DEV Community: Daniel Layfield

Navigating the SaaS-pocalypse Part 2: Winning in the Short Term

Daniel Layfield — Mon, 30 Mar 2026 18:59:24 +0000

In Part 1, I made the case that despite all the news, the rules of the game aren't changing.

You still need to find an arbitrage, build distribution, and protect it before it gets competed away.

This post is about what that actually looks like across the three levers that matter: User acquisition, Creating user value, and Monetizing that value.

Velocity Still Wins, But the Bar Is Higher

From what I see in companies, teams that ship the fastest are the most successful. Hands down.

Of all the companies that I've worked with, those that ship multiple times per week are much more likely to hit double digit growth numbers, raise money, get acquired, etc.

Velocity is relative. There is no "fast enough."

You're in a race that you need to win. There is no "acceptable speed", there is only faster than your competition.

With AI, the obvious move is fewer employees doing more, but that's only partially right.

If you've touched Claude Code or Cursor, you already know you can work 10-100x faster than before.

The temptation is to do the same work with 10 people instead of 100. That's going to happen — Block just fired half their staff.

But the best path is 100 people moving 100 times faster.

Speed only matters with direction.

The constraint isn't shipping anymore. It's knowing what to ship.

The work that was always important — defining the right metrics, knowing your highest-LTV persona, building a proper north star — becomes more important now.

Measurement compounds the velocity advantage.

The product development loop hasn't changed: Ideate → Decide → Build → Measure → Learn → Repeat.

Companies that can validate faster can ship faster. That means traffic volume matters more, frequent-use products have a structural advantage, and the discipline to ship with a measurement plan in place is more valuable than ever.

Acquisition: Distribution Matters More, Channels Shift

Free products are about to multiply.

It's cheaper than ever to build software, so the incentive to launch a free product that cannibalizes a competitor's paid product is stronger than ever.

At Codecademy, we spent 30-40% of engineering capacity on the free product — deliberately, because it built the brand and the funnel. A lot of new entrants can now do that for much less.

If your paid product competes in a category where a good free alternative can exist, you need to be thinking about that now.

CAC is going up in most channels.

More competitors entering more markets means more competition for the same attention and ad inventory.

This puts more pressure on monetization — specifically on shortening payback periods. You can't afford to wait 18 months to recoup your CAC when CAC is rising and churn is still churn.

Not all acquisition channels are equal here.

Paid ads get more expensive as competition rises. Content SEO is also getting hit — AI-generated content is flooding search results.

The channels that hold up best are the hardest to replicate: brand, word of mouth, community, referral loops.

Brand is one of the few genuinely defensible things in this environment.

When every competitor can ship a comparable product quickly, trust is one of the things that can't be copied overnight. Brand compounds in a way paid channels don't — lower churn, higher referral rates, better conversion, all at once.

User Value: The Bar for PMF is Moving Up

"We're the only team that can build this" is no longer a moat.

If your defensibility was execution speed or engineering talent, that moat is shrinking. You need to be thinking about the other kinds: network effects, switching costs, proprietary data, brand.

The average product quality is going to rise dramatically across every category.

Think about what happened to physical goods when mass manufacturing arrived. The cost to produce quality dropped, and the definition of "good enough" reset upward permanently. The same thing is happening to software.

More product choices means onboarding & faster time to value is key.

When users have more options, evaluating them gets harder. You have maybe 2 minutes on day zero.

Data from RevCat's 2025 State of Subscriptions shows roughly 80% of users who start a trial do so on day zero. If you don't activate them immediately, you don't get a second chance.

Nuanced understanding of the problem matters.

Understanding why a user showed up, what problem they're trying to solve, and getting them to their "aha moment" — this work becomes foundational.

Monetization: More Important than Ever

Winning monetization allows you to win distribution.

"Whoever can spend the most to acquire a customer wins" — this Dan Kennedy quote is more true than ever.

The better you are at making money from your users, the more you can spend on acquisition.

Get to monetization best practices ASAP.

Setting up smart payment retry logic, building a proper cancellation flow, running pricing tests — this used to require meaningful engineering investment. That barrier is dropping.

Move up the value chain or get squeezed.

If your product takes on more complex, higher-stakes work, you have more pricing power than ever. Software is going to start taking over more complex tasks, which means budgets that are usually reserved for salaries.

What's Next

The companies that win through this shift treat it as an accelerant on the fundamentals, not a replacement for them.

Move faster on the work that was already important — churn reduction, monetization optimization, acquisition efficiency. Invest in measurement. And keep moving up the value chain.

More on the long-term defensibility side in Part 3.

Originally published on Subscription Index.

Navigating the SaaS-pocalypse Part 1: Do We Panic?

Daniel Layfield — Mon, 30 Mar 2026 18:59:22 +0000

If you work in SaaS right now, your inbox is full of people telling you the world is ending.

In early February about 300 billion dollars was wiped out from public SaaS stocks valuations.

Yesterday, Block (the parent company of Square, Cash App, Afterpay and others) fired roughly 50% of their 10k+ staff.

So yes, a lot is going to change.

But I think the most important thing to understand is this: the game isn't changing.

The fundamentals of how businesses are built, how money is made, and how competitive advantages are protected have not changed.

What AI is doing is reshuffling the deck on how you execute against those fundamentals — faster and more aggressively than anything we've seen since the internet.

This mini series is about navigating that shift without losing sight of what actually matters.

The impact of this change will be massive, however we are all still playing the same game.

Find a successful arbitrage
Focus on improving Acquisition, User Value, Monetization
Build Defensibility via the 7 Powers

AI's Impact Will Be Larger Than the Internet

Marc Andreessen — who founded Netscape, started A16Z, and sits on the board of Meta — says AI's impact will dwarf the internet. When he says that, it's worth taking seriously.

But "take it seriously" doesn't mean panic. It means understanding what is actually changing — and what isn't.

All Business is Arbitrage. This Doesn't Change

Making money is simple, not easy. All business is arbitrage of some form.

Your company:

Takes some inputs
Does something to them
Sells the output for (hopefully) more than they cost.

In the software world, we find a problem, hire a development team, load them up with caffeine and snacks, and sell recurring subscriptions until we're profitable.

All Arbitrages Shrink With Time

Arbitrages don't last forever.

If there's a way to spend a dollar and get $2–10 back, people find out. The early winners grow big, people notice.

Blogs get written, consultants show up, courses get created, and the ROI compresses.

Building SaaS around important, nuanced problems has been one of the great arbitrages of the last decade.

A Brief History of Arbitrages

Big companies are built when they find these arbitrages, but they don't last forever.

2013–2016: Facebook Ads — CPMs were dirt cheap. Early e-commerce and app companies scaled massively on $0.01–0.05 clicks.

2014–2018: Content SEO / Programmatic SEO — Google was still rewarding volume. Companies like NerdWallet and HubSpot built massive moats by publishing at scale.

2015–2019: Instagram Influencers — Before disclosure rules tightened, micro-influencers drove extraordinary ROAS.

2016–2020: YouTube Ads — Severely underpriced relative to attention.

2017–2021: Podcast Ads — CPMs were low, audiences were highly engaged.

2018–2022: TikTok / Organic Short Video — Massive organic reach in the early algorithm.

2019–2022: Product-Led Growth / Freemium — Slack, Figma, Notion used free tiers to bypass sales cycles.

2022–2024: AI Content + SEO — Short window where AI-generated content ranked before Google's helpful content updates.

2023–present: AI Agents / Workflow Automation — Still early. Building internal tooling and customer-facing AI features is cheap relative to the leverage.

