I get it. Most PLG advice out there talks primarily about optimization and innovation — the later stages of PLG maturity, and is targeted towards more mature companies with established teams, clean data, significan resources.

Very little is dedicated to the Build phase, where most startups actually live.

Here’s the truth: you don’t need lots of money or resources to start. What you need is clarity on what matters now, and understanding the right time to do each thing. The rest is execution.

This practical, no-BS guide will help you understand:

• What exactly we’re talking about when we talk Product-Led Growth

• Whether your product and company are ready for PLG

• What to focus on in the very beginning (and what to ignore)

• How to kick off PLG in your organization the right way

This guide focuses exclusively on the Build stage and is tailored for startups with constrained resources and limited clarity.

I’ve been working in and with startups for almost two decades. This guide cuts through the noise and gives you a practical roadmap for getting your PLG motion off the ground without requiring perfect data, huge teams, or endless experimentation cycles.

Table of contents:

1. Product-Led Growth in B2B

   1. What is B2B Growth

   2. Dual nature of Product-Led Growth

   3. Three pillars of Product-Led Growth

   4. Advantages & disadvantages

   5. Product-Led motion vs. tactics

2. Are you ready for Product-Led Growth?

   1. Is your product eligible for PLG?

   2. Is your startup ready for PLG?

   3. Is your company ready for PLG?

3. ULTRA Framework: 0-1 PLG Roadmap

4. 0-1 PLG Project setup

   1. Setting expectations

   2. Metrics and targets

   3. Team setup

   4. Basic operations

   5. Tools & Documentation

5. Summary

🚀 Let’s get started!

1. Product-Led Growth in B2B

Before we dive in, let’s make sure we’re aligned on some PLG essentials.

Our human reality is inherently semantic, and words matter — they create our worldview. So let’s spend a few minutes here to come to a shared understanding and save ourselves time down the line.

What we think about when we think about growth?

For most people, it’s what they’re lacking at the moment. It’s a fluid concept with no hard rules.

But…

In the early days startups’ main objective is to get more customers, right? It’s natural they’re obsessed with acquisition. It becomes a problem when founders carry on this acquisition-first mindset into further lifecycle stages of their startup — this can break a business, for real.

And even though 80%+ of people equate growth to new customer acquisition (no data, just personal experience), this is just one type, or lever, of growth.

What happens to these new customers is an even bigger question:

• Do you lose them or do you keep them?

• Do you just keep them or do you grow them?

• Do you keep and grow them at all costs or profitably?

All this doesn’t happen by itself. It’s actual work. Deliberate growth work. And there’s more than one way to do this work.

There are different approaches and operating models of how companies can drive consistent full-funnel growth — growth motions. There are three distinct growth motions for a B2B business: Sales-led, Marketing-led, and Product-led growth.

Each has its own strengths and limitations. A strong B2B growth strategy is not an either/or game — it smartly layers these motions to maximize results and secure all flanks.

B2B vs. B2C growth

Growth approaches for business and consumer software differ because they have different focal points:

B2C products focus on user acquisition and free-to-paid conversion. Consumers are budget-conscious, so success means increasing user volume.

B2B products focus on strong product engagement that drives long-term usage and upsells. Business customers aren’t as budget-constrained, allowing to sell more value to existing customers: additional products and features, larger usage volumes, more seats.

Why this matters: Product-Led Growth is often equated with traditional B2C growth. While PLG has its roots in consumer apps, these different focal points mean PLG in B2B is far more a product engagement strategy than an acquisition strategy.

The dual nature of Product-Led Growth

Unlike Sales-Led Growth (driven by relationships and negotiations) or Marketing-Led Growth (driven by campaigns and demand gen), Product-Led Growth makes the product itself the main vehicle for distribution and sales. Users can discover, adopt, purchase, and continuously engage with the product without human intervention.

This is what makes PLG unique: it’s both a Go-to-Market (GTM) strategy and a Product strategy.

As a GTM strategy, PLG defines how you bring the product to market: who you sell to, how you position it, which channels you use, and how you acquire customers. It’s front-loaded, focusing on launch, market entry, and scaling into new segments.

As a Product strategy, PLG is a repeatable model for how your product and product org drive ongoing growth after you’ve landed customers. The emphasis shifts to scaling usage, monetization, and expansion. It can’t live outside the Product territory and must be tightly aligned with it.

Read more on Product Growth here:

As such, Product-Led Growth has a dual nature:

• It’s the market entry strategy geared toward lower market segments

• It’s an engine that sustains and compounds growth through increased engagement and built-in expansion pathways

Or, as I call it, a full-funnel growth strategy :)

Three pillars of Product-Led Growth

This motion relies on three foundational pillars.

1) Showing product value before a purchase

The idea is to facilitate a purchasing decision by proving that the product can deliver on its promise. Sharing this value for free allows potential customers to make an informed selection and increase their intent to use (and buy!) by experiencing the benefits firsthand.

The tradeoff? You’re giving away product value for free without any guarantee of conversion, which especially problematic for products with high cost-to-serve, like AI products.

2) Capturing individual users, not enterprise buyers

The main idea is to win over end-users who adopt the product themselves, then bring it into their team, and eventually across departments until it lands an enterprise deal.

This is an ideal scenario. In reality though, this means casting a wide net: acquiring not just employees of various enterprises who promote adoption within their organization, but also prosumers with individual use cases, and SMB employees with small teams.

This means that only a fraction of new accounts will translate into enterprise deals, and even for them, it may take months before meaningful revenue materializes.

3) Self-serve product exprience

The product itself guides the user through the entire customer journey, without any humans in the loop. If done right, PLG becomes your most cost-effective growth engine.

But building frictionless user flows (from signup to payment) and the infrastructure behind them takes months or years before you can scale.

All in all, PLG is not cheap growth — it’s a long-term operating model that front-loads effort and investment, but compounds over time once it’s in place.

Advantages & Disadvantages of PLG

Let’s quickly summarize the benefits and contributions of Product-Led Growth to your business growth:

Customer advantages:

• Improved customer experience

• Faster and smoother time-to-value

• Higher satisfaction and advocacy

Product development advantages:

• More focused product development

• Accelerated feedback loops and expanded data pool

• Data-driven and faster decision-making

• Better alignment across departments as the product serves as the unifying focus

Competitive advantages:

• Higher chances of customer consideration for procurement

• Market defensibility (competitors won’t eat away your market share in a different segment)

• Natural market expansion

Business advantages:

• Acquisition efficiency and lower CAC via product-led channels

• Higher conversion rates from engaged, higher-intent users

• Shorter sales cycles

• Higher CLTV and retention

• Natural growth paths within the product

• Higher ARR/FTE

The main disadvantages:

• Significant investments into building and optimizing performance to the maximum

• Longer timeframes for materializing true B2B revenue from upper market segments

• Making peace with the fact that many accounts will be free or low-ticket value

Product-Led Growth motion vs. product-led tactics

Product-Led Growth as a motion enables the product itself to acquire, engage, retain, and monetize users—moving them from signup through adoption, habit formation, payment, and expansion.

Product-led tactics are separate techniques that improve product adoption and engagement within a sales-led motion. They help, but aren’t accountable for growth.

For example, a sales-led product can have a trial and product-led onboarding, but all purchasing (initial conversion and upsells) goes through Sales. Yep, this happens. These tactics improve the product experience and user engagement, but they don’t replace the sales motion.

Some products can’t support a full Product-Led Growth motion due to their product context and value structure (more on this in Section 2). They can’t do PLG in its entirety, but they can still employ product-led tactics to improve the product and provide a better end-user experience.

Besides, I’m convinced that pretty much every PLG effort starts with tactics. Tactics help teams get started, prove PLG viability and potential, get leadership buy-in for further investment, test hypotheses, and gain necessary learnings.

And this is what this guide will be about.

