Great work - you did your research, tested your idea, and launched your product. The upfront research (steps 1-3) helped you reduce the risk along the key 4 questions we asked ourselves.

However, and I’ve seen it hundreds of times - whenever products hit the real world, stranger things happen. People might not use your software the way you thought they would. Or you might attract completely different customers than planned. Or customers might have said they’d love to use your product in your research, but then in reality are motivated by other drivers they didn’t mention, and their real life behaviour becomes vastly different.

This is why it’s so crucial to continue to gather feedback and product usage analytics post launch, and continue to iterate on your product and features before moving on to the next shiny thing.

4.1. Tracking Quantitative and Qualitative Data
The most important part you need to know is that you need a mix of both quant and qual data:

- Qualitative data: this is feedback from customers in language format - e.g. gathered through customer interviews or analysing product review comments
- Quantitative data: this is the hard numbers - e.g. the analytics of your customers such as number of sign ups, number of conversions to a paid plan and so on and so forth.

The quant data will tell you “the what” and give you the facts - while the qual data you get from understanding customer feedback gives you the “why”. For example you might see x amount of people not converting to a paid plan, but only once you talk to them do you understand the reason behind it.

It’s important to track both types of data, and if you want to learn more techniques I wrote an in depth article on how to gather both quantitative and qualitative data here.

4.2. Measuring Product Usage

For tracking how customers are using your software, you also want to setup some basic product analytics to start with. One of my go to’s over many years and product teams has been to use Mixpanel for this purpose.

Tracking customer usage is an absolute non-negotiable in any mature software team, and something you should adopt for your product idea.

The key metrics to track in your application are typically:
1. Customer acquisition
How many new customers sign up? Ideally, you also track the key steps it takes customers to sign up to measure whether customers are dropping off at any particular steps in the onboarding.
2. Customer activation
How many of them use a key feature or get started using the product in a meaningful way within the first week or month? (This is also called the “aha moment”)
3. Customer engagement
How many users use your product min. once per day / month? This is often called MAU (Monthly active user) or DAU (Daily Active User). You could measure this by tracking the number of customers who use an important feature every time period.
4. Customer retention
How many customers are still “active” 1, 3 or 12 months after signing up? How many % of customers have churned? This describes how “sticky” your product is and if you can drive long term value for your customers.

💡 Common mistake to avoid:
Tracking vanity metrics! This is a huge industry wide problem and something you should also be aware of. Too often, people track customer engagement on the basis that a customer has opened the app. This is not true engagement.

You want to identify what it means to get value from your product - for example we use Attio as our CRM, and using the product for us means successfully running a weekly sales report, or entering a new entry to the CRM a minimum of once per week. This is what defines customers getting “value” from your product. Do not track just pure log ins or app opens, please.

Dig Deeper into Tracking Product Usage

If you want to dive deeper on how to effectively track how your customers are really using your product, I’ve also written a book on this topic - The Insights Driven Product Manager. It’s an easy to digest read, a #1 release on Amazon and a staple for Tech Product Managers on this topic - with all the tools and frameworks you need to measure product success, including for AI products.

Final 2 Cents

Now more than ever, we are reminded that people crave good quality products and experiences, not more average apps. With the ability to build so much faster with AI now, it also means there is more competition and crowded markets than ever, so it’s even more important to build things that truly make a difference and delight your customers, especially if you want to build a successful business around it.

Don’t skip the hard part of spending time with customers just because you can build it now. Use the timeless principles software development teams have honed in over decades on how to do customer research and validation before you start building. The learnings on what doesn’t work come one way or another, so you might as well get them early and not waste time building to then redo a lot of the features.

Try these techniques for your current AI coding project, and let me know how you go in the comments, or email me on corinna@insightsdrivenpm.com. I read every response. 💜

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Corinna

Founder, InsightsDriven · CEO & Co-Founder, Bizzy · Author, The Insights Driven Product Manager

Get in touch for Coaching & advisory - I work with select clients for advisory and mentoring. I’d love to see if I can help.

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What I’m thinking about

It seems new news on policing AI content just keeps rolling in every week. The latest: Claude is adding watermarks to texts created by AI.

The trigger for that is a new EU regulation (of course it’s from the EU 😂) that came into place August 2: it requires AI generated content to be marked as such by all providers. Claude’s watermark won’t be visible to readers, and it’s unclear what various platforms will decide to do with this information in the long term.

My guess is that following recent user sentiment, social media platforms will start to favour human written content in their algorithms in some shape and form. The hard part will be deciding how to treat content that was not entirely created, but supported or polished with AI. Anthropic itself says the watermark can only answer the question “What is the likelihood this was partly written by Claude?”

Here is how I use AI to create my articles:

• I’m very anti outsourcing your thinking to AI - I always always always write my first drafts myself

• I use AI to keep a running backlog of ideas I want to write about, I add items to it whenever I have an idea and pull this backlog up when I start on a new issue

• Once I created my draft, I typically use AI to check my writing for grammar or spelling mistakes, overall coherence and a cross check of sources

• At times I use AI to do keyword research, optimise article titles, or create summaries of what I have already written for reshares

The data I’m pondering

While most people only talk about Claude introducing their watermark, 190 AI companies have actually signed the EU Code of Practice requiring watermarking of AI created content.

What I’m working on

I just finished running my cohort workshop on Maven on how to build business acumen and it was very fulfilling! Too often I see very smart product and design leaders who share their amazing work to stakeholders, but struggle to articulate their “business impact”. It’s part confidence, part understanding the terminology, but something I am convinced everyone can learn.

I am opening a new workshop slot on Oct 1, reach out if you’re curious to learn more.

Quote I’m sitting with

“Create more than you consume.”

Unattributed and repeated by many sources over the years. This has been a very powerful reminder for me personally this year.

That’s it for this fortnight.

If something resonated, hit reply, I read every response. And if you know someone who’d find this useful, forward it on.

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Corinna

Founder, InsightsDriven · CEO & Co-Founder, Bizzy · Author, The Insights Driven Product Manager

You’re receiving this because you subscribed to InsightsDriven. Every second week, I share RawData - my musings on business, technology, and what I’m reflecting on. I also publish long form articles on building technology products. You can manage your email preferences on Substack for both InsightsDriven and RawData.

What I’m thinking about

Just in the last issue of RawData we talked about the amount of “products” being created with AI. We have the same issue with creative content - which is now being heavily monitored by major platforms: Substack just rolled out an AI detection feature, and LinkedIn introduced a “Seems like AI Slop” button.

I think it will be very hard to police and draw the line, but I’m all for doing something to fight the amount of empty and inauthentic content where everyone suddenly sounds exactly the same on the internet.

The data I’m pondering

A study referenced by Andrew Huberman earlier this year found that having your phone nearby is having huge impact on your brain capacity and focus. Even if it’s off and upside down, or in your bag - it’s still “calling” you even if you don’t realise it.

The main advice here is to put your phone entirely away into another room.

What I’m working on

I just ran a Lightning Lesson on Maven last week on how to incorporate business thinking in your day to day. We had over 200 sign ups! You can watch the recording here. This was my setup - don’t mind the Working Backwards book used as a prop - still a classic. 😁

Quote I’m sitting with

“It’s never been faster to build, which means it’s never been easier to run 10 times faster in the wrong direction.”

- Hilary Gridley, AI Product Leader

That’s it for this fortnight.

If something resonated, hit reply, I read every response. And if you know someone who’d find this useful, forward it on.

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Corinna

Founder, InsightsDriven · CEO & Co-Founder, Bizzy · Author, The Insights Driven Product Manager

You’re receiving this because you subscribed to InsightsDriven. Every second week, I share RawData - my musings on business, technology, and what I’m reflecting on. I also publish long form articles on building technology products. You can manage your email preferences on Substack for both InsightsDriven and RawData.

The data I’m pondering

Lovable, one of the leading vibe coding platforms, recently shared that users are creating 1 million new projects on its platform every week. E.v.e.r.y week!

The majority of its users are non-technical: founders, designers, salespeople building tools they previously would have had to hire a developer for. While I thought it would be high, this truly blew my mind given we’re still in the early days of adoption.

What I’m thinking about

Before everyone was able to build software with AI, teams would usually do some research on the customer, see whether there is demand, test the feasibility and viability of the business idea. Now, we tend to ship products and then ask the question "should we build it” later.

I am all for building faster and in a way, it has simply shifted the validation phase. But I believe the fundamental issue is that we’re testing often poor quality software with real people’s time and attention (which is already scarce), adding to the general sense of overload in the world. Not to mention the effort to make it scalable and reliable later.

Now more than ever, we are reminded that people crave less, not more. Don’t skip the thinking just because you can build it now.

Quote I’m sitting with

“Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.”
— Stephen Few

I just published a longer piece on InsightsDriven on how to present data to stakeholders so it actually drives decisions. Many times I see brilliant leaders spend hours gathering data, refining dashboards - but lacking the skills to really tailor the message to their audience. In this article I share my monthly process of analysing and presenting data in our business.

That’s it for this fortnight.

If something resonated, hit reply, I read every response. And if you know someone who’d find this useful, forward it on.

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Corinna

Founder, InsightsDriven · CEO & Co-Founder, Bizzy · Author, The Insights Driven Product Manager

Join my Maven Lightning Lesson on July 31 - Accelerate Your Career with Business Thinking

You’re receiving this because you subscribed to InsightsDriven. Every second week, I share RawData - my musings on business, what I’m reading or thinking about. Occasionally, I publish long form articles on building technology products. You can manage your email preferences on Substack for both InsightsDriven and RawData.

The meeting comes, the team shares their fancy charts, stakeholders are impressed with all the data - but 9/10 times the audience completely misses the key points because there were too many data points on the screen. The meeting ends again with everyone feeling they saw some data as an “FYI”, rather than getting to a real “aha moment” that drove a meaningful change or decision.

Whether it’s to show the latest product usage, or the latest customer acquisition KPIs - once you’ve gathered all the data - is where the actual work starts.

It’s actually my favourite part. It’s where the reflections start. It’s where I now look through my freshly gathered numbers, and just sit on them for a minute (or twenty).

After some time, the first thoughts start to come in: wait, why is this number so high, but this one over here is so low? Is there a correlation between those two numbers over here that are both trending up? I wonder why that is.. Have we changed something that made this part go out of whack? Oh, there’s a broader pattern here - awesome to see this number going up slowly but steadily if you zoom out over a longer period.

This is where the gold is. The actual reflections, the actual insights. THIS is what you want to put onto the slides. The graphs aren’t the story, they are just supporting data of whatever point you want to make.

One of the things I do in our business is a monthly recap of KPIs - I then share this with our team, investors, partners, and even leverage the insights for our content and partner newsletter.

The actual data gathering is really just the first step of this monthly process - the main work is to distill the actual insights, prepare it for the different audiences and do some tailoring to what they actually want to know.

In the next section of this post I’ll run through my usual data analysis process and how it can help you do better data presentations.