Rules of the Game Are Not Changing. How You Play it Is.

The playbook has always been the same three steps:

Find an arbitrage
Get profitable distribution
Build defensibility to protect it

A useful analogy: when the forward pass was introduced to American football, it changed how the game was played — but not what it takes to win.

AI is the forward pass. The scoreboard is still the scoreboard.

The Short Term Goals: Acquisition, User Value, Monetization

On a week-to-week basis, everything that matters in a software company roughly maps to one of three buckets:

Acquisition - How do you find users at a sustainable cost?
User Value - How do you build something they want so they stay around?
Monetization - How do you capture enough value to fuel the other 2 with profit left over?

Do all three well and you can build real enterprise value.

AI will change how you execute on all three. It will not change their importance, or how they depend on each other.

The Long Term Goals: Defensibility via the 7 Powers

The best definition of "strategy" is how you're going to protect the arbitrage that you have created.

The only real book on strategy is the 7 Powers Framework by Hamilton Helmer.

The core idea is that there are 7 ways of protecting the arbitrage you have from getting competed away. AI will impact some of these massively and some less so.

What's Next

In Part 2, we cover how this changes how you act in the short term to generate profit across Acquisition, User Value, and Monetization.

Originally published on Subscription Index.

New Benchmarks for Voluntary & Involuntary Churn

Daniel Layfield — Sun, 15 Feb 2026 13:37:17 +0000

When I was at Codecademy, I would've killed to know other people's churn numbers.

We had a really good understanding of our metrics. We'd monitor them every day.

The business was growing well, but could we do better? Where should we focus? When is it time to stop working on a metric?

As the company grows, you have more and more options. More features you could build, more flows you could optimize, more experiments you could run.

The backlog gets longer. The debates get louder.

Bach then, there were no good benchmarking reports in our industry.

Thankfully that has changed.

Churnkey published two great benchmark reports in partnership with Stripe and their data.

This is real data broken down by industry and price point.

Stripe processes 1.4 trillion dollars in payments, 200 million subscriptions.
Churnkey processes 5.4M failed payments and 25M subscriptions over the course of a year.

How to Use Benchmark Data

Benchmarking data is extremely useful if you are aware of the inherent tension within it:

Benchmarks allow you to grade yourself, which can show you where to focus. Focus is everything.
Benchmarks are averages. The average startup dies.

Benchmarks tell you what's normal, not what's good.

If you're dramatically below benchmark (on the bad side), that's a clear signal.

You're probably missing best practices. Basic stuff that everyone else has figured out. Start there.

If you're at benchmark, that's not success. That's table stakes. You're average. You want to be best-in-class, not average.

Use benchmarks as an input to your planning not as the goal.

The Voluntary Churn Numbers: Insights

For those unfamiliar with the term, voluntary churn means people actively cancel via your cancellation flow.

This is what jumps out to me:

Voluntary Churn Across Industries

1. The main driver of retention is use case

The main driver of your retention numbers is going to be how long the user's have the problem that you solve.

Travel is inherently a short-term use case, while things like leisure and insurance are inherently long-term use cases.

Companies in these industries can impact these numbers but not fundamentally change them.

2. Low monthly churn numbers are still high annual churn numbers.

Most ed tech companies would be extremely happy with 3-4% monthly churn rate, but that means you’re still losing 1 in 3 users each year.

You need a massive marketing machine to keep the growth going.

This is one of the reasons I push all startups I work with and talk to to work on their churn fundamentals early.

Plug your numbers into their calculator and do the math for yourself.

3. Cancellations happen when users don’t see value anymore

Any time I've ever seen cancellation flow data that it shows the same thing:

Price is the number one reason people leave
Lack of usage is number two

To me these are both the same objection, users don’t see value.

They are not willing to pay with either time or money.

These objections never go away, they are almost always #1 and #2.

I think the actual way of solving them is improving your core product and not lowering your prices.

That said, cancelation flow tactics (listed here) are very effective and Churnkey data shows this.

The more you offer a mitigating step that matches the reason the user leaves, the more effective it's going to be

The Involuntary Churn Numbers: Insights

These are the users that churn without actively canceling. Typically these are payment-based reasons.

Here’s what jumps out to me.

1. The lower your price the lower the quality of your users

This confirmed something that I suspected for a long time

The cheaper your product is the more likely you deal with bad payment methods and accounts with low balances.

This is a hidden impact of raising your price 😀

You actually raise your ARPU/LTV in two ways

The users actually pay you more
You attract better users who fail fewer payments

Products that inherently attract affluent face dramatically lower payment processing problems. I've seen this multiple times.

if your product caters to wealthy executives, you probably don't even know what payment churn is.

2. Most payments still fail due to lack of funds.

Overwhelmingly you have payment problems because customers don't have money in their account at that time.

The three big buckets of effective tactics here are:

Auto-retry failed payments
Email and/or text people when cards fail
Nudge people to update their cards when they are back on your product

Churnkey expands on these tactics here and I also have this post on the underlying logic here

Reason That Payment Methods Fail

So What Do You Do With This Information?

Implement the best practices ASAP

There is no reason to not have these best practices set up for all web based subscription products. It makes a huge difference

If you need to convince someone, convert your monthly churn numbers to annual churn numbers.

At 5% monthly: ~46% annual loss. At 10% monthly: ~72% annual loss. At 15% monthly: ~886% annual loss.

This compounding can also work in your favor, but only if you start now.

Every month you delay is a month of users gone forever.

Your cancellation flow is an untapped A/B testing surface

A final tactical point. Not nearly enough companies run cancellation flow-based A/B tests.

There are three things that impact 100% of your paying users:

Your purchase flow - pricing page/paywalls + checkout page
Your payment processing
Your cancellation flow

Most companies run A/B tests on #1. #2 is very hard to do. Very few run them on #3

We’ll cover more on minimum detectable effect next week, but if you have 500 visitors your cancelation flow per week and you see about 300 of those people cancel, you will be able to detect a ~10% drop in 4 weeks.

This is enough firepower to run real experiments. Test different offers, different copy, different pause lengths.

This is how you build your own benchmarks and move from average to best-in-class.

Churnkey makes this easy and I would highly recommend using it.

Good luck out there.

Dan

Sponsored by Churnkey: Churnkey helps subscription companies like Superhuman, Veed.io, and Copy.ai, to drastically reduce voluntary and involuntary churn. On average, Churnkey saves companies 20%-40% of subscription revenue that would otherwise be lost to churn.

Originally published at Subscription Index

Usage-Based Pricing: Killer Growth Strategy or Fatal Mistake?

Daniel Layfield — Sun, 15 Feb 2026 13:35:17 +0000

Usage-based pricing is all the rage right now.

I understand why companies love it. The math looks incredible on paper.

But for most consumer companies, it's probably a bad idea.

For those who implement it, or a version of it, it’s harder than you think.

Flat Fee Pricing vs Usage Based Pricing

For those unfamiliar, usage-based pricing is the concept that you charge per usage of the product as opposed to a flat fee per month.

Blockbuster video charging you for every movie you rent = usage based pricing

Netflix allowing you to stream as much as you want = flat fee pricing.

While the vast majority of software products are flat fee and for good reason, more companies are exploring usage based pricing.

Why is that?

Why Companies & Investors Love It

If you can make it work, usage-based pricing can massively increase your revenue and your company valuation.

This is driven by unlocking what’s called “net negative churn” which is only possible with some form of usage based pricing.

Net negative churn means if you sign up a cohort of users this year that pays you $100, next year they're worth more than $100.