For more on the PLG spectrum and motion vs. tactics, check out:

2. Are you ready for Product-Led Growth?

For B2B startups, I recommend to always start with Sales, then layer on a Product-led motion.

But when exactly? With Product-Led Growth, there is, in fact, such a thing as “too early.” Building a new growth motion isn’t free, and we don’t want to prematurely invest into too much ambiguity.

I suggest checking your readiness on three levels:

1. Is your product eligible for PLG?

2. Are you at the right stage to start building PLG? Is your startup ready?

3. Is your organization ready for a new growth strategy?

2.1. Is your product eligible for Product-Led Growth?

While employing some product-led tactics is absolutely for everyone, building a full-scale Product-Led Growth motion isn’t: there are products that by their nature won’t be able to realize PLG’s full potential.

Let’s bust another myth here: lots of people think that enterprise-focused products are ineligible for PLG. But the market segment focus isn’t the right criterion here: if this logic were correct, then every B2B SaaS would be ruled out, as all of them eventually want to go for enterprises (after all, this is where the real B2B money lives), and we wouldn’t hear about such PLG-Behemoths like Miro, Figma, etc.

What truly matters is the nature of your product value, and there are really just two hard rules your product needs to fulfill to “qualify” for Product-Led Growth:

Rule #1: Bottom-up adoption should be possible in your market

If people in your market can’t adopt tools on their own, PLG will break before it even starts.

There are markets (e.g. highly regulated environments) where employees must stick to strict rules for security and safety, and have a formal chain of command. They are not empowered to find and test software independently and mandate its adoption within their team — this lives with a central authority.

Even then all may not be lost: you can target middle managers who have the authority and budget to test tools and mandate its usage in their unit (e.g. regional office) without the need to escalate to higher management — a “middle-out” adoption motion. It’s not always possible, but it may be.

Rule #2: Your product must deliver clear value to individuals and small teams

This is a structural limitation. Remember that PLG captures individual users, gets them to adopt the product in their immediate team, and then potentially promote product adoption in other teams and departments.

Some products, however, can’t show their value to individual users or small teams. For example, enterprise access and identity management systems that aren’t designed to manage individual accounts (you could, but is it worth the price?) These only make sense when managing hundreds or thousands of employees across dozens of applications.

This is why people say PLG isn’t for products serving enterprises. But let’s be semantically correct: if some enterprise software isn’t made for Product-Led Growth, it doesn’t mean Product-Led Growth isn’t for enterprises.

If one of these two fails, PLG is not your motion.

But even if they both hold, it may not be the right time yet for your product to go PLG. There are also “soft” rules in the sense that it is in your control to make conditions fruitful. But until they are, your PLG investment will likely be wasted.

Rule #3: Your product should have a collaborative use case

Without a collaborative use case, your individual signups won’t grow into team accounts, and you won’t be able to sell into bigger organizations over time.

If you’re a prosumer SaaS that wants to move upmarket, you need to deliberately manufacture, promote, and scale collaboration in your product.

I started using Notion as soon as they launched, and back then they didn’t have team collaboration capabilities: they were purely a tool for individual notes and documents. Notion developed projects and documentation collaboration over the years, and now they’re one of the most successful productivity B2B tools!

Rule #4: Your product should solve a recurring problem

If your product solves a one-off or rarely occurring need, it’ll be difficult or impossible to promote natural usage habits and integrate your tool into the customer’s value chain.

Daily or weekly usage frequency is ideal, as it drives stronger engagement, retention, and customer growth. Otherwise, you’ll need strong human relationships to stay top-of-mind for their next problem-solving cycle.

While your main use case can occur annually, you can develop additional use cases with higher frequency. Say, your SaaS for annual employee performance reviews may be supplemented with more frequent use cases, e.g.:

• Employee development planning and tracking (weekly or monthly)

• 1:1 meetings tracker and documentation (weekly or bi-weekly)

These use cases can attract middle managers into your Product-led motion. Although by that time, you may not be a startup anymore. 🙂

Rule #5: You should have clear market positioning and messaging

Simply put, if you’re trying to create a new market category, it’s too early for PLG: the problem-solution space should be well-known to the market and end-users to start looking for a new tool themselves.

Rule #6: Your product shouldn’t require your assistance to set it up

Many B2B products are complex and need some setup to start generating value. Sometimes, the initial user isn’t the right one to do the setup and will need assistance from someone, like an engineer, which makes the self-serve setup even a bit more complex.

Lots of early-stage startups haven’t yet figured out an effective and clear self-serve setup process and assist their customers with that. That’s ok, you’ll get there with time.

But if you’re not planning to ever allow your customers to set up the product by themselves, then a fully-fledged PLG motion isn’t for you.

Understanding these constraints early helps you make realistic decisions about your growth strategy and avoid investments into something that is not even meant to work.

2.2. Is your startup ready? Signs of Product-Market Fit

Everyone says “Don’t do PLG before you have PMF.” Product-Led Growth scales the distribution of your product, and it needs to work first.

Truth. But when is PMF?

There isn’t one universal definition of Product-Market Fit, so we have to rely on its signals:

• Sustained usage: a signal you’ve built a valuable solution for your defined market.

• Traction: a signal that you’ve found the right way and words to capture your target audience’s interest, relate to their problems, and convince them of your solution.

Sustained usage

Simply put, you see that users consistently return to your product to derive its value.

Look at your user retention curve: in the beginning there’s an inevitable drop-off, but you want to see that this curve flattens out (don’t have to be perfectly flat). If it does, that’s an indication your product is valuable for someone. If you have different personas within your target audience, make sure to track them separately.

• Min. 20% of your new usersbase should maintain activity (perform the “core action of value”) for 6+ weeks.

• 40-60% indicates a sound Customer-Problem-Solution fit.

• Your engagement metrics (e.g. WAUs) should also be trending up.

Traction

More customers should be joining each month than the previous one. We’re not looking for crazy growth, just consistent. In general, acquisition should just be getting easier.

This says you’ve more or less figured out your targeting, positioning, messaging, and a suitable channel.

• Ideally, a portion of your new customers should come through organic channels — this is truly a signal of the value to the market.

• Your avg sales cycle duration and sales conversions should improve: ~10% CR without warm intros is a great sign.

Other signs

It’s also great to witness:

• Some renewals are happening.

• You increase your prices and it actually works: a very strong indicator.

• Your NPS is going up, you start seeing more 9s and 10s

Some people say that $1M ARR signifies Product-Market Fit. While it may be true, it’s not a definitive benchmark and should be supplemented with the signals above. Besides, so much depends on the market, competition, your solution, pricing, etc. Remember that correlation is not causation.

Crucially, for ARR to signify PMF, it should be coming from the same customer segment. If it’s split across multiple customer segments and use cases, that cannot be seen as a PMF signal.

Having evaluated hundreds of companies for signs of product-market fit, I can assure you that there’s no specific revenue benchmark that definitively signifies product-market fit.

— OpenView

2.3. Is your company ready for a new growth strategy?

If your product is ready for PLG, it doesn’t mean your company is. It’s nothing short of an organizational mindset shift, and you have to have just a few things in place to ensure it’s not going to be a total mess.

I’ve been working in and with startups for almost 20 years now, so I get the constraints (hence this guide). A lot of PLG advice assumes you have the resources and infrastructure of a scaling company. You probably don’t. So let’s focus on what actually matters at the start.

NB: I’m deeply convinced that clarity is the main resource you truly need: it’s far better to start with clarity and little data than with abundance of data and little clarity. Sorry to all “bias for action” folks — I’m in the camp of “let’s figure out what we’re doing first, then do it.”

To ensure your PLG start will be effective (not fast and smooth), this is what you need:

2.3.1. Customer Intelligence

This is always important, but even more so for PLG: selling via Sales can be pretty forgiving in this regard (you can adapt, adjust and improvise). But product-led distribution is unforgiving: you can’t just create a random self-serve product — it should mirror the user’s reality as close as possible.