The 5 step process to analyse, prepare & present data more effectively

This is a process I follow when I create our monthly insights, that you can copy for your KPI and data reporting if you want people to actually get away with an aha-moment:

1. Collect the data

This is the base level step - whether it’s already setup in a self serve dashboard, or there is some manual gathering involved. In my case I like to have one “master spreadsheet” with the key KPIs from all business areas pulling together from different tools. In your case, it might be that you already have all your relevant data in one place for example in your product or marketing analytics dashboard. Either way, gather it, do a cross check to make sure you’re analysing the correct and complete latest data set before you proceed to the next steps.

I repeat - this is what people call “analysing the data”, when really it’s just the collection.

2. Find the trends

Now is when we actually get into the “analysing” part. In its most simple form, this is where I now often just stare at the numbers, until I stumble across some interesting numbers or spot trends that spark my curiosity.

Sometimes, although not always needed, I run AI over the data to fuel the ideas: “What patterns can you spot in our monthly KPIs? Review the KPIs for the month, but also compare and put into context with the previous quarter, and share a list of observations, trends and anomalies”.

This is great to get the creative juices flowing and direct my attention to particular areas of the data, which I may even have missed otherwise. However, it still misses the point often (even if it’s plugged in to plenty of business context), makes broad generalisations or misses some of the nuances that you might be aware of.

My guide to using AI in my data analysis process is that I never use it to replace the thinking and I definitely don’t take any conclusions at face value - but it is useful to aid the analysis part by helping you spot additional patterns that our brain would not necessarily be able to see.

3. Find the why’s

Now that you have stumbled across the interesting trends in your data, can you explain the why behind it? For example, we recently saw a 2x increase in our customer acquisition compared to previous quarter. We were running some campaigns for a while, but what really impacted this was that we tightened our positioning and wording, and revamped all our landing pages accordingly. This was our true “why” that led to the acquisition growth.

Another number on our KPIs was lagging - I knew this was most likely due to seasonality, as we’ve seen those trends historically. It's not enough to share what happened - stakeholders really want to know why it happened, and how the numbers compare to benchmarks or different time periods.

4. Set the key message

Now that you’ve done the data collection and analysis, and connected the dots of what any trend can be attributed to - it’s time to think about what you want to do with this insight, how you want to share it, and what actions you want to inspire in your organisation based on this data.

For example, after distilling the key channels and messaging that worked really well for our customer growth, my recommendation was to increase spend for next quarter on those channels and campaigns. On another KPI where I saw a drop off in customer conversion in the product, I wanted us to agree to invest in a new product capability that I believe would drive conversions to healthier levels. These are your key messages.

Whether it’s a “good” or “bad” data trend, it’s absolutely critical to think about the key message you want to convey, and tailor your message to the audience you’re presenting to. Different stakeholders will want different things from the same data, and your key message should adjust depending on who's in the room.

Lastly, don’t be afraid of sharing your opinion here. Many times, stakeholders listening to your presentation won’t have all the benchmarks and context in mind, so I always find it helps to clarify in the key message whether I think a certain trend is “good”, “average”, or whether we’re really dissatisfied. This opinion is what sets the tone and inspires action.

5. Present the data

Too often I see otherwise very data driven teams make the mistake where they put a graph on a slide, hoping this graph will tell the story on its own and stakeholders will just come to the same realisation as you. You want to start this presentation with your insight, your key message, your opinion, and the graph will be supporting this message in the background.

If it’s a slide, I sometimes even put the key message right into the title “We suggest increasing spend on channel x” - then show the graph, with a sentence around what trend we observed and what led to it. Simple.

So the process and format for creating better data presentations where your stakeholders actually go away with a big “aha moment” is:

1. Collect the data

2. Find the trends

3. Find the why’s

4. Set the key message

5. Present: Key Message - Trends & The Why - Supporting Data

The final test for a good data presentation to stakeholders

You essentially never want your audience to look at your data, and have to ask themselves: so what?

Make sure you start with the “so what”, aka your key insights and message. Then use the data to do the convincing and reasoning behind it. This ensures you avoid two very common traps when sharing data with stakeholders:

1. You avoid losing your audience: You start high level, and offer the detail only if people are interested. If your stakeholders want to dive deeper at this point, great, you can. But if you start with an overwhelming amount of data first and not everyone wants to see it, you’ve already lost half your audience. Let the room guide how much you’ll go into geeky data levels.

2. You avoid distractions: Your stakeholders won’t need to get into active thinking mode while listening to your presentation to work out what this actually means. Our brains naturally will want to try to interpret the data to get to some sort of conclusion, so why not give them the main interpretation upfront. This way the audience can relax and fully focus on what you want to walk them through.

Remember: Collect > Find the trends > Find the whys > Set the key message > Present.

Don’t be afraid if presenting this way feels like you’re “oversimplifying” things. You’re still offering to dive deep into the data if the room wants it, but you’re also making sure you’re starting from the top level, to then dive deeper where needed.

It’s much harder to present things in a simple way, than to just throw ten different graphs on a screen and overwhelm your audience. By simplifying things, you’re actually building a crucial communication skill.

Try this format for your next presentation, and let me know how you go in the comments! I read every response. And if you know someone who’d find this useful, forward it on.

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Corinna

Founder, InsightsDriven · CEO & Co-Founder, Bizzy · Author, The Insights Driven Product Manager

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Welcome to the 2nd RawData edition - my biweekly musings on business, what I’m building, reading, and thinking about. This time: some big fintech numbers, a book I keep recommending, and a mindset shift I’ve heard in a few places now that genuinely made a difference to me. Let’s get into it.

The data I’m pondering

The 100 largest private FinTech companies generate more revenue than the 100 largest public ones. Their combined valuation sits at roughly $1.9 trillion, which is 3x the total market cap of the top 100 public fintech companies.

This report by FY Partners argues we are heading into what they call a “fintech liquidity supercycle” - eseentially increased M&A and IPO activity over the coming years as this private capital looks for an exit. As someone building in fintech, it’s been awesome to see the momentum locally and globally.

What I’m reading

I recently finished Ultra Processed People by Chris van Tulleken. I posted about this book earlier this year on LinkedIn and people still come up to me about this recommendation, so I wanted to reshare it here. Apart from the brilliant research and funny writing style, it changed my entire awareness of how the food industry is marketing to us and creating addictive food for commercial gains.

“A food that is consumed less will never sell as well as a food that’s consumed more.”

What I’m building with

Mixpanel just launched their AI agent in May 2026 and as a long time Mixpanel user this one is exciting me. I’ve said for a few years that one of the powers of AI will be the ability to ask your own data questions in plain language, without writing a query or even having to navigate a dashboard. Super cool to see this on our Mixpanel setup.

Quote I’m sitting with

“You don’t *have to do something, you *get to do something.”

I’ve heard this in a few places recently - I don’t believe in hacks, but changing out this word in your internal framing genuinely makes a difference when the the day feels packed with obligations sometimes.

That’s it for this fortnight.

If something resonated, hit reply, I read every response. And if you know someone who’d find this useful, forward it on.

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Corinna

Founder, InsightsDriven · CEO & Co-Founder, Bizzy · Author, The Insights Driven Product Manager

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You’re receiving this because you subscribed to InsightsDriven. Every second Friday, I share RawData - my musings on business, what I’m reading or thinking about. Occasionally, I publish long form articles on building technology products. You can manage your email preferences on Substack for both InsightsDriven and RawData.

Welcome to the first issue of RawData. This is a new section within InsightsDriven and something that frankly I’ve been thinking about doing for quite some time. It always takes time to find the courage to hit publish!

Every fortnight, I will share some of my musings on business, what I’m building, reading or thinking about. The goal is to share a more personal series of notes alongside the main blog, with real time reflections and data points (of course!) that keep me and others inspired to build meaningful things. Let’s get into it.

The data I’m pondering

Reddit’s Q1 2026 results show Daily Active Uniques were up 17% year-over-year to 126.8 million, with revenue growing 69% to $663 million. According to a BBC article, women now make up more than half of Reddit’s users, and amongst women in the UK specifically, Reddit is the fastest growing social platform.

While most platforms and companies are racing to flood feeds with AI generated content, Reddit’s bet is on authenticity and it appears to be working. This broader feeling is a big part of why I’m starting RawData. While everyone else doubles down on AI content, I want to spend more time creating, and reading human content.

What I’m reading / listening to

This set from Polaroit on YouTube. They’re one of my favourite German DJs and this set is an oldie but goodie. I love it for focused mornings, it’s not too slow, not too upbeat and beautifully melodic.

What I’m building or thinking about

Keeping on the theme of data, we just launched our first insights series for Bizzy, the fintech I’m running. It’s a marketplace connecting business lending requests with a wide range of lending providers, and we get a unique view on demand trends, what’s working and what isn’t for business owners seeking capital. Fascinating.

Quote I’m sitting with

“Don’t try to take the whole world on your shoulders, that’s impossible. Choose whatever is closest to your heart or where you have special expertise, then you can make the biggest change.”

- Yuval Harari

This feels like the right note to start RawData on. There’s a lot happening in the world that we can’t do much about. But the things close to me - building meaningful things, learning, creating - those I can do something with. RawData is my outlet for that.

That’s it for this fortnight.

If something resonated, hit reply, I read every response. And if you know someone who’d find this useful, forward it on.

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Corinna

Founder, InsightsDriven · CEO & Co-Founder, Bizzy · Author, The Insights Driven Product Manager

You’re receiving this because you subscribed to InsightsDriven. Every second Friday, I share RawData - my musings on business, what I’m reading or thinking about. Occasionally, I publish long form articles on building technology products. You can manage your email preferences on Substack below for both InsightsDriven and RawData.

“We need our teams to show more business acumen.”

If you’re a product or technology leader, you’ve likely heard this feedback before.

This pressure is in my opinion creating a lot of confusion and tension between often very senior leaders, and their executive teams. How do we know we’re really contributing to the most important business goals? What the *** does business acumen actually mean, and do we now all need to get a finance degree or do an MBA while working full time?

As someone who has moved from product management into business leadership roles and now running a business as a CEO, I found that the people who show the most “business acumen” are not necessarily always finance and business people. It really isn’t about being able to analyse every single line on a financial statement - but it’s about being clear on answering some core questions about your business drivers:

1. What are the largest drivers of revenue in the company?

2. Who is the ideal customer who is willing to pay?

3. What are the major cost drivers in the business?

The more time you spend in the world of business, the more you realise everything runs truly and only based on incentives. This was something I kind of knew (duh!), but now really see how it drives every single decision business leaders make.

The key incentive in any for profit company is - well - revenue and profit. In this article, I want to explore the first topic in more depth: what are the largest drivers of revenue in the company that really drive the business, and how can you understand them without sifting through dozens of financial spreadsheets.

How to really understand your business drivers

I like to think about businesses like a system of complex but yet simple set of levers, almost like an engine that needs everything aligned and firing in a certain rhythm to keep running. Every business has their own levers that drive the efficiency of this system. You change one input, the rest of the pieces may be impacted and become out of whack, or go even faster if you optimised it the right way.

Now the big question is what are those levers in your business? This is a lot more of a fundamental question than most people think - let’s go through a few examples:

1. You’re in the business of “entertainment” - you create content in some shape and form that people consume. The more people consume your content e.g. watch content through your streaming app, the more money you typically make, whether that’s through a direct subscription, advertising revenue or sponsorships that result from the volume of content consumed.