Not less. More.

This means instead of churning revenue from every group of users that sign up each month, you are gaining revenue.

This has a massive impact on your ARR and valuation.

Usage based pricing is common in web hosting and storage industries.

You can see this in Snowflake’s public filings that they hit 178% net revenue retention at its peak. That means for every $100 they signed up, they were making $178 from that same cohort a year on.

They trade at a 16-17x price to earnings multiple whereas the “average” SaaS company is close to 7x ARR.

Understandably, if you run a company that could potentially unlock net negative churn, you’re probably feeling some pressure to do so.

Why Product Teams Love It

On the product side, usage based pricing also elegantly solves multiple difficult problems at once.

1. It makes pricing tiers much easier to understand.

One of the hardest problems when you design a paywall or multiple tiers of a product is making the difference easy to understand.

As the saying goes, "a confused mind never buys."

Centering your product around usage makes (most) of this problem go away.

In the Twilio example below it's really easy to see the difference between the tiers when they're based on usage.

The user can mentally calculate where they are and understand what's right for them

2. Users who get the most value from your product are more likely to convert and have high LTV.

A really good mental test if your monetization system works is "Do the users who get the most value from your product end up paying you the most?”

If the answer is no, then something in your monetization system is off.

Usage based pricing both helps fix this problem and inherently self selects for good personas.

3. It aligns customer & company incentives beautifully

Company and customer incentives should be aligned but they are frequently not.

It's really easy for product teams to get lost down meaningless rabbit holes and ignore the core value of the product.

With usage based pricing:

The better your onboarding, the more money you make.
The better the habit loop, the more money you make
The better you layer in more use cases, the more money you make

Now when your teams get lost in things that don't drive usage, you feel that pain financially

So what's the problem?

The Downsides

Even if usage-based pricing makes sense for your market, there are real costs.

It makes user activation much harder. Every click feels like it costs money. Users hesitate instead of exploring freely. Hesitation kills habit formation. And habit formation is how you fight churn.

The hardest part of growing a product is activating users early. If you add friction to that journey with usage anxiety, you hurt retention downstream.

It adds massive complexity in your product and company

You now have to figure out:

How do upgrades and downgrades work?
What happens to unused credits?
How do you handle overages? What happens during cancellation?
What about reactivation - do old credits come back?

Additionally it triggers a lot of organizational complexity

How does your finance team recognize this revenue?
How do you forecast upgrades and downgrades
How will the customer service team know which plan someone’s in when they email you
Product has to build metering infrastructure, dashboards, and alerts.

Every one of these needs to be designed, built, documented, and supported. It's a tax you pay forever.

When Usage-Based Pricing Actually Works

I think that pure usage-based pricing only works when three things are true:

There's a real marginal cost to providing the service.
The customer understands and believes that cost really exists.
No competitor can offer unlimited and survive

To me, point #3 is the key one.

If everyone is already offering a flat fee, it's tough for you to go usage-based unless you have a massive differentiator.

Because of these factors, its more common in some industries vs others:

Usage Based Pricing Dominates

Cloud infrastructure (AWS, GCP, Azure)
AI/LLMs (OpenAI, Anthropic API)
Payment processing (Stripe, Square)
Utilities (electricity, water, gas)
Telecommunications (data overages, international)
Shipping & logistics (FedEx, UPS)
Data & API providers (Twilio, SendGrid)

Flat/Subscription Pricing Dominates

Streaming media (Netflix, Spotify, Disney+)
News & content (NYT, The Atlantic)
Consumer productivity (Notion, Evernote)
Dating apps (Hinge, Bumble)
Fitness & wellness (Calm, Headspace, Strava)
Password managers (1Password, Dashlane)
Design tools (Canva, Figma)

In all of these cases, customers intuitively understand there's a real cost.

Nobody expects unlimited electricity for $50 a month.

So What Do You Do With This Information?

Here's how I'd think about your choices:

Pure usage-based pricing (Stripe, AWS, OpenAI API): Pay for what you use. No tiers, no flat fee. Only do this if you meet all three criteria above.
Usage within tiers (Beehiiv, Twilio, Zoom, Mailchimp): Fixed monthly price per tier, but usage limits define which tier you're in. This is the default for most B2B companies. You get expansion revenue without the activation friction.
Flat fee (Basecamp, Netflix, Spotify): Everyone pays the same. This is the default for most consumer companies.

For the vast majority of consumer companies, you should do number three.

In certain high usage scenarios, you can use #2 if you can execute on it well.

My favorite version of #2 is using it as the line between a free and paid product.

DocuSign (used to) gives you 5 free signatures per month
NYT gives you a limited amount of free articles per week
Shazam gave limited the number of skips you could have per day

If you are an up-and-coming company that is still working on core product development, I would start with a clear line between free/trial and paid.

Once you start to understand your personas, you can add additional tiers on top of paid based on usage numbers.

Good luck out there

Originally published at Subscription Index

Don't Run A/B Tests without Understanding MDE

Daniel Layfield — Sun, 15 Feb 2026 13:24:37 +0000

A/B testing can be one of the highest-ROI tools in growth.

It's a major unlock in optimizing a business.

I have personally launched hundreds of tests. When I was a PM at Uber, every single change we made was A/B tested.

Everything. Layout updates, infrastructure improvements, button positioning, everything.

This made sense for us because:

We have the traffic/users to measure down to .01% improvements.

We had the tooling set up and some of the best data scientists in the world.

Even a 0.01% in our core metrics is worth tens of millions of dollars.

Seeing this process permanently changed my perspective on product management.

However, most of the tests that I see run are a giant waste of time.

How A/B Tests Waste Time

Even with all of my experience I've still launched many stupid A/B tests that I regret.

I have learned this painful lesson multiple times. At Codecademy, we once tested three different pricing page layouts.

Three variants meant 3x the build time. It also meant we needed way more traffic to reach statistical significance.

The more variants you test, the more you split your traffic, the longer it takes to get a clear answer.

The test ran for months. It came back inconclusive.

We basically wasted an entire quarter—engineering time, PM time, design time—and learned nothing actionable.

Additionally, and maybe more painfully, we blocked any other work in that area as changing something near the pricing page would have polluted the results.

The core mistake wasn't the idea of the test itself.

It was not calculating whether we could actually measure a meaningful difference before we started building.

This is where Minimum Detectable Effect comes in.

Why MDE is So Critical

Minimum Detectable Effect or “MDE” is the smallest change you can be statistically confident in.

You can think of it as the “floor” below which you can’t measure reliably.

If your MDE is 10%, you could have a 9% improvement and never see it. The change happened.

Your instrument just isn't precise enough to detect it.

You could run a test, see no statistical significance, and conclude "this didn't work" when in reality it worked—you just couldn't see it.

Your MDE is determined by three things:

Your sample size (i.e traffic).

Your baseline conversion rate,

How long you're willing to run the test.

Personally I have never found an online calculator that I liked so I built this for everyone.

Its free and not behind an email gate. This allows you to:

Select the metric you’re trying to improve

Plug in the numbers relevant to that

See what level of impact you could measure in what time

Before you run any test, you need to know: can I reach a reasonable MDE in a reasonable timeframe?

If the answer is no, don't run the test. You're just guessing with extra steps.

When to Actually A/B Test

Not everything needs a test. Most things don't.

Here's the decision tree I use:

Is there real risk or genuine uncertainty here?

If you're implementing a well-known best practice—like adding a pause option to your cancellation flow—you probably don't need to test it.

The industry has already tested it for you. Just ship it.