Too many companies think they know their customers, while in reality they... kinda don’t 🤷‍♀️

Where to start? With your ICP!

The ICP (Ideal Customer Profile) topic is complex. It’s counterintuitive. Think about it: to get your business going, you need to sell it only to one segment of the market. This goes against all intuition we have about this world.

Everyone tells you to focus on ONE Ideal Customer Profile. The word “ideal” literally means one, the best. But too many founders don’t listen until their business starts failing (The “Everything for Everyone” Startup Death Trap). Only then are they able to understand the true meaning of selling to an ICP.

Let’s not be them. Trust the experts: sticking to your ICP can make or break your growth.

Simply put, your ICP is the type of a customer that would have the most success with your product. Or, as I put it, who “stays, pays, and prays for your product”.

ICP is defined through data: acquisition and conversions, engagement and retention, satisfaction, and expansion. In the very beginning you won’t have enough of it to give you a definitive answer. But when you have signals of PMF, you should already have good directional signals of who your ICP is.

With PLG, we should go a step further and define your Ideal Individual User Profile: typically a persona within your ICP who’s most likely to discover, adopt, and engage with your product. For this you should have a deep enough understanding into your user personas, not only market intelligence and buyer profiles.

2.3.2. Data

Product-Led Growth and data are like two sides of the coin. But in the beginning you don’t need loads of data or advanced data skills and setup, so don’t be intimidated by this. What you actually need is:

a) Clean data

Before you start, make sure to:

• check your main metrics definitions and events, and ensure there’s just one event per metric (because every department loves to have their own!)

• establish a clear event naming conventions for everyone in the company to stick to.

• unmess your existing reports and dashboards (or you’ll get drowned in them).

b) Access to data

If you have self-serve analytics, great! But if you don’t, it’s not the end of the world. Use spreadsheets, SQL, AI, pair with an analyst — it doesn’t matter.

What matters is that you can answer your questions swiftly, without going through the hoops of creating Jira tickets for the data team who will never have time for you (if you know, you know).

c) Clarity on what is success & impact

You have to understand the structure of your business and revenue to be successful at growing them.

In the beginning, you won’t need a lot of targets (more on them later). Nonetheless, I urge you to get your entire leadership team to create a “Driver Tree” for your business success to understand and outline your entire system of levers you can pull to increase revenue (or other form of impact). This is an invaluable artifact for the entire company that will help you stay aligned and clear in the future, so do it early.

By the way, if your product team doesn’t yet operate in an outcome-driven way and their goal is to deliver features instead of customer and business impact — this is your sign to start learning and doing modern product management.

This is enough for now. In the future you’ll get more advanced and will start to correlate end-user data with account-level patterns, and connect product data with marketing and sales. But for now don’t get carried away by this.

LOW-DATA B2B ENVIRONMENT

Everyone knows that Growth relies on experiments. But B2B products have far fewer customers and users than B2C, especially startups. So how do you experiment in this reality?

First, you don’t need to A/B test everything. There are other validation techniques that give you enough insights without a need for loads of traffic.

You’ll have more traffic on the homepage, pricing page, and signup flow, so A/B test there. But even then you may not hit 95% significance. Lower it to 80% or 70%. Early on, you don’t need precision — you need direction. Precision matters at scale where every percentage equals thousands of dollars.

Second, less quant data means more qual data. And the less customers you have, the more you can stay close to them, talk to them, observe them, in order to raise your confidence (the goal of experimentation).

When traffic is low and every signup is precious, put rigor in your hypotheses: understand well what you want to learn, how you’ll define success, and who your audience it. The more precise and homogenous your segment, the lower sample size you’ll need.

2.3.3. Alignment

Misalignment, lack of buy-in, and wrong expectations are the most common source of growth problems. Let’s go through the main pitfalls that will get in the way of your success.

a) No leadershup support and buy-in

Your CEO and immediate manager must be bought into PLG and act as your “sponsors.” Without their support, everything you do will be an uphill battle. Worse, they may diminish the significance of your wins (it’s human nature to resist what we don’t like) and/or penalize you for “rogue” actions.

Sometimes they’ll be natural supporters. Other times you’ll have to build the case: outline the experiment, agree on success criteria, and address their (likely valid) concerns. It’s all about communication and leadership.

It’s okay if other company leaders don’t support you yet. Your goal is to prove PLG viability with real results and earn their trust.

b) Wrong expectations

When people hear about Product-Led Growth, they often think about explosive overnight viral growth. That’s rarely a reality.

PLG is long, deliberate work. Most progress is slow, incremental, and unremarkable. Big wins are compounded small wins over time. If your execs expect fireworks and you deliver campfires, you’ve already lost.

The other common trap: expecting immediate wins. Growth is based on a scientific method, and early days are all about learning, not impressive results. It’s understandable that founders want to see tangible ROI even before they greenlight a new growth team. But if that team faces pressure for immediate results, they’ll drown. They need runway to learn and gain velocity.

с) Misunderstanding Product-Led Growth implications

I’ve learned that PLG is perceived very differently by different people. If your CEO thinks about PLG as cheap virality, and you think about strong engagement that translates into retention and expansion, it’s best you uncover this early.

And... Product-Led Growth should be treated seriously.

Execs need to understand that it’s a company-wide growth strategy, not a PM team toy, not an initiative on a product roadmap, and certainly not a couple of tickets on a platform team’s backlog. Every functional department will have to contribute to make it work. And it deserves not only its own strategy, but its own team that has different skillset, priorities and goals, and MO than core teams.

d) Product <> Sales Friction

Fact: Sales are often nervous about PLG. They’ll say “Product should focus on customers and leave revenue to Sales!” or “Why even bother with these low-ticket customers?”

This tension is understandable. Sales used to control customers and deals. With PLG, they think they’re losing that control. That can feel threatening: they all have quotas to hit, and commissions to earn.

Position PLG properly: it captures customers outside the sales target segment—those with simpler use cases, earlier in their problem-solving cycle. Product nurtures them into high-intent, potentially high-ACV PQLs that Sales can then close. It’s not a threat, it’s a pipeline builder.

The real friction? PQLs take months or years to develop. Sales may see this as missed opportunity, but remind them: they wouldn’t have captured these users anyway. Sales doesn’t work with individual users, and buyers rarely just stumble upon tools.

The reality is: clarity beats perfection every time. Get clear on these three areas, and you’ll have a foundation strong enough to start building your PLG motion.

3. ULTRA Framework: 0-1 PLG Roadmap

You’re clear on what Product-Led Growth means for B2B, and whether your product and company are ready to start building this new motion.

Now what?

PLG experts (including yours truly) will tell you that it’s a complex system, and growth strategy and execution requires expertise and experience. Legit.

But this is what I have come to understand through my conversations and work with startup founders: their initial problem is universal. So is the solution that I’m offering in this guide.

What do all startups care about the most? Increasing the pool of paying customers. This capital will fund all future work (not only in money, but also in data and learnings). If they do not have the right mechanisms to convert customers, there’s no sense in TOFU growth, elaborate engagement strategies, or monetization optimization.

It’s an ideal place to start with Product-Led Growth: generally a low-risk and lower-investment area with plenty of optimization opportunities, and a fruitful learning environment. Conversion rates also make a perfect North Star Metric (not too complex, but significant enough to build trust and prove viability).

If you already have some of the steps in place, skip them. The task is to tailor this framework to your context. But make sure you have all 5 parts in place.

Step 1: User Profile

If you have established your ICP (at least a sound qualitative hypothesis), you can derive the profile of your Ideal User.

Who is the person within your ICP (role, function, seniority, etc) who is most likely to be:

• Motivated enough to discover and try your product?

• Able to use and adopt it without external help?

• Have the permission to invite teammates to work on projects together?

• Potentially, pay for it too (not the enterprise tier, of course)?

