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Kodak invented the first digital camera. They held a vast portfolio of digital photography patents. And in 2012, they filed for bankruptcy. The reason? They were too afraid to cannibalize their own film business. Meanwhile, Apple looked at the iPod, its most successful product at the time, and said "let's kill it" by launching the iPhone. Same decade. Opposite instincts. Wildly different outcomes.

This issue's articles hit different because the writers aren't theorizing from the sidelines. John Utz, Gaurav Nukala, and Noa Ganot are in the trenches, wrestling with the exact tension that killed Kodak: when do you protect what's working, and when do you burn it down for something better?

The thing I keep coming back to is how emotional this decision is. It's not a spreadsheet problem. It's a courage problem. John Utz captured Kodak's failure perfectly: "Despite being a pioneer in the photography industry and holding a vast portfolio of digital patents, Kodak failed to prioritize the transition to digital photography effectively. This reluctance stemmed from a fear of cannibalizing their highly profitable film business."

They had the technology. They owned the patents. But the comfort of existing revenue held them hostage.

John continues: "Kodak's hesitation and misaligned priorities led to its inability to capitalize on its technological advancements, ultimately resulting in the company filing for bankruptcy in 2012." That's the quiet tragedy of product cannibalization anxiety. It doesn't look like a crisis at the time. It looks like prudence. It looks like protecting the business. Until it's too late.

Steve Jobs understood this better than most: "People think focus means saying yes to the thing you've got to focus on. But that's not what it means at all. It means saying no to the hundred other good ideas that there are." And sometimes that 'no' is aimed at your own current cash cow.

I wonder if most product teams even have the vocabulary for this conversation. Our roadmaps are full of "new features" and "growth initiatives." I almost never see "product sunsetting" or "feature deletion" as a top priority. John Utz addresses this head-on in Delete to Accelerate: Transforming Your Product with a Reverse Roadmap. He points out something that made me pause: "We have a roadmap that is largely, if not entirely, focused on releasing new stuff while maintaining what we have. But for some reason, we don't have a roadmap focused on decluttering and simplifying the product."

I found myself nodding at that. Launches get celebrated. Retirements get mourned, or worse, avoided entirely.

John describes his own frustration: "I was pissed. So what did I do? This was around the time that the idea of a separate, reverse roadmap came to mind." His proposal is simple and a bit radical: "Spend as much time planning for what you will get rid of as you do planning for what you will add." Adobe, Atlassian, and Salesforce all maintain separate retirement roadmaps. It's not a fringe idea. But it still feels rare in practice.

This isn't about arbitrary deletion though. Gaurav Nukala brings the bigger picture with How To Determine Product Priority: A Portfolio Management Framework. He makes the uncomfortable case that sometimes you have to sacrifice individual product success for the health of the whole portfolio. "We have to sub optimize individual products to optimize the portfolio," he writes. That sounds like a tough conversation to have with the team that just shipped a solid quarter on a product you're about to sunset.

And then there's the AI acceleration. Noa Ganot's Product Strategy in the AI Era hits this point hard. "Without a clear product strategy, AI will help you go fast in every possible direction at once." That line has been rattling around in my head since I read it. Everyone is shipping AI features. But without a strategy for what to keep, what to replace, and what to let go of, you end up with a bloated mess. Noa warns, "You can't afford to do it blindly, spray and pray, and hope that somehow one of your attempts will work."

Jeff Bezos put it another way, as quoted in John Utz's The Power of Product Lifecycle Management: "All businesses need to be young forever. If your customer base ages with you, you're Woolworth's." That's not about ageism. It's about relevance. And staying relevant sometimes means your best product today becomes the thing you retire tomorrow.

What I Am Hearing on the Podcast

This tension between protecting what works and building what's next keeps surfacing in my podcast conversations.

I was talking with Dan Balcauski in EP83 about pricing and packaging when he asked something that stuck with me: "Is this new set of capabilities, the benefits, the use case that we're talking about, is this talking to the same buyer? Is this an extension of that same buyer's life? Or are we talking to the person in the cube next to him or in the building next to him?"

That question matters for cannibalization more than people think. If you're building for the same buyer, you're creating a choice. And if the new thing is better, the old thing has to go. Dan also said something that's hard to hear but important: "The customers don't care about how many story points it took to build your feature and they don't care about the variable costs of your expenses. The value is not in the feature. The value is in the mind of the customer." Internal effort doesn't equal external value. And when the new product delivers more value, sentimentality about the old one becomes a liability.

In EP93 with Nicole Segerer, the conversation got into AI and cloud migration. Nicole shared something sobering: "80% of companies will have launched some sort of AI functionality. But when you really go deeper and you look at, okay, are customers using this? And is it something that carries some value that people would actually pay for? That creates a whole different picture."

She also named the classic cannibalization standoff: "Sometimes the profitability is still sitting with on-premise products. That's okay. But then you also need to think about how do I grab that customer cohort and move them to my cloud and SaaS products." The old product is still paying the bills. The new product is the future. Managing that transition without blowing everything up is one of the hardest things in product.

Nicole finished with something that keeps echoing for me: "People need to unlearn to just build features and capabilities for the sake of it. It doesn't work, especially when you think about AI use cases." Unlearning. That might be the most important skill in product right now. Unlearning the instinct to protect, to add, to preserve. Learning instead to subtract, to sunset, to cannibalize your own work before someone else does it for you.

I keep wondering though. How do you build that muscle? How do you create a culture where killing your own successful product isn't career suicide but career-defining? I don't have a clean answer for that. Maybe nobody does yet.