If you're making a change where you genuinely don't know what will happen, or where being wrong would hurt the business, that's when testing starts to make sense.

If no real risk or uncertainty, just ship it.

Are your metrics stable enough to observe the change directly?

If your conversion rate holds steady at roughly the same level week over week, you might not need a formal test at all. Ship the change and watch the line move.

Look at the below graphs. If your numbers look like the ones on the left, you'll probably see a 10% jump visually just by watching it and don’t have to test anything.

However if you are more like the one on the right, then you won’t be able to visually pick out changes.

If your metrics are stable, just ship it and watch.

Is this in a high-traffic area of your product?

Low-traffic areas can't support A/B tests. You simply don't have the volume to reach statistical significance in a reasonable timeframe.

If you're testing something in a part of your product that only gets a few hundred visitors per month, you're not going to learn anything useful.

The test will run forever or come back inconclusive.

If not enough traffic, just ship it.

Can you reach a meaningful MDE in a reasonable timeframe?

This is the critical calculation. Plug in your numbers: your traffic, your baseline rate, your test duration.

Here’s the link again.

What's the smallest effect you'll be able to detect?

If your MDE is 15% and you're hoping to see a 5% improvement, don't bother. You won't see it even if it's there.

If your MDE requires 4+ months to reach, don't bother. You'll block development for too long.

If you can't hit a reasonable MDE in 6-8 weeks, just ship it.

If your answered “yes” to all of these, run the A/B test.

One caveat worth mentioning: even at 95% confidence, 1 in 20 tests gives you a false answer. I've seen tests flip after looking conclusive for weeks. Statistics reduce risk. They don't eliminate it. Don't bet the company on a single test result.

The Real Cost of Bad A/B Testing

Here's the math most teams don't do.

A feature takes 4 weeks to build. If you A/B test it, that 4-week project becomes 12+ weeks:

4 weeks to build the variants

7-8 weeks to run the test

1 week to interpret results and decide what to do

That's 3x the timeline. And you still have to explain to executives what "inconclusive at 95% confidence" actually means.

Testing also blocks adjacent work.

If you're running a test on your pricing page, you can't materially change your homepage without contaminating the results. Same goes for the checkout flow, the onboarding sequence, or anything else that feeds into the thing you're testing.

The blast radius is bigger than people realize.

You're not just blocking one feature—you're blocking an entire area of your product for weeks or months.

I'd be skeptical of any A/B test that needs more than 6-8 weeks to reach significance.

At that point, you're dramatically slowing down your velocity. The opportunity cost is enormous.

So What Do You Do With This Information

Go to this link and bookmark this calculator.

Before your next test, plug in your numbers. See what MDE you can actually reach. See how long it will take.

If the math doesn't work, don't build the variants. Save yourself a quarter.

Good luck out there,

Dan

"One More Feature" vs Monetization

Daniel Layfield — Tue, 10 Feb 2026 13:13:08 +0000

"One More Feature" vs Monetization

Most founder aren’t ready to work on monetization “just yet”.

They just want to get done with the next phase of their roadmap. Or get one more thing done. Or ship that feature they've been promising for months.

This is a mistake.

The uncomfortable truth is, your product is never as good as you want it to be.

The product is never ready. There's always something else that needs to happen first.

"Just Build a Great Product"

You've heard this advice a thousand times. Just focus on the product. Make something people love. The money will follow.

This is partially true. But there's more to the story that you're not being told.

This advice (and most advice) comes from the outliers who were massively successful and had books written about them after the fact.

Facebook, Google, OpenAI, Oracle, etc

They ignored monetization for years and focused purely on building something incredible.

So why shouldn’t you do the same?

Dangers of The Unicorn Playbook

"Ignore monetization and focus on product" is just one part of a much larger playbook.

Here's what the rest of it looks like:

Raise a ton of money to offset your losses
Target a massive opportunity created by technology and/or distribution changes
Hire the best team you possibly can, which means an expensive team
Lose money for years investing in team, product, & acquisition
Eventually make massive money for you and your investors

For the Unicorn Playbook to work, you have to run the whole thing.

Just blindly focusing on product, after you have product market fit, won’t get you unless you have the other parts.

The Business Olympics

The venture-backed startup game - is basically “the business version of the Olympics.

It's where the best founders go to compete with maximum resources, talent and focus.

Just like real olympians, the companies that win have incredible raw potential + funding + access resources + luck/timing.

Lets look at the actual P&L from each of these companies and how long it took them to make not only profit, but “meaningful” profit based on their ambitions.

Facebook - raised $ 895.7M, starts making “real” profit by year 7

Year

Money Raised

Annual Rev

Profit/Loss

2004

$500k

-$1.5M

|
|

2005

$12.7M

-$5M

|
|

2006

$27.5M

-$50M

|
|

2007

$360M

$150M

-$15M

|
|

2005

$175M

$280M

-$30M

|
|

2006

$200M

$777M

+$25M

|
|

2007

$120M

$1.9B

+$606M

Google - Raised $35M, making “real” profit by year 5

Year

Money Raised

Annual Rev

Profit/Loss

1998

$100k

-$1M

|
|

1999

$25M

$220k

-$10M

|
|

2000

$10M

$19.1M

-$14M

|
|

2001

$86.4M

+$6.9M

|
|

2002

$439M

+$99.6M

|
|

2003

$1.4B

+$430M

I was going to build a table about OpenAI, but they still aren’t close to generating profit. They have raised $57.9B in about 10 years.

Who You Should Copy

If you want to become a better athlete, don't study the genetic freaks. Study the people who were good and figured out how to become great.

The vast majority of the people on this list aren’t raising billions to try to make 100 billion.

Study the people ones who built great businesses without the Unicorn Playbook:

Mailchimp bootstrapped entirely and sold for $12 billion. The founders never took a dime of VC money. They each walked away with roughly $6 billion because they prioritized profitability from day one.
Basecamp bootstrapped to tens of millions per year. The only outside money was Jeff Bezos, who invested after they were already profitable.
ConvertKit went from zero to over $30 million ARR bootstrapped.

What do these companies have in common? They prioritized profitability early. They focused on a specific customer. They monetized aggressively rather than waiting.

Why This Matters For Your Exit

Most people reading this aren't building toward an IPO. You're building toward acquisition.

The goal is a $10-20 million ARR business that sells for $50-100 million or more. That's life-changing money.

Here's the problem: you don't know when those acquisition offers are coming.

They show up when they show up.

A strategic buyer gets interested. A PE firm is doing a roll-up in your space. A competitor decides to acquire rather than compete.

You don't control that timeline.

And when those offers come, the vast majority of you will get valued on your revenue, not your features. Nobody's paying a premium for your roadmap or your "potential." They're paying a multiple on your ARR and your trajectory.

The better your revenue trajectory when that offer lands, the higher the valuation.

Every quarter you delay investing in monetization is a quarter of weaker trajectory when the offer might come. That's the difference between a $30 million exit and an $80 million exit.

The One Exception

If you don't have product-market fit, work on that first.

But after product-market fit, you need to start monetizing so you can afford the acquisition and product development that makes a great product.

You can't run out of money. And you need to figure out who you actually have product-market fit with - not everyone, but a specific someone. (I wrote more about this here.)

So What Do You Do With This Information?

Invest in monetization every quarter.

This means measuring things correctly. If you're not tracking ARPU and usage-based retention, you're behind. You need to understand which of your investments are actually worth it.

Run a pricing test at least once or twice a year. This doesn't have to be a price increase - it could be packaging changes, new tiers, or repositioning.

Calculate your ARPU and conversion rate numbers. Look at them every week.

All subscription companies hit a growth ceiling. You want yours as high as possible to fund the product development and acquisition that makes a great business.

Stop waiting for the product to be ready. It never will be.

Good luck out there.

Dan

A Free Gift for You

Daniel Layfield — Tue, 30 Dec 2025 13:00:08 +0000

There are a lot of growth ceiling calculators out there and they do a great job illustrating the concept.

However, I find them practically hard to use for forecasting as you metrics don’t remain consistent across time. So I built one.

Check it out here

Its free, not even behind an email sign up form.

This allows you to:

Compare up to 3 scenarios
Add changes to your core metrics at different points in the future
Easily see the differences in your LTV, MRR, Growth Rates, etc

Happy Holidays

Dan

PS - Want to see more features? See a bug? Reply and let me know

PSS - I can’t figure out how to remove the bottom up loading animation. It drives me crazy. Apologies.

Web to App Funnels: Is Saving 30% on App Store Fee's a Good Idea?

Daniel Layfield — Fri, 19 Dec 2025 13:00:24 +0000

You can now save the 30% app store fee and let your users pay you directly.

This seems like a no brainer. 30% more profit for a simple change? That’s just free profit right?

I don’t think its that simple and for most companies I think its a mistake.

To break this problem down, you need to figure out:

Which is better at the initial transaction?
Which is better at recurring transactions?
What else should we consider?

What we (always) care about on this site, is which of these will make you more money?

Revenue: Initial Transaction

You can break the initial transaction into these elements:

Revenue from Initial Purchase = Checkout Page Conversion x AOV x Initial Payment Processing Success - Refund Rate - Fees

Now lets look at each of these elements to see who likely wins this.

Factor

Likely Winner

Checkout Page Conversion

App Stores, by a lot.

It has a higher degree of trust, saved payment methods, one tap payment (which is huge).

|
|

AOV of Initial Order

Likely a Tie

I don’t see any reason these should be different

|
|

Initial Payment Processing Success Rate

App Stores

They process 10000x the volume that you do, have a higher merchant rating, use multiple gateways, etc

|
|

Refund Rates

Likely a Tie

The giant caveat here is that web checkout can be much worse if you’re not managing the experience well.

If the user hits material bugs or friction right after they purchase and don’t activate on your product, you’ll see refunds go up.

|
|

Fees

Web, by a lot

Assuming you’re using stripe its “2.9% + $0.3” by default.

Both apple and google stores charge 30% by default. 15% for the second year of subscriptions and developers in the SMB program with under $1M of revenue.

Verdict: I think the app stores win here.

For the web based payments to win, you need a really good checkout page.

Even then I think you won’t be able to overcome the power of single click checkout.

Cornell did a study where they concluded that single click checkout increased order frequency in e commerce by 43% and items purchased by 37%.

This is different than session conversion level, but these numbers are huge. There is a reason that Amazon does it by default and that Stripe is building the same feature.

The team at RevCat wrote a great articletesting web checkout vs app store.

They found that the web billing lead to slightly less take home. My suspicion is this is driven by the smoother checkout experience.

Revenue Recurring Transactions

Now lets look at the recurring side. You can break this into:

Revenue From Recurring Transactions = Monthly Price x Active Churn Rate x Passive Churn Rate - Refund Rate - Fees

Factor

Likely Winner

Monthly Price

Likely a Tie

I can’t see a reason this will be different.

|
|

Active Churn Rate

Web, by a lot

Your hands are pretty tied within the app stores and I’ve seen this universe of tactics drop churn by 10-30%

|
|

Passive Churn Rate

App Stores

Same rational as the payment processing point above. Whatever gateway you use won’t be able to compete with the app stores volume and dedicated teams.

|
|

Refund Rate

Likely a tie

App stores might have a small advantage here as they have slightly tighter policies but it might not be material.

|
|

Fees

Web, by a lot

As noted above, you pay a lot less of your revenue

Verdict: I think the web wins out here, driven by the bottom of funnel tactics that you can use to win back users.

Tactics like this matter a lot. Read more here.

What Else Should You Consider?

As I’ve written before, your ultimate enemy is the complexity tax.

You need to protect your ability to ship code quickly and drive results in the long term.

Arguments to Stick with App Stores

Apple/Anroid app stores give you a lot of out the box functionality that you’d need to recreate (i.e pay engineers to recreate).

High converting, trusted checkout page - and one that you don’t have to spend any engineering capacity to maintain.
Multi currency support - both within the UX and on the payment processing side
Multi language support - across the same surfaces
Tax calculations - across all countries
Discount code support - so you can run promos
Saved payment methods - this allows both single click checkout, which is huge, and back up payment methods if the initial card fails.

This is a lot of functionality that you’ll have to build or pay another vendor to manage.

Arguments to Switch to Web Billing

You cancellation tool box is stronger outside of the app store
You run a product that get used across desktop and web, so you can actually consolidate this with time.
You acquire most of your users via paid ads, so you can get users to pay a step higher in your funnel, therefore increasing conversion and ROAS.

The Hidden Impact: ASO Penalty

Its my suspicion that monetizing off of the app stores will reduce your distribution in these app stores. I have no proof, but I think its going to happen either directly or indirectly.

Firstly, all of the marketplaces use ranking algorithms to determine what you see. Ebay, UberEats, Doordash, FB marketplace, app stores, etc

All of these look at some combination of “whats best for the user” + “whats best for the business”.

I was on the team at UberEats that rank the ranking of the homepage, so I can say this from first hand experience.

At UberEats, we explicitly say that% feeyou pay Uber directly impacts your organic ranking. This would be direct impact.

The harder one to determine is indirect impact.

All ranking algorithms are fed by signals from the product and the algorithm basically determine their weight of importance.

Apple would almost certainly take “payment retention” as a sign of quality and use that to boost the ranking of apps that retain well as they are seen as good for the user.

If you monetize off the app, Apple can’t get that signal and this is effectively held against you.

How material is this? I don’t know. But I think its there.

So What Do You Do With This Information?

As with most advice, you should care less about what is best overall and care more about what’s best for you.

For the average start up, I don’t think the complexity is worth it. You have a limited amount of time and focus.

Once that 30% is millions of dollars and you can pay additional people to handle that complexity for you, then it can be worth it.

Good luck out there,

Dan

How to Cut Waste & Drive Profits from Product Development

Daniel Layfield — Sat, 13 Dec 2025 13:00:11 +0000

A typical product team in the US costs $900k–$1.2M per year.

Most companies have no idea if that investment is paying off.

Marketing gets measured. Sales gets measured. Product gets vibes. That’s a problem.

Every other domain of start ups has a rough framework they follow.

Marketing is held to a CAC to LTV ratio. 3:1 is the often quoted target.
Sales teams are expected to product 4x to 6x their salaries in new deals.

Why does product not have the same? Lets discuss.

You’re Burning More Money Than You Realize

A product team is typically 4-6 engineers, a product manager and a designer.

Lets say they’re all in the US all making 150k per year. So that’s $900k to $1.2M per year to run the team.

You have 52 weeks in a year, so that’s 26 sprints. Assume you’re losing 2 of those to holidays and time off.

Remove 2 sprints for holidays have 24 sprints in a year, so that $37.5k to $50k every two weeks.

If you aren’t building things that make you more than $ 50k, then you’re going to go out of business.

Do Your Features Actually Do Anything?

As we talked about last week, the best way of measuring the health of your product is thoughtfully setting a north star metric.

A well defined north star metic measure both the user value and business value that your product creates.