Start with a qualitative hypotheses informed by your existing insights and intuition, and validate or refine with quantitative data.

Look at the first users in your successful accounts, their characteristics and behavior trends, and what role they take in further account adoption and engagement. Do they advocate your solution or serve as champions in the buying process? Do they invite other users and share their work with them? Do they leave good testimonials and give proactive feedback?

It may be challenging as B2B tools can be horizontal and serve multiple roles and use cases. In this case, you need to prioritize one user profile — a strategic tradeoff that will help you strengthen your offer, focus on value delivery and onboarding, and decrease churn.

If your product serves SMBs: The first user can also be the buyer.

If your product requires technical setup: The first account user may not necessarily be the first value recipient. For example, an engineer setting up product analytics prepares the ground for others to use it. Remember this when you look at your data to avoid confusion or frustration. Your customer knowledge should pay dividends here.

Work with them like you normally would with a product persona. Understand their desired outcome, main problems and gaps in current solutions, and how your product can benefit them. Understand their individual use case as well as how they work with their team, and their collaboration dynamics: Who do they work with more closely? What is the nature of this collaboration? What are the tasks, outputs and outcomes? Which users have which roles: who creates content, who consumes it, who shares?

This is ground work that is a foundation for every net step (not just 0-1 phase, but subsequent optimization and innovation phases), so make sure you do it diligently.

At the same time, don’t let yourself get stuck in analysis paralysis. It’s a constant iterative process where you have to be robust about updating and documenting your knowledge that will inform your next iteration.

Put your rigor into discipline rather than reaching 100% certainty (you won’t).

Step 2: Landing Page with a tailored offering

This is a simple but crucial step that a lot of teams skip or get wrong.

If you’re a sales-led B2B startup, your homepage is oriented towards buyers and focuses on capturing leads. Because many B2B SaaS products are complex, so is their homepage: it covers a variety of use cases, industries, and segments.

As a result, this page very often has fuzzy messaging and product offering: it will be hard for your target user to understand that this product is for them, why, and how it helps them do their job well. Remember, their problems are of smaller scope (individual or team-level) and are more concrete, oriented at immediate benefits and outcomes, not company-level ROI.

So what a lot of teams do is they start redesigning their homepage🙄

This is a massive initiative that involves multiple stakeholders and takes a lot of time and approvals. It doesn’t only make it hard to launch, but iterate on it, too. Every version will have to go through the same hoops.

Besides, such a big change guarantees initial decline of performance. But no one has relieved yout homepage of responsibility to work with leads and buyers just it. It’s not what anyone wants or needs right now.

Other teams understand that and just slap the “"Try for Free” button in the navigation. The rest stays the same. Needless to say, their trial signups aren’t doing well.

I recommend starting with a separate targeted landing page to attract your ideal users and convince them to try it in a language and design that resonates with them, and use this page for your paid, content, and social campaigns.

You’ll have full ownership of this page and can iterate on it to your heart’s desire, and it will be far easier to track the results. It’s super easy to build today, so there’s no excuse.

After confirming your hypotheses and ultimately proving PLG viability for your product, you can adjust your homepage to accommodate the new product-led motion.

Step 3: Try-before-you-buy model

The core premise of Product-Led Growth is to let users try the product by themselves and see it in action before they commit to a purchase.

There are two most common “Try-Before-You-Buy” models: Freemium and Free Trial.

I always recommend starting with a Free Trial, because Freemium is really not easy to do right, and it’s a lot of product work. A trial, on the other hand, doesn’t require too much thought process, and it’s faster and easier.

Of course, simpler doesn’t mean better. Ultimately, you’ll have to choose a model that allows users to experience the product value in the best way, and can drive attention in your competitive space. It can be freemium or a special version of a trial (e.g. usage-based or credit-based trial). I just suggest you postpone this important work to later stages.

I also recommend using the standard 14 days duration, as 7 days is usually too short for business software to really feel it. Even 14 days may not be enough, and in the future you may build a freemium or a sandbox environment. For now, consider trial extensions for promising users.

Should you ask for credit card information during signup? I recommend not doing this, or you’ll significantly limit your new active userbase and deprive yourself of the necessary learnings (not every employee is permitted to make a purchase, and they won’t use their personal data for work software).

If your product has high cost-to-serve (e.g. you’re a fully AI product with obvious costs), then usage- or credit-based trials are for you. But if you only have some AI feature(s) in your product, provide a standard 14-day free trial for your main product and add credits for a limited trial of your AI feature.

POTENTIAL ADDITIONAL DEVELOPMENT

a) Self-serve signup

If you don’t have one yet, you necessarily have to create it.

b) User profiling survey

You must ask your new signups essential information about them and their company to identify and Ideal User and send them to a relevant experience flow. Also, this data is critical for your product analyses and reporting.

Don’t overdo it with questions and only ask truly relevant ones:

• About them: their role, department, intent, use case.

• About their company: company or team size, industry, and whatever else is relevant for your ICP.

c) Self-serve purchasing flow

In Product-Led Growth the product should be able to sell itself, so if you don’t yet have self-serve payments, it’s time. Integrate Stripe or an equivalent, use the most common payment options, don’t do anything customized just yet.

If for some reason you can’t do it right now (e.g., tech, operations, or even legal constraints), it’s okay. When the trial expires, direct the customer to book a meeting with a sales rep. Not everyone will do it, but it’s better than nothing.

🚨 If you do not have open pricing for self-serve product: I’m not covering this complex topic in this guide, and will be working on it in the near future (subscribe!) If you have questions about how to create your first pricing system, feel free to send me an email or connect on LinkedIn, and I’ll do my best to help you.

Step 4: Reach the Aha! Moment

For your user to make up their mind about purchasing your product, they need to understand what it gives them, how it helps them, what are they going to pay for?

This is tacit knowledge, not just information. They have to see it, touch it, feel it, to understand if it fulfills their needs or not. And your product has to guide them. Where exactly? To the moment when the user understands what consititutes the product value, the Aha! Moment. And do so fast (ideally in their first session), because people get distracted and forget.

This is not a guide on Activation and onboarding, so we will not spend time discussing the details. There’s also a high chance you know about it too well, as this knowledge is a commodity now. If not, here are a couple of foundational resources to get you started on the topic:

• The Ultimate Guide to Product-Led Onboarding

• Ultimate Guide: Activation

• Bowling Alley Framework for onboarding

Here, instead, I want to share some important nuances of B2B product activation that are rarely spoken about and are one of the most common points of confusion.

1. Aha! Moment ≠ Setup completion

This is still a wildly common misconception, unfortunately. It typically concerns products with a complex setup — it may involve a few steps, and teams think that once it is complete, the user is activated. It is not so.

Consider an Analytics SaaS as an example: it requires installing their code into the customer’s website in order to collect data that will then be interpreted and visualized in the product dashboard. Having the code installed and confirmed is the Setup moment, but not the actual value. The customer doesn’t pay money for this product to just collect data, right? They pay to be able to see performance, analyze data, derive insights, share reports with others, and so on — this is the value they expect.

Setup is just a necessary prerequisite for your product to function and generate value for users, not value itself.

2. Complex Aha! Moment

Often it is really not easy to define this moment in your product.

The Analytics SaaS is a great example — when does the user truly understand the differentiated value of it? Is it when they create their first report? Or dashboard? Or receive a notification about some error or performance issue? It can be truly, truly hard to define the Aha! Moment.

In such cases I recommed to look for value proxies. This is how:

1. Understand what real-life outcomes (not actual metrics yet!) correlate with your users’ success. In our example it is likely to get an insight from data. But there is no action within the product that directly represents what’s going on in the user’s mind, right? Don’t panic.

2. List a few things you can measure that connect to this value definition as close as possible. Think like your customer, not an engineering team. At the same time, it’s important to let go of perfectionism, or you’ll get stuck in analysis paralysis.
   In our example it can be creating a report, working with the report (e.g. applying fiulters), setting up daily alerts for a report, etc.