John Utz, Gaurav Nukala, and Noa Ganot are doing the work that matters here. John isn't just writing about reverse roadmaps as a concept. He lived the frustration, got mad about it, and built the framework. Gaurav is bringing rigour to portfolio decisions that most teams make on gut instinct. And Noa is asking the hard questions about AI strategy that everyone is thinking but few are writing down. These are practitioners sharing what they've learned in the middle of doing it, not from the comfort of having figured it all out. That's what makes their work worth reading.

👋 Jay

Sources:

• John Utz, Delete to Accelerate: Transforming Your Product with a Reverse Roadmap

• John Utz, It Means Saying 'No': How to Master Prioritization Like Steve Jobs

• Gaurav Nukala, How To Determine Product Priority: A Portfolio Management Framework

• Noa Ganot, Product Strategy in the AI Era

• John Utz, The Power of Product Lifecycle Management

• EP83, Dan Balcauski

• EP93, Nicole Segerer

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Somewhere around 72% of SaaS companies will change their pricing model within the first 18 months of launch. Not because the product failed, but because the way they asked people to pay for it did.

These are product practitioners who've wrestled with the question most teams avoid: not "what should we build?" but "how should we get paid for it?" Nick Chasinov, Bart Krawczyk, Noa Ganot, Scott Middleton, Advait Lad, Bhavik Patel, and their fellow writers have turned monetisation from a finance team afterthought into a genuine product discipline, and their work in Product Coalition is a masterclass in thinking about money as a design problem.

I have been guilty of treating pricing as the last slide in the deck. You build the thing, you ship the thing, and then someone in a meeting asks "so what do we charge?" and the room goes quiet. The truth is, the pricing model is the product experience for a lot of your users. It shapes what they try, what they ignore, and whether they come back.

Nick Chasinov puts it bluntly in How To Select the Right Pricing Model for Product Monetization Opportunities: "The success of a product, apart from the product itself, often hinges on how a company chooses to leverage its monetization opportunities." He's right. And he goes further: "Setting a price involves several factors: costs, competition, profit margins, demand, pricing objectives, and more." Most product teams I know handle maybe two of those variables well and guess the rest.

The problem gets more interesting when you zoom out from individual pricing decisions to the business model itself. Bart Krawczyk wrote what I think is one of the most underrated articles in our publication: Products Don't Exist in A Vacuum: A Product Manager's Guide to Business Models. It is a reminder that the monetisation model isn't a lever you pull after the product is built. It is the architecture the product lives inside.

The Freemium Trap and the Upgrade Nudge

Here is a number that should make you uncomfortable: according to industry benchmarks, typical freemium conversion rates sit between 2% and 5%. That means up to 98% of your free users may never pay you a penny. The question is whether that 98% is working for you or against you.

Advait Lad digs into this in Freemium to Premium: How Companies Nudge You To Upgrade. The article maps out the psychological machinery behind the upgrade moment, from feature gating to usage limits to the subtle art of making the free tier feel generous but incomplete. It is a fascinating read because it sits right at the intersection of product design and commercial strategy, the exact space where most PMs feel least confident.

But here is the thing about freemium that rarely gets discussed honestly: it is not just a pricing model. It is a distribution model. The free users are not freeloaders. They are your marketing department, your word-of-mouth engine, your living proof that the product works. The conversion rate matters, but the virality coefficient matters more. If your free users are not talking about you, freemium is just an expensive demo.

Beyond the Subscription Box

The SaaS pricing conversation has gotten stale. Sriram Parthasarathy does a thorough job of mapping the landscape in The Right SaaS for You: How to Break Down SaaS Pricing Models. He explains that "Companies may need to experiment with different pricing models to find the best fit for their particular product and audience." That sounds obvious on paper. In practice, most companies pick a model at launch and defend it like a castle wall, even when the moat has dried up.

Serhat Pala takes this further in SaaS 2.0: When the Software Becomes the Worker, arguing that AI agents are about to blow up the per-seat pricing model entirely. When software does the work that humans used to do, charging per person makes no sense. You start charging per outcome, per task, per unit of value delivered. This is not a future prediction. It is happening right now.

The API Economy's Quiet Revolution

One of the most thoughtful monetisation frameworks I have read came from Scott Middleton. His multi-part series, API Monetisation: A Comprehensive Guide (Part 1) and A Comprehensive Guide to API Monetisation (Part 3): Indirect Monetisation, lays out how APIs have created entirely new monetisation surfaces. The indirect model is the most fascinating part: you give the API away or charge a nominal fee, and the real revenue comes from the ecosystem it creates. It is Stripe's playbook. It is Twilio's playbook. And increasingly, it is the playbook for any product with developer-facing surfaces.

The Price Experiment Nobody Wants to Run

Bhavik Patel asks the question that keeps pricing teams up at night: Are Pricing Experiments Unethical? It is a genuinely brave article because most product people will A/B test a button colour without blinking but refuse to test a price point because it "feels wrong." Bhavik makes a compelling case that not experimenting with pricing is the more dangerous option, because you end up charging based on gut feel and competitor mimicry rather than evidence.

He follows it up with You're Using the Wrong Metric for Pricing Experiments, which gets into the mechanics of what you should actually measure. Spoiler: conversion rate alone will mislead you. Revenue per visitor is a better north star.

When Customers Don't Actually Want Cheaper

Noa Ganot drops perhaps the most counterintuitive insight in this entire collection. In Do Your Customers Really Want to Pay Less?, she compares two service providers. One optimised for minimum cost and effort. The other optimised for maximum value delivered. The one that charged more won, because "even in times of an economic downturn lowering your prices blindly is not necessarily the right thing to do." Every PM who has ever panicked and slashed prices during a tough quarter should read this piece. Sometimes the customer does not want to pay less. They want to feel like they are getting more.