2. You’re in the business of “efficiency” - you have automated an otherwise manual workflow by bringing things from paper onto digital or now into AI agent workflows. You make money by onboarding as many users as possible under the promise of saving your customers time, typically monetised by subscriptions or a pay per use / token usage model.

3. You’re in the business of “transactions” - you are running some sort of marketplace, ecommerce or FinTech where you make money whenever people purchase something or get matched with a service or product successfully. This could include a wide range of products e.g. investment apps, affiliate platforms, marketplaces, payment providers, finance companies or ecommerce stores.

4. You’re in the business of “improvement” - customers use you to improve their health or gain knowledge. The outcome is measured by the impact on customers’ aspirational goals, but monetisation varies on how exactly the business delivers this value - this could be through the volume of hardware sales, services, subscription revenue or content consumption.

This list won’t cover 100% of all business types, and you may find your business may even operate in a combination of both - but typically by thinking about these different types of drivers you can quickly categorise your business and help you understand “how the machine really runs”.

For example, I currently run a FinTech company called Bizzy - we connect business owners in need for growth capital with the right finance providers. Our business model is strongly aligned with successfully matching business owners to those providers, so we clearly fall into the transactions model. It’s how we deliver value to customers, and what drives our monetisation and business success.

The big question is: which one does your business fit in, and does your current roadmap actually reflect it?

Once you understand your business mechanics

You’ve done the hardest part: identifying the core lever that drives your business. Now, think about how this trickles down into what your company does. Are you able to connect the dots of your current work and initiatives to this core driver? What are the metrics you’re measuring and are they aligned to this?

When talking to product and tech leaders I often find that when reviewing their current priorities, many things they work on or measure tend to fall outside of this core area. Unless it’s a deliberate choice, for example when the goal is to diversify from the core business, it can be an incredibly helpful exercise to bring the focus back to the true driver of the business.

In our case, our business driver has become everyday language for us: how can we achieve more successful matches? We use this as a quick evaluation filter of everything we do across product, sales, marketing and operations.

True Business Focus in Action

I was working with a product leader last year whose objective was to improve monthly revenue for the company. The company was a marketplace and making money of transactions - think matching services to open jobs. The initial reaction from the executives was to increase revenue by increasing company’s monthly active accounts through new customers, so the teams got busy with building and optimising every single bit in the product acquisition funnel.

But the product leader had actually found a great insight that customers could be retained by adding certain tools, and with that are more likely to transact more often. By bringing it back to the basic question of how we can improve transactions volume, they changed from a list of 20 onboarding improvements to increasing adoption and retention of those additional tools.

They were now able to present their roadmap with completely new clarity: “We’re in the business of driving more transactions in our marketplace. We found we can actually achieve this by retaining our customers through additional tools - and here is what we’re building that will help with that.” 💥

The moral of the story is to take a second to truly zoom out - and that a goal can often be achieved through many different paths. The hardest part is usually to zoom out enough, get clear on the key driver for the business, and then work backwards from this, rather than get lost in a long list of initiatives and metrics that we assume will get us there, or got us there before.

Want to learn more on building roadmaps that actually drive the business?

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Productboard’s 2024/2025 State of Product Management report highlighted that nearly 60% of product leaders believe business acumen will be the most critical skill for PMs over the next two years. I can confirm this is also what I see executive teams expecting from Product Managers these days - but we are yet to close the gap and grow our confidence in business metrics.

When I talk to PMs, many still don’t know how they should actually grow their commercial skills. They often tell me they feel financial metrics are “someone else’s responsibility” - the executive team, finance or commercial team. Or, they simply feel they aren’t “businessy enough” to be in those conversations.

It’s time to close that gap. To grow your influence, you don’t have to read every single line in a financial statement. But what your executive team expects is that Product Managers can connect the dots between a user’s action and the company’s business goals. That’s why I want to help demystify the most commonly used business metrics in this article.

What are Business Metrics

I like to think of Business Metrics as operational health metrics. While Product Metrics tell you if people are using your feature, business metrics describe whether you are actually running a healthy and efficient business.

The danger of not being clear on the key business metrics that truly matter is that efforts across the company become more and more siloed. Marketing is tracking leads, Sales is tracking demo bookings, Product is tracking engagement - but nobody is looking at how they link together towards the broader goals. As a Product Manager, mastering business metrics means you stop being a backlog manager and start being a trusted cross functional leader who understands how the whole engine works.

Disclaimer before we get into it: this is a list of key operational business metrics, not an exhaustive list of detailed financial metrics - but if that’s something you want to dig into later, let me know!

1. Company Financial Goals

Every financial year, your leadership team sets a target. In companies that operate for profit, this is typically a financial goal. In a nutshell, every company is aiming to either increase revenue, reduce costs, or usually a combination of both to drive profitability - but those financial goals may be shared in a few different common ways, for example:

• Increase annual revenue by X%

• Grow the company by X% (typically this also means growing the annual revenue)

• Grow Monthly Recurring Revenue (MRR - often used in SaaS)

• Grow Average Revenue Per Customer/User (ARPC/ARPU) - the average amount of revenue generated per customer (or account) over a specific period.

• Reduce Operational Costs - for example to acquire new customers or support existing customers, in order to drive profitability.

• Increase Profitability - this is either achieved by increasing revenue or reducing costs, or a combination of both; it’s important to be aware whether the company is trying to reduce direct costs of delivering the product (such as product development or hosting costs) which determine your Gross Profit, or whether they aim to reduce broader operating costs (such as sales, marketing or finance), which impact your overall Operating Profits.

The PM’s Job: You need to get crystal clear on where the increase in revenue, or cost reductions are expected to come from - this is essentially the business strategy that will lead to those business outcomes if successful.

Your company may be looking to grow their customer base in a new geographic region, or increase revenue by introducing a new product line. If the business goal is to grow revenue without necessarily increasing the number of customers, your focus as a PM could shift to expansion - introducing premium features or add ons to existing customers that help drive this number up.

If you’re working for a bank, revenue growth might come through growing the amount of loans in a particular sector. If you’re in SaaS, it might be expanding your conversion to paid subscribers, and so on and so forth.

These business goals should ideally already be shared with you in company wide sessions. If you’re working for a publicly listed company, you can also read up on your goals in the annual shareholder reports. If neither is available to you, or the goals are still not quite clear - reach out to your business leaders and ask. There are no stupid questions, and in my experience good business leaders will always appreciate when their teams show genuine interest in the business’ goals and performance.

If you want to dig deeper into how to unpack your company’s business goals, I’ve written an in depth article with a real life example here.

2. Commercial Efficiency Metrics

In the growth at all costs era, we were taught that a growing user base was the only metric that mattered. But as the market has shifted toward profitability, we’ve learned that it’s not just about how many users you have, but how much it cost to acquire them - and how quickly you can make that investment back. While some of those metrics may read as sales or marketing related metrics, our products often have significant impact on those areas as well.

Here are a few key business metrics that help you track the efficiency of acquiring and retaining your customers:

• CAC (Customer Acquisition Cost): How much does the company spend on marketing and sales to acquire one paying customer? For Product Managers, better onboarding conversion can have significant impact on CAC by driving onboarding conversions.

• LTV (Lifetime Value): How much total revenue do you expect to get from a customer over the entire time they use your product? Driving up product engagement typically increases Lifetime Value of customers.

• LTV:CAC Ratio: This is the ultimate comparison metric. If it costs $100 to get a customer (CAC) but they only bring in $80 of value (LTV), you lose money on every new customer. While it’s not always a simple equation like this and those new customers may contribute to larger vision in the future, it’s a useful metric to measure efficiency of your customer acquisition.

• Payback Period: This is the number of months it takes for a customer to pay back the cost it took to acquire them (CAC). If your payback period is 18 months, the company is in the minus on that customer for 18 months - if you as a Product Manager can improve onboarding to lower the CAC, you shorten this window and free up cash for the business faster.

• Retention & Churn: While we often measure this in our product metrics already, it’s worth listing here as a key factor to increase Lifetime Value (LTV) of customers, which in turn will have positive impact on your LTV:CAC ratio - in other words, every dollar spent acquiring customers brings in more revenue per customer if we keep customers retained for longer.

3. Industry Specific Business Metrics

This is a bit more of a catch all list of “it depends” metrics. I found over the years that no matter how much I want to standardise the common list of metrics, every industry and company has its specific health metrics that are unique to its business model.

These are essentially operational metrics that ensure business continuity and success beyond just revenue - for example working in highly regulated industries, revenue goals can only be achieved if the company continues to offer their products in a compliant way. Or in other words - if s*** hits the fan, there is no more revenue growth no matter how good your new product feature was.

A few examples of business critical metrics I’ve come across are:

• Platform Stability: For an API-based product, platform uptime and latency are critical business metrics because the business is the platform and its availability to power other products.

• Fraud & Compliance: In FinTech and Insurance for example, fraud reduction rates are critical. A 10% increase in revenue means nothing if fraud losses increase by 20%.

• AI Accuracy: With the new wave of AI first products, customer trust is lost very quickly if the product hallucinates or generates incorrect results.

• Data Accuracy: The same is true for any data products that generate some sort of calculations, reports and insights - data accuracy is key for customer trust.

How to Start Connecting the Dots between Product and Business Metrics

If you want to grow your business acumen and become a more business savvy product leader, start by mapping your company’s key business metrics and how your product initiatives link to these business metrics. In my experience this doesn’t have to be a perfect calculation, but the pure exercise of thinking through how things connect with each other is immensely valuable.

I like to create a Metrics One Pager for this - with the business metrics at the top, and product metrics like product acquisition, activation, engagement and retention underneath. The key is to get started mapping them out, and that you can tell a story how those things are linked.

When you are clear on the business goals and you propose a new feature, instead of saying “This will improve engagement”, you can now say “By improving this specific user behaviour, we expect to increase the Lifetime Value (LTV) of this customer segment, which supports our 10% annual revenue growth goal.”

That is the language of the C-Suite, and what they mean when they ask Product Managers to be more business savvy.

Final Thoughts

Business metrics shouldn’t be someone else’s problem. They are the bridge that connects your daily product work to the growth of the company. Especially as building products becomes more democratised and companies are under more pressure to deliver financial returns, Product Managers who can speak the business language will become invaluable.

My two cents - ignore all the “Product Management is dead” chatter. Focus on delivering true value to both your customers, and your business, and you'll be a trusted leader and huge asset to any company. When you understand the operational health of the business, you earn a seat at the table. You move from being someone who ships features to someone who drives impact.

Which of these metrics does your team currently track? What have I missed? If you want to dive deeper into more financial metrics, leave a comment or reach out!

Want to master product & business metrics?

We provide online workshops and self learning resources for product teams at Fortune 500 companies and startups to help product leaders align their metrics and speak the business language.
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- one of the top resources for PMs in ‘25 by Mind The Product!
📈 Our self paced video course Master Product &Business Metrics

Get in Touch

In my experience as a product leader now running a company, commercial acumen isn't about analysing every single line on a financial statement - it's about being hyper aware of your business outcomes. It’s about understanding the patterns, the core mechanics of how your business makes money.