3. Correlate each such action with overall engagement strength (e.g. frequency, longer sessions) and retention, and pick a winner.

It’s not perfect, but it is reality for some products. As they grow and improve and get more learnings, they will keep iterating on this. Just don’t start creating elaborate metrics that are hard to understand and maintain — it’s only going to confuse you.

IMO, it is better to remember you’re using a proxy than trying to retroactively fir the Aha! Moment metric to create a good reporting picture and forget what are the real customer outcomes and expectations.

3. Delayed Aha! Moment

In some products it is not possible to experience the Aha! Moment soon after a signup. For example in seasonal businesses. Or, say, when the Aha! Moment can only be experienced through collaboration, but to get there the first user needs to invite their team and wait for them to complete the required steps (I’ve seen this taking weeks).

This is truly inconvenient for teams working on activation. I’ve seen them redefine the Aha moment to fit users’ reality to the product. But they’re only tricking themselves. They may get “great activation rates,” but what’s the point if they don’t convert into retention and monetization? Reality is what it is.

There are three ways to deal with this:

1. Go for a usage-based trial or freemium to not abruptly end the trial before the user has a chance to get to the Aha! Moment. It may not be as easy as a time-based trial, but definitely makes more sense for your product.

2. Consider creating sandboxes and test projects that emulate value delivery on dummy data (and even dummy collaborators). But this requires development and isn’t the best option for an early stage.

3. Create an interactive in-app demo to guide the user through all the necessary steps consequently to help them “learn by doing” without the need to create any projects themselves. This should be a temporary solution until you find a better way, but it’s better than nothing.

4. User profiling ≠ Setup

Profiling survey helps you identify the type of a user and slignshot them to the relevant onboarding experience.

Setup moment is the milestone when the product is properly configured to provide the intended value to the user. It can be just one simple action or a process with multiple steps. It may even be absent at all, it happens — doesn’t mean you have to create one artificially. Stay close to reality.

5. Setup moment can be your biggest dropoff point

But more often, B2B SaaS have rather complex set processes that may even require support from other roles, making it a #1 reason for activation dropoffs.

If you’re a product with a high setup barrier, put your focus on this first milestone: provide lots of information (text and video guides, webinar recordings, even ticket examples for engineers), offer support of your technical team, and consider turning on a trial only after the setup is complete.

6. Don’t get advanced in the beginning

There are two additional crucial parts of performing B2B activativation and PLG overall:

• Habit Moment

• Team-level activation

My advice is not to work on them during the 0-1phase!

The abundance of Activation knowledge out there often leads teams to work on this pretty advanced stuff for which they simply don’t have the capacity, data, and knowledge just yet. This work drags them down the rabbit hole, they get stuck and confused and/or constantly change their activation hypotheses, workflows and metrics.

Instead, focus on getting the user through the Setup and Aha! moments, collecting more data and customer behavior insights, and forming your first hypotheses for the Habit moment.

As for team activation — sometimes it is truly simple, other times it’s way harder.

Do promote team invites and shares by all means. But if your case is closer to “complex” on this spectrum, don’t yet invest a lot of time into building and measuring team activation workflow — instead nail individual users onboarding (especially the first account user).

7. Only focus on your Ideal User now

As we talked about before, in the real world you may have a horizontal product with different personas. And while we can prioritize one - ideal - persona for this new motion, it doesn’t mean we should leave all the other users hanging and unattained (i.e. without proper onboarding).

Pretty fast you’ll understand that creating an activation journey is no easy feat. Hence the advice is to focus only on the Ideal User in this phase and invest in improving their onboarding to better reflect their needs vs. spreading yourself too thin and building onboarding for other user types. Get the main thing right!

When you define the Ideal User through profiling, slingshot them to the right onboarding experience. For others (if they have different needs, goals, and behaviors) you can create a super simple onboarding guide, like a video explanation in a popup, and let them figure out the rest by themselves. You’ll focus on them later in the Growth journey (that’s why they say that Activation work is never over!)

Step 5: Assist with emails

Many teams assume that activation only happens in product and neglect emails.

In reality, your users don’t think about you all the time, they may not understand the product well even if they’re technically activated, and they may not necessarily understand the entire process (it is far from perfect at this point).

So it’s always good to fuel them along the way, nudge, and provide clear next steps via email.

Onboarding emails is a certain form of art, but at this 0-1 stage we do not have a goal to build a perfect campaign. We only want to make sure that we welcome the user and help them move further in case they drop off, and provide clarity about what’s next.

Welcome email

No need to be too fancy here and stuff this email with aspirational marketing promises, case studies, and links to all your support documents.

Include a heartfelt welcome from the founder and clearly outline the next step the user has to take to accomplish their goal in your product. Most likely this CTA will lead the user to the Setup moment. If your product has a complex setup, include a few links for different options, e.g., one leading to developer documentation, another to invite their technical team, or a link to request help from your support team. (NB: only for the first user in this account!)

Additionally, don’t send it from a “No reply” email: provide the ability to reply to the message if they have questions or want to share any feedback.

Aha! Moment nudge

If the user hasn’t reached the Aha! Moment during their first session after the setup is complete, nudge them with an email on their third day.

You don’t have to create a perfect behavior-based email and suggest the exact next step based on what steps they’ve already taken. It’s enough to just tell them that they haven’t completed the main task that would result in a desired outcome, and also remind them of the main value proposition and a few key benefits of your product.

If this doesn’t prompt action, send another nudge on their 7th day.

Activation email

This one is not mandatory at this moment, but you can use this email to congratulate them on their onboarding and provide a few possible next steps for them, especially if your activation journey is scarce with prompts.

This is a perfect opportunity to introduce the user to the collaborative benefits of your product: suggest to invite their teammates and/or share an existing project with them. Don’t forget to outline a few key benefits of collaboration, and include a link to a case study (if you have it) which clearly shows how a similar team has reached success.

Trial expiration email

This one is a very important email that you shouldn’t miss.

Actually, there are two of them:

1. Send the first email 3 days before their trial expires. Talk about the paid account benefits, share a case study and testimonials from paid clients with their results and ROI.
   Make it clear that after the trial expires they won’t be able to use the product.
   Don’t forget to include the “Upgrade to paid” CTA in the email that shall lead to the pricing matrix.

2. Send the second email on the day of expiration notifying them about this even. Offer the possibility to talk to Sales about a purchase — this may not be a huge driver but gives a clear direction to users who have the intent but no permission from their company to make a purchase.
   For activated an active users, you can offer a trial extension, too.

Make this a two-way street and ask them to share their feedback with you, especially if they have decided not to go with a purchase — it’s a valuable feedback source for startups.

Again, don’t go crazy with personalization and behavioral triggers.

Your main goals are:

1. Not let the user fall through the many cracks of your imperfect onboarding, and provide additional clarity and support to set them up for success.

2. Improve awareness of paid account benefits and the process to purchase for higher-intent users.

In my opinion this is the bare minimum of emails you need to create in your sequence.

4. Making It Happen: Your 0-1 PLG Setup

Now that you have a good understanding of what to do, the question is: how to actually pull it off? Where to begin, what resources are needed, and how to set yourself up for success?

The guide below is designed to help operators in the first place. If you’re a founder, you’ll have to assign a responsible person to lead this project (for example, your product leader).

There are 4 main areas that will need your attention.

4.1. Setting the right expectations

In addition to 2.3.3. Alignment, there is one more important thing to be explicit about, regardless of your company’s readiness and your own weight in the organization.