Marketplace Models: Aligning Incentives

Lucas Fonseca Navarro rounds out the picture with The Marketplace Starter Pack, arguing that "commission is always preferable when possible, and the reason for this is that you guarantee the greatest retention of value for your company." Commission-based models are the ultimate incentive alignment: you only make money when your users make money. He notes that platforms like eBay capture around 10% in commission while advertising-based models capture less than 1%. The gap tells you everything about which monetisation model has real staying power.

And then there is Gabriyel Wong with A Revenue Growth Model That Breaks the Price War Conundrum, who brings behavioural economics into the conversation. When everyone in your market is racing to the bottom, the answer is not to race faster. It is to change the race entirely.

What I Am Hearing on the Podcast

Three recent conversations on the podcast have all circled around the same uncomfortable truth: most product teams treat monetisation as someone else's problem until it is too late.

Dan Balcauski, founder of Product Tranquility and pricing strategist who teaches at Northwestern's Kellogg School of Management, put it most directly on EP83:

Dan's advice was to spend most of your time on what the price tag goes on and very little time on what number goes on the price tag. The packaging decisions, the price metric, whether you charge per seat or per outcome or per API call, those structural choices are where the real leverage lives. He also called out a pattern I have seen everywhere: "There's really only three ways to grow a SaaS company: acquisition, retention, and monetization. In that zero interest rate environment, the only thing that gets any oxygen is acquisition. Retention is not really considered and monetization is sort of an afterthought." That afterthought era is over.

Nicole Segerer, a monetisation specialist with over a decade at Revenera and Flexera, reinforced this on EP93:

She was talking about usage intelligence, the practice of instrumenting your product to understand how customers actually consume it before you build a pricing model around it. Her warning was sharp: "When companies implement too quickly, without having the right data, monetization models go wrong. And when that goes wrong, you have a customer satisfaction issue. Then you've got to retract and change your pricing models, and that erodes trust."

Nicole also challenged the lazy use of "subscription" as a catch-all: "Subscription always gets used as this broad talk term. 'Oh yeah, we monetize as a subscription.' But really that's not the statement. The statement is what is your subscription based on?" With AI reducing the number of human users in many software products, she argued that per-seat subscription models may need to be fundamentally rethought.

And Dave West, CEO of Scrum.org, connected it all to a wider product leadership crisis on EP74:

He is talking about Scrum and agile, but the monetisation parallel is exact. We have spent a decade training PMs to be technically excellent, to understand APIs and data pipelines and system architecture, while the commercial muscle has quietly atrophied. Monetisation strategy is not just a finance problem. It is a product problem. And if you cannot have a meaningful conversation with your CFO about how your product makes money, you are only doing half the job.

So here is the question worth sitting with: if your product's pricing model was invented before your product existed, who is actually making the monetisation decision? The answer, for most teams, is "someone who left three years ago." And that should terrify you more than any competitor launch or market downturn. Because the companies that win the next decade will not be the ones with the best features. They will be the ones who figured out that how you capture value is itself a product decision, one that deserves the same rigour, the same experimentation, and the same creative ambition as anything else on your roadmap.

The writers featured in this issue are doing something that matters: they are treating monetisation as a product discipline, not a business team handoff. They are the PMs who sit in pricing meetings and actually have opinions backed by frameworks. They are the builders who understand that how you charge shapes what you build. Nick Chasinov mapping monetisation opportunities, Bart Krawczyk connecting business models to product architecture, Noa Ganot challenging the "cheaper is better" assumption, Scott Middleton dissecting API economics, Advait Lad reverse-engineering the freemium nudge, Bhavik Patel making the case for price experimentation, and Lucas Fonseca Navarro aligning marketplace incentives. This is what Product Coalition exists for: giving working practitioners a platform to share the commercial thinking that rarely makes it into product conference talks.

If you are not already part of our community, subscribe below for more content like this, delivered to your inbox every week.

Sources

- Nick Chasinov, How To Select the Right Pricing Model for Product Monetization Opportunities, Product Coalition - Sriram Parthasarathy, The Right SaaS for You: How to Break Down SaaS Pricing Models, Product Coalition - Advait Lad, Freemium to Premium: How Companies Nudge You To Upgrade, Product Coalition - Bart Krawczyk, Products Don't Exist in A Vacuum: A Product Manager's Guide to Business Models, Product Coalition - Scott Middleton, API Monetisation: A Comprehensive Guide (Part 1), Product Coalition - Scott Middleton, A Comprehensive Guide to API Monetisation (Part 3): Indirect Monetisation, Product Coalition - Bhavik Patel, Are Pricing Experiments Unethical?, Product Coalition - Bhavik Patel, You're Using the Wrong Metric for Pricing Experiments, Product Coalition - Noa Ganot, Do Your Customers Really Want to Pay Less?, Product Coalition - Lucas Fonseca Navarro, The Marketplace Starter Pack, Product Coalition - Gabriyel Wong, A Revenue Growth Model That Breaks the Price War Conundrum, Product Coalition - Serhat Pala, SaaS 2.0: When the Software Becomes the Worker, Product Coalition - Stephen Butts, Monetizing Your Product: A Guide to Pricing Strategies and Techniques, Product Coalition - Dan Balcauski, EP83: Profit by Design: Mastering SaaS Pricing and Packaging to Maximize Growth, Product Coalition Podcast - Nicole Segerer, EP93: Monetize This! How You Can Turn Data, AI, and Innovation Into Revenue, Product Coalition Podcast - Dave West, EP74: Embracing the Product Mindset with Scrum.org CEO Dave West, Product Coalition Podcast

👋 Jay

Between 20% and 60% of users churn after their first session with a product. I had to read that number twice. Not after a month. Not after a quarter. After a single session, up to six out of ten people decide they've seen enough and leave.