If you are good at product development, you can reliably improve you north star metic.

Full Stop.

If you have a solid north star metric and its not moving, you are working on the wrong things.

The Portfolio Approach

This is probably not the right balance

At Uber, I was able to see product management happen at a huge scale. Dozens of teams with hundreds of engineers.

At the beginning of each quarter, we spit all the projects that we wanted to do into three categories.

Big bets
Tech Debt
Run the Business

Leadership would then check:

Are we “spending” the right % of our resources on each area?
Within each area, are we working on the right things?
Are we balanced the right way across the teams to hit our goals?

Think of your engineering hours per quarter as dollars (because they are) and your projects as investments.

You want the highest risk adjusted ROI for your spend.

A typically quarter for us with Uber Eats would probably be 40% big bets, 30% tech debt, 30% run the business items. But this depends on the team and the maturity of your area.

The more mature your product, likely the more technical/design/feature debt you have to keep paying down to

If you are an early stage product, you might be 70% big bets, 10% tech debt, 20% run the business.

The exact ratio can change depending on your product and what you want to do each quarter, but you should measure this each time.

Big Bets

To grow, you need to make enough big bets and control the risk.

These are the most exciting projects, but also the riskiest. New features, new platforms, etc.

These projects are where you create impact on your north start metric.

You only get a few of these per quarter, if you miss, it really hurts.

If enough of these projects don’t produce results, that’s how PMs get fired and companies go out of business.

You should do your homework ahead of time during planning:

Potential Headroom
Cost Estimates

Once you have both, you want to stack rank these projects to make sure you get the most impact for your eng investments.

Headroom Analysis - If you ship this, can it actually work?

Headroom is an industry term for estimating potential impact of a project on your north star metric.

What you don’t want is to spend 3 months making a 50% improvement to something that only 3% of your users see.

When it matters, do the math.

Said another way, you should be estimating the impact that each idea for a big feature can have on your north star metric.

Lets say that I was running a team and the north star metric was “activation”.

If I wanted to rebuild my lifecycle email campaign, that could be estimated as

Emails sent per month x open rate x the CTR x the potential % of people activate back on the product

If I wanted to re do my homepage to increase sign ups.

Traffic x potential lift in conversion rate x activation rate of current sign ups

the key point is that you measure the potential impact of each feature in your North Star metric.

Cost: Keep the Timeline Under Control

Equally important to the impact is how much each project is going to cost, typically measured in weeks. Even more typically, engineering weeks.

To do this you need to be able to describe to the engineers roughly what you want and to have them estimate them.

There are a lot of frameworks that work here.

T Shirt Sizes (S, M, L, XL)
Weeks
Stack rank in relative cost.

I won't go too deep into this here. Each of them work better in different scenarios but the important thing is that you do one of them.

A few tips:

You want to do the estimation relatively quick, take 1-2 days not weeks.
Get your most senior engineers involved.
this isn't the time to discuss detailed scope. If there are a few ways something can be implemented, pick the average cost.

Technical Debt

The bigger a codebase, the more things the engineers could in theory do to improve it.

If you don’t routinely pay down this debt, the codebase becomes too hard to work on and you’re velocity slows to a crawl over time.

If you can’t ship quickly, you can’t innovate as the market changes and you slowly lose PMF.

They key points is to work on things that will increase the velocity of the other work that you’re going to do.

Think about this as “clearing the path”.

The clearer you can describe where you want to go, the better your engineers will be able to help you here.

Typically choosing these investments should be up to your engineering team. However you need them to articulate the impact. Don’t let this be a black hole.

Still follow all of the project management best practices. Set milestones, clear scope, metrics if possible, etc.

Run the Business

This bucket is for things that just have to happen, and typically quickly.

Sometimes you know them ahead of time, if so, put them in your roadmap. Other times you just leave capacity for them when they come up.

These can be bugs in key areas, small UX optimizations, helping out other teams team, installing new tools etc.

If you make it too hard to get the simple things done, especially across teams, it kills collaboration, which kills innovation, which eventually kills growth.

Don’t over think them, just ship them. If you can measure them, great. If not, its fine.

So What Do You Do With This Information?

Typically this is easiest to do at the beginning of a quarter and as a part of your annual planning process.

Make sure you have a north star metric.
Start with your big bet projects. List all of you ideas. Estimate cost and impact.
Work with engineering to get technical debt idea
Add any known run the business ideas
Calculate how much engineering capacity you have using whatever framework you want
Build your roadmap in the highest ROI configuration that you can.

Good luck out there,

Dan

P.S - Interested in scaling your revenue and fixing issues like this?

Click Here to Book a Call

About Me

Dan has help drive 100M+ of business growth across his years as a product manager.

He ran the growth team at Codecademy from $10M ARR to $50M ARR, which was acquired for $525M in 2022. After that he was a product manager at Uber.

Now he advises and consults with startups & companies who are looking to increase subscription revenue.

Learn more about consulting >>>

Stop Building for “Everyone”: The Simple Persona Playbook That Prints $$$

Daniel Layfield — Sat, 06 Dec 2025 12:30:09 +0000

Most companies don’t plateau at $2–10M ARR because of product or marketing.

They plateau because they’re building for “everyone who might buy,” instead of a crystal-clear someone.

This typically involves developing “personas”, “ideal customer profiles (ICPS)”, “jobs to be done” or something similar.

The tricky part here is that most of the traditionally processes here are a waste of time.

I’ve seen teams waste months here with very little to show for it.

This is the process that I’ve found to be much faster and impactful.

What a Persona and Why Does It Matter?

The single biggest inflection point in most companies happens when they know these groups, collect the data, user the data correctly and then change their strategy.

Let's say you sell a product that digs holes. What kind of hole does your customer want?

Well that depends if your customer a is:

5 year old at the beach?
A hobbiest gardner?
A construction worker?
An oil prospector?

All of these people want a hole. However, why they want a hole dug and what that hole has to look like is very different.

Because of that, the value that they put on a hole is also very different.

A 5 year old at the beach might only want a single hold dug and doesn’t have any money to pay you. An oil prospector might pay $100k per hold and might need hundreds of them.

Granted these holes need to look different, but that’s kind of the whole point.

Problem Centric Personas

There are lots of different methodologies to define your users personas.

In my experience, the most useful one by far is grouping users by the underlying problem they are coming to your product to solve.

This is the best indicator of both their retention potential and willingness to pay.

At Codecademy, we had four main personas:

Hobbyists : People who find coding interesting but don't need it for anything specific.
Students : People learning for school assignments.
Career Upskillers : People adding coding to their existing job skills, so a financial analyst who wants to move from excel to python + SQL.
Career Switchers : People who want to become full time engineers or data scientists. So this could be a journalist who wants to become a fulls stack engineer.

After we started collecting this data and using it, we saw some unsurprising trends.

“Hobbyists” don’t really need to learn to program, it is literally a hobby. They were typically retirees or people who just like learning.
“Students” had a need, but learning to program didn’t unlock any immediate earnings, so they retained well on the free product but didn’t have high willingness to pay.

They typically didn’t buy the paid product. If they did, they didn’t stick around.

However, “Career Upskillers” and “Career Switchers” had a real problem we could solve.

If they gain new skills, they can make more money relatively quickly, so not surprisingly they had the highest LTV.

When we used this segmentation information in our data, we saw something like the below. Our best personas had a meaningfully different retention rate and LTV.

How to Build Your Personas

Like all good consultants, I came up with a catchy & complex framework for you to solve this:

Have your founder guess.

In my experience most of the long expensive research frameworks here are a waste of time.

Founders should have been in your problem space long enough and spoken with enough users that they can probably segment people with about 30 minutes of thinking here.

If they don’t, you have other problems.

You’re better off forming some rough categories and filling in details as you speak with users in the course of routine user research.

As with most things in the startup game, velocity is everything.

Using This Data to Make More Money

For this data to be useful, you need to do the following things:

Collect it
Integrate it across your analytics so you can see the differences
Modify the early experience to maximize activation rates.
Use the overall lessons to drive product development & marketing.

The easiest way to do this is with onboarding surveys.

I've never seen any material drop-off in early-stage engagement from onboarding and if you use this data to customize the early-stage experience, which you should, you will likely increase activation.

Let's take a look at a few examples.

Duolingo does this. they asked for the core motivation up front as they can't infer this from the course data.

Notion asks for your organizational information, likely as they have materially different PLG motions in selling to these users

Airtable collects the role of the users, likely as they both customize the early stage experience and their product gets used on the team level primarily.

So What Do You Do With This Information

As stated above, the basic playbook looks like this:

Map out your personas with your founder. Do it quickly.
Set up something during onboarding that allows you to segment them.
Use this segmentation data in all of your key metrics. Look at it weekly.
Pick your ideal persona
Make sure both marketing and product optimize for this over the others.

The hardest part of this by far is having the discipline in to focus on the best persona in your marketing and product development.

This cannot be over stated.

The larger your team and/or the longer you’ve been doing this, the more resistance there will be to stop doing things that kind of work and start trying to find thing that really work.

North Star Metrics: How to Set and Use Them

Daniel Layfield — Sat, 29 Nov 2025 12:36:36 +0000

There is a dirty secret in product management.

Most features that get shipped do nothing. Absolutely nothing.

These features might sound can look great, sound fancy, and get a lot of praise internally. But when it comes to impacting the business, they do F all.

They do nothing, not because they aren’t designed well, but because they don’t drive your business model in any meaningful way.

The only way to impact your business is to have sustained focus on the things that matter.

Without defining what matters, its easy for teams to scatter changes around the product, get lost in rabbit holes or micro optimizations that are a waste of time.

This quote from Alice in Wonderland captures it perfectly:

Alice: “Would you tell me, please, which way I ought to go from here?”

The Cat: “That depends a good deal on where you want to get to.”

Alice: “I don’t much care where—”

The Cat: “Then it doesn’t matter which way you go.”

Don’t be Alice. Direction matters.

What is a North Star Metric?

All the great software development companies solve this problem by defining North Star Metric.

We did this at Uber and Codecademy. Meta does it, Google does it, Slack does it and you should do it too.

A North Star Metric is a way of quantifying the combined user and business value that your product produces.

Users come to your product to solve a problem. Your business makes money somehow.

You need to find a way of measuring the leading indicator of both.

Lets look at some examples and what their north star metrics could be:

Facebook - “Daily Active Users” - FB is designed to be a daily use product so this measures successful user engagement. FB makes money on ads, so user have to show up to see the ads.
Uber - “Trips Completed” - They’re a marketplace, so they make money when a ride or delivery happens. “Completed” is key here because it drives value to both sides of the marketplace and captures liquidity + quality.
AirBnB - “Nights Booked” - Also a marketplace so this measures value to both the host and the guest. Their revenue up if this number goes up.
Slack - “Messages Sent per Team” - B2B, so revenue driven by accounts. The more active the account, the higher the retention. They also charge per seat, so the more messages, likely the more people in that account, the higher the revenue.
Notion - “Documents Created” - Notion has both solo users and team users. In team environments, the real value is the creation of a document for other to read. The more documents, the more valuable the workspace, the higher the retention.

No metric is perfect, but all of these orient product development towards growing the business and helping their users.

Why Use a North Star Metric and Not MRR?

Product development, if not measured carefully can be insanely wasteful.

We learned this painful lesson at Codecademy.

It was roughly in 2018 and we hired our first data scientist (shout out to Hillary) who correctly calculated our retention numbers for the first time.

I remember staring at our fancy new dashboards and seeing that the last 2 years of our product development did absolutely nothing to improve our usage retention.

Nothing. A flat line.

We probably spent millions of dollars on engineers, PMs, designers, office space, snacks etc to get no movement in our retention, which is the main thing that matters.

This taught me a valuable lesson.

Good product development isn’t just shipping things. It also isn’t just shipping things that your users like.

It getting the highest ROI for the dollars you put into developing your product.

North star metrics give you a currently to denominate the value of your work in.

Without a clear and thoughtfully chosen North Star Metric, you can’t quantify this ROI.

Just optimizing around MRR can by very misleading as MRR is a lagging indicator.

If you remember the concept of the growth ceiling from a few posts ago, your MRR might still be growing just from past momentum and not your current work.

MRR can still go up even if your current features aren’t working. It can also go down when you’re shipping things that work.

If you follow this as a signal, its really easy to go astray.

How to Set a North Star Metric

There are 2 basic choices you have to make here.

What are you going to measure?
How are you going to measure it?

For “what to measure”, you should try to capture “one complete unit of value” from the user’s perspective.

Typically value exists at a few levels and its tricky to find the right altitude.

For example at DuoLingo, this could be the completion of a question, pack of questions, collection of packs, etc.

The first time you do this, pick something that you can easily measure that signals user value and you can get more specific with time.

For “How to measure it”, there are 3 traditionally ways of doing it.

“Count of Something” - e.g Uber’s total completed trips per month
“Average of Something” - e.g Slack’s average number of message per team.
“X per Y” - e.g Facebook’s 7 friends in 10 days metric for new users.

There are no perfect metrics and all of these involve tradeoffs.

“Count of Something” - This number is sensitive to new user acquisition. So it might go up, just because you have more users not because you are improving the product. That might still be a good thing, but be aware of that.
“Average of Something” - This is great at quantifying user level health but it also hides outliers/power users. This is important in certain models like content creation, marketplaces, or anything involving usage based pricing.
“X per Y” - Setting thresholds are really powerful, but you need a lot of data to know you’re setting this threshold correctly. Optimize for the wrong level and you can go off course.

Advance Topic: North Star Metrics Drive Strategy

The details of how you define this metric will also impact what strategy you take.

Airbnb’s metric of “Nights Booked” has only a loose relationship to profit.

They could drive much more profit if that was factored into their metric.

Not focusing on profit means the teams can focus on expanding geographically as all nights booked anywhere in the world are equal under this metric, which means they can take more marketshare.

If they set a metric of:

“Profit per Night Booked” - this would likely favor optimizing the cities where they already have density.
“Avg Nights Booked Per User” - They would focus on high frequency travelers and addressing their specific needs.

When I was at UberEats, we actively optimized for profit.

To do that we shut down countries that we didn’t think would be profitable (Italy, Hong Kong, Argentina).

We definitely lost marketshare doing this but it was clearly the right thing to do for the business.

So What Do You Do With This Information?

North Star Metrics are a simple framework but deceptively hard to get right.

Go from Broad to Narrow - Start with a metric that has broad coverage and narrow your focus with time.
Make Sure Its Easy To Explain - your team can't optimize for it if they don't understand how it's calculated. Simple is better.
Stay focused - in my experience the life cycle of a North Star metric is somewhere between 1-3 years. If you are struggle to move you north start metric, it might not be the metrics fault, you might be working on the wrong things.

Additionally, here are some good gut checks:

Can everyone in the company impact this metric in some form?
If you are seeing this metric improve, does your MRR eventually go up?
If you see this metric improve, is your usage retention improving?
Is this based on a user action of some kind? E.g not just landing in your product.

As in most things, simple is typically better.

Good luck out there.

P.S - are you looking for help setting a metric like this? Reply to this email with “NORTH STAR” and we will chat.

About Me

Dan has help drive 100M+ of business growth across his years as a product manager.

He ran the growth team at Codecademy from $10M ARR to $50M ARR, which was acquired for $525M in 2022. After that he was a product manager at Uber.

Now he advises and consults with startups & companies who are looking to increase subscription revenue.

Learn more about consulting >>>

The Four Horsemen of Churn: Why Your Subscribers Leave—and How to Stop Them

Daniel Layfield — Thu, 23 Oct 2025 18:35:37 +0000

Why You Can’t Fight Churn All At Once

Churn isn’t one problem. It’s four—and the worst part?

Three of them aren’t even about your product. Until you separate them, you’ll keep bleeding cash for no reason.

In my 10 years of experience doing this, the only way to lower churn rates is to break this number down into seperate buckets.

For most businesses, I’d categorize them as:

Payments or technical churn
Selling to the wrong persona
Failure to deliver value fast enough
Happy user churn

I’d suggest that you tackle them in roughly this order and I’ll walk you through the

Below I’ll walk through each one as well as the best ways to fight them.

The Impact of Improving Churn is Massive

Its tough to understate the value of lowering your churn rate. It impacts basically every metric in your business.

Before you understand how to fight churn, it seems impossible. But it is not.

However, if you break your churn apart into the underlying reasons, you can make massive impact.

I personally have seen companies with 25% churn rates get down to 10% by focusing on the below problems.

But this is only a 15% improvement right? No it is a 250% improvement.

Because Churn makes such a big impact on the metrics of your business, it has a huge impact across the board.

For now, lets just focus on LTV, which you can calculate as:

❝

Life Time Value (LTV) = Monthly Price / Monthly Churn Rate as %

This mean that lowering churn from 25% to 10% means that you raise your LTV by 250%, which can raise your revenue by 250% which can raise your enterprises value by 250% (likely more because your business is seen as more stable)

Churn Rate

Monthly Price

LTV

25%

$20

$80

|
|

10%

$20

$200

Additionally, when you calculate the annualized impact of your churn rates, its pretty shocking:

There are 4 core types of churn. To help your business grow, you need to fight them in different ways

#1 Payment/Technical Churn

This type of churn happens when you lose an otherwise happy user to payment or technical problems.

Lets take Whoop (the fitness tracker) as an example.

Payment churn would be if my membership was canceled because my card didn’t have enough balance when they tried to charge me.

Technical churn would be if the strap lost connection to my phone and I couldn’t use it anymore.

Both of these scenarios are painful because you’re losing happy users to preventable problems.

Payment churn is (typically) the larger problem and easier too measure within this category.

If you pull up your Stripe homepage and you will see a chart that looks like this.

The orange part is bad. The more orange you have, the worse this problem is.

In my opinion this chart understates the impact dramatically because it doesn’t factor in the future payments from these users.

Lets assume you have a $10 per month product and your LTV is $100.

If a user fails their 2nd month’s payment, you didn’t lose $10, you lost $90.

This chart will only show you a $10.

Big difference. Very painful.

How To Fight This: Best Practices + Stripe Settings

If you’re read this newsletter for any period of time, you heard me stress the importance of good payment processing.

Best practices here won't radically change the business, but you can do the basics in ~10 minutes via Stripe.

When I consult with companies - this is literally the first thing I do.

While you can never get this number down to 0, this is the highest ROI work you can do.

Short Term Fixes with Stripe:

Go to the “Revenue Recovery” page in stripe and make sure you have the “Smart Retries” feature enabled. I prefer 8x in 1 month.

Email users when their cards are failing or going to expire. Turn these on.

A few notes. Stripe moves these settings around, so these pages might look different by the time you see this. Just look up the term “revenue recovery” and find the “emails” and

#2 Selling to the Wrong Persona

This is a simple but deadly effective strategy for reducing churn.

Identify who your highest LTV users are
Only spend money to acquire those users
Only build features for those users.

Sounds simple but a shockingly low number of companies do this.

Everything you do costs money and adds complexity, so you should find the highest ROI way of spending that money.

The right data really makes this clear.

If you have 3 core personas, and you can chart out their retention like below.

Assuming you have a decent volume of this person, you should obviously be spending money to acquire “Persona 3”.

So why don’t more companies do this?

How To Fight This: Data Collection, Unified Goals & Feedback Loop

In my experience, this is an organizational problem at heart but it comes down to the following problems.

Not collecting and using data effectively: - Do you actually collect enough data to segment by persona? Can you easily apply that segmentation to all the metrics of your business?
Marketing & product teams have different goals: - Typically marketing has a goal to acquire a total number of new users and product has a goal to retain users. No one is looking at the connection between the two.
No feedback loop: Even if the data exists in the product world, its not making it back to the other team and actually changing their strategy.

When you don’t have aligned goals, you won’t have aligned behaviors.

Persona one might be the easiest to acquire, but if you focus there, you’ll increase your churn numbers, which will lower your LTV, which will lower the value of your company.

#3 Failure to Deliver Value Fast Enough

Users come to your product to solve a problem. You need to show them that you can solve this problem as fast as possible.

This is know as getting user to the “Aha” movement, or the time they first see value in the product.

This data from RevCat’s 2025 state of the subscriptions illustrates the point.

For mobile apps ~80% of users who start a trial will do so on day 0 downloading an app.

You can set up emails to try to win them back but this won’t be nearly as effective as improving the experience to deliver value on day 0.

You have to deliver value fast.

Assume a new users will only show up once and you have maybe 2 minutes to get them to see the value of the product.

How to Fight This: User Journey Mapping & Onboarding

There are 3 core things you need to figure out here.

What is the “aha” moment for my product? Note this might vary per persona.
What is standing in the way of this?
How can you design your onboarding to get users there faster.

There is a reason the dating apps just drop you directly onto a list of matches and get you swiping immediately.

They also (allegedly) boost your visibility to you are more likely to connect with someone in the first 24 hours.

Does this inflate your sense of dating possibilities, make you more selective, less likely to settle down and damage society in the long term?

Yeah probably, but it also helps their activation rate.

#4 Happy User Churn

Users came here to solve a problem. Your product helped them solve that problem.

Now they are leaving.

They are probably leaving happily with fond memories of your product, which is nice, but they’re still not going to pay you anymore.

We had this problem a lot in Codecademy. You came to us to learn SQL. We taught you SQL. Now you’re leaving happily.

How To Fight This: Use Case Expansion or Extension

This, in my opinion, is one of the harder forms of churn to fight because you need to stand up net new product lines or really big features.

Inherent in that is hiring new teams, validating new ideas, and/or launching MVPs.

All of which are expensive.

Spotify added Audio Books that you can listen to in the same subscription
Bumble built Bumble BFF where you can connect with people for friendship.
Airbnb built Airbnb Experience where you can find activities to do in the city that you’re traveling to or

These products work because you’re finding either the next problem that a users has or another problem that happens in parallel for users that you already have.

That last part is key. Pick your best users, the ones that you already have distribution channels set up for and give them something else to do.

So What Do You Do With This Information?

The advice within this article is easy to say and much, much harder to do. If you can lower your churn rates materially and transform your company.

How to fight each type of churn is listed above, so follow those and stay focused on each for long enough to make an impact.

Good luck out there.

Dan

DEV Community: Daniel Layfield

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