At its heart, it's answering a few core questions:
1. How does value get created and exchanged in your business?
What are the largest drivers of revenue in the company? If it’s a marketplace, the business may make money at the point of matching two sides successfully, for an ecommerce business the focus may be on volume and average size of transactions, or for a usage based business the value may lie in the continuous use and retention of paid subscriptions. If it’s a more complex business like a bank or large enterprise, it’s worth understanding what product lines contribute to the largest share of revenue.
2. Who is the ideal customer in your business?
Think about both who gets the most value out of your product, and who is therefore the most willing to pay for your service or product. The pricing model of the business should ideally reflect this of course and not be in conflict with it like in the old days where customers were locked into year long licences for example.
3. What are the major cost drivers in the business?
Consider the costs to acquire customers and operate the business. One of the most useful parts to understand are the different channels to acquire customers, and the cost of each channel. Some business might also be running high operational costs to support customers or develop products, for example if hardware or very specialised technology is involved. Again it’s not about a line by line spreadsheet analysis, but being clear about the key drivers in the business here.

I personally learnt a ton about the mechanics of a business by publishing my book The Insights Driven Product Manager. I first saw how different distribution channels, like Amazon versus Gumroad, had a massive impact on my final revenue. I also had to make choices like the quality of paper that in turn would impact the cost to print and the profit I make on each book sale. The next step to figure out was advertising: at one point I doubled my monthly ad spend on Amazon to promote the book, only to realise that sales didn’t double. I ended up scaling down the ads to a manageable amount again - back to where I made more money from sales than I was spending on ads.

This wasn't necessarily rocket science and certainly not about complex financial modelling - it was about understanding the fundamental mechanics of a business. While I knew a lot of these things in principle, there was nothing better than testing this even on a small scale and learning by playing with these different levers of the business. That experience taught me that every choice, from the sales channel to the pricing model, has a direct measurable impact on the health of your business, and I found those principles highly transferable to the day to day in my roles leading digital products.

From Product Manager to Business Owner Mindset

To truly contribute to a company's financial success, you have to shift your mindset outside just the product roadmap. Sometimes the business goals can be achieved by improving your features and your platform, but sometimes the answer might also lie in collaborating more closely with sales or marketing to make it happen. The point is to try think like a business owner, not just a Product Manager, to make better product decisions. This mindset has been central to every role I've held, and it helped me immensely with creating greater impact and collaborating better within companies.

One of the most powerful tools I personally use to align teams is a simple metrics one pager workshop. By getting everyone on the same page about our business goals and how we'll measure success in each contributing department, we ensure that every action, from marketing campaigns to new features, directly supports our collective business health. It’s an effective way to cut through “us versus them” thinking and ensure everyone understands their contribution to the bottom line. If you need help with this, I’ve run workshops on this with my own teams and dozens of external companies and am always happy to chat.

Practical Steps to Build Your Business Acumen

So, how can you start building business acumen as a Product Manager? The good news: you don't need a finance degree. Here is what I always recommend:

• Get Crystal Clear on Your Business: Start by asking the "dumb questions." Talk to your CEO or other leaders about what they report on. Read your company's business updates and look for the key headlines that reveal how the company is performing. The goal isn't to analyse every data point, but to get a sense of the big picture, the major trends and the strategic direction.

• Do a Side Project: This is perhaps the most practical and powerful lesson of all. Take on a project outside of work, whether it's selling a product online, running a small service, or creating a course. You'll be forced to learn about channels, costs, and profit and loss statements in a way no spreadsheet ever could. I personally found it hugely valuable as it’s a hands on way to understand how all the different parts of a business fit together.

• Lose the Mental Blocks: Avoid saying "I'm not good with numbers." You just lack confidence and practise, and that is different. Again, the goal is not to become an absolute spreadsheet wizard either. Instead, challenge yourself to use numbers in conversations and presentations: the more you say them out loud, the more comfortable you'll become. Your expertise isn't tied to your comfort with math, it’s tied to your ability to communicate business value.

Commercial Skills - The Future of Product Management?

Let’s get to the hot topic question: should a Product Manager own the financials?

I like to go back to the origins of Product Management in more traditional businesses like Procter & Gamble where the role of a Product Manager first appeared. The back then called “brand men” (where there any brand women too?) essentially were general managers of their product, owning everything from packaging to product development to distribution. The benefit was clear ownership of different product lines, and that's a model we should aspire to in my opinion.

Of course, this ownership is only possible if the organisational structure supports it. You can't be responsible for something you have no control over. But my short answer to whether Product Managers should own the financials is yes, strong product leaders should have business skills and act as the owner of the product end to end, if the company supports this structure. To me, that's the very essence of being a product leader.

But what if things go wrong? A very smart mentor once said to me: “You're not proving yourself, you're proving that the business has legs.” I think about this sentence a lot. Throughout my years of leading large product teams I’ve learnt that one of the worst things someone can say is "business outcomes are out of my remit." Acknowledging that you care about the wider impact, taking accountability for the things that could influence the business, even if the end results may vary, will always take your career further in my experience.

Want to align product & business metrics?

We run online workshops for product teams at Fortune 500 companies and startups to help product leaders align their metrics and speak the business language. Or, we offer self learning resources, including the Metrics One Pager, if that’s more your thing:
📈 #1 Amazon New Release book The Insights Driven Product Manager
📈 Our self paced video course Master Product &Busines Metrics

Get in Touch

Over the last few years, I’ve run dozens of coaching workshops with product teams across different tech industries through The Insights Driven PM. In one of our key exercises - the Metrics One Pager - we map product initiatives to key metrics like activation or retention. The idea of this exercise was initially to help product leaders articulate their current product initiatives in context of their key product metrics. 

But every time, we hit the same roadblock when filling out this one pager: product leaders develop a good idea which customer metric they were impacting, but couldn’t articulate how their work connects to actual business outcomes. Because of that, their work or role often didn’t get appreciated, or even deprioritised in favor of initiatives that had clearer “return on investment”.

I found the reasons for this can often be quite nuanced: sometimes, it’s due to a fundamental gap in knowledge of how the business is currently doing, and where it’s heading. Sometimes, it’s because the business goals have not been shared with the teams clearly. Sometimes, it’s because certain initiatives are important but really hard to exactly tie to specific numbers, and we get stuck trying to do the impossible maths. Or sometimes, it’s because the initiatives are simply not a good investment in the first place.

Whichever the challenge, with investments and budgets being more conservative and business profitability more in focus than ever, we’re finally putting the large spotlight on business acumen in product management it deserves. And as a long time advocate for business savvy product managers, I’m here for it!

Common Reasons Product Managers Lack Business Acumen

My personal take from my experiences is that the lack of business acumen in product management has come from one of these three things, or a combination of all: 

1. Organisational structure: in many companies, product leaders have shifted too far into pure execution functions, and are far removed from business metrics. Product roles have increasingly been brought in to purely run agile delivery teams, which is very far removed from the original notion of a much more generalist, commercial, strategic product leader. Product Managers often tell me that “commercials are not part of their department”, which sit mainly with finance, sales or marketing. If and whenever those financial metrics and goals are shared with them, no one wants to ask the “silly questions”.

2. Lack of confidence: especially when purely focused on execution for a long time, product leaders often feel intimidated by business jargon, believing they lack the financial or “business” skills to understand the numbers and strategy. It’s the same phenomena I’ve seen for many years why product managers shied away from using analytical tools, due to a fear of not being “an analytical data person”. 
   

3. Too much in the weeds: lastly, others can fall into the trap of over-measuring and attempt to draw direct lines between every single minor product change and its financial results, even though in reality dozens of people and different functions have contributed to any given change. In the end, they lose sight of the big picture and get trapped in endless debates over impact and prioritisation against other functions in the organisation. 

The truth is, you don’t need a finance degree or rush to do an MBA to understand business strategy as a product leader. And you certainly don’t need to waste time playing the detailed attribution game - trying to prove the calculations of how every single little feature change led to a huge increase in revenue. 

I believe what really matters is business curiosity: asking the fundamental questions, understanding the broader business context and learning how to connect the dots between your work and the company’s strategic goals. How does money come in? What are our biggest costs? What else really matters to the company? How are we currently doing, and what is our focus going forward? 

Breaking Down Business Context

At its core, every company that works for profit operates with the same fundamental principles and at any given time, has one or probably both of these goals: 

1. Make more money (grow revenue)

2. Spend less money (reduce cost) 

The important thing to understand here is: where does the business think the increased revenue will come from, and where are the biggest opportunities to reduce costs?

Usually, these two goals come with some sort of assumption or plan - for example the company may believe (or in other words: place its bets) that revenue growth will come from doubling down or expanding certain offerings or customer segments. This could be geographical expansions, focus on specific product lines or types of customers. 

The same goes for cost reduction: the company may not be happy about last year’s customer acquisition costs and places big focus on streamlining acquiring new customers, or decide to consolidate or sunset certain offerings. Both of those will drive up the overarching goal of profitability. 

Now as a product leader, you don’t need to analyze every line in the financial statement to understand this. But what you do need to understand is the big picture, the big levers that bring in money and cost money to the business. This also means having a sense of where your company currently is, what challenges it faces, where it’s headed, and then figuring out how your work contributes to its overall success.

How Product Leaders Can Quickly Unpack Business Goals

One of the best ways to start is by looking at annual company or investor reports (if the company is public), or internal quarterly or yearly company updates. These sources should give you a sentiment on where the company is and outline what the company is prioritising for the next year - whether that’s expanding into new markets, improving customer acquisition efficiency, or reducing other operational costs.

Let’s go through an example from the accounting software Xero. If you read their FY25 H1 Investor Briefing, you can draw out some fundamental themes. I personally don’t typically jump into the financial slides, but rather start with scan reading sections that mention:

• “performance summary”, “summary of results” or similar to understand the current state, and

• sections that mention “strategic themes”, “key priorities” or similar to understand the focus going forward. 

What we can draw from Xero’s example, as an absolute outsider, at a glance:

1. Revenue Growth Goal: the company is aiming to grow at a certain rate (“rule of 40”, a widely used financial metric meaning a growth rate of 40% or more)

2. Customer Segments: they aim to grow revenue by focusing on three core markets: the UK, Australia, and the US

3. Reducing Costs: one of their strategic goals is also “A Winning GTM Playbook” - a good assumption to make here is that it’s important to the business to drive customer growth in more efficient ways, or in other words to reduce costs to acquire new customers. This is in line with mentions of their growing customer acquisition costs in other sections of the report, and a general focus on this industry wide. 

Now scrolling two pages further down, we can also see that:

4. Product Lines: they aim to grow revenue by focusing on 3 key product lines - accounting, payroll, and payments

This is something we can start to work with. Even without deep financial analysis, product leaders can grasp the company’s top level goals and strategic focus in a fairly short amount of time by reading these reports, in order to connect their work accordingly.

These top level themes can be derived from reading their summaries and their strategy on a page, without having to analyse the financial statements line by line. The key part is to get started.

Now that you have that context, you can start crafting a narrative that connects your work to those business priorities. The trick is: don’t overdo it. 

Why Chasing Perfect Attribution Is a Waste of Time

One of the biggest mistakes I see product teams make when trying to build more business acumen is to try to precisely calculate how much revenue a single feature or UI change contributes. The reality is that business growth comes from multiple touchpoints - marketing, brand awareness, paid acquisition, sales conversations, customer support, onboarding, and of course the product experience itself.