The numbers in this issue come from product people who got tired of watching dashboards turn red and decided to do something about it. They are not retention consultants selling frameworks. They are practitioners who opened their spreadsheets and shared what they found.

I think about this stat every time I see a startup announce a huge funding round alongside a user acquisition number. The number always goes up. The retention number is never mentioned. There is a reason for that, and it is not flattering.

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The Sign-Up Is Not the Win

I spent an hour last week trying to unsubscribe from a meal kit service that I signed up for during a moment of late-night ambition. It took four clicks and a forced survey just to stop the boxes from arriving at my door. I sat there watching the screen and realised that I was just another data point in a very expensive game of musical chairs.

Ant Murphy captured this distinction perfectly in Acquisition vs Activation and How to Predict Churn. He writes: 'Acquisition is when you get a user on board. Activation is when you get them to use your product in a meaningful way.'

I wonder how many product teams confuse those two moments. Ant asks a question that I find genuinely uncomfortable: 'Just think how many things have you signed up for and never used?' I look at my own phone and see at least a dozen apps gathering digital dust. Every one of those was someone's acquisition metric. None of them became an activation story.

He had a FinTech client whose mobile team was tracking daily active users as their core metric. His challenge to them: 'How often do you look at your bank account? Do you check your app every day? I don't.' They were measuring engagement on a cadence that did not match how people actually use banking products. The number looked good on a dashboard. It meant almost nothing.

His advice is the bit that stays with me: 'Don't just track acquisition, activation and retention. Track the nuanced states between each of them.' The space between active and churned is not a cliff. It is a slow, quiet drift that most dashboards are not designed to show you.

The 90% Problem

If you ignore those nuances, you end up fighting a war you cannot win. The team at UXCam laid out the stakes in How to Measure, Analyze, and Reduce App Churn: 'All the user acquisition in the world won't matter if you've got a high churn rate on your app.'

It is a simple truth that most teams acknowledge and then proceed to do nothing about. They also note that 'it's generally much easier to retain customers than it is to gain new ones.' Everyone nods at this in meetings. Very few teams actually resource retention work the way they resource acquisition campaigns.

The statistic that I keep coming back to from their piece: 90% of users have stopped using an app due to poor performance. Not missing features. Not bad pricing. Poor performance. The app was slow, or it crashed, or the thing they wanted to do took too many steps. Product teams obsess over building the next feature while the foundation is cracking under their feet. I suspect that fixing a slow loading screen is often a better investment than building a new dashboard, but that fix never makes it into the quarterly roadmap presentation because it is not exciting enough.

The Unicorn Graveyard

Nick Chasinov puts the human cost of this in focus in User Retention Defines Your Product's Success: Here Are 3 Ways to Improve It. He mentions that 'the number of users who churn in the first session ranges from 20% to 60%.' That is the stat I opened with, and it is a sobering thought for anyone whose job depends on growth.

He also cites research showing that 'improving retention by just 5% can boost revenue by 25% to 95%.' A five percent improvement. Not a rebuild. Not a pivot. Five percent less leaking from the bucket.

The examples he picks are the ones that should keep product leaders up at night. Fab, the design e-commerce site, 'was valued as a unicorn and raised a $336 million funding round prior to closing its doors.' Homejoy, the home services platform, raised almost $40 million and shut down 18 months later. Both grew fast. Neither retained.

His framing is the one I wish more product teams would internalise: 'It's easy to show investors top-line growth, such as gaining 100,000 new users in a week, but retention paints a more accurate picture of long-term success.' The leaky bucket looks full if you keep pouring water in fast enough. It is still leaking.

Putting a Number on the Leak

If you want to move this from philosophy to action, Scott Middleton provides a framework in How to Calculate the Value of Your Retention Improvements. He acknowledges upfront that 'calculating the return on investment of improvements to retention can seem like a daunting or complex task.'

His approach is deliberately simple: take your churn rate, multiply it by the number of customers, and multiply that by your lifetime value. The number you get is what churn is costing you right now. I find this math useful because it removes the emotion from the room. It turns a product problem into a business reality that a CFO can understand and act on. No philosophical debates about user experience. Just a number on a spreadsheet that says 'this is what we are losing every month because people leave.'

The simplicity is the point. Scott argues that without a clear and uncomplicated formula, your calculation would get caught up in sensitivity analysis and endless debates about the inputs. Most retention initiatives die not because they are bad ideas but because nobody could agree on how to measure whether they worked.

The Ratio That Matters Most

Nick Chasinov returns in Why Are Customer Acquisition Cost and Lifetime Value Important to Calculate? to explain the core tension that sits underneath all of this. He states that 'the money, time, staffing, and other resources used to acquire a new customer must be lower than that customer's lifetime value for the business to be successful.'

He suggests aiming for a 3-to-1 ratio between lifetime value and acquisition cost. If it costs you $1,000 to acquire a customer, you need at least $3,000 in lifetime value to have a sustainable business. I find this useful as a North Star because it forces product and marketing teams to have the same conversation instead of optimising in opposite directions.