Frame this initiative as an experiment. Here’s why:

a) Everything is a hypothesis now

Your user profile, product value, positioning and messaging, try-before-you-buy and pricing models, activation journey — everything is a hypothesis. Even if you have high conviction about Product-Led Growth viability for your product, your goal is to prove it. And this is what experiments are about.

b) You’ll need runway to learn

You’ll be constantly getting new insights and data about your product and users in the scope of this new work. You’ll not only have to gain new learnings to pave your way forward, but learn to work with this new information iteratively and continuously.

Even if you have great product management practices, this still may be new to a different pace and MO. You need runway to accumulate a critical mass of these new insights and put them to work.

c) This is a new type of work

And it requires new skills, tools, setup and workflows, and even a team. This in itself will take time to figure out and adjust.

To put it bluntly: right now you can’t make concrete promises about timelines (that we tend to underestimate) and impact (that we tend to overestimate).

Even if this is not an entirely foreign work to your team, I recommend min. 4 months to factor in new team dynamics, workflows, time for learning, and inevitable friction and setbacks. If it is — go for 6 months. While team’s velocity is gradually improving today with AI-assisted development, the 0-1 phase is all about gaining clarity and experience.

If you don’t explicitly frame this as an experiment, you may find yourself under constant stress to deliver impossible results, which will be very hard as you don’t have the right foundation to do so just yet.

4.2. Metrics and targets

Don’t reinvent the wheel here — focus on 3 types of KPIs:

Impact KPIs

You started this all to prove that Product-Led Growth is possible for your product, meaning that your product can sell itself. So at this moment, your north star metric should be Trial-to-Paid conversions for your target segment.

Don’t go beyond that. Don’t focus on revenue metrics (MRR/ARR, Self-Serve revenue), churn reduction, or efficiency metrics just yet — they will dilute your focus and may only get you confused.

Make sure that you segment your data by your ICP & Ideal User, and build your reports accordingly. Segmentation may make your numbers look not very impressive, but your goal now is to build and prove your motion, not scale it.

Leading indicators of success

In order to track your progress, build a simple funnel:

Landing page visit → Signup → Aha moment → Trial to paid conversion (self-serve and via sales)

You can extend this funnel to include more steps (e.g., break down the signup and profiling process) for detailed analysis. But for communication and reporting, keep it simple.

Track not only the step conversions per se, but also time to convert. This should give you more insights on improving your Time to Value and activation overall.

Guardrail metrics

You also need to be tracking down-the-line engagement, retention/renewal, and even upsell metrics to see how your experiments influence them.

Be careful not to turn them into your goals at this point. You only want to see correlation between these metrics and your 0-1 PLG work: ideally both should increase. At a minimum, engagement and retention shouldn’t get worse. If they do, this is a signal for you that you’re doing something wrong.

Remember that most of these metrics are lagging, so it may take you a few months before you notice clear correlations. So don’t fixate on them just yet, but don’t forget to keep your hand on the pulse.

Which targets to set

So what numbers should you aim for? Even if you don’t commit to ambitious promises, you still need to understand when you consider your 0-1 project a success and when move on to the next steps.

It’s not an easy question to answer for 3 reasons:

1. In the beginning you may not even have the entire funnel built out, so you won’t have the baseline for each metric. Doing this will require some time.

2. Without historical data and deep customer understanding it’s hard to give any reasonable projections for numbers.

3. Industry benchmarks may not be the best way to assign your targets: not only their relevancy to your business is questionable (benchmarks are generalized and unsegmented), but also getting there will take you many, many more months. It’s better to use them as lightpost goals, not early targets.

Your first goal in this project should be to build out the entire funnel and establish clear and correct baseline for each of your metrics. Depending on where you start, it can take you 2-4 months to get there (you’ll need a month to accumulate this data).

With the baselines in place, you can set yourself some goals. Don’t be a hero and target for a 15% Trial-to-paid conversion rate! Instead, aim for small but consistent MoM improvements, for example +1% each month. Sometimes you’ll achieve more, other times less — these are all learnings that will tell you what works and what doesn’t, and foundations of your growth experimentation engine.

In my experience, it takes 9-12 months to collect the necessary data and learn what works in order to give more or less confident projections of impact for your initiatives.

4.3. Team setup

I recommend creating a separate team for this with single focus on this initiative. This work can’t be a part of the backlog of the team with least projects on their hands — they will inevitably get bogged down in other projects and maintenance

But you also don’t have to hire a new team. In fact, it’s better you don’t: at a minimum you don’t want to spend additional time on new employee onboarding; at a maximum there is a chance that this team will be dismissed after a few months because of failed efforts. Such is life.

Create a temporary “Tiger” team focused on this single project and without any prior obligations, and “hire” your existing employees. Here’s who should be there:

Product Manager

Responsible for planning and running the growth work, doing the analyses, working with teams on solutions, maintaining documentation, working with stakeholders and leadership — the usual stuff. This is always a lot of work, so it should be a full resource.

You want a person who is fluent with data and metrics, a good communicator, and has a bias for action.

Even for a regular PM this shift can be significant, so if you have someone who fits the requirements, has your trust, and is eager to learn, but doesn’t carry the PM title (e.g. Product Marketing, Engineer, Analyst) — you can give them a go.

Engineers

IMO, this team shouldn’t have more than 2 engineers, or they will be slowed down, and it will be harder for the PM to service them.

The thing here is these engineers shouldn’t just be good performers, they should be comfortable working in an entirely different setting: while your core engineers are craftsmen who love solving problems and care about sustainable and beautiful code, your growth engineers should be fast, scrappy, write disposable code, and care about your business.

Designer

You may not even need one in the very beginning if you have a good design and frontend system — your engineers and PM can do the basic work.

Still, I recommend involving a designer early so they can learn the growth context alongside the team. You’ll definitely need one in later stages of your Growth work.

Growth designers need a specific mindset. Look for someone who:

• Moves fast and iterates rather than perfecting upfront

• Collaborates fluidly without rigid process gates

• Is comfortable with experimentation where “right” answers emerge from data, not design authority

In growth work, there’s no time for extensive UX research phases or debates about design ownership. The team needs to work quickly. Everyone owns outcomes together, and solutions come from rapid testing, not upfront certainty.

You won’t need their full resource at this time — aim for 0.5. But help them prioritize Growth work so their availability is immediate, not scheduled for the next sprint in 2 weeks.

Product Analyst

Same applies here: if you have a strong PM and clean self-serve data, you may not need an analyst immediately, but involving one early is smart, as they add depth to insights, optimize data processes, and help the PM stay sharp and make better decisions.

Look for someone who can work at speed in an experimental setting, where analysis serves rapid decision-making rather than comprehensive reports.

Again, 0.5 resource will be enough with clear Growth work prioritization.

Product Marketing Manager

This can be a really good resource to help the team move with user profiles and landing pages, as well as activation emails.

As well, they may be the team’s liaison for the Marketing team to work on campaigns to get traffic for the experiment.

If you don’t have anyone at all for this role right now, you’ll survive, but if you do, make sure the team gets their help.

Act based on your existing resources. Some startups have more, others less. Get creative: a product leader may take on the Growth PM role for now, an engineer can handle mockups, a designer may excel at copy, sometimes a CEO helps with data analysis. Make the best choice from what’s available — don’t aim for perfection or let resource constraints intimidate you.

A note on reporting lines: Keep it simple at this stage. Don’t formalize anything, this whole setup is experimental and you don’t want to introduce unnecessary processes. Leave existing reporting lines as is, but ensure regular communication and alignment between this team and your Growth leader (whoever that is —your CPO, CEO, or the PM leading this initiative).

4.4. Basic operations

New teams inevitably go through a Forming-Storming-Norming cycle. Your job is to make this transition as smooth as possible and help them focus on the work, not the process.

I don’t aim to outline an entire Growth team operating system here, just the critical elements for your Tiger team and pitfalls to avoid.

There are two common failure modes:

• The team inherits the operational overhead of their existing product org: standups, sprint planning, retros, grooming sessions, stakeholder reviews, etc.

• Or they spend weeks figuring out processes, rituals, optimal sprint duration, handoffs, etc.

None of this matters much at this stage.

This team should optimize for speed, cutting corners and decreasing operational complexity. Your job is to guide them toward this and help remove friction rather than adding rigid structure.

The key principle: start minimal, add only what’s necessary.

Instead of keeping the standard setup and subtracting, start with a clean slate and add only what this specific team absolutely needs. Let them figure this out without too much oversight. Throw them in the water, so to say.

In practice, this means:

• Don’t impose specific rituals or delivery systems on them

• Actively help them save time on unnecessary meetings

• Relieve them of obligations like support, maintenance, and infra work

• Let them experiment with their own processes (in my experience, this is inevitable anyway)

Some teams tailor Scrum to their needs. Others don’t have any formal system at all and just continuously plan and release without specific cadence. If it works for the team, it works for you.

One caveat: This lean approach needs protection. Your growth team may have a mandate to skip some processes, but other teams don’t. This creates friction. Your growth team needs urgent support from core teams with packed sprints. Marketing requires formal review processes. Legal needs three weeks notice. You’ll need to actively negotiate exceptions, or your team’s speed advantage disappears.

One last note: if your team can be co-located and meet at least once a week to review work, collaborate and make plans together, do it. This is rare these days, but if you have the opportunity, use it. Things naturally go faster.

4.4.1 Required meetings

To continue the previous message, relieve this team of any former obligations, including meetings. There are only three types of meetings truly necessary for this team.

Daily team syncs

Standard team standup where they discuss progress, blockers, and any incoming news. Not much to add.

Weekly team meetings

Call it whatever you want, just don’t let the name itself limit you — the agenda for this meeting can be fluid and change contextually.

The main thing is that this is the dedicated weekly slot for the team to get together without leadership, review their progress and results, and make plans for the next week.

What the team should discuss:

• Progress: Current work, running experiments, and core metrics.

• Learnings: From experiments, data, interviews, research, other teams and company updates.

• Mini-retro: Pay attention to planning, execution and team dynamics. Just don’t overdo it with processes and turn into a real retro.

• Planning: Next week priorities, experiments to launch, work to be done, any collaboration or workshops to be planned.

That’s it. Make it light: they should feel like they’re working in a garage on a new startup (but don’t forget about their responsibility). And pizza always helps.

Two rules: don’t split this meeting into several, and never skip it.

0-1 PLG Program Review (bi-weekly)

Instead of running separate meetings with each leader (CPO, CEO, Marketing, etc.), create a Program for all leadership and the team to meet together, discuss progress and plans, and make decisions.

This is a higher-level meeting that focuses on results, progress and blockers, priorities, tactical changes, issues and critical decisions, not execution details.

Typical agenda:

1. Metrics & Targets: Results and progress, learnings, questions.

2. Hypotheses and experiments: Reviews, learnings, necessary adjustments.

3. Issues and open questions: Discussion that needs input and/or decisions.

4. Requests: For help and collaboration.

There is no concrete recipe here: the idea is that it’s bi-directional open communication.

Leadership needs to understand what’s happening, whether any plans or expectations should be changed and why, what’s the team’s progress and what blocks or limits them, provide cross-functional input for help, and make any decisions that the team cannot make on their own.

The team needs to be open about their progress and learnings, manage expectations, and proactively ask for help from their sponsors and stakeholders.

As you see, I’m not recommending specific time durations, who exactly should be in the meeting, or specific formats — it’s different for each company’s culture and habits, and changes slightly every week. It’s on you.

The only rule is to make this an open discussion, not a forum for hearings, proceedings and reprimands. But you know this already, don’t you.

4.4.2. Tools & Documentation

Just like with team workflows, don’t overburden this speed-focused team with elaborate tooling.

It’s not about having every possible tool and process, but about removing friction from the core tools and workflows and creating a minimum but working system for the team.

Data tools

As mentioned before, the main thing is that your team has access to up-to-date information and data analysis. It’s okay to start without a self-serve product analytics tool, but sooner or later you’ll have to install one to democratize access to data and shorten the insight loop.

When you do, invest in creating a feasible number of most important dashboards and/or reports (it’s easy to drown in them!) and relevant segments and filters. Remember, clarity and speed is what you’ll need 80% of the time. Don’t make your team spend hours preparing for the Program Review meeting.

Other tools you may consider:

• Session replays: Help the team see how users navigate new experiences and what problems they encounter. It’s way faster to spot friction and issues in new launches. Recommend!

• A/B testing: Not necessary for a couple of ad-hoc tests (engineers can help you there), but if you find yourself doing this more regularly, get a tool.

• CES & customer surveys: Getting qualitative data and feedback from users about your onboarding or other product workflows truly helps.

• User onboarding: Such tools give you access to main onboarding techniques like tours, tooltips, checklists, and so on. No need to reinvent the wheel here.

Documentation

The team should be rigorous about one thing: creating their Learnings Library. With time, this documentation becomes priceless.

Document:

• Hypotheses and experiments

• Learnings from experiments

• Results from data analysis & research

• Market, customer, and user intelligence

The rest isn’t that important.

When everyone shares the same context and understanding, they don’t need elaborate Jira tickets with step-by-step instructions. They can figure out their own execution because they understand the why behind the work. Knowledge in tickets creates dependencies and slows teams down. Knowledge in shared context creates autonomy and speeds teams up.

Summary

Building Product-Led Growth doesn’t require perfect conditions. It requires clarity.

You don’t need massive teams, pristine data infrastructure, or unlimited budgets. Those are excuses that conceal the real blocker: lack of clarity on what actually matters at your stage.

This guide gave you that clarity:

You now understand:

• What Product-Led Growth actually means for B2B (and what it doesn’t)

• Whether your product and company are truly ready

• The ULTRA framework: a concrete 0-1 roadmap focused on conversions

• How to structure your team and operations to move fast without drowning in process

The path forward is simple:

1. Validate your readiness with the criteria in Section 2

2. Follow the ULTRA framework step by step

3. Frame it as an experiment with clear metrics

4. Start small, learn fast, compound over time

Product-Led Growth is daunting. The challenges are real, the learning curve is steep, and meaningful results take time. But it may well be a worthy investment—one that compounds into your most cost-efficient growth engine.

The decision to start is yours. The roadmap is here.

No more excuses. Just clarity and execution.

Thank you!

You made it to the end!

Thank you for reading and for supporting my work.

These guides take weeks to create. They’re a reflection of almost two decades working with startups, distilled into practical frameworks you can actually use.

If this guide helped you, here’s how you can support:

• Share it with a founder or PM who’s wrestling with PLG

• Subscribe to get future guides and insights

• Like this post to make me smile

• Reach out if you have feedback or want to work together (alexandra@growth-zen.com)

Good luck!

It seduces founders into daydreaming about people lining up at the gates of their B2B castle in the sky, frantically waving credit cards and shouting "Shut up and take my money!" while golden coins rain down from the towers onto prancing pink unicorns.

There’s a seed of truth to this, only it becomes reality much later in a startup's lifecycle.

To get there, you need mundane, old-school sales. And no, not only for enterprise-focused products — the whole concept of attributing certain growth motions to market segments, while generally true, is an idea "with an asterisk" — too nuanced to be taken at face value.

Regardless of market focus, early B2B startups need human-led sales to reach PMF.

Here's why:

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1. Market Intelligence

While the product team takes care of building product value with the help of insights they get from talking to the product users, founders need to grasp the bigger picture around the problem their software solves, such as:

• Who finds it most pressing

• What outcomes they want to achieve

• How they prefer to learn about solutions and how they evaluate options

• What they're willing to pay

• What conditions they expect

• What holds them back & what they fear

• Which words resonate with them

This is a different level of abstraction - think population sampling versus individual data points.

Prospecting, outreach, demos, and sales conversations are by far the best ways to study your market and gather intelligence that will lay the groundwork for scaled distribution and marketing, all while staying flexible and responsive to customer context to help secure those crucial early wins.

And let's be real — in B2B, the product itself is rarely the real driving force for customer acquisition, especially in the early days when many product-led acquisition approaches aren't even viable yet.

2. Foundation before Automation

Early startups are fluid by nature.

If we imagine a business as an equation, an early-stage startup would contain mostly variables, not constants. In order to solve the equation, the task is to determine the values of the variables to lock them in, turn into constants.

To give a product the ability to handle certain aspects and milestones of the customer journey autonomously (i.e. without human intervention), it must rely on carefully designed and programmed product flows that can't be built on shifting sands.

This doesn't mean everything needs to be figured out with 100% confidence to create self-serve flows, but product development isn't magic — it relies on concrete knowledge about the product user, their ecosystem of needs, problems, tasks, actions, and expectations.

​Until reaching stable market traction and product usage, a startup doesn't have the luxury to fully settle with these definitions — attempting to fixate them too early could mean solving for the wrong equation entirely.

This flexibility allows for rapid response and value building based on emerging customer and market insights.

​​

The Growth Sales Generalist

Who should be doing this work?

It all starts with founders, but at some point, there's more clarity about the market, some things start coming together (even though still messy), and in real life, founders have plenty of other responsibilities. Eventually, they need to delegate.

The challenge is that a typical salesperson might not be very effective at this stage — their value typically lies in scaling proven tactics through proven playbooks. Moreover, their motivation is linked to predictability of their pipeline, which is far from a given in a startup.

On the flip side, PMs and UXRs usually don't understand sales dynamics or relationship building with potential clients, viewing customer conversations purely as data collection opportunities rather than actual sales opportunities.

What the startup needs is a one-person orchestra who combines multiple roles: doing prospecting and outreach like an SDR, showing demos and talking price like an AE, helping with product training and coordinating problem-solving like CS.

This person should be driven to experiment — not just find 100 leads and email them, but try different templates, communication channels, materials, styles, and tones of voice. They need to work closely and transparently with other functions like marketing and product, not afraid to step on each other's toes, sharing and incorporating new knowledge from all available sources, united by a shared purpose.

​A Growth Sales Generalist, if you will. This person will develop the first version of the sales playbook that will repeatably earn that first million(ish) in ARR, only after which the startup can begin proper scaling and automating its sales function.

The Sales Breaking Point

The B2B software landscape is changing.

Before, the only way to sell software to a company was to find someone authorized to make purchasing decisions, and convince them of a dire necessity to use your product.

Today, employees and subordinates of that authorized person independently find products to solve their individual and team problems, and champion the purchasing decision in their company.

In the early startup days, this isn't a top of mind because nobody comes knocking. But upon achieving some degree of traction, you must open the gates to individual end-users and build infrastructure for them to explore, adopt and champion your product in their company, if you want to harness this Bottom-Up adoption force.

This is when human-powered sales breaks down, because salespeople are the wrong tool to drive end-user-driven adoption, and it would be a waste of money.

This processes must be delegated to product automation: convincing users of your solution effectiveness, turning them into signups, gathering their data, engaging them and their teams with the product, and translating this engagement into money.

By this time, the experimentation-fueled early sales has paid off — critical constants in the PMF equation are defined, making the startup ready for this transition.

The "Everything for Everyone" Startup Death Trap

Product-led growth is challenging and requires investment.

Some companies choose to focus on their sales growth motion and leave end-users unattended— not necessarily preventing usage, just not proactively investing in it. After all, the business is still shaky and needs those high-ACV clients to survive, so the product centers around their needs and wishes, leaving no time for anything else.

Eventually, the product becomes too complex, with too much unnecessary and unclear functionality, requiring even more human support to help existing customers make sense of it all.

While one client, for whom a new feature was just rolled out, stays — that previous one, neglected for six months, churns away. Then another.

​What do sales-led B2B companies do in this case?

They try to compensate by attracting new clients with redoubled effort. So what if the product has low ratings and clients complain? This is Product’s problem — they need to pull themselves together and start doing their job! Except product teams can't, because they're busy developing absolutely necessary features that must be done yesterday to land that promising prospect (who, predictably, doesn't land after all). No worries — we'll market these features to other customers and spend a great deal of human-hours convincing them they need these features.

​Meanwhile, sales pushes marketing to expand into adjacent markets (forcing product to create a new strategy to serve them — in the best case; in the worst, product remains unaware), which naturally brings in unqualified leads that now need to be converted with tripled effort. No worries, we can do it, we’re great at Sales! We just need to expand the CS team so that they have capacity to retain current clients through incredible effort (just for another quarter!)

And so the company falls into the trap of becoming Everything for Everyone which I deem to be one of the top reasons for B2B companies demise: it becomes impossible to turn down another sale that doesn't fit the ICP profile ("To hell with ICPs, they're limiting us!"), and money is now needed for more marketing, more sales, more customer support. ​

Product teams end up confused and burnt out, while product, design, and engineering leaders get replaced because they can't motivate their reports to deliver results - unacceptable given their salaries

So What?

Start with conversations with the market, find the best currently available way to reach and sell through creative, smart experiments, build a temporary sales system to earn the necessary capital for growth, and start investing in a long-term product-led growth system.

Good luck!

Subscribe to not miss my 0-1 PLG guide for B2B Startups to help you avoid the death trap and set up yourself for Product-Led Growth success!

Today, I want to discuss how to understand churn to draw the right conclusions and form a relevant action plan.

When your house burns down, you see the destruction, but the real problem might be a costruction problems, or constant safety violations. Churn works the same way — customer cancellations are what you see, but the underlying causes require deeper investigation.

But first:

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Churn ≠ Retention

Churn and retention are inverse metrics, like two sides of the same coin.

At the same time, there's a nuanced distinction worth making:

Retention is a more operational metric for product development and may serve as a (somewhat) leading indicator of success.

It is a behavioral metric that represents regular value consumption in alignment with the customer problem occurence frequency (daily, weekly, monthly). You can track retention across different timelines, e.g. 1-month retention, 3-month, 6-month, and so on — this data gives you granular insight into customer engagement, depicting and somewhat predicting their value consumption.

Churn is backward-looking and more strategic — it's about where you lose customers and their money.

It's measured by billing cycles (monthly, quarterly, annually) and represents customer’s reluctance to pay for the product, not use it per se.

So while churn is essentially the opposite of retention, they measure slightly different things. It's like comparing oranges to… grapefruits, perhaps? 🤔

Three Types of Churn in B2B

Customer (or account) churn: The entire company (can be a “logo” or its division) cancels their paid usage. This is the most common variation and what we typically refer to when saying "churn."

Revenue churn: This is a contraction of your product's revenue, not customer base size. Consider two scenarios:

1. The customer doesn't churn and continues as a paid customer, but downgrades to a lower plan.

2. One of your highest-ACV accounts decides to leave. They may constitute as little as 0.5% of total annual customer churn, yet their annual revenue contribution may represent 20% of the company's total revenue! While your customer churn rate may look impressive, your business is bleeding.

   This reflects the nature of B2B products where revenue isn't evenly distributed among customers.

Track both customer and revenue churn at all times, and compare the trends. This will help you understand where your value delivery is falling short and what type of intervention is needed:

• Many small customers churning? Likely issues with Product-Market Fit and/or poor self-serve adoption pathways.

• Few large customers churning? Likely feature gaps or massive shifts.

For the rest of this article, I'll mostly focus on customer churn. Still, let’s also note

User churn: When individual user accounts within the customer account are deleted. This may happen for various reasons and isn't necessarily catastrophic. But a persistent trend in the account size contraction can be an indicator of upcoming account churn.

Team expansion or contraction dynamics are important components of the account health, and are factored in the “at-risk” or PQA qualification systems.

P.S. