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When ‘Ping Them Again’ Was the Plan (2019)

Before anyone was saying ‘product-led retention,’ the playbook was straightforward. A 2019 Product Coalition editorial on app retention laid out the accepted wisdom:

“A good onboarding experience can improve retention by up to 50%.”

The toolkit: onboarding flows, push notifications, personalization, re-engagement campaigns. Nudge the user. Remind them you exist. Hope they come back.

It wasn’t wrong. But it treated retention like a marketing problem. The question was always ‘how do we get them to open the app again?’ Nobody was asking ‘why did they close it?’

The Structural Turn (2018–2020)

The smarter thinking was already percolating. In 2018, Rob Finney wrote about network effects using the VHS vs Betamax story:

“What I had witnessed was a platform based network effect kicked off by a small advantage. The small advantage was then accentuated by a positive feedback loop.”

A system where more users equals more value equals less reason to leave. Retention was becoming structural, not tactical.

Then 2020 cracked things open. Carlos Gonzalez de Villaumbrosia named the shift to PLG: “It’s getting easier to build a business, but not easier to grow one.” When competitors replicate your features in six months, the product itself has to be the retention engine.

But my favorite retention concept in the entire archive came from Derek Skaletsky that same year. He’d built a search engine with genuinely excellent tech. Users kept churning after 90 days. His diagnosis was brutal: “Search is the most perishable activity on the web. You search for something, find your answer, and… close the tab.”

His conclusion? When users pour their data, workflows, and routines into your platform, switching feels like losing a part of yourself. He called this the ‘All My Shit’s In There’ factor. Still the most honest description of SaaS retention I’ve read.

The Number That Stops You Cold

That same year, Dennis Meisner pulled the Amazon Prime data:

“A stunning 93% of Amazon Prime members renew their subscription after the first year. After the second year, this number goes up to an incredible 98%!”

98%. By year two, Prime isn’t competing for your loyalty. It has become infrastructure. You don’t cancel your water bill. That’s the endgame of retention done right: you stop being a product and start being a utility.

From Tool to Infrastructure (2024–2025)

Lee Fischman wrote in 2024 about maintaining 90%+ B2B retention, and his framing was disarmingly simple: “The first reason our retention was so high was simply because customers needed our stuff. When they didn’t, they cancelled.”

No growth hacks. Just be so embedded in your customer’s context that removing you requires rewiring how they work.

Then Asher Atlas wrote in 2025 about a retention crisis when a music service lost its catalog overnight. His team’s reframe changed everything: “What if this isn’t a search problem? What if this is a discovery problem?” They turned a broken search into a trust-building experience. Retention, it turns out, lives in how you handle the moment when things go sideways.

What I’m Hearing on the Podcast

Nesrine Changuel (ex-Google, Spotify, Microsoft) came on the show and pushed this further. Her argument: “User loyalty is significantly enhanced by emotional connections.” The products people stay with longest are the ones they genuinely love, not just the ones they can’t figure out how to export their data from.

(Listen to EP82 with Nesrine Changuel)

So Where Does This Leave Us?

The arc is clear if you step back far enough. Retention went from ‘remind them you exist’ to ‘make leaving expensive’ to ‘make them not want to leave at all.’ I think most teams are still stuck in the ping-them-again era, measuring retention without asking what’s actually retaining people.

If your retention strategy starts with ‘how do we reduce churn,’ you might be asking the wrong question. The better one: why would someone choose to stay?

Reply and tell me where your team sits on this spectrum. I’m genuinely curious whether the AMSIT factor resonates, or if you’ve found something completely different keeping people around.

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.

Jay Stansell · Lisbon, Portugal · Product Coalition

Then I hit delete.

That single action, hitting delete, was one of the best product decisions I’ve made on ExecReps. Maybe it’s also the most contrarian thing I’ll write in this entire series.

The Seduction of the Easy Answer

Nobody talks about this in product right now. LLMs have become the new ‘just throw it in a database.’ Every feature. Every recommendation. Every personalisation decision. The default instinct in 2026 is to ask a model. I get it, I’ve done it too. ExecReps uses GPT–4 for assessment, and it’s brilliant there.

But recommendations are a different beast.

When we started planning personalisation, that moment a user logs in and sees curated practice scenarios instead of a generic wall of content, the temptation was overwhelming. Embed the user’s profile. Embed the content. Cosine similarity. Done. Ship it. Put ‘AI-powered recommendations’ on the marketing site.

Except I kept hitting one problem I recognized from Don Norman’s work. I couldn’t explain why the model would recommend what it recommends. Norman calls this the Gulf of Evaluation, the gap between what the system did and why it did it. I wonder if this is the biggest long-term trust issue with LLMs. When your user can’t bridge that gulf, you’ve built a black box. They’ll eventually stop trusting it.

The Explainability Problem Nobody’s Solving

In EdTech, and executive coaching is EdTech even if we don’t always dress it that way, recommendations aren’t like movie suggestions. If Netflix recommends a mediocre rom-com, nobody gets hurt. You scroll on.

If ExecReps recommends the wrong practice scenario to someone preparing for a board presentation next Tuesday, and they spend their limited practice time on a scenario that doesn’t stretch the right muscles? That’s someone’s career development time wasted. I was talking to a product leader last week, and he mentioned how frustrating it is when ‘AI’ gives him generic suggestions. He just wants to know why. So when they ask ‘why did the platform have me practice this?’, ‘well, the embedding similarity was high’ isn’t an answer.

Any product team that’s applied Nielsen’s Heuristics knows this tension. His sixth heuristic, Recognition over Recall, says users should never have to guess why the system made a decision. They should see the reasoning. With an LLM, the honest answer was we couldn’t show it. With math, we could.

Nir Eyal’s Hook Model shaped our thinking too. The Hook cycles through Trigger → Action → Variable Reward → Investment. In a coaching context, the variable reward needs to feel earned and understood, not random. If users can’t connect the recommendation to their own growth, the reward loop breaks. The thing I keep coming back to is this. We needed variability in the content surface, yes, but not opacity in the logic underneath.

Five Factors, Zero Magic

So we built what I now think of as the honest recommendation engine. Five scoring factors. Each weighted. Each grounded in learning science. And each, I’d later realize, maps to established behavioral frameworks that any product team would recognize:

1. Content Relevance. Does this scenario match what you need to practice? A structured mapping between your stated goals, your role, your industry, and the content metadata we’ve tagged on every scenario.

2. Difficulty Match. Straight from Vygotsky’s Zone of Proximal Development, which anyone who’s studied Csikszentmihalyi’s Flow theory will recognize. Optimal challenge sits just beyond current ability. Daniel Pink calls this the Mastery component of his Drive framework, the intrinsic pull toward getting better at something that matters. The engine doesn’t just serve content. It serves the right difficulty to keep users in that Flow channel.

3. Skill Gaps. Where are you weakest relative to where you need to be? If your scores show strong Command but shaky Eloquence, the system pushes you toward scenarios demanding clarity and articulation. The Kano Model would call this ‘must-be’ quality. Users expect a coaching platform to know where they need work. Not a delighter. Table stakes.

4. Recency. When did you last practice a particular skill area? Spaced repetition works. Kahneman and Tversky’s research on memory availability tells us the same thing from a different angle. Recently practiced skills feel more accessible than they are, creating an illusion of competence the system needs to counteract.

5. Diversity. This one’s mine, not from any textbook. I kept noticing in user testing that people got stuck in loops, practicing presentations over and over because that’s what felt comfortable. From a Hook Model perspective, this is where variable reward design becomes critical. Eyal is explicit: predictable rewards lose power. The diversity factor nudges you into unfamiliar territory. Difficult conversations, upward feedback, client escalations. The things you avoid are often the things you need most.

Each factor produces a normalized score. The weighted combination gives us a final ranking. No black box. Every recommendation traces back to defensible reasoning. ‘We recommended this because you haven’t practiced delivery skills in 12 days, this scenario is one level above your last difficulty rating, and it targets the dimension where your gap is widest.’

Norman would call that bridging the Gulf of Evaluation. Nielsen would call it Visibility of System Status. I just call it treating adults like adults.

141 Skills Walk Into a Bar

Let me be honest about a mistake here. It’s one that any PM who’s read Norman’s work on conceptual models should have seen coming.

When we first built skill tracking, I let users self-report their target skills. Free text. ‘What skills do you want to develop?’ Very user-friendly. Very product-manager-who-reads-too-many-UX-books, me included.

We ended up with 141 unique skills in the database. Think ‘Executive presence.’ Then ‘Executive communication.’ Then ‘Presence in meetings.’ Then ‘Being more present in executive meetings.’ Four users, four entries, functionally the same thing.

The recommendation engine couldn’t work on top of that. In Norman’s terms, we had a catastrophic conceptual model mismatch. Every user had built a different mental model of what they were working on, and the system had no shared language to operate from. You can’t do gap analysis when the gap dimensions are fuzzy, overlapping, and user-generated.

So we did something painful. We threw all 141 away and started from psychometric research. Validated communication competency frameworks. Published, peer-reviewed dimensions of what ‘communication skill’ means when you measure it rigorously.

We landed on eight dimensions. Eight.

That collapse, 141 to 8, was one of the hardest product decisions I’ve made. In Self-Determination Theory terms (Deci & Ryan), it felt like violating the Autonomy principle. It felt like telling users ‘we know better than you what you’re working on.’ The psychometric evidence was overwhelming though. When you let people self-categorize communication skills, they create endless synonyms and miss critical dimensions entirely. Almost nobody listed ‘consistency’ as a target, for example. I’m not sure if this is a universal human trait or specific to communication skills, but it’s a pattern I’ve seen often. Consistency, maintaining quality under pressure, across contexts, is one of the strongest predictors of communication effectiveness in the literature.

User-generated taxonomies feel democratic. Validated taxonomies work. The Kano Model has a concept for this. Users will say they want freeform input, but their satisfaction doesn’t improve from getting it. What improves satisfaction is getting recommendations that work.

Solving Cold Start Without Asking Boring Questions

The classic cold start problem. New user, no data, and you need good recommendations immediately. Most platforms solve this with a questionnaire. ‘Rate your interest in these 15 topics.’

BJ Fogg’s B=MAP model told us that was wrong. Behavior equals Motivation plus Ability plus Prompt. A new user arriving from their manager’s email has moderate motivation at best. A fifteen-question onboarding form tanks their Ability. Too much time, too much mental effort, too much friction. Fogg is clear: when motivation is low, make the action trivially easy. A questionnaire is the opposite of easy.

We built two things instead.

First: industry/role/seniority profiles. Before any user tells us anything personal, we know statistical priors. A VP of Engineering at a Series B startup has different communication challenges than a Marketing Director at a Fortune 500. We built baseline profiles from research. What skills matter at each seniority level, what scenarios each role encounters, what difficulty level fits each experience bracket.

Second, and this is the one I’m proudest of, the Challenge Picker and Confidence Slider.

Instead of asking ‘what do you want to improve?’ we show three real scenarios and let users pick which feels most relevant. Steve Krug would call this ‘Don’t Make Me Think.’ Recognition, not recall. Show options, let them point.

Then, before their first practice: a Confidence Slider. ‘How confident do you feel about this scenario?’ Zero to ten. Two seconds.

That slider is pure signal. It’s where Kahneman and Tversky’s work on cognitive bias becomes directly useful in product design. Someone who rates themselves 8/10 confidence and delivers a 550 EPS score has a calibration problem, what Kahneman calls overconfidence bias. Someone who rates 3/10 and delivers the same 550 is underestimating themselves. The recommendation engine treats them completely differently, and it should. The gap between self-assessment and performance is one of the richest signals any coaching product can capture.

All of this runs without a single API call to any language model. Zero tokens. Zero latency. Zero risk of prompt injection or model drift affecting what learning path someone follows.

The Quiet Confidence of Boring Technology

I’m not anti-AI. ExecReps runs on AI, yes. Our assessment engine is genuinely powerful and would have been impossible three years ago. AI was the wrong tool for recommendations though, because the recommendation problem was well-defined enough to solve with math.

Here’s my rough heuristic, for what it’s worth:

Use AI when: the input is unstructured, the output needs to be generative, and you can’t write rules for what ‘good’ looks like. Assessment of free-form speech? That’s AI.

Use algorithms when: the input is structured, the output is a ranking, and you can define ‘good’ with research. Matching users to content? That’s math.

Use neither when: the problem is a UX problem disguised as a technical one.

There’s a confidence that comes from building something you can fully explain. When a user asks ‘why did you recommend this?’ and you can say ‘because your skill gap in Eloquence is 23% wider than your gap in Command, you haven’t practiced client-facing scenarios in 9 days, and this sits one difficulty tier above your last successful attempt’, that’s a different conversation than ‘our AI thought you’d like it.’

In executive coaching, where people are developing skills that affect their careers, their livelihoods, their sense of professional identity, they deserve the transparent answer. Someone making decisions about their own development should understand the reasoning behind the recommendations shaping that development.

Executive presence should not be a privilege. Neither should understanding why your learning platform steers you in a particular direction.

It’s not the flashiest thing we’ve built, I know. ‘Startup uses math instead of AI’ isn’t exactly clickbait. Still, when I look at our recommendation accuracy, users completing recommended scenarios at a significantly higher rate than self-selected ones, I think about that moment with the GPT–4 prompt written and ready to ship.

I’d delete that prompt again every time.

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.

Brought to you by ExecReps, AI coaching that helps product leaders practice the conversations that matter most

Just Ship Something, Please (2015–2019)

In December 2015, Fabrice Wegner wrote ‘The Vicious Circle of Preparation’ for Product Coalition, and reading it now feels like stepping into a time capsule. Fabrice was watching teams get stuck in endless preparation loops. Stakeholders would prioritize half-baked epics, product owners would scramble to validate them, engineers would wait around, and nobody shipped anything. His frustrated question cut through the noise:

“Is it wrong to want ship fast?”

The answer back then was a loud no. Speed was the gospel. Fabrice also dropped a line that I think most MVP evangelists never fully internalized: “Your customers won’t get happier or pay you a dollar if your team has learnt something. But the learning increment is the basis of a successful product.” Learning was supposed to justify shipping ugly. The trouble was, a lot of teams took that permission and ran with it.

Four years later, Vaibhav Gupta published ‘How to Define MVP’ on Product Coalition as part of a Product Management 101 series. His framing was textbook Lean Startup: “It is the smallest thing that you can build that would deliver some value to your customers and also help you gather validated learning about customers with least effort.” He walked through the Uber example (Travis Kalanick driving a car, a basic web app for hailing it), the skateboard-to-car analogy, the Build-Measure-Learn loop. All the hits.

This was still peak MVP optimism. The assumption was clear: build the smallest useful version and the path forward reveals itself. What nobody was really asking yet was whether the smallest useful version might also be the version users hate.

Ship Fast, But Please Make It Good (2020)

OK so here’s where it gets interesting. I pulled up two articles from 2020 and they basically argue with each other without meaning to.

In March of that year, Yellow Systems wrote ‘An MVP Can Save Your Product’s Life’ and was still firmly in the pro-MVP camp. Their argument: “launch delays have killed millions of startups and will probably kill millions more.” Fair enough. Ship now. Worry later. But then they snuck in a line that stopped me: a reduction in features is expected, they wrote, but “there should be no reduction in quality.”

Wait. Quality? In an MVP? I had to reread that. The whole MVP playbook, at least how most teams ran it, was basically trading quality for speed. Yellow was drawing a line nobody had drawn before. Strip the features, sure. But the stuff you keep? It better work. And it better look like someone cared.

Then Carlos Gonzalez de Villaumbrosia dropped ‘What Is a Minimum Lovable Product?’ that same summer and I remember reading his opening line twice. He basically said: meeting needs isn’t enough. Your users want to be delighted. They don’t want to tolerate your product. They want to love the thing.

I keep coming back to his next line: “being Viable just doesn’t cut it anymore. You need to be Lovable.” Carlos wasn’t really killing the MVP. He was renaming it, adding a higher bar. The MLP, he explained, is like an MVP “but with more thought and care taken in design and UI.”

And honestly? He was right about the timing. By 2020, users had lived with Slack and Notion and Figma. Their baseline for ‘acceptable first version’ was miles above where it sat in 2015. The floor had moved and a lot of teams hadn’t noticed.

RIP MVP? (2025)

This is where the backlash got loud. Garima Srivastava, one of our contributors, wrote a piece titled ‘Minimum Viable Product (MVP) is Dead’ earlier this year and she pulled zero punches. The MVP, she wrote, was supposed to get you early feedback. What it actually does? It produces “half-baked products that nobody loves.”

Garima’s analogy is the kind that stays with you. She compared most MVPs to someone serving you an uncooked pizza and telling you to imagine how great it’ll be once it’s done. Nobody wants that. Nobody shares that with friends. Her point: viability isn’t the bar anymore. Loveability is. People don’t recommend products they merely tolerate.

I’m not sure I fully agree. The original MVP concept, the one Eric Ries actually described, was never supposed to be a product you ship to the market and walk away from. It was a learning tool. But the PM community took ‘minimum viable’ and heard ‘minimum effort,’ and that misreading created a decade of products that launched to crickets.

The correction isn’t wrong. It’s just late. And honestly, I wonder if MLP will get misread the same way in another five years.

What I’m Hearing on the Podcast

Raksha Vashishta joined us on the podcast for an episode she called ‘The MVP of My Career.’ She talked about her journey as an immigrant woman in tech, applying MVP thinking not just to products but to her own career path. Start small. Validate. Pivot when the data says to. Her story of building from an accounts receivable internship to a product leadership role mirrors the MVP-to-MLP arc in a way I wasn’t expecting: the first version of your career doesn’t need to be polished. But at some point, you stop iterating on the minimum and start building something you’re genuinely proud of.

So What Does This Mean for You?

The MVP isn’t dead. But the era of shipping something ugly and calling it strategy? That part is over. What actually changed isn’t the framework itself. It’s the floor. User expectations in 2025 are so far above where they were in 2015 that the ‘minimum’ in ‘minimum viable’ now means something completely different.

When was the last time you shipped a true MVP, the ugliest working version you could get away with, and actually got useful feedback from it? Or has your team already moved past that into MLP territory without calling it that? Reply and tell me. I genuinely want to know if the skateboard still works.

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.

Jay Stansell · Lisbon, Portugal · Product Coalition

I had one of those mornings recently. You know the kind. Staring at a screen, coffee going cold, and the data just… mocks you. Not the company, not the product, but the dashboard itself made me want to quit.

We’d spent weeks building acoustic voice analysis into ExecReps. Pace, filler words, clarity, confidence. Now, users could see everything.

Their content scores, broken out across structure, persuasion, and relevance. Their delivery scores, measured across four vocal dimensions we called Command, Eloquence, Engagement, and Consistency. Eight archetype classifications. Historical data on every dimension. Charts for days.

It was, objectively, impressive engineering. It was also terrible product design.

I knew this because I watched myself use it. I’d finish a practice session, get my results, and then spend two minutes trying to figure out whether I’d gotten better. Was my Eloquence going up, but my Command going down? Did the content improvement offset the delivery dip? Was a 620 in Engagement good? Compared to what?

This is the bit I keep getting stuck on with some of the data visualization trends. We collect so much, but what do we dowith it?

Don Norman calls this the Gulf of Evaluation: the gap between what a system shows you and your ability to figure out what it means. Our dashboard had created a canyon-sized gulf. Tons of data, zero answers. If I, the person who built this thing, couldn’t quickly answer ‘am I improving?’, no user stood a chance.

The Data Trap

Engineers and PMs love data. Users love answers.

I’ve learned that the hard way, multiple times, across multiple companies.

There’s a seductive logic to giving users more information. More data means more transparency, more trust, more engagement. It’s a clean little story that falls apart the moment a real human opens your app.

Barry Schwartz’s Paradox of Choice shows more options create more anxiety, not more satisfaction. Nielsen’s eighth usability heuristic, aesthetic and minimalist design, exists because every extra element on screen competes for cognitive bandwidth. Show someone eight metrics and you haven’t given them eight insights. You’ve given them decision paralysis wearing a dashboard costume.

The mistake isn’t in collecting the data. It’s in thinking the data is the product.

What Fitbit Already Figured Out

The breakthrough came from a conversation I had with a friend who’d just gotten a Garmin watch. She was showing me her stats: heart rate variability, VO2 max, sleep stages, body battery. I asked her what her resting heart rate was.

She had no idea.

What she did know was that her ‘body battery’ was at 73 that morning, which was higher than last week. Her training status said ‘Productive’. That’s all she needed.

In the Kano Model, Garmin’s raw biometric data is table-stakes quality: expected but not exciting. The ‘body battery’ synthesis is the delighter. Same sensors, but the experience shifts from ‘here’s your data’ to ‘here’s your answer’.

Fitbit figured this out years ago. It wasn’t ‘here are your steps, your distance, your calories, your active minutes’. It was: you hit your goal today. You’re on a streak. You’re improving. The raw data lives behind a tap for the nerds who want it. The product is the answer.

We needed our own version of that. I wonder if this lesson applies to every ‘pro’ tool. Do finance professionals want every row of a spreadsheet, or just the insights from it? I’m not sure.

Building the Executive Performance Score

So in late November, we built what we call the Executive Performance Score. The EPS.

One number. 400 to 1,000 scale. It tells you how effective you are as a communicator. Full stop.

Under the hood, EPS combines two systems we’d already built. Think of it like this:

Content quality. Our GPT–4 analysis layer evaluates what you say. Structure, clarity, persuasion, relevance, depth of thinking. This is the stuff traditional coaching often focuses on.

Delivery quality. Our acoustic analysis layer evaluates how you say it. Command, Eloquence, Engagement, and Consistency. This is the stuff that usually requires a live coach or, more often, never gets measured at all.

In Norman’s terms, we were creating a new conceptual model. Instead of ‘I have eight separate scores I need to track’, the mental model becomes ‘I have one number that goes up when I get better’. One number. Two systems. A graspable picture.

The 60/40 Fight

This is where it got contentious. If EPS combines content and delivery, what’s the weighting?

My instinct was 50/50. Clean. Fair. Democratic.

But content and delivery don’t matter equally. Pretending they do is the kind of elegant-sounding product decision that makes your product worse.

We went back and forth on this for longer than I’d like to admit. The arguments for 50/50 were aesthetic. The arguments against were practical.

First, content has a higher ceiling of differentiation. Two people can deliver a message with nearly identical vocal quality but wildly different levels of insight and persuasion. Academic communication research suggests content predicts audience perception of competence more strongly than delivery alone.

Second, we’re building for professional contexts. In a board meeting, a pitch, a difficult conversation, what you say carries more weight than how you say it.

Third, and this is the one that settled it, a 50/50 split created a perverse incentive. Kahneman and Tversky’s work on framing effects applies directly. How you frame a metric changes what people optimize for. A 50/50 split would tell users with strong voices but weak content that they were ‘good’ when they were just pleasant-sounding. That’s not what we’re building. We’re not a voice beautification tool.

So we went 60/40. Content gets 60%, delivery gets 40%.

It’s not a perfect split. It does create the right incentive structure: if you want a high EPS, you need to think well and communicate substantively. A beautiful voice won’t carry you past 700.

The product lesson is that ‘weights are product decisions, not math decisions’. Every composite metric encodes values. Spotify’s Discover Weekly weights novelty against familiarity. Google’s search algorithm weights freshness against authority. The weights aren’t neutral, they’re your product’s opinion about what matters. Own that opinion explicitly, or you’ll end up with a metric that doesn’t drive the behavior you want.

The 400–1,000 Scale (And Why Not 1–100)

Small decision, but it mattered more than expected: the scale.

Early versions used 1 to 100. It felt intuitive. Everyone understands percentages. It created two problems though.

First, nobody wants to be a 43. This is Kahneman and Tversky’s anchoring bias in action. Decades of school grades have anchored everyone to read anything below 70 as ‘bad’. A score of 52 out of 100 is above the mathematical median, but it feels like failure. The anchor is stronger than any disclaimer you put next to it.

Loss aversion tells you why this kills a self-improvement product. If users feel they’re ‘failing’ on their first session, the emotional cost of continuing outweighs the potential gain. They don’t come back. You’ve lost them to a technically accurate but emotionally devastating number.

We borrowed the 400–1,000 scale from standardized testing (SAT, GRE, GMAT). It’s population-normalized, so a 600 means you’re at the median, not ‘failing’. A 580 doesn’t trigger the same shame response as a 58.

Same underlying data, different emotional frame. That’s product design. I wonder if there’s a limit to how much you can ‘gamify’ negative feedback. Or if all products should just avoid grading entirely.

The Backfill Decision

This is the part I’m most proud of. It was almost the part we didn’t do.

When we launched EPS, every user had existing data. Practice sessions from weeks or months of using the platform. They didn’t have EPS, because EPS didn’t exist when they did those sessions.

The easy path: EPS starts now. Old sessions get old scoring. New sessions get EPS. A clean cutover.

But that meant users would open the dashboard and see… nothing. No trend line. No trajectory. Just a single data point. The whole promise of ‘am I getting better?’ requires comparison over time. One data point isn’t a trajectory. It’s a dot.

So we went back and calculated EPS for every historical submission in the system. Every practice session, every scenario, every user. We applied the same 60/40 weighting, the same population normalization, the same scale. When users opened the new EPS dashboard for the first time, they didn’t see a blank slate. They saw their entire journey.

This matters more than it sounds like it should. Multiple well-established frameworks explain why.

Teresa Amabile’s progress principle research at Harvard shows that the single strongest driver of motivation at work is the sense of making progress on meaningful work. Not recognition, not incentives. Progress. Deci and Ryan’s Self-Determination Theory identifies competence, the feeling that you’re getting better, as one of three fundamental human needs. Daniel Pink’s mastery research in Drive makes the same case. The trajectory line directly feeds the deepest drivers of human motivation.

From a behavioral design perspective, Nir Eyal’s Hook Model would call the backfill data ‘stored value’, the Investment phase of the habit loop. Users who see their history have something invested in the system. That investment loads the next trigger: ‘I wonder if my next session will push me past 700.’ Without the backfill, zero stored value. With it, they start already hooked.

When users opened ExecReps and saw a line going up, even slightly, something clicked. I could see it in the engagement data. Session frequency increased. Users who’d been doing one practice a week started doing two or three. They weren’t responding to a new feature. They were responding to evidence that they were getting better.

BJ Fogg’s B=MAP model explains the mechanism: Behavior = Motivation × Ability × Prompt. The backfill didn’t change ability or the prompt. It changed motivation. Seeing a rising trajectory makes the next session feel not just possible, but inevitable.

The engineering cost was maybe two days. The impact on retention was disproportionate. If you’re building any kind of self-improvement product, I’d argue the backfill is the feature. Not the score itself. The score is just a number. The trajectory is the product.

Measure What Matters, Show What Motivates

We ended up with a design philosophy we still use: ‘measure what matters, show what motivates.’

We still collect all the granular data. Every acoustic dimension, every content sub-score, every archetype shift. That data lives one tap away. Progressive disclosure in Nielsen’s terms. Or what Dan Saffer’s microinteraction patterns might call a ‘long tap to reveal’ secondary layer. Some users dig into it and love it.

The product surface, the thing you see first, is one number, one trend line, and one answer: you’re getting better.

This philosophy cuts against every instinct a data-driven PM has. We want to show our work. We want credit for the engineering. All of that is ego, not product sense.

The best products are like the best coaches. They take in a huge amount of information, synthesize it, and tell you the one thing you need to hear right now. That’s what EPS became. Not just a metric, a design principle. Every feature we’ve built since, I ask: are we showing what motivates, or are we showing what we measured?

The Uncomfortable Truth About Metrics

I’ll end with something that still makes me slightly uncomfortable.

EPS is an opinion. It’s our opinion about what makes effective communication, encoded as math. The 60/40 split is a product decision. The population normalization is a modeling choice. The scale is a psychological design. None of it is ‘objective’ in the way users might assume when they see a number with decimal precision.

Every metric in every product is like this. Your credit score is an opinion. Your Uber rating is an opinion. Useful approximations dressed up as measurements.

I think founders and PMs have an obligation to be honest about this. Cialdini’s research on commitment and consistency shows that once people anchor their identity to a number (‘I’m a 720’), they’ll work to stay consistent with that identity. That’s powerful. The behavior your metric incentivizes should be the behavior you want. Get the weights wrong, and you build a precise system that makes users worse at the thing they’re trying to improve.

We spent more time on the values behind EPS than on the engineering. I think that’s exactly right.

It reminds me of a conversation I had with one of our Product Coalition podcast guests, Marty Cagan, who often talks about how product strategy is value decisions. Metrics are just a manifestation of those values.

Because that’s the point. Not the number. Not the dashboard. It’s the person on the other side of the screen, trying to close the gap between where they are and where they want to be. They’re trusting us to tell them whether it’s working.

If this idea resonates with you, and you’re curious about how a tool like ExecReps could help your team communicate with greater impact, you can learn more about it on our website. It’s been a fascinating journey building it in public, and I’m genuinely excited about what we’re helping people achieve.

“Should we add streaks?” Our designer sent it.

I typed “yes” immediately.

Then I deleted it.

Then I typed a longer answer. Deleted that too.

I stared at the cursor for a solid minute. Behind those six words was one of the most consequential product decisions we’d make. Not technically. Streaks are trivial to build. Philosophically, though, it was huge. Because the answer would define whether ExecReps helps people build a practice, or just guilt-trips them into opening an app.

Very different things, those two.

The Duolingo Problem

Let me back up a little. We’d just shipped v2.5, our personalized experience release. We had role-based navigation. Smart empty states. Progressive disclosure that showed people only what they needed. Don Norman would call that reducing the “Gulf of Execution,” narrowing the gap between intent and interface.

But we had a retention problem. People who practiced three times stuck around. The challenge was getting people to practice three times in the first place. Our activation curve looked like a cliff with a plateau. You either fell off in the first week or stayed for months.

When we ran our engagement features through Nir Eyal’s Hook Model, we scored about 4 out of 10. We had a decent trigger (email invite) and a reasonable action (do a workout). Our Variable Reward was flat, though, and our Investment phase was nonexistent. Users completed a workout and… nothing loaded the next trigger. No real reason to come back tomorrow. Any product team using the Hook Model would recognize that open-ended loop as the problem.

So, the conversation turned to engagement mechanics. And someone said “streaks.”

If you’ve ever uninstalled Duolingo, you know why that word made me pause. Duolingo’s streak system is a masterclass in behavioral design. And, I think, an object lesson in crossing the line from helpful to hostile. The sad owl. The passive-aggressive notifications. The sheer guilt of it all. That thing where you feel worse about breaking a streak than you ever felt good about maintaining one.

I’ve talked to people who kept their Duolingo streak alive for 300 days without learning a single new word. The metric had completely decoupled from the outcome. In Kano Model terms, what should be a ‘delighter’ – a sense of momentum – had degraded into a ‘must-be’ obligation that only generated dissatisfaction when broken.

That isn’t retention. That’s a hostage situation. A podcast guest told me something similar about fitness apps a few months back. The moment the streak becomes the point, you’ve lost the plot on what the product was supposed to do.

BJ Fogg Changed My Mind

I almost killed the streaks idea entirely. Then I re-read BJ Fogg’s Tiny Habits, and his B=MAP model reframed everything.

Fogg’s framework is pretty standard product science now. Behavior = Motivation × Ability × Prompt, all converging at the same moment. Miss any one, nothing happens. The mistake most products make is trying to increase motivation. More notifications, more badges, more guilt. Fogg argues you should instead decrease the effort required. Make the behavior so easy that even low motivation is enough.

That distinction changed everything. The question wasn’t “how do we motivate people to practice?” It was “how do we make practicing the path of least resistance?”

We spent two weeks mapping the behavioral science before writing a single line of code.

BJ Fogg’s B=MAP. Design for the low-motivation moment. Not eager Monday morning, but exhausted Thursday afternoon. If the streak system only works when people are fired up, it’s not a system. It’s a fair-weather friend.

Kahneman & Tversky’s Prospect Theory. People feel losses roughly twice as intensely as equivalent gains. The engine behind every streak system ever built. We’d use loss aversion, but carefully, with an escape valve.

Nir Eyal’s Hook Model. Trigger → Action → Variable Reward → Investment. Our 4/10 audit exposed the missing Investment phase. Our morning nudge system needed to close that loop.

Deci & Ryan’s Self-Determination Theory. Intrinsic motivation requires autonomy, competence, and relatedness. Daniel Pink popularized this as Drive’s AMP framework: Autonomy, Mastery, Purpose. This became our ethical guardrail. Any engagement system that undermines autonomy will eventually backfire.

What We Built (And What We Didn’t)

Streaks, But With an Exit Ramp

Yes, we built streaks. But with one critical difference. We designed the break as carefully as the streak itself.

When you practice, your streak increments. You see a small visual celebration. Dan Saffer calls this a ‘signature moment’ in his microinteractions framework. Not confetti cannons, just enough feedback to trigger Fogg’s “celebration” mechanism. The key is immediacy. Celebration fires within 200ms of completion. Delayed celebration doesn’t wire the habit. Hit milestones (7 days, 30 days, 100 days) and the feedback gets richer. It’s a progressive loop where the microinteraction evolves as your behavior matures.

The part I’m quite proud of? Streak freezes. Miss a day, spend a ‘freeze credit’ to keep your streak alive. You earn these by practicing consistently. It’s Kahneman’s loss aversion working for you instead of against you. You’re spending a resource you banked through good behavior. Cialdini’s Commitment & Consistency principle is baked in. The freeze credits are earned through past commitment, making the current streak feel like something you’ve invested in, not something imposed on you.

If your streak does break? We don’t show a sad owl. We show your best streak as a personal record. “Your best: 23 days. Let’s start a new one.” The streak dies quietly. No funeral. No guilt. Just a clean slate.

Someone on the team argued a broken streak should feel like something. They thought mild pain of loss is what makes streaks work. They weren’t wrong about the psychology. But they were wrong about the ethics. Our users are professionals trying to get better at communication. If they miss a day because their kid got sick, the last thing they need is an app eroding their confidence. That’s a direct violation of SDT’s autonomy principle. Once autonomy is undermined, Deci & Ryan’s research shows intrinsic motivation doesn’t just dip. It collapses.

Morning Nudges, Timed to the B=MAP Window

Our nudge system sends a morning email, not a push notification. That’s deliberate. It’s timed based on engagement patterns. Content varies. It could be a prompt related to something you’re working on, a micro-insight from your last session, or simply “Your 3-minute warm-up is ready.”

The timing isn’t random. We anchored nudges to what Fogg calls ‘anchor moments’. That’s the Tiny Habits recipe: “After I [existing behavior], I will [new behavior].” Most professionals have a cortisol spike in their first waking hour. Alert, but not yet consumed by chaos. That’s when B=MAP converges. Motivation is fresh, ability is high, and the email is the prompt.

The variable reward, straight from Eyal’s Hook Model, comes from the content. You don’t know what the nudge will contain. It might surface a skill gap you hadn’t considered, or a scenario relevant to a meeting that afternoon. Variable rewards of the “hunt” type create curiosity. Curiosity beats obligation every time.

Every nudge has a one-click opt-out. Not buried in settings. Right there in the email. Nielsen’s Heuristic #3, “User control and freedom,” in practice. The moment a nudge feels like spam, trust is gone permanently.

Re-engagement, An Invitation Not an Intervention

For users who go quiet: one email after two weeks. “Here’s something new since you’ve been away. No pressure.” One email. Not a drip campaign. If they don’t come back, we leave them alone. That’s Pink’s Autonomy principle in action. And it matters more than juicing a metric.

The “What We Don’t Build” List

Every product team has a roadmap. Very few have a formal anti-roadmap. We started one during v2.6 planning, and it’s become one of our most valuable documents.

No leaderboards. Communication skills aren’t a competition. Ranking people against each other undermines what Deci & Ryan call ‘relatedness’. That’s the sense you’re part of a supportive group, not fighting for position.

No public scores. Your EPS, streak, practice frequency. All private by default. Nielsen’s Heuristic #7: flexibility and efficiency of use means users control their own data.

No shaming mechanics. No “you’re falling behind” messaging. We learned from our social proof analysis that showing “3 of 15 members practiced today” is negative social proof. Cialdini warns that framing the minority as the example normalizes inaction. We use absolute counts instead. “3 team members practiced today.” No denominator, no implied judgment.

No engagement theater. No features that increase time-in-app without increasing value-to-user. Norman calls this a conceptual model violation. When the product’s goals diverge from the user’s, the relationship erodes.

The Product Lesson: Easiest Beats Loudest

This is an insight I keep coming back to. It applies to virtually every product with a retention challenge.

The best retention features don’t motivate people to do hard things. They make the right thing the easy thing.

The B=MAP alternative to amplifying motivation: make the behavior so frictionless that even a bad day’s motivation is sufficient.

• Shorter sessions, not longer ones. A three-minute warm-up is better than a thirty-minute workout you skip. That’s Fogg’s “Starter Step.” An entry point so small it feels absurd to say no.

• Fewer choices, not more. Progressive disclosure means one clear next step, not twenty options. Norman calls this ‘constraints’. Limiting action possibilities to guide behavior without instructions.

• Gentler nudges, not louder ones. A calm morning email beats five push notifications. Saffer’s microinteraction principle: triggers should match the emotional context, not overwhelm it.

• Graceful failure, not punishment. A quiet streak reset beats a guilt-trip notification.

Our early v2.6 data is encouraging. Streak cohort users practice about 40% more frequently. More importantly, session quality metrics haven’t dropped. People aren’t checking a box to keep a number alive. They’re doing the work. Not sure yet whether that holds over six months, but it’s the right signal.

That’s the difference between a habit and an obligation.

The Uncomfortable Truth About Behavioral Design

Building this system forced me to confront something uncomfortable. Every behavioral design technique is morally neutral. Kahneman & Tversky’s loss aversion can build a healthy practice or trap someone in a toxic loop. Eyal’s variable rewards can spark curiosity or create compulsive checking. Cialdini’s social proof can inspire action or manufacture peer pressure. The framework doesn’t care about your intentions.

I wonder if that’s the biggest design challenge we face today. Not building the features, but building the ethics around them.

We optimized for practice quality, not app opens. For skill growth, not session count. We built explicit escape valves, too. Freeze credits, quiet resets, one-click opt-outs. Because these tools were powerful enough to do real harm if deployed carelessly. I’m not sure we got every detail right, but the intent was right.

That three-week internal argument about streaks – the one that started with six words in Slack – was the most valuable product discussion we’ve had. The most important product decisions aren’t about what you build. They’re about why you build it. And what happens when it works exactly as designed.

If you’re building engagement systems, write your own “what we don’t build” list before you write a single user story. You might be surprised how much clarity it provides. It sure did for us, and it’s why we built ExecReps the way we did.

And if you ever find yourself designing a sad owl, stop. Close your laptop. Go for a walk. You’ve lost the plot.

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.

I’ve been pulling North Star Metric articles out of our archive all week, and honestly? The pattern surprised me. In 2019, our contributors were so sure. One metric. One number. That’s all you need. Three years later, some of those same people were writing about how that advice nearly wrecked their product teams. I don’t think most of us caught the shift as it happened.

Brought to you by ExecReps, AI coaching that helps product leaders practice the conversations that matter most

One Metric to Rule Them All (2019-2020)

If you want to feel the vibe of peak single-metric evangelism, go read Smit Shah’s ‘Tracking ‘One Metric That Matters’ (OMTM)‘ from September 2019. I pulled it up again this week. The certainty in it is wild:

“The concept of One Metric that Matters (OMTM) states that any organization should focus on optimizing only one metric that matters at that particular stage of the product’s lifecycle.”

Smit was channeling the spirit of the time. Pick one number. Align the team. Ship toward that number. He even acknowledged that the metric should shift as the product matures, but the core belief was unwavering: at any given moment, there is ONE number you should care about.

Six months later, Rashmi Shukla wrote ‘Product Metrics — How to Measure a Product‘ and slipped in the first crack. She argued for “chasing a north star metric accompanied by a few major metrics.” A few. Not one. Rashmi also dropped a warning that didn’t get enough attention at the time: focusing on MAU is “just another vanity metric” that hides the real picture of product stickiness.

I almost skipped that ‘a few’ caveat when I first read it. Seemed like a throwaway line. Turns out it was probably the most important sentence in the piece.

The Year the North Star Cracked (2021)

OK so here’s where it gets uncomfortable. Two pieces from 2021 basically describe the wreckage. People who followed the OMTM advice, did everything right by the book, and still ended up in trouble.

Tiziano Nessi wrote ‘How to Position Yourself as a Strong Product Manager From the Get-Go‘ and did something I don’t see often enough in our industry: he confessed to the exact mistake that the previous era’s advice had produced.

“I did the mistake to set the total number of users as an NSM. This led to a focus on the wrong value lever, and despite constant growth, the engaged users were decreasing...”

Read that again. Growth was going up. Engagement was going down. The North Star was shining and the product was quietly dying underneath it. That kind of failure mode doesn’t show up in a framework diagram.

Around the same time, Sebastian Straube published ‘North Star Metric — Measure the Right Thing‘ and reached back to 1956 for ammunition. He cited Ridgway’s classic critique:

“What gets measured gets managed — even when it’s pointless to measure and manage it, and even if it harms the purpose of the organisation to do so.”

Sebastian proposed a three-tier system: vanity metrics at the bottom, proxy metrics in the middle, business-aligned metrics at the top. The North Star should sit at Tier 1, sure, but only if it demonstrates “a clear and direct relationship between your product’s problem and the degree to which it is solving it.” Even that wasn’t enough on its own, because you still needed the lower tiers for context.

From Star to Constellation (2022-2023)

This is where the thinking got genuinely interesting.

I remember reading Elena Seregina’s May 2022 piece ‘Metrics Hierarchy and Metrics Pyramid‘ and thinking: finally, someone said it. She named the thing that had been bugging me for months:

“NSM is a product metric: most likely, it will measure customer behavior, not business goals.”

That distinction sounds small. It changes everything. Your North Star can be humming along perfectly while your business loses money. Elena proposed a layered pyramid with business goals at the top and product metrics below, each level checking the one above it. She also shared a story that stuck with me: “Our business metrics were rising. But as we were cracking a bottle to celebrate, the metrics suddenly dropped.”

By 2023, even the evangelists were hedging. Sriram Parthasarathy wrote ‘Decoding Business Growth: The Art and Science of Choosing the Right North Star Metric‘ and spent most of the piece making the case for NSMs. Then he landed here:

“While the North Star metric is a critical guide, it’s not the only measure of success. Companies should monitor other KPIs to ensure a balanced approach to growth.”

Sit with that for a second. Sriram just spent a whole article making the case for North Star Metrics. And his own conclusion is ‘not the only measure of success.’ That hedging at the end? That’s the sound of a correction happening in real time, right on the page.

What I’m Hearing on the Podcast

Bhav Patel came on the podcast to talk about fighting feature factories, and his rant connected directly to this whole metrics story. His argument: PMs became “feature managers” precisely because they lost the discipline of measurement. Not because they measured too little, but because they measured the wrong things and then stopped owning the numbers altogether.

“Product folks need to get their head out of their asses and start measuring what they do,” Bhav told us. His point isn’t that the North Star idea was wrong. It’s that most PMs never did the hard work of picking the right one, and when the single-metric approach failed them, they abandoned measurement entirely instead of fixing the metric.

So What Does This Mean for You?

The North Star didn’t die. It got demoted from solo act to lead instrument. The product community learned (sometimes painfully) that a single metric can focus a team, but it can also blind one. The fix isn’t to stop measuring what matters most. It’s to make sure ‘most’ isn’t doing all the work alone.

Here’s what I want to know: does your team still rally around one North Star, or have you moved to something more layered? Reply and tell me. I’m genuinely curious whether the correction has reached the teams doing the work, or if it’s still mostly happening in the articles.

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.

Jay Stansell · Lisbon, Portugal · Product Coalition

The user, a senior PM at a mid-size SaaS company, had scored well on our platform. Her content was structured, her arguments logical, her delivery measured. By every objective metric we’d built, she was a strong communicator.

Her feedback, though, said: “I guess I’m just not a natural speaker. My scores are fine but I still don’t feel like I know what I’m doing.”

Fine scores. No identity.

That’s when I realized we’d built a measuring stick when people wanted a mirror. The thing I kept coming back to was this idea of identity.

The Score Trap

Let me rewind. By November 2025, ExecReps had acoustic voice analysis working. V1.0 shipped with AssemblyAI-powered analysis of pace, filler words, clarity, confidence.

Users would record a practice response to a scenario like ‘Your CEO just asked you to explain why the roadmap is slipping, go,’ and we’d break down both content quality and vocal delivery.

The scores were… fine. People used them. People improved. Something, though, was missing.

I’m not sure if it was the numbers themselves, or just how we were presenting them. I wonder if it’s just human nature to want more than a number.

I kept hearing the same questions: “What’s a good score?” “Am I normal?” “How do I compare?”

These aren’t questions about improvement. These are questions about identity. People weren’t asking “How do I get better?” They were asking “Who am I as a communicator?”

Anyone who’s read Deci and Ryan’s Self-Determination Theory would recognize the pattern. Scores satisfy competence (one of three basic psychological needs) but completely miss relatedness and autonomy.

A number tells you where you rank. It doesn’t tell you where you belong.

I’d spent fifteen years building products and somehow missed one of the most documented insights in motivation research. People don’t optimize for numbers. They optimize for a sense of self.

This reminds me of a conversation I had with a founder recently, talking about how a lot of fitness apps fail because they focus on calories burned, not on the feeling of being an ‘athlete.’ It’s the same thing.

The Research Rabbit Hole

Once I saw the problem, I couldn’t unsee it. So I did what any product person does when they sense an insight. I went deep into the research.

I started with the usual suspects. Big Five (OCEAN) is scientifically validated, but measures who you are, not how you communicate. DISC is closer, but too static. Myers-Briggs has too much pseudoscience baggage. Sixteen types is a nightmare for product design.

None of these were designed for voice. They were built for survey responses and check boxes.

We had actual recordings of people speaking under pressure, with measurable acoustic properties. So we built our own.

Four dimensions emerged:

Command. How much authority and conviction comes through. Not volume. Certainty. Vocal steadiness, declarative phrasing, pacing that says ‘I know what I’m talking about.’

Eloquence. The precision of expression. Vocabulary range, sentence structure, the ability to find exactly the right word instead of four approximate ones.

Engagement. How much you pull people in. Vocal variety, storytelling instincts, tonal warmth. The difference between reading a report and making someone lean forward.

Consistency. How stable your communication is across contexts. Some people are brilliant in a 1:1 and fall apart in front of a group. This dimension measures the gap.

We called it the Voice Performance Score, VPS. We scaled it 400 to 1000, like the SATs.

That familiar-scale choice comes straight from Nielsen’s Recognition over Recall heuristic. If your scoring system requires a user manual, you’ve already lost. The SAT scale gave people an instant mental model. No learning curve.

The VPS wasn’t the breakthrough, however. The archetypes were.

Eight Ways to Speak

What I saw at a company in education (as well as at “MyersBriggs”, “Working Genius” etc ), was this: when you give someone a category to belong to, they engage differently with your product.

At FYG we profiled students into career pathways. The moment a student could say ‘I’m a Builder’ or ‘I’m an Advocate,’ their engagement doubled. Not because the content changed. Because their relationship with the content changed.

If you know the Kano Model, this is the difference between a ‘must-be’ feature and a ‘delighter.’ Scores are must-be. You expect them in an analysis tool. Archetypes are a delighter.

Nobody asked for them. Once people had them, though, they became the reason people came back. Kano’s insight is that delighters create disproportionate satisfaction precisely because they’re unexpected.

So we mapped the four VPS dimensions into eight communication archetypes:

The Analyst. Clarity is their superpower. Blind spot: clarity without engagement can feel cold.

The Commander. Walks into a room and people listen. Not loud, but certain. Blind spot: steamrolling without realizing it.

The Diplomat. Reads the room better than anyone. Builds consensus. Blind spot: prioritizing harmony over directness.

The Maverick. Brilliant in bursts. The off-the-cuff comment everyone remembers. Blind spot: inconsistency.

The Mentor. Natural teachers who make complex things simple. Blind spot: over-explaining when the room just needs a decision.

The Practitioner. Reliable, trusted, steady. Won’t dazzle you, but will never let you down. Blind spot: disappearing in Commander-dominated rooms.

The Storyteller. Turns data into narrative, problems into quests. Blind spot: not every moment needs a story.

The Visionary. The full package when they’re on. Paints futures people run toward. Blind spot: so compelling nobody challenges them.

Choosing eight was a deliberate product decision. Four would have been too broad. People wouldn’t see themselves. Sixteen too granular. Too many edge cases where users fall between types.

This is what Don Norman calls building the right conceptual model: if the model is too simple, people distrust it. If it’s too complex, they can’t hold it in their heads. Eight sits in the cognitive sweet spot. I still wonder if there’s a ‘perfect’ number, but eight just felt right, you know?

We debated whether archetypes should be fixed or fluid. Fixed archetypes are easier to build product around. Fluid archetypes are more scientifically honest. Your style does shift based on context.

We chose a middle path. Your archetype is your dominant style based on your body of work. We surface how your dimensional scores shift across scenarios.

So you might be a Diplomat overall, but skew Commander during board presentation practice. That nuance maps to Daniel Pink’s Autonomy principle from Drive. It gives users freedom to explore their communication range rather than boxing them in. The dominant archetype gives you identity. The dimensional shifts give you agency.

The LinkedIn Moment

We shipped archetypes on November 16th. Within the first week, something happened that I did not expect.

People started sharing their archetype on LinkedIn. Not their score. Not their improvement percentage. Their archetype.

“Just found out I’m a Storyteller on ExecReps. Honestly tracks 😂”

“Apparently I’m a Commander. My team is not surprised.”

“Diplomat here. Which explains why I spend half my life translating between engineering and sales.”

The product insight that hit me then, and that I hope lands for you too, is this: people share identity, not metrics.

Nobody posts their credit score on LinkedIn. Nobody shares their Duolingo XP. They’ll share their Hogwarts house, their Enneagram number, and now, their communication archetype.

Through the lens of Nir Eyal’s Hook Model, we’d accidentally built a complete engagement loop: someone sees an archetype post on LinkedIn (external trigger). Curiosity drives them to try ExecReps (action). They discover their archetype, any of eight possibilities (variable reward, the ‘Hunt’ type). They share it publicly, loading the next trigger for their network while creating a public identity commitment (investment).

We hadn’t designed this loop. Every piece was there, though. I wonder if we could have planned it, or if some of the best product moments just emerge like this, almost by accident.

And what made it powerful rather than gimmicky: the archetypes were grounded in real behavioral data. This wasn’t a quiz telling you what you already knew. It was acoustic analysis of how you speak under pressure.

Cialdini’s Authority principle explains why it stuck: the archetype carries weight because the source is rigorous. A personality quiz calling you creative is forgettable. A voice analysis confirming it is not.

The Diplomat Problem

The title of this post is about Diplomats, and I want to get specific.

When archetype results started rolling in, something interesting emerged. Diplomats were disproportionately likely to have previously described themselves as “bad communicators” in their onboarding surveys.

Read that again. The people most skilled at reading rooms, building consensus, and managing complex interpersonal dynamics… thought they were bad at communication.

Why? Because in most professional environments, ‘good communicator’ is coded as ‘Commander.’ Be decisive, be loud, be certain.

If your natural style is diplomatic. If you listen before speaking, build bridges instead of podiums, choose precision over volume, you’ve been told your whole career to “speak up more.”

Kahneman and Tversky’s work on framing effects explains this precisely. The professional world framed ‘communication skill’ around a single archetype, and Diplomats measure the gap between their style and the dominant one. They interpret that gap as deficiency. It’s a framing problem masquerading as a skill problem.

This hit close to home. I still remember being told my Cockney accent, my natural speaking voice, disqualified me from a design job early in my career. The implicit message then was ‘the way you communicate doesn’t belong here.’ It wasn’t about the content of what I said, but the sound of it. It was about identity, about belonging.

That’s the same message every Diplomat gets in a Commander-dominated culture. Every Mentor told they’re ‘too soft.’ Every Analyst told they’re ‘too in the weeds.’

The archetypes reframed what ‘good communication’ even means. There isn’t one model. There are at least eight.

Personas As Product

This is the product lesson I keep thinking about, because I believe it’s underappreciated:

Personas aren’t just a marketing tool. They can be the product itself.

What if the persona isn’t something you build about the user, it’s something you build for them?

Spotify Wrapped understood this. ‘I’m a top 1% listener of Japanese city pop’ is more powerful than any feature Spotify has ever shipped.

When we built archetypes into ExecReps, three things changed, and they map to frameworks any product team would recognize.

Retention jumped. Self-Determination Theory explains why: an identity satisfies all three psychological needs at once, competence (‘I’m a strong Diplomat’), autonomy (‘I choose to develop my style’), relatedness (‘I belong to the Diplomat tribe’). That’s Pink’s Mastery drive, visible progress along a path you’ve claimed as your own.

The product got easier to build. Archetypes gave us a design language. ‘You’re becoming a more commanding Diplomat’ is infinitely more motivating than ‘Your Command score increased by 40 points.’ Norman’s conceptual model principle: when the mental model is rich and intuitive, every communication taps into it.

Word of mouth ignited. BJ Fogg’s B=MAP model explains the mechanics: Motivation was sky-high (identity validation), Ability was trivial (screenshot and post), and the Prompt was the archetype reveal itself. All three maxed at the moment of discovery. We’d accidentally engineered sharing behavior at the exact point where Fogg’s equation was maximally favorable.

Two weeks later we shipped v1.3, the Executive Performance Score. Technically more sophisticated. Content weighted at 60%, delivery at 40%. Population-normalized. Objectively better.

Nobody shared their EPS on LinkedIn. They shared their archetype.

What I’d Tell Myself

If I could go back to early November 2025, deep in acoustic analysis, obsessing over signal processing, I’d tell myself: the most important feature you’re about to ship isn’t the most complex one. It’s the one that helps someone say ‘Oh, that’s who I am.’

Because the biggest barrier isn’t skill. It’s self-knowledge. Most people aren’t bad communicators. They’re communicating in environments that reward a style that isn’t theirs, and they’ve internalized that mismatch as a personal failing.

You’re not a bad communicator. You might just be a Diplomat in a room full of Commanders.

Once you know that, once you see it, you stop trying to be something you’re not, and start figuring out how to be the best version of what you already are.

This idea, this reframing, is at the core of what we’re trying to build with ExecReps. It’s about helping people find their voice, not just change it. I’m still learning how to articulate this, but it feels important.

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

In 2016, the PM job felt clean. Three jobs, one framework, minimal existential dread. Ten years later, a PM writes about burnout so deep that “just take a break” feels like a joke. Somewhere between those two moments, an entire profession lost the plot on what it was supposed to be.

I spent the last week reading through a decade of Product Coalition articles about the PM role. What I found is less a career arc and more an identity crisis playing out in slow motion.

Brought to you by ExecReps, AI-powered executive coaching for product leaders

“Three Jobs, Not Enough Time” (2016)

Christina Wodtke wrote The Three Jobs of Product Management back in 2016, and it still reads like the clearest thing anyone has said about the role. Business Owner. Vision Holder. Team Coordinator. That’s it.

But Christina also flagged something most people glossed over. “A Product Manager often has influence without authority,” she wrote. “She can’t fire anyone, or even get them off her team.” Think about that for a second. We spent years calling ourselves mini-CEOs when we couldn’t even choose who sat at our own table.

She also nailed something about the daily reality: “Product Managers, like all humans, tend to do the work that has the most screaming associated with it.” If you’ve ever abandoned your roadmap to fix a billing escalation, you know exactly what she meant.

“The Title I Prefer Is Servant Leader” (2022)

John McDonald didn’t tiptoe around it. His 2022 piece Why Acting Like the “CEO” of Your Product Might Be Doing More Harm Than Good went straight at the Horowitz memo that started it all. “Many a product managers use this line as a justification for their behavior,” he wrote. And honestly? I’ve seen it happen.

His replacement? “The title I prefer is ‘servant leader’. I get it, it’s not as sexy as ‘CEO’, but I think it’s a lot more evocative and representative of the role.”

Meanwhile, something awkward was happening. PM jobs had grown 230% between 2017 and 2022. But Kasey Fu confessed in The Hidden Value a PM Brings to the Product Team that engineers were still asking the uncomfortable question: “What value do you folks even bring?”

That line still makes me wince. Not because it’s unfair, but because most PMs, if they’re honest, have struggled to answer it clearly.

“Wait, Are We Even Needed?” (2023-2026)

Then AI walked into the room. Jing Hu put it bluntly in No Such Thing As An AI Product Manager: all product managers now have to swim in the AI pool. There’s no special “AI PM” title. There’s just PM, with a whole new set of capabilities to understand.

What fascinates me about Jing’s piece is the diagnosis underneath. PMs had become “button placers, conversion makers, document writers” for so long that bold technology actually felt unfamiliar. AI didn’t threaten the PM role. It exposed how small the role had become.

Then Dan Apps published the title that stopped the scroll. Product Management Is Broken: This Is Where It Ends. After 16 years in the field, Dan wrote that he’d been “quietly haunted by the same dysfunction” across every company. His verdict: “Product Managers need to stop internalising blame for a system that’s broken by design.”

And in early 2026, Irina Bulygina made it personal. I’m Burned Out and “Just Take a Break” Is Not Helping reads less like an article and more like a late-night conversation with a friend. “I sit on both sides of the barricades,” Irina wrote, “trying to understand everyone at the same time.”

This is the part that gets me. She’s not questioning the strategy frameworks or the org chart. She’s questioning whether the emotional cost of the job is sustainable. “Fear that AI is replacing us. Fear that the product manager role is becoming obsolete. Fear that we are watching the beginning of the end.”

What I’m Hearing on the Podcast

This tension showed up on the Product Coalition podcast too. In EP98, Margaret-Ann Seger argued that the best product ideas shouldn’t come from product managers at all. That’s the servant leader thesis taken to its logical conclusion: your job isn’t to have the ideas, it’s to create the conditions where good ideas surface from anywhere.

Brian Glassman explored the other side of the coin in EP81, making the case that generative AI can actually free PMs from the button-placing and document-writing that Jing described, letting them get back to the bold thinking the role was always meant for.

So What Does This Mean for You?

Here’s what I keep coming back to. The PM role has survived every identity crisis of the past decade. Mini-CEO, servant leader, empowered teams, AI disruption, burnout. It survives because the need for someone to hold the thread between business, technology, and users never goes away. The title changes. The job description changes. The core need stays.

So try this at your next dinner party. Someone asks what you do. You can’t say “product manager.” What comes out instead? I genuinely want to know. Hit reply and tell me.

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.

Jay Stansell · Lisbon, Portugal · Product Coalition

She was a product director from a Series B startup. Smart as hell. Her answer to our practice prompt, a mock board presentation on quarterly growth, was structurally flawless. Perfect, even. Clear thesis, supporting data points, strong close.

Our AI gave her a stellar score.

Then I played the audio.

She said “um” twenty-three times in ninety seconds. Her pace swung between racing through numbers at 210 words per minute and grinding to a near-halt when she hit anything she wasn’t sure about. Stretches where her voice got so quiet it almost disappeared.

None of that showed up in our assessment. For the first four months of building ExecReps, our AI couldn’t hear anyone. It could only read.

I keep coming back to that moment when someone asks about finding product-market fit. The thing that makes your product ‘real’ isn’t always the thing you build first. Sometimes it’s the thing you were accidentally ignoring.

The Text-Only Era

Let me back up. When we launched v0.1 in July 2025, the core loop was simple: you record yourself speaking, we transcribe it, GPT–4 evaluates the transcript, you get feedback. Record, Transcribe, Assess, Improve.

If you’ve read Nir Eyal’s Hook Model, you’ll recognise that structure. External trigger (the prompt), action (record yourself), variable reward (personalised AI feedback), investment (your practice history building over time). It worked.

GPT–4 is good at analysing structure, directness, and evidence. Users were coming back daily, practising pitch decks, preparing for all-hands, rehearsing difficult conversations. BJ Fogg’s B=MAP model would have given us decent marks: motivation was there (real presentations coming up), ability was easy (speak for two minutes, get instant feedback), and the prompt was the product itself.

I almost fell into a trap here. The product was working. Users were engaged. Growth was happening. Every instinct in my PM brain said ‘don’t touch it. Optimise what’s working.’

But something gnawed at me. We were building a communication coaching tool that only analysed what people said, not how they said it. We were a voice product that was functionally deaf.

This reminds me of a conversation I had with a guest on the Product Coalition podcast a while back, talking about the gap between perceived and actual performance. In Don Norman’s terms, we’d created a massive Gulf of Evaluation. Users would record themselves, get text-based feedback, and think “great, I’m improving.” They had no way to evaluate their actual delivery. That’s the part that mattered most for their career. The system was hiding their most critical signal from them.

The Origin Story Collision

I need to tell you why this bothered me more than it probably should have.

Years ago, I graduated from university in London with a design degree. Portfolio polished, creative director at a prestigious agency loved my work. Then the call came: “Jay, I have to be honest with you. Your work is great. Your attitude is great. But we can’t employ people who talk like you.”

My working-class Cockney accent. That was it.

Not what I said. How I sounded.

I was building a product whose entire origin story was about the injustice of being judged by your delivery, and the product itself could only judge content. The irony was not lost on me.

Every time I reviewed a user submission and saw a clean transcript score next to audio that told a completely different story. Rushed pace, vocal fry, filler words stacking up. I felt the gap widen. Kahneman calls this WYSIATI: ‘What You See Is All There Is.’ Our users were suffering from it. They’d look at their text-based score, see nothing flagged about delivery, and conclude their delivery was fine. The investor who later told us “I thought I sounded the same the whole way through”? That’s WYSIATI in action. If the system doesn’t show you a problem, your brain concludes no problem exists.

Going Acoustic

In September 2025, I started researching acoustic analysis providers. Building it ourselves lasted about a week. Training acoustic models from scratch is a rabbit hole that has swallowed entire startups. Build vs. buy was an easy call.

AssemblyAI became our answer: pace detection, filler word identification, pause analysis, and raw data we’d need for confidence and clarity metrics.

The hard part wasn’t the API calls. It was deciding what to do with the data. AssemblyAI gives you a firehose. Every millisecond of audio has extractable features. Which ones matter most? I’m still not sure we’ve found the perfect answer here, but we’re getting closer.

Nielsen’s heuristic of “recognition rather than recall” shaped our thinking here. We didn’t want to dump a wall of acoustic data on users. We needed to surface metrics they’d immediately recognise as relevant. Pace variation they could hear in their own head, filler words they’d felt themselves say. The data had to match their fuzzy memory of the recording, then sharpen it.

The 80/20 Decision

This is the product decision I think about most from that period. I still wrestle with it sometimes.

We now had two signals: content quality and delivery quality. My first instinct was 50/50. That’s what every public speaking book tells you. Mehrabian’s “7–38–55 rule” even suggests delivery matters ‘more’.

Mehrabian’s research is about emotional communication, not professional presentations. When a product director presents quarterly numbers, the content matters more than her pacing. If the analysis is wrong, no amount of vocal confidence saves it.

We went with 80% content, 20% delivery. It felt conservative. Part of me wanted to weight delivery higher because that was our differentiator.

A product team that’s worked with the Kano Model would recognise the trap. Acoustic analysis was our ‘delighter,’ the feature that surprises and differentiates. Content quality was our ‘must-be,’ the foundational feature users expected to just work. Kano’s research is clear: over-invest in delighters at the expense of must-be quality and the whole product collapses.

The 80/20 split evolved to 60/40 by v1.3 as our delivery analysis matured. Starting conservative was right. You can always increase the weight of a signal as it improves. Decreasing it after users have relied on it is harder. That’s Kahneman and Tversky’s loss aversion at work. People feel losses roughly twice as intensely as equivalent gains. Telling a user “your delivery score now counts less” feels like taking something away. I wonder if we could have communicated that better, in retrospect.

The First Real Analysis

I remember the exact day we ran the first complete acoustic analysis. Early November 2025, right before v1.0 went live.

A beta user doing a practice investor pitch. Transcript score was strong: clear problem statement, solid market sizing. Under the old system, encouraging feedback, move on.

The acoustic analysis told a different story. His pace averaged 178 WPM, fine, but variance was extreme. Racing through market opportunity at 220+ WPM (nerves) and crawling during financials (uncertainty). He used “basically” seventeen times. Two pauses over four seconds. Not power pauses, but the kind where someone loses their thread.

When we showed him the combined assessment: here’s what you said (strong), here’s how you said it (needs work), here’s exactly where. He sent us a message I still have saved:

“I’ve given this pitch eleven times. This is the first time anyone told me I speed up when I’m excited and slow down when I’m unsure. I thought I sounded the same the whole way through.”

That message illuminated something about what Daniel Pink calls Mastery in his Drive framework. People can’t improve what they can’t see. The text-only version gave users mastery over ‘content.’ Delivery mastery was invisible. They had no feedback signal, no progress to track, no way to know if they were getting better or just more comfortable with bad habits. Acoustic analysis opened an entire mastery dimension that had been locked.

The Product Lesson: Finding Your 10x Moment

If you’re building a product, especially an AI product, there’s a concept I keep coming back to: the 10x moment. It’s the point where your product stops being “useful” and starts being ‘I can’t go back to not having this.’

Text-based feedback was useful. Valuable. Users came back.

Acoustic analysis made ExecReps ‘real.’ It was the difference between a product that told you ‘what to say differently’ and one that told you ‘how you sound when you say it.’

In Hook Model terms, acoustic analysis transformed our variable reward. Text-only feedback was becoming predictable. After a few sessions, users could roughly guess their content score. Eyal is explicit: predictable rewards lose their pull. Acoustic analysis reintroduced variability. Every recording surfaced something unexpected. ‘I didn’t know I did that’ is the most powerful variable reward a coaching product can deliver.

I’ve learned this about 10x moments: they’re usually hiding in the thing your product should do but doesn’t yet. They feel obvious in retrospect. ‘Of course a voice coaching tool should analyse the voice.’ When you’re in the weeds of building, though, the temptation to optimise what exists instead of building what’s missing is enormous.

The framework I use now is simple: “What is the most obvious thing our product should do that it currently can’t?” Not the clever thing. The ‘obvious’ thing. The thing a non-technical friend would ask about at dinner. ‘Wait, your voice coaching app doesn’t listen to people’s voices?’

That’s where your 10x moment lives.

What Acoustic Analysis Taught Me About the Mission

When communication coaching was something only Fortune 500 executives could access at $500–$1,000 an hour, those coaches were listening. They caught the pace changes, the filler words, the confidence drops. Human ears doing acoustic analysis with years of experience.

Every product that had tried to democratise this before us stopped at text. Grammar checkers, script analysers, AI writing assistants: all content, no delivery. None could hear you.

By going acoustic, we closed the gap between what a $1,000/hour coach provides and what everyone else has access to. That’s the mission we started with at Product Coalition, in a way: making product management knowledge accessible. This feels like a natural extension of that.

Executive presence isn’t about talking points. Anyone with ChatGPT can generate those. It’s pace and pause and conviction and clarity. It’s what got me rejected from that design agency. Deci and Ryan’s Self-Determination Theory says people need competence feedback to stay intrinsically motivated. Our text-only product gave users an incomplete competence signal, half the picture. Acoustic analysis completed it. When people feel competent, they don’t need external pressure to practise. They come back because they ‘want’ to.

What Came Next

Shipping v1.0 with acoustic analysis in November 2025 was just the beginning. Once we could hear, we needed to understand. The Voice Performance Score in v1.2 broke delivery into Command, Eloquence, Engagement, and Consistency. Then v1.3’s Executive Performance Score unified content and delivery into a single metric tracking progress over time.

Each was its own battle. All downstream of the same decision: stop being deaf, start listening.

Four months of a product that could only read. One afternoon hearing a user who didn’t know she said “um” forty-three times. And a bet that the obvious missing piece was worth the engineering investment.

Because the metrics were fine. The product was still incomplete.

That dissonance, between what the data says and what your gut knows. If you’re building something that matters, learn to listen to it. Pun fully intended.

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.

The Numbers Behind the Nomination

I sat down last week and pulled the actual data on what Product Coalition has done since I started it in 2015. The numbers honestly shocked me:

• 3,870 articles published across Medium and Substack

• Nearly 10 million views (9,860,219 to be exact)

• 3.67 million reads — people who actually engaged with the content

• 100+ podcast episodes featuring product leaders from companies you know

• 8,500+ newsletter subscribers opening our emails twice a week

• 11 years of continuous publishing without a break

Our most-read article — “10 Fun Virtual Icebreakers to Take Remote Working to the Next Level” — hit 656,324 views during the pandemic. It wasn’t a product management framework. It was a piece that helped people feel connected when the world fell apart.

Our second most-read — “So You Want To Manage A Product?” — has been read by 254,746 people, many of them at the exact moment they were deciding whether to pursue this career.

“How to Write Epics and User Stories” sits at 208,543 views — a practical guide that gets shared in Slack channels and onboarding docs at companies I’ll never even know about.

What This Nomination Actually Means

Thinkers360 is the world’s premier B2B thought leader platform. Their annual awards recognize people who’ve made genuine, sustained contributions to their field — not just viral moments, but years of showing up.

The process works like this:

1. Public voting (now through April 30) determines the top 3 nominees in each category

2. Final judging by Thinkers360 Ambassadors selects the winner

3. Winners announced at the virtual ceremony in Fall 2026

I’m nominated in the Thought Leader of the Year category — the biggest individual award they give.

Why I’m Asking

I’ll be honest: asking for votes feels uncomfortable. Product Coalition has always been about the community, not about me.

But here’s what I’ve realized: a win here doesn’t just validate me. It validates the model.

Product Coalition was built on a bet that the best product thinking comes from practitioners sharing openly — not from consultancies, not from credentialed gatekeepers, but from people doing the actual work. Every article published by a first-time writer. Every podcast guest who shared a real failure story. Every newsletter reader who forwarded an issue to their team.

Winning Thought Leader of the Year would tell the world that community-driven thought leadership works. That you don’t need a book deal or a TED talk to shape how an entire industry thinks. You need consistency, generosity, and a community that cares.

How to Vote

It takes about 2 minutes:

1. Go to the voting page: https://www.thinkers360.com/tl/annual_awards/voting

2. Create a free Thinkers360 account (they require this for voting integrity — no spam, I promise)

3. Find “Jay Stansell” under the Thought Leader of the Year category

4. Click Vote

Voting is open until April 30, 2026.

A Thank You, Regardless

Whether or not I win this thing, I want you to know: the last 12 years have been the most fulfilling work of my career. Not because of view counts or download numbers, but because every week I get messages from product managers who say this community helped them.

That’s the real award.

But if you’ve got 2 minutes and you think what we’ve built here matters — I’d be grateful for your vote.

Thank you for being part of Product Coalition.

Jay

Brought to you by ExecReps, AI coaching that helps product leaders practice tough conversations before they satisfice easy ones

The Quiet Recommendation (2016)

Before OKRs became a mandate, they were a suggestion. Christina Wodtke, author of Radical Focus, mentioned them almost casually in her 2016 piece ‘The Three Jobs of Product Management.’ Her take:

“Vision holding is the most neglected job of a PM, yet it is the one that makes teams and product succeed. (OKRs can help!)”

That exclamation mark and parenthetical still make me smile. OKRs weren’t yet the thing every VP demanded in their quarterly planning session. Christina framed them as a useful tool alongside team norms and mission statements. A helper, not a religion.

“30 Days and You’ll Never Look Back” (2018)

Two years later, the tone changed completely. Brad Dunn’s ‘Moving to OKRs in 30 Days‘ captured peak enthusiasm perfectly. He opened with an admission that tells you everything about the era:

“I have been talking to a LOT of people over the last few months about OKRs, more than I thought I would actually. It is such a hot topic.”

Brad’s confidence was infectious. Follow his plan, and in 30 days you’d be transformed. But I love this moment of honesty buried deep in the optimism: “Trust us here. The first cycle you do will be a mess, just accept that and get the cadence and routines down.”

The seeds of disillusionment were planted right inside the hype. Most people skipped past that line. I bet they wished they hadn’t.

The Warning Shot (2019)

Then Itamar Gilad showed up. A former Google PM (the company most associated with OKR success), Itamar wrote ‘5 Ways Your Company May Be Misusing OKRs‘ and dropped what I consider the single most important sentence in a decade of OKR discourse:

“OKRs are just containers for goals. They serve bad goals just as well as they do good goals.”

That line changed my thinking about frameworks in general. Containers don’t care what you put inside them. Itamar went further: “Bad OKRs can amplify the issues the org is troubled with rather than fix them.” Coming from inside the Google house, this wasn’t a hot take. It was a warning from someone who had watched OKRs fail at scale.

Seeking Alternatives (2023)

By 2023, the community had started publishing “what to use instead of OKRs” articles, which is the clearest signal any framework has crested its hype curve. Lee Fischman’s ‘Goal-Question-Metrics: A Holistic Alternative to OKRs‘ proposed GQMs as a more rigorous approach. His critique was specific:

“Why is asking questions so worthwhile? Look at the examples above and notice how their Key Results are basically arbitrary.”

Arbitrary. That word landed hard with anyone who had sat through a quarterly planning session watching leadership pick Key Results out of thin air. Lee didn’t reject OKRs entirely, but he exposed a real weakness: the framework skips the “why are we measuring this?” step.

The Framework Survives (2024-2026)

This is the part that fascinates me most. Noa Ganot wrote about OKRs for Product Coalition twice, two years apart, and watching her thinking deepen is its own story.

In 2024, her piece ‘OKR Planning: Break the Chains‘ treated OKR problems as therapy problems: “OKRs are meant to bring your dreams into reality. If you don’t eliminate these limiting beliefs, you are left with a lot of reality that shapes and limits your dreams.”

By January 2026, her perspective in ‘When OKRs Don’t Work, the Problem Is Elsewhere‘ had sharpened into something more profound. OKRs fail because “they surface gaps teams have learned to live with: unclear priorities, postponed decisions, and ownership that isn’t as solid as it looks.”

And then the line that closes the loop on a decade of debate: “The framework forces the question, but it can’t force the answer.”

Noa didn’t flip-flop between those two articles. She deepened. That’s rarer than you’d think in product writing.

What I’m Hearing on the Podcast

Andrea Saez joined me recently to talk about product strategy alignment, and her thinking connects directly to the OKR misuse thread. She’s been vocal about the difference between vanity metrics and meaningful customer KPIs, which is exactly where most OKR implementations go sideways. Teams pick Key Results that look impressive in a slide deck rather than ones that reflect actual customer outcomes.

Alex Blinov made a related point about how the PM role is shifting from “mini CEO” to strategic architect. When your job definition changes, the goals you set and how you measure them have to change too. Most OKR frameworks haven’t caught up with that shift.

So What Does This Mean for You?

OKRs survived the hype cycle. Not because they’re perfect, but because the problem they force you to confront (do we actually know what we’re trying to achieve?) never goes away. The framework is just a mirror. If you don’t like what you see, the answer isn’t a better framework.

Are you still using OKRs? Have you modified them beyond recognition, or abandoned them entirely? I’d love to hear what actually works on your team. Hit reply and tell me.

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.

Jay Stansell · Lisbon, Portugal · Product Coalition

I had just graduated from university in London. Design degree. A portfolio I was proud of. I’d interviewed at a design agency I’d been obsessing over for months. The kind of place with exposed brick and people who used ‘kerning’ as a verb. The creative director had spent forty-five minutes going through my work. He loved it, laughed at my jokes, and shook my hand, saying he’d be in touch.

He was.

“Jay, I have to be honest with you. Your work is great. Your attitude is great. But we can’t employ people who talk like you.”

People who talk like you.

I’m from East London. Cockney. Not the charming Guy Ritchie kind. The actual kind, where “th” becomes “f” and sentences end before they technically should. The kind that, apparently, disqualifies you from working at a place where your job is to design things with your hands.

I didn’t argue. I didn’t feel angry, not at first. I felt something worse. I felt like he was probably right. That’s what rejection based on how you sound does. It doesn’t just close a door. It makes you believe the door was never meant for you.

The Hidden Filter

Something nobody tells you about professional life is that there’s a filter sitting underneath every other filter. It’s not on any job description. No recruiter will mention it. It’s there in every meeting room, every pitch, every interview, every promotion committee.

It’s how you sound.

Not what you say. How you say it. Your accent, your cadence, your filler words, whether you uptalk or downspeak, whether your voice carries the invisible markers of a particular postcode or tax bracket.

I remember reading Daniel Kahneman’s Thinking, Fast and Slow and it felt like someone was explaining my own life back to me. He and Tversky showed that humans make snap judgments, what he called System 1 thinking, based on surface signals, then rationalize them after the fact. That creative director didn’t spend forty-five minutes evaluating my portfolio and then make a reasoned assessment. He made a gut decision in the first thirty seconds of hearing me speak. Everything after was confirmation bias in a nice chair.

Any product team that’s studied cognitive biases would recognize the pattern. We just usually apply those insights to button colors. Not to someone’s career. I sometimes wonder if that’s the biggest blind spot in all of product management.

I spent the next twenty years building a career despite that phone call. I led product teams, founded companies, and grew Product Coalition to over a million readers. I sat on the Harvard Business Review Advisory Council. I built Find Your Grind into a $20M EdTech company.

At every single stage, I watched the same pattern repeat. Not just for me. For everyone.

The engineer who has brilliant ideas but gets talked over because she pauses too long between thoughts. The sales leader who loses deals not because his product is worse but because his delivery doesn’t carry what someone like Cialdini would call ‘authority signals’. These are the vocal patterns and confidence markers that make listeners trust the speaker before they’ve processed the content. The first-generation professional who has all the skills but none of the communication patterns that come from growing up around boardroom tables.

Communication coaching exists. It’s just not for them. It’s for Fortune 500 CEOs who pay $500 to $1,000 an hour. Everyone else gets a line in their performance review that says “needs to work on executive presence,” with precisely zero guidance on what that means or how to do it.

The Realization That Broke It Open

Sometime in early 2025, I was watching the AI space consume everything. Writing emails. Polishing documents. Drafting presentations. Generating images. Building code. Every piece of professional output was getting an AI layer.

Except one.

Your voice. The way you speak, persuade, inspire, connect was completely untouched. No infrastructure. No tools. Nothing.

Then I realized something I haven’t been able to un-think since: your voice is your last IP.

In a world where AI can write anything, design anything, code anything, the one thing that’s still authentically, irreducibly you is how you communicate when you open your mouth. It’s about autonomy, that fundamental human need to express your authentic self. Your presence in a room. Your ability to land a point, hold a silence, turn a hostile question into a moment of connection. That’s not something you outsource to a language model.

Yet there’s nothing helping people develop it at scale.

Building the Thing

I started building ExecReps in the spring of 2025. The concept was deceptively simple. AI-powered practice for high-stakes conversations. You speak. It listens. It gives you feedback on pace, clarity, filler words, confidence, structure. An Executive Performance Score to track improvement over time.

Not “speak like a TED Talk.” Not “sound more corporate.”

Not “lose your accent.”

That last part matters to me more than any feature we’ve ever shipped.

If you’ve worked in product, you know BJ Fogg’s model. Behavior happens when Motivation, Ability, and a Prompt converge. My entire v0.1 was designed around one question from that framework: what’s the simplest possible action at the moment someone is motivated to improve?

Press record. Speak for three minutes. Get feedback.

No onboarding carousel with seven screens. Just a microphone, an AI, and the lowest possible Ability threshold Fogg would accept.

On July 7th, 2025, I shipped v0.1. Google OAuth. Stripe. Voice recording, transcription, GPT–4 assessment, admin dashboard, support chat. It was all duct tape and ambition.

That first version could do exactly one thing well: listen to you speak and tell you what you could do better. No archetypes. No voice performance scoring. No team analytics. Anyone who’s used the Kano Model would recognize what we shipped. The must-have quality, nothing more. The basic expectation that has to work before delighters matter.

It was enough.

It was enough because it proved the one thing I needed to prove. That people would open a browser, press record, speak into the void, and care about what came back. It was a test of Nir Eyal’s Hook Model. I needed to know if the basic loop would close. Would an external trigger (an email invite) lead to an action (recording a workout), produce a variable reward (personalized feedback that surprised them), and create enough investment (seeing their score, wanting to improve it) that they’d come back without being asked?

That first version was a test of whether the hook existed at all.

The Product Lesson: Founder-Market Fit Is Not a Buzzword

I want to talk to the product managers reading this, because this is the part that took me fifteen years to understand.

We talk a lot about product-market fit. Important questions. There’s something that comes before all of it, though. The thing that determines whether a product has a soul or just a roadmap.

Founder-market fit.

Not “I spotted a market opportunity.” Not “I read a McKinsey report that said the executive coaching market is worth $15 billion.” I mean: has this problem left a mark on you?

Daniel Pink’s book Drive breaks this down perfectly. Extrinsic motivation, like money or market opportunity, fades the moment things get hard. Intrinsic motivation is built on Autonomy, Mastery, and Purpose. The founders who survive aren’t optimizing for TAM. They’re driven by Purpose, a problem they can’t stop thinking about because it happened to them.

That phone call in London wasn’t market research. It was a scar. Scars make better products than spreadsheets.

The first reason this matters is survival. Every product hits a trough where nothing is working. Users aren’t growing. The feature you shipped didn’t move the needle. Pink’s research is unambiguous. Purpose-driven motivation outlasts reward-driven motivation under sustained difficulty. If your motivation is “the market opportunity is large,” you will quit during the trough. If your motivation is “I got a phone call that told me I wasn’t good enough because of how I sound, and I know millions of others got the same call,” you will not quit.

It also calibrates your intuition. When you are the user, you make a thousand micro-decisions correctly without needing data. Kahneman’s work on expert intuition shows it develops in environments with clear feedback loops and repeated practice. This is exactly what founders get when building for their own problem. You know which feedback to give first because you know what would have helped you.

Finally, your story becomes your moat. In a market that’s about to get crowded, and the AI communication space will get crowded, the company with the founder who lived the problem will always sound different from the company that identified an opportunity. Users can tell.

When I tell people what ExecReps does, I don’t start with features. I start with a phone call from a creative director who liked my work but not my voice. Every single time, something shifts in the conversation. They’ve had their version of that call too.

The Uncomfortable Part

I want to be honest about something. This is a building-in-public series, and I’d rather be uncomfortably honest than comfortably polished.

When I shipped v0.1, I wasn’t sure it would work. Not the product. The idea. Previous voice coaching products had failed because they misjudged the Ability variable in Fogg’s equation. They asked users to do too much. Record a full presentation. Complete a 30-minute module. Invite colleagues to listen. The Motivation was there. The Prompt was there. Yet the Ability gap killed them.

My bet was that a three-minute solo workout with instant AI feedback was below the threshold where behavior happens. Not “I should do this” but “this is easy enough that I just did it.”

I didn’t know it would be different. I just knew I had to try. I’m still not sure we have the balance exactly right. I wonder whether we’re asking for too much, or even too little. We’re constantly tuning it.

There’s a version of my career where I keep running Product Coalition, keep advising startups, keep writing articles about product management frameworks. It’s a good career. Comfortable. Respected.

But there’s that phone call.

Every time I hear about a talented person who can’t get past the invisible communication filter, who gets the performance review that says “needs more executive presence” with no tools to develop it, I hear that creative director’s voice again. We can’t employ people who talk like you.

Fifteen years of building products taught me that the best time to start is before you’re ready. So on July 7th, 2025, I pressed deploy on something that was emphatically not ready. A foundation. A starting line. Voice recording, transcription, AI feedback, and a deeply personal bet that this problem was worth solving at scale.

What Comes Next

So this is the first post in a series. I’m not sure what the cadence will be, or even what the second post will cover. Maybe the tech stack. Maybe our first ten customers. The point is to do it in the open. I’ve spent years writing about how other people build products at Product Coalition. It feels right to turn the lens on myself.

I wanted to start here, at the very beginning, because the origin matters. It’s the thing I come back to.

It’s my simple filter for every hard decision. Does this feature serve the person who got that phone call? Does it make executive-level communication development accessible to someone who can’t afford $1,000 an hour? Does it help someone’s talent get heard rather than filtered?

If yes, we build it. If no, we don’t.

Twenty years between a phone call and a deploy button. Let’s see what happens next.

I don’t usually write posts like this. But some things deserve more than a Note.

Tremis Skeete, the editor of Product Coalition on Medium, just won the ‘Champion of Emerging Voices’ award at Medium’s 2026 Editor Awards.

It’s a peer-nominated award. Editors across Medium nominate the editors they respect most. It was announced at Pub Crawl to 1,900+ people from 79 countries.

The award description: ‘For their tireless work uplifting new and emerging voices on the platform.’

I want to tell you why this matters more than a trophy.

What Tremis actually does

I run the podcast. I think about strategy and partnerships and all the business stuff. But the reason Product Coalition has published 2,300+ articles since 2014 is because of Tremis.

He’s the person reading every submission. Finding writers nobody else is paying attention to and going ‘this deserves to be read.’ Not rewriting their work. Finding what they were actually trying to say and helping them say it clearly.

Most of you reading this found Product Coalition through a newsletter or the podcast. But there are hundreds of product managers out there whose first published article was on Product Coalition. Some of them went on to build entire careers around their writing. That started with Tremis opening their draft and deciding it was worth his time.

That’s not glamorous work. There’s no follower count for it. No vanity metric.

So when Medium itself says ‘this person champions emerging voices better than anyone,’ I feel something about that.

The numbers behind it

I know some of you are data people. So here’s what 11 years of championing new voices looks like:

• 2,300+ articles published on Medium since 2014

• 127 countries reached

• 50,000+ podcast downloads across the full library

• 11,000+ subscribers on Substack

• Hundreds of first-time writers given a platform

And now: Medium’s Champion of Emerging Voices 2026.

The part that matters most isn’t the number of articles. It’s the writers who published their first piece with us and went on to do things I never expected. That’s Tremis.

What this means going forward

Product Coalition has always been about giving product managers a place to share what they actually learned. Not thought leadership. Not hot takes for engagement. Real lessons from real work.

That mission hasn’t changed. If anything, the award is a reminder that it’s working.

If you’ve been sitting on something you want to write about product management, our doors are open at medium.productcoalition.com. Tremis is still reading every submission.

And if you’re already subscribed here on Substack, you’re part of this too. The community we’ve built together over 11 years is what made this possible.

Congratulations, Tremis. This one’s well deserved.

Read Medium’s announcement of the Editor Awards →

If you’re not subscribed yet, Product Coalition publishes two newsletters a week: Temporal View on Tuesdays traces how PM thinking evolved over a decade. Product & Profit on Thursdays covers the commercial side nobody teaches. Plus a podcast with guests from OpenAI, Slack, Samsung, and GoPro. Subscribe free below.

In this episode, Griffin Parry, CEO of Madduck, explores the evolving landscape of SaaS pricing, especially in the context of AI-driven features. We discuss the shift from traditional seat-based models to usage and outcome-based pricing, the operational challenges of implementing these models, and how companies can strategically navigate this transformation.

Key topics

1. Shift from seat-based to usage and outcome-based pricing

2. Operational challenges of implementing new pricing models

3. Impact of AI on SaaS value and pricing strategies

Chapters

• 00:00 Introduction to SaaS pricing evolution

• 00:12 Griffin Parry’s background and expertise

• 01:40 Limitations of legacy billing systems

• 02:34 AI’s impact on SaaS valuation and pricing

• 05:02 Adoption of usage-based pricing before and after AI

• 07:13 Cost modeling for AI-driven features

• 08:01 Outcome-based pricing and its challenges

• 14:41 Operational bottlenecks in legacy systems

• 19:01 Strategies for convincing stakeholders to change pricing

• 22:04 Meta’s approach to pricing and monetization

• 24:15 When to invest in a pricing strategist

• 25:52 How Meta’s pricing enables competitive advantage

• 27:28 First principles of pricing experimentation

• 29:23 Market trends in B2B and B2C pricing

• 32:15 Future of B2C pricing models

• 33:32 Closing remarks and resources

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 — we want to hear from you. Apply to be a guest on the Product Coalition podcast: go.productcoalition.com/become-a-guest

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.

I’ve been reading through our archive of Product-Led Growth articles this week, and something funny kept happening. The earlier the piece, the more certain the author sounded. By 2023, everyone was hedging. That arc tells a story worth tracing.

Brought to you by ExecReps, AI coaching that helps product leaders practice tough conversations before they satisfice easy ones

When the Product Sold Itself (2021)

In 2021, Michelle Yick wrote a piece for us called ‘How to Spot a Product-Led Company‘ that captured the PLG dream perfectly:

“If sales, marketing, and customer support disappeared, the core product would still attract and retain users (albeit at a slower growth rate).”

Think about that claim for a second. If your entire go-to-market team vanished, you’d still grow. That was the promise. And for a handful of companies, it seemed true. Michelle pointed to Zoom as proof: “Remember when you tried Zoom and immediately told your friends, coworkers, and overseas relatives with terrible wifi to switch over?”

Around the same time, Sandhya Hegde published ‘The 3 Modes of Product-led Growth,’ tracing PLG’s roots back further than most people realize. She argued that “1983 was the true birth year of product-led growth,” pointing to AOL free trials and the GNU open-source movement. By 2006, companies like Box and Skype had the playbook: “giving away free plans and trials to entice their end users to adopt software that could grow virally within companies.”

Every startup wanted to be the next Slack. PLG wasn’t just a go-to-market strategy. It was an identity.

Everybody Tried to Be Slack (2022)

Surrbhi Soni’s ‘Slack: A Product Led Growth Strategy Case‘ reads like peak evangelism. Slack, Dropbox, and Zoom had “removed the myth that without product manuals, traditional lead generation, sales demos, and POC setup, a product cannot grow.”

But buried in the same article was a counterpoint nobody wanted to hear:

“Microsoft launched Team in 2016 and took over many customers of Slack, and currently has almost twice as many daily active users as Slack.”

Microsoft Teams didn’t win on product experience. It won by being free with Office 365. Enterprise CTOs picked the bundle, not the better tool. That should have been a warning sign for the whole PLG movement. But in 2022, the hype was still running hot.

Meanwhile, Noa Ganot was already seeing the backlash build. In ‘Three Wrong Reasons to Give Up on Product-led Growth,’ she pushed back against lazy dismissals. Her observation was sharp: “Product-led company and product-led growth are two different things. While you can’t do PLG without being a product-led company, the other way around is not necessarily true.”

That distinction matters more than most people realize. A lot of companies thought they were doing PLG when they were really just not hiring salespeople.

The Confession Booth (2023)

This is where it gets honest. Guy Barner, a founder who actually built a PLG company, wrote ‘To PLG, or not to PLG?‘ and dropped this line:

“Even though we went with PLG, I still don’t think it’s the best choice for most startups.”

That’s a PLG founder saying PLG probably isn’t for you. He went further: “Trust me, there are zero sales-led startups with good onboarding.” Sounds like a point for PLG until you read the rest. Guy explained that PLG requires you to nail everything at once: lead generation, website conversion, onboarding, product quality, and a freemium model that converts. With sales-led, you need a good product and good salespeople. That’s it.

Then Noa Ganot came back with ‘Product-Led Growth Is a Misleading Name‘ and shared a stat that stopped me cold:

“Research shows that up until $10M ARR, it’s much easier to succeed without PLG than with it. After $10M ARR the opposite is true, and PLG wins big time.”

So PLG works best for companies that already have serious traction. But most of the advice and tooling is aimed at early-stage startups. That gap explains a lot of the wreckage.

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The Hybrid Reality (2024)

By 2024, the conversation had matured. Mart Objartel wrote about the challenges of choosing between product-led and sales-led strategies in ‘Navigating Challenges in Product-Led vs. Sales-Led Strategies for B2B SaaS,’ and he dropped the either/or framing entirely. His warning was practical: companies shift between strategies all the time, “sometimes subtly, akin to the proverbial frog boiling.” The real danger isn’t picking wrong. It’s changing direction without telling your product team.

Mart also highlighted something that rarely gets discussed. In sales-driven organizations, “aspiring product managers often feel constrained in effectively managing their products.” The PLG dream wasn’t just about growth metrics. For a lot of PMs, it was about autonomy. That’s partly why the backlash stung.

What I’m Hearing on the Podcast

Dan Balcauski joined me on the podcast to talk about SaaS pricing and packaging, and his perspective connects to PLG’s blind spot. Freemium tiers, trial-to-paid conversion, the whole pricing ladder that PLG depends on: those are the execution details where most companies fall apart. Dan’s insight about understanding customer value through qualitative research (not just usage data) explains why so many self-serve funnels leak. The product might sell itself. But if your pricing doesn’t match how customers experience value, the self-serve model collapses anyway.

(Listen to EP83 with Dan Balcauski)

So What Does This Mean for You?

PLG isn’t dead. But ‘PLG alone’ was always a mirage. The companies that tried to be Slack learned that being Slack requires being Slack: massive network effects, a product category that spreads virally, and enough runway to survive years of free users before revenue catches up. Most products don’t have those conditions.

What actually works for most teams is a hybrid. A product good enough that users want to try it, paired with humans who help close the deal. Not glamorous. Very effective.

I’m curious: did your company try PLG and pivot away? Or are you still running a self-serve motion? Reply and tell me how it’s going. I genuinely want to know.

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.

Jay Stansell · Lisbon, Portugal · Product Coalition