The detail that landed hardest for me: '93% of people trust friends and family to obtain information about services and brands, while only 30% of consumers trust companies.' If you focus on retention, on making the product so good that people actually stay and tell others about it, you build the best acquisition channel that exists. Not an ad campaign. Not a growth hack. A product worth talking about. That is the cheapest customer acquisition cost there is, and the one least likely to show up in your marketing budget.

The Question I Keep Asking Myself

When I look at the products I have worked on, the honest answer is that I spent far more time on acquisition than retention. Most of us do. New users are exciting. Churned users are depressing. It is easier to celebrate a sign-up than to investigate a cancellation. But the math does not care about what is more fun to work on.

The practitioners I have quoted in this issue are not writing from corner offices. They are product managers, founders, and consultants who sat down and worked through the numbers because they were tired of watching their buckets leak. That is what Product Coalition exists for. Not the view from the top, but the lessons from the people doing the work.

When you look at your own product, are you spending more time trying to plug the holes in the bucket, or trying to find a bigger hose to fill it?

You're reading the Product Coalition newsletter. Every week, I dig into the archive of 3,500+ articles and 100+ podcast episodes to trace how product thinking has evolved. If someone forwarded this to you, subscribe here so you don't miss the next one.

Got a product story worth telling? Whether you're an exceptional talent shaping the future of product, or you've got a career journey others can learn from, I want to hear from you. Apply to be a guest on the Product Coalition podcast.

👋 Jay

Sources

Ant Murphy, Acquisition vs Activation and How to Predict Churn (Apr 2023); UXCam, How to Measure, Analyze, and Reduce App Churn (Jun 2022); Nick Chasinov, User Retention Defines Your Product's Success: Here Are 3 Ways to Improve It (Nov 2022); Scott Middleton, How to Calculate the Value of Your Retention Improvements (Mar 2022); Nick Chasinov, Why Are Customer Acquisition Cost and Lifetime Value Important to Calculate? (Jun 2023)

In 2017, Kate Bennet surveyed 50 product managers on Product Coalition and found something that should embarrass the entire industry. “In 46% of respondents’ companies the leadership team or head of product decide what will be built next. Only 13% of product managers have the authority to decide themselves.”

Gut feel and CEO preference were each cited by about 43% of respondents as inputs to what gets built. We invented an entire profession around deciding what to build, then gave the actual decision to someone else.

Since then, the framework machine has been working overtime. RICE. MoSCoW. Kano. WSJF. ICE. Opportunity scoring. Value vs. Effort matrices. Jordan Lamborn reviewed over 100 of them on Product Coalition and found they almost all share the same blind spots. “Unsurprisingly, some type of value is the most prevalent [factor]. This includes anything like impact, benefits, or return. Second place is the effort category.”

Over 100 frameworks, and most of them are just impact-vs-effort wearing a different hat. The variables that actually matter, customer centricity, confidence in your hypothesis, strategic alignment, barely show up.

Carlos Gonzalez de Villaumbrosia walked through the textbook trio in 2020, but even he acknowledged the limits: “So much of Product Management has to be unscientific. Although data should be used as much as possible, sometimes you have no choice but to rely on intuition and gut-feeling.”

I think he is right that gut feel is unavoidable. But the gut feel needs guardrails. Garrett Rysko made the counter-argument on Product Coalition and called it the McClane Mentality, after John McClane from Die Hard. PMs shooting from the hip, hoping nothing explodes. “When you’re working on a portfolio worth millions, with employees’ jobs banking on the success of your product, your decisions need to be as informed as possible.”

Fair point.

Here is what I think the framework conversation consistently misses. Every feature on your roadmap is an investment, and the cost goes well beyond the sprint points.

Caio Flores wrote about this on Product Coalition: “It’s easy to think that the lack of features is causing your startup to not grow enough, but remember that you have an infinite number of variables that could be causing that.” The instinct is always to add. Build feature X and Y, then growth happens. Except sometimes you have the wrong market, wrong timing, or wrong value proposition entirely, and no feature fixes that.

Hans-Jörg Roser pushed this further, pointing out that even sophisticated frameworks like WSJF miss the downside: “Also a new risk is created! Think for example of IT systems that could lead to new security aspects or simply the risk that comes with the future maintenance you have to do for a software feature.” Every feature you ship creates new liabilities. Security surface. Maintenance burden. Complexity that compounds quietly until it doesn’t.

John Utz was blunt about what this actually feels like: “The pain of prioritization almost made me quit.” No framework removes the emotional cost of telling someone their idea did not make the cut. That part is on you.

Margaret-Ann Seger said something on my podcast that keeps nagging at me: “Empowering teams leads to better outcomes than top-down mandates.” If the CEO is still picking what gets built, no RICE score will save you. You are just laundering someone else’s opinion through a spreadsheet.

The PMs I have seen get this right don’t treat prioritization as a feature-ranking exercise. They treat it as capital allocation. Each item on the roadmap has an expected return, a risk profile, and a maintenance cost that extends years past launch. When you frame it that way, the conversation changes. You stop asking “what should we build next” and start asking “where should we invest, and what is the cost of being wrong.”

What does your team actually use to prioritize? And does it work, or is it theater? Hit reply and tell me. I am genuinely curious whether anyone has cracked this.

Sources: Kate Bennet - “Product Prioritization by the Numbers” (Nov 2017), Jordan Lamborn - “Throw Away Your Prioritization Framework” (Feb 2021), Carlos Gonzalez de Villaumbrosia - “3 Prioritization Techniques” (Jan 2020), Garrett Rysko - “McClane Mentality” (Oct 2021), Caio Flores - “The Real Cost of Adding a New Feature” (Aug 2020), Hans-Jörg Roser - “Feature Prioritization: Apply the Highest Counter-Value First” (Sep 2022), John Utz - “It Means Saying No” (Sep 2024), Margaret-Ann Seger - Product Coalition Podcast EP98. All published on Product Coalition.

I keep hearing the same pitch: “We replaced three FTEs with an agent workflow.”

Cool. But who owns the budget line when that agent hallucinates a contract clause? Who absorbs the cost when your inference bill triples because a customer’s edge case sent your agent into a retry loop? And when you show up to quarterly review, is that agent spend capex or opex?

These are the conversations happening in every finance review I hear about right now. And most product leaders are walking in unprepared.

The 95% problem is a money problem

Here’s a number that should keep every CPO up at night: 95% of GenAI pilots deliver zero return on investment. Bulbul Pandya put it bluntly in Product Coalition last year: “AI doesn’t absolve us from doing the product work. It magnifies the consequences of skipping it.”

That’s not a technology problem. That’s a commercial discipline problem. Teams are shipping agent features without a unit economics model. No cost-per-task target. No margin threshold. No kill switch when the math stops working. And CFOs are starting to notice.

The cost of inference dropped 99% in two years. Sounds like a gift, right? It is, until you realise that cheaper tokens mean teams use 100x more of them. I’ve talked to product orgs where their monthly AI compute bill went from a rounding error to their third-largest line item in under a year. When nobody owns the P&L for agent spend, nobody notices until the quarterly close.

Per-seat is dead. Per-outcome is terrifying.

Serhat Pala wrote something in Product Coalition this week that crystallised what I’ve been thinking. He called it SaaS 2.0: “SaaS 1.0 charged per seat. Customers paid for access. SaaS 2.0 charges per outcome. Customers pay for work completed. A support ticket resolved. A tax return filed. A contract reviewed.”

That sounds clean on a slide. In practice, it flips the entire risk model. When you charge per outcome, your margin depends on how efficiently the agent delivers. If the agent needs human intervention 20% of the time, your gross margin might be 60%. If it needs help 40% of the time, you’re running a service business with software pricing. And good luck explaining that variance to your board.

This is the part most product leaders haven’t internalised yet. Per-seat pricing meant predictable revenue regardless of whether the customer got value. Per-outcome pricing means you eat the cost of every failure. Your agent’s accuracy rate isn’t just a product metric anymore. It’s a margin metric.

The three questions your CFO will ask (and you’d better have answers)

I’ve been collecting the questions that finance teams are actually asking product leaders about agent spend. Three keep coming up:

First: “What’s the fully loaded cost per agent-completed task, including compute, orchestration, evaluation, and human escalation?” If you can’t answer this, you don’t have a business case. You have a demo.

Second: “What’s the error rate, and what does each error cost us?” Jon Matheson learned the hard way that product managers who don’t understand how their decisions affect revenue, COGS, and margin are doing half the job. With agents, the stakes are higher because the cost of a wrong output isn’t just a bug ticket. It can be a compliance event.

Third: “At what volume does this become cheaper than the alternative?” Not cheaper than the old software. Cheaper than the humans, contractors, or BPO teams doing the same work today. Thomas Schwenger described it perfectly: “The translation layer between business ideas and working software has been the most expensive bottleneck in technology for decades. It is collapsing.” True. But collapsing doesn’t mean free. It means the cost moved from headcount to infrastructure. Your CFO wants to know exactly where it landed.

The product leaders who thrive in this era won’t be the ones who ship the most agents. They’ll be the ones who can show the unit economics on a single slide and defend every number on it.

What’s the hardest financial question your CFO or board has asked you about AI agent spending - and did you have an answer? Hit reply. I want to hear the real stories.

I asked a VP of Product last week what her product’s LTV-to-CAC ratio was. Smart person, ten years in product, ran a team of twelve.

She did not know. Not roughly. Not ballpark.

She could tell me her NPS was 62, her activation rate had improved 14% quarter over quarter, and her team shipped 23 features in the last cycle. All good numbers. But the one number the board asks first? Blank.

I keep seeing the same pattern. The product team walks into a QBR with activation funnels and cohort charts. The board wants to know when customers pay back their acquisition cost. Both sides leave frustrated, and nothing changes until someone gets cut from a budget review.

Nick Chasinov put it bluntly on Product Coalition: ‘If your CAC is higher than your LTV, it’s only a matter of time before the business closes its doors.’ Nick recommends a 3-to–1 ratio as the minimum healthy benchmark. I suspect most PMs reading this cannot say whether their product hits that or not.

And honestly, I do not blame them. I never learned this in any PM course I took either. Most of us pick it up only when a CFO forces the conversation, which usually means we are already behind.

John Utz shared a story that still bothers me. Fab, the e-commerce darling once valued at a billion dollars, tracked user registrations and website traffic religiously. John writes that they ‘overlooked the necessary metrics of customer retention and lifetime value.’ They poured money into acquisition, the top-line numbers looked incredible, and then the whole thing cratered.

Every metric Fab tracked was trending up. The business was dying anyway.

Olivia Tanuwidjaja explained on Product Coalition that CLV measures ‘the healthiness of our customer acquisition.’ I keep coming back to that word. Healthiness. Not size, not speed. Health. If your acquisition costs outpace what customers generate over their lifetime, you are not growing. You are spending faster.

The PMs who figure this out tend to move differently. Jon Matheson described the shift in his own career: he structured pricing so that ‘the LTV of premium users offsets the CAC within six months.’ That is not product language. That is board language. And it completely changed how his finance team engaged with the product roadmap.

Nicole Segerer made a related observation on my podcast. Most teams sit on mountains of usage data but have almost no visibility into how those numbers connect to revenue per customer. The data exists. Nobody is wiring it together.

Here is what I keep thinking about. Everyone in product is scrambling to learn AI tools and prompt engineering right now. Fair enough. But the PM who can walk into a board meeting and connect their roadmap to payback periods and gross margin? That person is rarer. And probably more valuable.

So here is what I want to know. What is the one unit economics number about your own product that you genuinely cannot calculate today? LTV-to-CAC ratio? Payback period by channel? Gross margin per feature? Hit reply and be honest. I am collecting these because I think there is a pattern in what product teams struggle to measure, and I want to write about it next.

Sources: Nick Chasinov - “Why Are Customer Acquisition Cost and Lifetime Value Important to Calculate?” (Jun 2023), John Utz - “From Vanity Metrics to Actionable Insights” (Aug 2024), Olivia Tanuwidjaja - “Customer Lifetime Value Explained” (Mar 2023), Jon Matheson - “Make Product Make Financial Sense” (Nov 2024), Nicole Segerer - Product Coalition Podcast. All published on Product Coalition.

There was a quarter where a product team I was advising felt good about their retention numbers. Churn was down. The trend line was moving in the right direction. Morale was high.

Then someone ran the revenue churn numbers.

Turns out they’d kept almost everyone, but lost a disproportionate share of the customers who mattered most. The ones on higher tiers. The ones who upgraded. The ones who stayed a long time. Those were the ones quietly walking out.

Customer count said one thing. Revenue said another. They’d been celebrating the wrong number for six months.

Most conversations about churn start and end with rate. What is it? Is it going up or down? How does it compare to benchmarks?

But Carlos Gonzalez de Villaumbrosia, writing for Product Coalition, points to a distinction that doesn’t get nearly enough airtime: customer churn and revenue churn tell completely different stories. He puts it like this:

‘it’s better to lose six $5 customers than it is to lose one $100 customer. (Not that this is the rule for all businesses — it depends on the model!)’

The parenthetical matters more than the main point. It depends on the model. And the bit I keep turning over is whether most PMs have properly interrogated which situation they’re in.

The other thing that rarely comes up: good churn.

Carlos gives the example of a dating app. Someone leaves because they found love. That registers as churn in your dashboard. It isn’t. It’s the product working exactly as intended.

I wonder how many teams are running retention initiatives they shouldn’t be running. There’s a version of this where you add friction to cancellation, drip in discount emails, make leaving feel awkward — and you keep the customer long after your product stopped being the right fit for them.

Lee Fischman wrote a piece on achieving 90% retention in B2B and the line that stuck with me was almost a provocation: ‘the first reason our retention was so high was simply because customers needed our stuff. When they didn’t, they cancelled.’

The goal isn’t to eliminate churn. It’s to make sure the customers who should stay, do. That’s a different problem.

What that requires is harder than any retention playbook.

Asher Atlas wrote the best case study I’ve read on what happens when retention breaks not because of product quality, but because of trust. A licensing shift meant a huge chunk of their music catalog disappeared overnight. Users weren’t annoyed. They felt cheated.

The team’s instinct was to fix search. The insight they landed on: ‘What if this isn’t a search problem? What if this is a discovery problem?’

That reframe is doing a lot of work. The churn signal pointed at one thing. The root cause was something else. A team that only read their retention dashboard would have shipped the wrong fix.

There’s no clean framework here. The thing I keep coming back to is that churn is a symptom, not a diagnosis. And teams that treat it as a metric to optimise — rather than a signal to interrogate — tend to optimise it in the wrong direction.

The question worth asking isn’t just: why are people leaving? It’s: which people are leaving, what were they worth, and should anything have been done differently?

Some of those answers are uncomfortable. Some of them are ‘no.’

- Jay

I counted once. In a single quarter, a team I was advising ran fourteen experiments on their onboarding flow. Fourteen. Button copy, tooltip placement, that little progress bar at the top.

You know how many experiments they ran on the pricing page in the same quarter? Zero. And this was a team that prided itself on being data-driven about everything.

I keep noticing this pattern and I am not sure I fully understand it yet. The pricing page is somehow sacred ground. Nobody runs real experiments on it.

Ivo Valchev wrote a case study on Product Coalition about what makes a pricing page work, and the opening line killed me: ‘”Oh wow! What a great pricing page.” Said nobody ever.’ I laughed and then felt a bit sick because it is so true. Ivo dug into the data and found visitors give you maybe 3 seconds before bouncing. Most pricing pages? Thrown together by someone in finance who built a comparison table and never came back to it.

What really gets me is how casually the actual numbers get chosen. Olha Mykhoparkina wrote about this and her phrasing stuck with me: companies just starting out ‘take their price figures out of thin air.’ Your whole revenue model, sitting on a number someone guessed over lunch.

I have a theory about why this keeps happening. Pricing feels risky in a way that button colors do not. Change the price and someone in sales will complain. A customer might leave. Your boss might ask uncomfortable questions about why you are tinkering with something that was working fine.

So PMs treat the pricing page like it is load-bearing. Structural. Do not touch it.

Except it is not structural. It is just a page nobody owns. Krishnan Hariharan spotted this gap back in 2018: ‘No one really thinks about the poor product manager who has to put together a pricing structure and is held accountable for its success.’ That was eight years ago and I genuinely wonder, has anything changed?

Something is starting to shift though. Eric De Castro published a piece recently that rewired how I think about pricing pages entirely. His argument: if your pricing page looks like a ‘hardware store receipt, charging by the seat, by the gigabyte, or by the API call,’ you have already lost the game.

In an AI world, features get cloned fast. A scrappy competitor can replicate your core product over a weekend, Eric says. So the real question is not what you charge per seat. It is what experience justifies your price. He calls it the Michelin-star model and honestly I have not been able to stop thinking about it since I read it.

Dan Balcauski made a related point on my podcast. He called pricing a ‘neglected business function.’ I think Dan is right. It is probably the most powerful commercial lever PMs have. And most of them have never actually pulled it.

I want to ask you something specific because it has been bugging me. When was the last time your team ran a real experiment on your pricing page? I mean a proper one, with a hypothesis and a control group and someone tracking whether it worked. Not a redesign meeting. A real experiment.

If the honest answer is ‘never,’ I am not judging. Most teams have not. I just think it is wild that we will test fourteen variations of a progress bar but not the single number that determines whether the business makes money.

Hit reply and tell me what happened the last time someone at your company suggested changing the price. I bet there is a story there.

Sources: Ivo Valchev - “How to Build a Killer Pricing Page” (Apr 2020), Olha Mykhoparkina - “Value Based Pricing for SaaS Startup” (Dec 2018), Krishnan Hariharan - “Pricing and Packaging Your Software” (Jun 2018), Eric De Castro - “The Economics of the Vibe” (Feb 2026), Dan Balcauski - Product Coalition Podcast EP83. All published on Product Coalition.

I want to tell you about a feature that nearly died.

Low engagement. Barely any daily active users touching it. By every dashboard metric a PM would check, it was dead weight. The kind of thing you flag in a quarterly review and say ‘let’s sunset this.’

Maret Kruve wrote about this on Product Coalition. The feature had poor usage numbers. Considering engagement alone, it should have been killed.

But here is what the dashboard did not show: that same feature made potential customers go ‘wow’ during sales demo calls. It was contributing to closed deals more than any other feature on the platform.

Read that again. The feature with the worst engagement metric was the strongest revenue driver.

This is the gap I keep coming back to. We measure what is easy to measure. DAUs, MAUs, feature adoption rates, NPS. These are product metrics. They tell you whether something gets used.

They do not tell you whether something makes money.

Tanay Agrawal learned this the hard way growing a product from zero to 100 customers. His team kept adding features and bundling them into pricing tiers. The plans got so complicated that customers stopped buying. Every feature had a carrying cost in maintenance and support, and nobody had visibility into which features actually drove revenue.

Tanay’s conclusion was blunt: everyone in the team should be able to look at and dissect subscription data at will. Not just the finance team. Not just the CEO. Everyone.

How many product teams can actually do that today?

Sebastian Mueller catalogued the damage when this goes wrong. The worst pricing mistake is not charging too much. It is underpricing so significantly that your unit margins are negative. You lose money by selling and scaling. Every new customer costs you.

Think about what that means. Your growth metric goes up. Your revenue goes up. And you are going broke.

This is why I think the most dangerous phrase in product is ‘users love it.’ Users loving something and that something being profitable are two completely different conversations. And most PMs only know how to have one of them.

Jon Matheson wrote a follow-up piece that landed on the same point from a different angle. His argument: stop asking ‘do users like this?’ Start asking ‘does this pay for itself?’

Not eventually. Not once you hit scale. Right now, at current volume, with current margins.

If you cannot answer that question about the last feature you shipped, you are not alone. But it is worth sitting with the discomfort for a minute.

I have a question for you. Have you ever killed a feature based on usage data, then discovered it was quietly driving revenue somewhere you were not looking? Or kept a feature alive that looked great on dashboards but was actually bleeding money?

Hit reply. I want to hear what happened.

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A product manager I know got let go after three years. Good with engineering. Great with UX. Solid on agile.

The feedback? ‘We need our product managers to have a commercial side to what they bring to the table.’

That quote is from Jon Matheson’s piece on Product Coalition, and it landed hard when I first read it. Jon was doing everything right by the traditional PM playbook. Launches, team management, user research. But his boss told him straight: ‘We need a product manager who understands monetization and how product decisions impact revenue, COGS, and expenses.’

He lost his job because nobody taught him the money side.

Here is the uncomfortable part. Most PM training skips this entirely. We get certified in discovery, delivery, strategy, stakeholder management. Almost nobody teaches you how to read a P&L or defend your roadmap in terms your CFO actually cares about.

Sebastian Mueller wrote about the four biggest pricing mistakes on Product Coalition back in 2020, and his observation still stings: ‘Pricing is incredibly important, yet very hard to nail down properly.’ Sebastian found that teams routinely underprice so badly their unit margins go negative. They literally lose money by scaling.

Think about that for a second. The better your go-to-market works, the faster you go broke.

What changed for Jon? He went back and learned the commercial side the hard way. In a follow-up piece, he describes walking into a GTM strategy meeting where the CFO wanted to see revenue projections, costs, margins, and ROI laid out in detail. Not user stories. Not sprint velocity. Financial evidence.

Jon writes that the moment he started presenting product decisions as investments with expected returns rather than features with estimated timelines, everything shifted. The CFO stopped being an obstacle and started being an ally.

Nicole Segerer made a related point on my podcast that stuck with me: ‘Evidence-based decision making is not just about user data. The most overlooked evidence in product teams is financial evidence.’

Most product teams can tell you their NPS to the decimal. Ask about gross margin per feature line and you get silence.

That gap is where the next generation of product leaders will separate themselves. Not by building better features. By understanding which features actually make money.

What is the one financial question about your own product that you genuinely cannot answer today? Reply and tell me. I read every one.