If a customer signs up for your product today, it’s not just because of a new onboarding flow. It’s because they saw an ad two months ago, read a blog post last week, talked to a colleague who recommended it, and then finally made the decision to give the free trial a go, and then they ultimately made the purchasing decision. 

This means direct attribution is nearly impossible in most cases. And even if you have the data, chasing perfect attribution is often a waste of time. Instead, the smarter approach is to focus on directional impact and alignment with business goals. I recommend a read of Kyle Ponar’s article on how to unify your Go To Market metrics if you want to learn more about how to do attribution, but better. 

How To Tell A Strong Business Narrative

Now that you have the business context, how do you communicate the value of your work? To truly build your influence and be seen as a strategic, business savvy product leader, you have to learn how to connect the dots and craft a narrative.

You’ve done the hard work now of getting the broader context of what matters to your business right now. The final part is now that every time you talk to a stakeholder, whether it’s an executive, marketing leader, or sales team, you need to be able to explain how your work ties back to the company’s priorities.

For example, let’s say you’re working on improving the onboarding experience.

Instead of saying:

“We’re redesigning the onboarding flow to make it more user-friendly.”

Frame it like this:

“We’re working to increase sign up conversion by 5%. I saw our company goal for this year is to reduce customer acquisition costs and ‘win at acquisition’ - this initiative plays a key role in improving our customer experience AND making our growth more cost efficient.”

Sharing your work this way does three things:

1. Gets stakeholder buy in: Executives and decision makers care about the business impact. When they see that your work is aligned with company priorities, they’re more likely to support and invest in your initiatives.
   

2. Sharpens your business acumen: The more you engage with company reports and strategic priorities, the more fluent you become in speaking the “business language”. Over time, you’ll naturally start thinking in terms of company wide impact, rather than just feature releases.
   

3. Motivates your team: at the end of the day, we all want to have an impact in everything we do. It’s easy to feel disconnected when you’re deep in the weeds, tweaking a UI flow or optimising a backend process. But when you can articulate how even small changes contribute to larger business goals, your team will feel more engaged and motivated.

How Business Acumen Will Boost Your Product Career

Beyond helping your company, learning to connect your work to business goals and “speak the business language” is one of the most powerful things you can do for your own career growth. Too often I see product managers acting solely on user feedback, without considering the business context. With this approach, you’ll be seen as a more strategic thinker, who thinks on business not just execution level. As Marty Cagan likes to say - our job as product leaders is to “solve hard problems in ways our customers love, yet work for our business”.

The more you develop this skill, the more you’ll be seen as a strategic leader - someone who not only understands product but also understands how product drives business success. Some of the best Product Managers I’ve hired into my teams over the years were the ones who had a side hustle or ran a business before, no matter how big or small. They were not necessarily finance or commercial experts, but they learnt which questions to ask about the business, and consider the broader context at all times.

Final Thoughts: How To Build Your Business Acumen in Practice 

The next time you’re in a meeting with stakeholders trying to get buy in for a new product initiative or share the impact of your work - try this exercise:

1. Before presenting your work, take a step back. What is the company focused on this year? What are the strategic goals? I personally always find it helpful to do a refresh by re-scanning the annual reports before those meetings.
   

2. Frame your initiative in that context. Think of creating a simple narrative - instead of just describing what you’re building, explain why it matters in the bigger picture.
   

3. Tie it back to impact. Remember it doesn’t have to be a complicated calculation of trying to attribute a change to specific numbers (in fact we established that can be a rabbit hole to go down) - we're looking for a clear logic and narrative that connects your work to business outcomes. The simpler the better.
   

So, next time you roll out a feature update, don’t just talk about what’s changing - connect the dots, tell a story why it matters. If you make this a habit, you’ll find that not only will stakeholders better understand your work, but your influence and credibility as a product leader will grow exponentially. Pick one initiative you're working on right now: how would you explain its business impact in 2–3 sentences?

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We run online workshops for product teams at Fortune 500 companies and startups to help product leaders align their metrics and speak the business language. Or, we offer self learning resources, including the Metrics One Pager, if that’s more your thing:
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In this video lesson I walk through a real world example of how we identified meaningful early signals in customer behaviour that helped increase customer retention, how this insight transformed our product strategy and how you can find your leading metrics specific to your business.

🎓 Explore the full course here and use code earlysignals20 for an exclusive reader discount.

Spotting The Signals Before It’s Too Late

Most product teams spend a lot of time measuring what has already happened: monthly active users, last quarter’s revenue, customer churn from two months ago. But if we want to course correct and influence outcomes before it’s too late, we need to start measuring the right metrics early.

That’s what this video is about: the difference between lagging and leading metrics - and how spotting the right early signals can drive a more proactive product strategy and drive greater future business impact.

Being data driven isn't just about tracking outcomes. It's about giving us insight to drive our decisions - ideally before it’s too late.

One of the biggest pitfalls I see is teams getting stuck in (over)analysing lagging metrics. Lagging metrics such as your past revenue or churn are still useful for understanding actual performance, but they’re not designed to help you course correct mid flight.

Leading metrics are forward looking. They help you see what’s likely to happen based on current customer behaviours. They let you catch problems early, double down on what’s working, and adjust when things aren’t. Using leading metrics effectively in your day to day is one of the most critical concepts in analytics to master if you're serious about driving long term impact as a product leader.

What you’ll learn in this video

In this 7 minute video module, I’ll walk you through:

• The key distinction between lagging and leading metrics

• Practical guidance for finding meaningful early signals in your own product

• A real world example where identifying a true leading indicator helped us increase customer retention and shape our product strategy

Whether you’re launching a new feature or refining onboarding, these insights will help you shift from reactive tracking to more proactive, data driven decision making. If you’re ready to stop guessing and start spotting what really drives your product forward - this is a great place to start.

What early signals you have found in your product? Share your experiences in the comments and help inspire other product leaders on their journey! 💜📈

From the course: Master Product Metrics

🎓 This video is a preview of my course Master Product Metrics: Leveraging Data, Metrics & AI to Drive Customer & Business Impact.

This isn’t just theory - it’s a concise, practical course designed for product leaders who want to make smarter decisions with data. The course is an extension of my #1 Amazon New Release The Insights Driven Product Manager, diving deeper into tools and how to put your metrics strategy into practise.

You’ll learn how to:

• Build confidence in defining and using the right metrics

• Align your product metrics to business outcomes

• Use analytics and experimentation tools to find signals that matter

• Build a data driven culture in your organisation

  
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Master Product Metrics Course

One of the biggest pitfalls I’ve seen is that teams often tend to track mostly lagging metrics. Lagging metrics are useful for understanding past performance, but the problem is that by the time lagging metrics show results, it’s often too late to take meaningful action. Or in other words, you never want to get to the end of the year and realise you didn’t hit your numbers, but now it’s too late to course correct.

Especially in today’s highly competitive markets, it’s critical for product and business leaders to measure frequently, and move fast to make adjustments if something doesn’t work to drive long term customer and business results. Understanding the difference between lagging and leading metrics can make or break your ability to drive impact effectively as a product leader.

In this article, we will go through the key differences between these two types of metrics and go through examples and strategies when to use which.

Lagging Metrics: Measuring Past Performance

Lagging metrics are retrospective - they measure past events. Because they’re based on historical data, they tend to be mostly accurate and reliable in comparison to leading metrics. More on this in a second. For example:

- Revenue last month: This metric tells you exactly how much money your business brought in

- Monthly active users (MAU): This shows how many users engaged with your product in a given month

Lagging metrics are great for reporting what actually happened. They give you a clear picture of what worked and what didn’t, helping you understand outcomes and create comprehensive reporting for the business.

Leading Metrics: Early Signals Of Future Outcomes

Leading metrics, on the other hand, are forward looking. They often start as hypotheses - you believe certain actions or customer behaviours will lead to certain outcomes. Over time, you gather data to confirm or refine your assumptions. This is why they tend to be less accurate when first setting them. Examples of leading metrics include:

- Customer behaviour signals: Specific actions that your users take in your application that tend to correlate with long term engagement retention, such as frequent use of key features

- Buyer intent signals: Actions potential buyers of your solution take that indicate interest or even intent to purchase such as watching product demos or interactions with your sales and support team

Even once you gathered historical data to confirm your assumptions, you can never be entirely certain that customers showing interest in your solution will actually lead to a final purchase of course. But, leading metrics can act as powerful early indicators for future outcomes and enable us to course correct if we don't see the early signs we were expecting.

How To Combine Leading & Lagging Metrics

While many teams fall into the pitfall of focussing only on lagging metrics, I want to stress that neither leading nor lagging metrics are inherently better or worse. They simply serve different purposes. Lagging metrics are incredibly useful to reflect on performance, but to stay competitive and leave room to course correct it’s crucial to also layer in leading indicators when defining your key metrics.

Typically, I find that the best starting point for finding leading indicators is customer behaviour, especially in the early stages of engaging with your product such as pre-purchase or onboarding actions. Business results such as acquisition costs or revenue made per customer are typically the results of those early signals, and with that lagging metrics.

Example: Activation Metric As Leading Indicator

One of the most impactful leading metrics is your activation metric. Activation metrics highlight the "aha moment" when a customer first experiences the value of your product. This aha moment can happen during the first time sign up process, or also during the first few days or week of using the product.

For example, while working on a insights dashboard tool for small business owners, we once discovered that customers who added two of their existing systems within their first week of onboarding were 20% more likely to retain over three months. This made sense as our key value proposition was to show business insights in one single view, and by adding two integrations users could see the value of the product early on.

By focusing on this activation metric, we were now able to focus on:

1. Improving onboarding flows to encourage app integrations

2. Use in app messaging and email nudges to guide users toward this key behaviour

Depending on your particular product, the activation metric could take many shapes and forms. For example, a Customer Relationship Management (CRM) product may encourage you to setup your first customer entry within the first week, or an entertainment product may aim for a certain time engaging with content after sign up. If you want to read more on this, I shared further examples on my previous guest article on the Mixpanel Blog.

What If We Don’t Have Any Historical Data Yet?

This is a very common question I get, especially in the early stages of a feature or product. I always encourage product leaders to go back to the value proposition and start with an assumption, even if you don’t have historical data to identify your activation metric yet. Here are a few quick actions you can take to get started:

- Brainstorming with your team: Reflect on your key value proposition and think about behaviours that might correlate with retention

- Conducting customer interviews: Ask users about the moments they felt your product solved their problem

- Using prototypes: Observe user interactions with interactive designs to identify moments of delight - this is hard to quantify, but look for the moment your customers eyes light up when they “get the product”.

Once you define your leading metrics and start collecting data, make sure you do use historical data over time to confirm whether those assumptions were the right ones to make in the first place in order to refine your early signals.

Once You Do Have Historical Data

If you have been collecting customer data on your product for a while, you can go back through historical data to find correlations for example between your retention and early customer behaviour signals. This can be done with the help of your data team, or you can use modern analytics tools such as Mixpanel or Amplitude to visualise correlations between leading and lagging metrics.

In this case, I recommend to start by identifying your most engaged and loyal customers, for example a group of customers with high retention. As a next step, you can explore through the data which early actions those customers have in common. In a tool like Mixpanel, this may look something like this:

A Note on Correlation vs Causation

Just because we see that customers who do x also do y, that does not automatically mean that one action also causes the other. Make sure you consider causation vs. correlation: do the leading indicators you set genuinely lead to the desired outcomes, or are other factors influencing the results?

One of the most common examples I see is seasonality or other factors like promotions that drastically increase your sales in a given period, which could heavily skew you towards looking at a certain customer cohort. I usually recommend to analyse data of longer time periods to identify broader trends, or if this is not available be highly aware of other external factors and use your best judgement.

In Summary

When you define your key metrics, it’s essential to maintain a healthy mix of both leading and lagging indicators. Lagging metrics measure past events and are great for final reporting, while leading metrics provide early signals, helping you course correct before it’s too late.

By tracking the relationships between leading and lagging metrics, you can prioritise metrics that drive long term growth and ensure you are measuring early signals in order to course correct and stay competitive.

Customer behavior metrics, such as activation and feature usage often serve as strong leading indicators for lagging metrics like revenue or churn rates. I’m personally a huge fan especially of tracking activation metrics.

Take a moment to reflect on your product’s value proposition. Have you identified the “aha moment” that shows a user is likely to stick around? How does this relate to long-term retention? Start brainstorming or gathering data to identify early signals for your activation metric. Remember, it doesn’t need to be perfect right away - start with a hypothesis and refine it over time.

Want to get more confident with data?

Every product role requires confidence with data—I’m here to help make metrics more approachable and actionable. My book The Insights Driven Product Manager—a #1 Amazon New Release— shares real-life examples, frameworks, and templates to unlock true data insights. 📈 NEW: Check out my course, Master Product Metrics: Leveraging Data, Metrics, and AI to Drive Customer & Business Impact.

Master Product Metrics

*Some links in this blog may be affiliate links, meaning I could earn a commission if you sign up or make a purchase through them. I always like to focus on teaching the principles and thinking first and foremost, but I know that having examples of recommended tools helps immensely to put things into practise. I only recommend tools and services I’ve personally used and found valuable. If you do decide to use them please use my unique referral links, as they help me keep track of demand and continue creating content for you. Thank you!

I was once designing an application for a large construction and manufacturing company to help factory workers track the efficiency and quality of their production line. The business goals were pretty clear: we need more data about the production line to make more informed decisions about what to improve. Do we need more staff? Do we need to refine our processes? Do we need to upgrade or replace our machinery?

All of these decisions were currently based on ad hoc feedback coming from the supervisors or team leaders from the factory shop floor. The data-driven German in me was stoked - more data, more measurement, awesome! It sounded like a no-brainer to me.

The vision was to invest in automation, for example by using more sensors in the machinery in the long term, but first we wanted to test some concepts through a tablet application for the production line workers. Through this application, the shop floor staff would be able to manually enter the output of a certain time period, machinery outages or issues, or raise other quality related feedback. A dashboard for team leaders and business stakeholders would then consolidate these inputs into efficiency and quality scores for reporting.

When I went into the factory to test an early version of the prototype with the shop floor staff however, we didn’t get quite the reaction we were hoping for. While I could clearly see benefits for them as well, for example they could now raise frustrating issues and get them resolved much faster than before, I could sense a general hesitation when I asked them for feedback.

One of the staff finally asked: will this be used to track our breaks or time spent on the machinery? Will our team leaders use this to compare our outputs against each other? What happens to our jobs if our numbers are not good?

The actual design of the application was not the issue. We really had to go back to the drawing board and reconsider the impact on the staff, their managers and how it would affect performance measurement and escalation processes across the company.

The "danger" of sharing your metrics

I have since come across similar patterns when working with product teams that try to become more data driven. Instead of producing physical items, their "production line" of course ships product features or updates. On one hand, more data about how those features are performing would mean they could improve their features and be more effective in their jobs. On the other hand, product teams often fear that the data could be used against them if the numbers don’t look good.

Similar to our ambitions for the shop floor tracking application, the initial intention was to learn. But instead of Build-Measure-Learn, many companies tend to replace the word learn with manage. Metrics are labelled success metrics that come with a certain weight and expectation: if your new feature or product is not performing well, you haven’t succeeded and you’ll be in trouble.

Past experiences have made teams wary of how this data will be used internally, so it’s often easier for them to share as little data as possible. If only a very small percentage of users adopt your new shiny feature, it’s easier to not have this metric showing in your company wide dashboards in the first place. The result is that teams rather keep those metrics to themselves. For important presentations, teams then carefully curate certain metrics to minimise any uncomfortable questioning.

The comments on a post from tech entrepreneur Dan McGaw stating “You can’t manage what you can’t measure” showed just how burnt some product people are from their past experiences: people were very quick to point out to be careful what to track as it could be used against you.

Dan’s statement in itself is not wrong, as ultimately the point of being more data driven is to know whether you’re doing well (or not) and to use this insight to inform future decisions. The problem lies in how companies typically treat their teams when they share those metrics and learnings.

Working with high levels of uncertainty

Building products is hard, and no matter how much research and testing you do upfront there is still a high level of uncertainty about whether something will work once you launch. See Marty Cagan’s inconvenient truths about building products (at least half of our ideas are not going to work) or this graph from Strategyzer that highlights the uncertainty product teams tend to work with, especially in the early stages:

The reason for this is that there are a large number of both internal and external factors that can have a huge impact on your success from when you first came up with an idea to when you actually launch: execution speed and quality, competitor moves, industry or regulatory changes, broader market and economic shifts to name a few.

Using metrics to learn, not to manage

My point is that even if you do all your research and test as much as you can upfront, we have to acknowledge that product teams in technology companies work in very fast paced and rather uncontrollable environments. Using metrics to punish teams when launches are not successful ultimately leads to teams becoming more risk averse, experimenting less and sharing their metrics less often which is usually the opposite of what those companies are trying to achieve.

The difference between those teams and the most data driven teams I’ve worked with is how much the company and stakeholders emphasise the importance of learning. Yes, you can’t manage what you can’t measure. And yes those insights will tell you whether a team, feature or product is doing well, or not. However, assuming the teams have followed the right process with early validation and research, companies need to provide a safe environment where learning is encouraged if they want to build a data driven culture.

In truly data driven teams that do this well, I’ve often observed the following habits or mindsets:

1️⃣ A Build-Measure-Learn instead of a Build-Measure-Manage mindset

2️⃣ Curiosity: asking “Can you help us understand?” instead of “Why didn’t this work?”

3️⃣ Showing appreciation for teams for sharing their metrics openly, good or bad

4️⃣ Reminding the teams in the day to day that the focus is on learning and improving

5️⃣ Viewing data as a way to capitalise and learn from failures

To expand further on the last point, I always love to share this quote from my interview with data leader Crystal Widjaja that I believe sums it up perfectly:

“When people make mistakes (failed experiment, failed deployment, etc), you've already paid that cost. We should think of data as a way to capitalize on mistakes and learn from them. Rather than "paying the tuition of the failure" and firing the individual, use the data insights to tell us WHY it was a failure, learn from it, and leverage it for the next iteration to be 10x better than the first.”

— Crystal Widjaja

Final 2 Cents

If we don’t openly share our metrics and data across the company, we are missing out on the many benefits of gathering insights to help us make better decisions and build more impactful products.

To build a truly data driven culture, we need to make sure teams feel safe to openly share their metrics and findings with us. We need to be aware of the high levels of uncertainty that product teams have to work with, assuming of course they have already done what they can to at least reduce the risk through research and early validation.

Data is ultimately not only about measuring how our production line or new feature is performing. Product metrics can help us capitalise on mistakes, increase our learnings and with that build a more data driven culture across the company - but only if we provide the right environment that encourages teams to share their metrics in the first place.

Want to get more confident with data?

Every product role requires confidence with data—I’m here to help make metrics more approachable and actionable. My book The Insights Driven Product Manager—a #1 Amazon New Release— shares real-life examples, frameworks, and templates to unlock true data insights. 📈 NEW: Check out my course, Master Product Metrics: Leveraging Data, Metrics, and AI to Drive Customer & Business Impact.

Master Product Metrics

A common theme I’ve seen in FinTech products for example is a drop-off when the application asks for ID verification or access to previous bank account data during onboarding. This is typically done to reduce fraud risk and assess the customer’s eligibility to access certain services. The easy answer to why users may be dropping off here would be that these steps simply take additional effort - the user might not have their ID or the password to link their bank account handy.

When actually talking to customers about their experience, product teams often notice there is a lot more behind those drop-offs: the customer might wonder why this information is needed, what this data will be used for, and if they can even trust the application to handle this data securely?

Not addressing those customer fears in the onboarding process would be a big missed opportunity to improve the experience and increase the onboarding conversion. Even though I love looking at clearly presented numbers, I’ve learnt that quantitative data is not a silver bullet on its own.

In this article I want to highlight the key differences between “quant” and “qual” and provide some concrete examples of when to use which.

The difference between quant and qual

In its definition, quantitative data is a numeric quantity like an amount or measurement. It’s clearly structured data that tells you the hard numbers on what is happening. Structured data means the data follows the same format and can be easily searched, aggregated or manipulated. Qualitative data on the other hand needs interpretation to be aggregated.

A practical example of quantitative data is when you look at the customer onboarding funnel of your product as in the FinTech example I just mentioned. You will typically have a funnel report in your dashboard that shows you exactly how many users perform each step of the onboarding journey and where your largest drop-off points are. It might look something like this:

Qualitative data on the other hand describes attributes or characteristics. It can tell you why something is happening. Qualitative data is non-numerical and unstructured, such as written or verbal language. I couldn’t just put it into a spreadsheet and easily turn it into a graph, I would need to go through each sentence to then aggregate a report on the overall sentiment. While this can be automated to a certain extent for example by using Natural Language Processing, it still needs some sort of interpretation.

While the onboarding funnel report above tells you that there are big drop-offs - you might want to look into the set password step in this case - you will most likely need qualitative data to tell you why customers are dropping off at this point.

I’m saying most likely because sometimes your dashboards might show you some pretty obvious factors that could lead to drop-offs such as the app always crashing at a certain point in the onboarding. But in most cases, it won’t be as obvious and this is where we’ll need to get actual feedback from our users to understand where the friction lies.

There are many easy and effective ways to get qualitative feedback. Many teams reach out to customers who have dropped off to arrange an interview or set up automated emails, in-app messages or survey links to ask for feedback using engagement tools like Intercom or Userpilot. You can also analyse customer reviews that mention frustrations with particular parts of the product. When analysing the onboarding performance, you can now add this qualitative feedback to your numbers which will make this funnel graph a lot more insightful.

In the earlier stages of building a product, for example if you’re testing the product market fit of a new B2B offering, joining sales calls can be another effective way to gather qualitative data. You can hear first hand from customers which parts of your value proposition resonate with them, or which hesitations they may have before choosing your product.

How to determine when to use which

When building products, you will always need both qualitative and quantitative data. Depending on the lifecycle stage of your product or product initiative, you might start with one over the other, but you will always need both to get true insights from your data.

For example, in the early research of a new product or potential new feature, product teams need to establish a deep understanding of the customer pain points and specific needs. Conducting customer interviews (i.e. gathering qualitative data) will typically be the best starting point to really dive into the problem space and uncover opportunities for the product.

Once you’ve identified a clear problem to solve and you’re trying to test and narrow down different solutions that will work best for your target customer, you will want to add in quantitative data as well. Products like Netflix do this all the time, for example by A/B testing different variations of a new feature with a subset of their users before rolling out the final solution to their entire user base.

Here are some common scenarios and questions you can ask yourself when trying to decide which type of data to use:

• Are you trying to go wide? Do you want to explore a problem or opportunities where you need human insights? Start with qualitative data for example through customer interviews or joining sales calls.

• Are you trying to narrow down? You understand the problem and opportunity but need to find the best solution. Quantitative data will help you with this, for example you could test different value propositions with landing pages or A/B test different versions of a feature with a subset of your customer base.

• Are you trying to understand what is happening? Do you want to find out what customers do in your product and whether you’re doing well or not? Quantitative data will give you an unbiased view e.g. by measuring your key metrics in your analytics dashboard.

• Are you trying to understand why something is happening? Gather qualitative data to dive deeper into problems or opportunities you’ve found in your dashboards, for example by sending surveys or conducting interviews with customers who have stopped using your product.

You can also use this handy overview to remind yourself of the key differences between quantitative and qualitative data:

Final Thoughts

The key takeaway is that if you want to get more insights from your data, you will need to combine both quantitative and qualitative data effectively. Just focussing on the “hard numbers” alone will likely lead to you missing big opportunities to improve the customer experience and impact on the product and business success.

To get started, you can use the questions and the cheatsheet above to determine when to use which type of data. You’ll find that with more practise it will become second nature and you will be able to intuitively decide when to apply which.

Want to get more confident with data?

Every product role requires confidence with data—I’m here to help make metrics more approachable and actionable. My book The Insights Driven Product Manager—a #1 Amazon New Release— shares real-life examples, frameworks, and templates to unlock true data insights. 📈 NEW: Check out my course, Master Product Metrics: Leveraging Data, Metrics, and AI to Drive Customer & Business Impact.

Master Product Metrics

At its core, being product-led means focusing on providing true value through your product. 

In contrast to more marketing- or sales-led models, product-led companies focus on acquiring and retaining customers through their product experience and unique value proposition. This does not reduce the importance of marketing or sales in any way though - it’s more a change in mindset and focus, as product-led companies center all of their activities around the product’s value.

The benefits of becoming product-led

It’s useful to understand how the industry of technology products has evolved over the years: previously, companies would launch products through a heavily sales- or marketing-led model by investing in their acquisition channels and offering licenses to use their software. Because the space was still evolving, companies that were first movers in, for example, offering customer relationship management (CRM) or design software were able to get customers on one-year or even longer licensing agreements.

Today, however, we have an overwhelming choice of products to choose from. To stay competitive, product companies need to truly differentiate their product offering. They need to solve a unique problem, focus on providing more value than their competitors, and change their pricing models to offer more flexible, customer-centric options. One of the most prominent examples of this transformation is Adobe, which shifted its heavily sales-led model to offering flexible monthly subscriptions (and free versions to try) and greatly transformed its business and revenue growth.

How to become product-led

It’s not just the pricing model though, the shift to becoming product-led can generally be seen in many areas of the business:

• Products offer free trials so customers can test the product before they buy

• Product onboarding journeys are being optimised to reduce the “time to value”, meaning customers can experience the value of the product very early on

• Product companies spend a lot of research and effort on truly differentiating from their competitor by finding new customer problems and value to provide

• Companies target more niche markets and their unique problems to provide even more value to those specific customers

• Marketing and sales teams craft tailored messaging centered around this unique value proposition

• Pricing models are optimised around the customer experience and provide more flexibility, like by offering subscription models

The common thread in all of those is the word “value.” If the customer doesn’t feel their needs are met and the product doesn’t provide value, customers churn and move on to the next product.

Because of this, the first step for companies to become product-led is to find out what customers value the most in their product. What are the biggest customer problems the product solves? Which features and functionality could your customers not live without and drive true long-term engagement?

Armed with this knowledge, companies can optimise their product offering, onboarding experience, marketing and sales messaging, pricing models, and trial options to ensure customers can experience the product’s unique value proposition in all parts of the journey.

The role of product analytics in a product-led strategy

This shift to becoming more product-led opens up completely new challenges for businesses on how to operate and measure their product and business success.

In the past, companies would analyse their sales and marketing performance through their CRMs, marketing tools, and a range of business intelligence tools that can plug into those. Once a customer converted from a lead to a paying customer, revenue was fairly predictable with long-term licensing agreements in place.

And while measuring the effectiveness of sales and marketing channels is still absolutely crucial today, the shift to becoming more product-led has introduced a new question: How do we measure “value”?

This is where the need for product analytics comes in. Depending on the value proposition of the product, product teams need to define their specific key metrics and customer behaviours that indicate whether their customers are using the core functionalities and with that the core value of the product. Tools like Mixpanel are then able to visualise those product metrics through their powerful analytics dashboards.

While not an exclusive list and the questions always need to be tailored to the specific product and value proposition at hand, here are some ideas of useful questions to ask to measure value:

• What is the key goal or outcome we want our users to achieve, and what customer behaviours can be tracked to measure this?

• What is the percentage of users who onboard and use our key feature in the first days of onboarding? (Customer activation)

• How useful the product is: How often do customers engage with the product in a meaningful way? (Customer engagement, with the measure of engagement being a key action rather than just logging in, for example)

• How sticky the product is: Do customers continue to use the product for long periods of time because it solves a very specific problem very well for them? (Customer retention)

The shift to becoming product-led is all about value. It’s not disregarding the importance of sales, marketing, or the financial or business viability when building products. It means that the entire organisation works toward providing and communicating the best value possible to their customers to stay competitive in today’s market where customers have so many products to choose from.

With effective product analytics tools and practices, businesses are able to measure customer behaviour and this mythical “value”, which will ultimately offer crucial insights on whether the business is performing well or where to course-correct to achieve the overall business goals.

— Originally published on Mixpanel’s The Signal blog.

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Master Product Metrics

I have learnt that focus is the first fundamental step to getting more insights from our data. While it doesn’t automatically translate to getting more insights from your data, it does create more time and space to spend on activities that will then make you more insights driven.

Instead of maintaining endless events lists and dashboard reports, tracking less allows us to dive deeper into the core metrics that actually matter and spend more time on learning about our customers’ behaviour. The ultimate goal to become more insights driven is to then use those learnings to drive future product and business decisions.

There is huge difference between teams that just go through the motions of tracking metrics, versus the ones that actually learn and create insights from their data. Let’s get into the activities that you should spend more time on.

Key activities to becoming more insights driven

0️⃣ Track less and create focus: this is the fundamental step I recommend to tackle first - it’s amazing how less cluttered dashboards can help us uncover essential problems or opportunities. Most teams I have worked with have defined their key metrics around four core areas: their customer acquisition, activation, engagement (e.g. your monthly or daily active users) and your long term retention / churn. You can read more about those standard key metrics here.

1️⃣ Improving the quality and actionability of your metrics: avoid the use of vanity metrics if you want to actually learn from your metrics, and make them easy to understand and actionable by using more comparative metrics. For example, a graph showing you how something compares to other time periods or how your sign ups compare to your acquisition spend is always more useful than a single total number in isolation. I’ve written about this in more detail in one of my previous articles if you want to learn more.

2️⃣ Digging deeper into the why: layer in qualitative data effectively and combine it with your quantitative data to not only understand what is happening, but also why something is happening. If you see a big drop in your week 4 retention, try to gather more feedback by sending a quick survey or personal email follow ups to those customers to find out why they stopped using your product.

3️⃣ Setting intentional goals for learning and experimentation: rather than tracking random experiments and hope for the insights to come to you later, we need to set a clear intention and objectives of what we’re trying to learn, before we start experimenting. Define the biggest assumptions or decisions you need to make, plan out how you will test and measure those experiments, and most importantly how different results will impact your ultimate decision making.  

4️⃣ Connecting the findings and creating data narratives to share with the organisation: just having good data is still not enough, we also need to make sure we connect the pieces of information in a meaningful way and form a strong narrative of what we have learnt. This is as much of a self reflection exercise to potentially uncover any gaps in our thinking, as well as a powerful tool for sharing insights with stakeholders and team members. A good data narrative should include what you set out to learn in the first place, how you tested, what the findings were, and how this may impact the product roadmap.

Especially the final point of sharing those insights effectively is often forgotten about. I want to highlight that this is a key step for teams to get stakeholder and team buy-in so the organisation can start to really include data insights more into their ways of working.

To sum it all up, the core idea behind this approach is that you go back to the essentials first and define the metrics you should really care about. You can then double down on those few key metrics and spend more time on making them more actionable, extracting and sharing more learnings to then drive product decision making that is based on real customer data insights. 😃✌🏼

Want to get more confident with data?

Every product role requires confidence with data—I’m here to help make metrics more approachable and actionable. My book The Insights Driven Product Manager—a #1 Amazon New Release—shares real-life examples, frameworks, and templates to unlock true data insights. 📈 NEW: Check out my course, Master Product Metrics: Leveraging Data, Metrics, and AI to Drive Customer & Business Impact.

Master Product Metrics

Today I want to cover two aspects of having good data practises that are a lot less talked about, but in my experience just as powerful.

Metrics and Strategy

The reason I personally love the data and analytics space so much is not because I like to geek out over conversion rates or funnel drop offs (well, maybe a little bit). I've realised that talking about data always brings the conversation back to the wider question of what we're tracking and - most importantly - why we’re actually tracking those things.

I often speak with teams that actually do a decent amount of tracking and reporting. However, when I ask them to tell me about their most important metric, they typically name about ten metrics that are all "equally important".

While data doesn't magically create focus and alignment, it does reflect your strategy. In Michael Porter's words, strategy is about making choices and deliberate trade-offs. If every metric is priority number one, it means there are too many competing objectives that the company is trying to achieve at the same time.

Good data practises allow us to uncover gaps in our strategy and create more focus by defining our key objectives and most important metrics.

Business and Product Metrics

Talking about metrics is also a great way to find out whether teams have a holistic view of how their work contributes to the wider business. Especially in larger organisations, product teams often get very disconnected from the business goals.

This hugely reflects in the language teams use. For example business stakeholders would track financial and operational metrics, while the product teams focus more on customer usage patterns in the product.

Unfortunately in most cases, neither side has a good understanding of how those metrics impact each other.

I can recount countless examples, but I'll share one of the more recent conversations I've had to illustrate this problem.

I was part of an analytics panel discussion with a Product Manager of a large very well known technology company whose entire team was solely focussed on improving customer activation. Their activation metric has remained pretty much the same over the years: customers should perform a certain setup action within the first x days of signing up.

But when querying why this activation metric has been chosen in the first place, the Product Manager quickly deferred that "the commercials are not the team's responsibility", and that they're "only focussing on the customer".

I find this example so powerful as it not only highlights the problem of teams not understanding the full business context, but also uncovers a subtle bias against using financial metrics for product teams.

I believe this is a bit of an unwanted side affect of the (very necessary) shift in product teams to "get out of the building" and become more customer focussed. While I don't ever want to move away from these practises again, it did set a hidden message for many teams that spending time with business stakeholders is a bad thing.

While we always need to start with solving a real customer problem (or there won't be a business at all), I strongly believe we should not forget to also spend some time inside the building to understand the mechanics and the goals of the business itself. It goes both ways: if the products we build don’t work for the business, we can’t continue to solve those customer problems.

Ask yourself: how do certain customer actions in our product impact business outcomes? Looking at our most engaged and highest paying customers, what behaviour patterns do they have in common and how can we find more of them?

Good data practises allow us to bridge the gap between the business and the product and align around common goals.

Final Thoughts

Imagine the Product Manager from the panel discussion would get across the financial metrics and find new surprising links between customer activation and long term revenue. The Product Manager might even uncover completely new customer segments and activation metrics that the business can now go after.

Data helps us speak the same language across the organisation and break down silos. Use metrics to show the impact you’re creating on the customer, but also on the business.

Data also allows us to get a good understanding of a company’s strategy and key objectives. If there is no clear answer, use this discussion as a trigger to revisit the wider strategy. With a better defined strategy you can now align on the most important metrics to create true focus for the company.

Want to get more confident with data?

Every product role requires confidence with data—I’m here to help make metrics more approachable and actionable. My book The Insights Driven Product Manager—a #1 Amazon New Release—shares real-life examples, frameworks, and templates to unlock true data insights. 📈 NEW: Check out my course, Master Product Metrics: Leveraging Data, Metrics, and AI to Drive Customer & Business Impact.

Master Product Metrics

If you want to read more articles on the topic of data and analytics, here are some more links you may find useful:
The big gap between product and analytics
Product Managers should drive analytics
Why we need to stop tracking vanity metrics

In my last post I wrote about building a wide range of skills in your PM team and the importance of having a solid foundation that allows you to scale. However your job is not done here yet.

As my Product Management team grew, new challenges arose: 

⏳ decisions started to become slower

🗒 consistency in processes across the various teams started to slip

⏰ I didn’t seem to have enough hours in the day to spend enough time with every report

Looking back a lot of my learnings on this journey came from trial and error. I would hold on to certain activities for a little too long, then adjust. Or hand over responsibilities without spending enough time coaching, then adjust again. The following advise hopefully prepares other PM leaders or also PMs to be more mindful of the necessary changes when the team is growing.

Set up your team for decision making

When you’re a small team, the Head of / Director / VP of Product and executive stakeholders are often involved in every big meeting or product decision. As I have experienced and other examples from Dropbox and Intercom echo, getting everyone’s sign off on every product decision does not scale. So how can you avoid lengthy review processes that become a bottleneck for all teams? 

I know you’re here to read about scaling PM teams and not about organisational redesigns, but it’s important to understand that often the root causes of slow decision making lies in how product teams are organised in the first place.

I regularly talk to a lot of PMs in different countries and industries and I almost always hear that they run “cross functional product teams”. But when I dig further, the company either still thinks and works in departments, has shared part time resources floating between the teams with little ownership, or have massive dependencies on some specialised teams like the core platform team.

The key is to organise product teams so they have all the skills and ownership over a specific product or product area. Whether product areas are organised by customer problem or functional area or something else does not matter for this argument - the key is that one product team should not have to largely rely on another product team to (hopefully) re-align their team objectives to get anything released to the customer.

Having a cross functional team isn’t enough yet. You might have all the different roles, but the team might still be operating as a feature team. For true scale and successful product outcomes, those product teams also need to be empowered to tackle problems and make product decisions.

As Marty Cagan has shared in his article on product vs feature teams, both teams can look very similar on the surface, but can dramatically differ in how they operate. A feature team’s main job is to deliver, while a true empowered product team has the ability to solve problems and be accountable for the outcomes.

Marty’s latest book Empowered is a fantastic read if you want to learn more about team topology and of course, empowerment of teams. John Cutler also has some great articles on the common signs that you might be working in a feature factory.

Why is this all important for scale?

If decision making about what needs to be built is being kept on leadership level, bottlenecks will arise very quickly as your team of Product Managers grows. It’s not only about efficiency - it also truly reduces the sense of ownership and accountability in your Product Managers and the product teams.

Beyond the team structure to enable scale and ownership, you as the Product Management team lead (whatever your title) is to provide the right tools, context and principles to make the right decisions without always requiring your input.

As a VP of Product I often feel like a broken record talking abut the importance to leverage our company frameworks and tools for designing and delivering products. The point of that is not to follow processes for the sake of following processes, but much more that it enables better decision making aligned with the core principles of how we work in the company. Assuming the company’s frameworks follow modern product practises of course.

This also leads to more trust from stakeholders in every product decision. It flips the conversation from “policing” every decision that is being made across the company (and trying to hold onto this process as more PMs join the company) to: have I as a PM manager enabled them with the right frameworks, ownership and knowledge to make good decisions in the day to day?

Final 2 Cents

Equip your PMs with the right skills and frameworks for better efficiency but also true ownership. It is certainly not the easiest, but the only truly sustainable and effective way to grow the Product Management and product teams.

I still go deep into some products or product areas sometimes when I’m being asked for assistance of course. This should always be done from a coaching lens by the way, not to dictate solutions. But it’s important to realise where to best spend your time as a PM manager, and that you can’t stay across all details as you grow. 

Letting go of the details has been hard for me, and coaching is even harder. There is also still a lot I am pondering about like how exactly to scale the coaching part of my role or team structures as we grow even further.

I always love to hear from other product leaders on their learnings and current challenges, so please reach out via LinkedIn or Twitter or leave a comment to share your wisdom! 🙏

Leave a comment

Is data a gap in your product team?

Every product role requires confidence with data—I’m here to help make metrics more approachable and actionable. My book The Insights Driven Product Manager—a #1 Amazon New Release— shares real-life examples, frameworks, and templates to unlock true data insights. 📈 NEW: Check out my course, Master Product Metrics:Leveraging Data, Metrics, and AI to Drive Customer & Business Impact.

Master Product Metrics

If you want to read more on building PM teams, check out my previous two articles on this topic:

When should you get your first PM?
How to strategically hire to grow your PM team

Now, it is easy to “just add more Product Managers”. Going through this exact journey several years ago, I have found it to be much harder to grow your Product Management practise with real purpose and intention. If done right, you’re setting yourself up for establishing a highly effective and scalable team of Product Managers that will have tremendous impact on your organisation and product success.

But be warned, it’s not an easy task to find the right timing of when to grow the team, what skills to look for, and how to set the right foundations at the start to allow for strong future growth.

In the first article of this series I talked about the importance of Product Management and the risk of waiting too long to hire your first PM. The same applies for waiting too long to grow the team: if your Product Managers get too stretched firefighting on their products or product areas, you will create gaps in some of the most important activities for your company such as:

• 💡Driving and evangelising the vision and value proposition, keeping focus on the target market and product positioning

• 🥅 Setting clear product goals that are aligned and contributing to the wider business goals

• 🗺 Driving strategic roadmaps, team and stakeholder alignment with the use of clear prioritisation frameworks

• 📊 Setting up product analytics, tracking the product north star and key metrics, measuring product experiments such as A/B tests

• 🤓 Aggregating and prioritising behavioural data, sales and growth insights, customer feedback, market research

• ⏲ Driving delivery timelines, coordinating releases, updating the wider stakeholders on deliverables

• 🤝 Facilitating team retrospectives, continuously improving tools and processes, driving team motivation and velocity

• 📃 Driving and refining product documentation, product team tools and frameworks

If you have grown to a stage where despite best efforts your first Product Manager(s) simply cannot allocate enough time to one or more of those key areas, it is time to grow the team.

Hire to your deficit to keep evolving

The trick is now to not only think about more headcount to spread the load, but to also think strategically about your product management practise itself. Any strong PM should be able to perform all of the activities above on their product or product area, but every Product Manager I have ever talked to also had at least one or two areas that they were clearly more strong in than others. This creates an opportunity to plan your hires very deliberately to create a wide range of skills in your team.

Ask yourself: which activities do you have covered well in our product management practise, and which ones do you need to get better at? The analysis of current skills versus your gaps should strongly inform the hiring for your team.

The reality is that product frameworks and demands in our role keep evolving incredibly fast. While you’re focussing on upskilling the team in one area, you’re already creating a new gap in another. I personally don’t think this should be disheartening by the way, but simply drive us to always keep learning. We can’t do it all at once of course, so it’s important to set clear focus and goals for the team depending on the most important needs.

You should however work towards creating a baseline to make sure everyone is skilled enough to perform all of the key activities at a sufficient level. Once you have created this baseline, you can set goals to advance the teams on certain frameworks and tools that you believe will have the biggest impact on your current product and business strategy.

Key Steps for growing the team effectively

1. Analyse the strongest and weakest skills in your Product Management team

Where are your most obvious gaps? You can use the product responsibilities checklist I created in Notion to analyse your coverage and identify the key skills your next hires should bring to the team.

2. Hire to your biggest gaps to create a balanced baseline

You ideally want full coverage of all key Product Management skills. This doesn’t mean everyone has to be an expert in all areas, but they should be able to perform all key activities at an acceptable level, or know who to reach out to to get support. You want to avoid creating an obvious skew towards one area while lacking skills in another.

The most common example I see is when PM teams are biased towards the delivery side while lacking skills on product strategy, or the other way around. One can’t live without the other, so be very deliberate about creating a balanced team. Product Intelligence skills are also often forgotten about or deferred to a separate, often disconnected analytics team.

3. Elevate the skills that will have the biggest impact to your product success

Creating full coverage is already hard, but if you have managed to establish a baseline of strong strategy, delivery, product intelligence and operational skills, you can now think about which areas would give your company and products the biggest advantage.

If for example your product is playing in a very saturated space, being first to market with additional features can have a massive impact on customer acquisition. In this case you could invest on further levelling up everyone on delivery techniques. It can also vary depending on the lifecycle stage of your product - you might have a stronger need for advanced product analytics skills as your customer base and amount of data increases.

Final 2 Cents

My general advice is ultimately that you need to hire with a clear strategy in mind. Identify what you’re already doing well and what you need to upskill your Product Managers on to fill gaps, then double down on on areas that will give you true competitive advantage.

Hiring PMs who have skills that you might not have yourself is one of the best things you can do - you can now use them as coaches and mentors for everyone in the team to evolve your overall Product Management practise. 💪🏼

If you have gone through or are about to go through a similar journey, I always love to talk to other product folks and exchange wisdom / tips / sorrows. Comment below or reach out directly via LinkedIn!

Is data a gap in your product team?

Every product role requires confidence with data—I’m here to help make metrics more approachable and actionable. My book The Insights Driven Product Manager—a #1 Amazon New Release— shares real-life examples, frameworks, and templates to unlock true data insights. 📈 NEW: Check out my course, Master Product Metrics:Leveraging Data, Metrics, and AI to Drive Customer & Business Impact.

Master Product Metrics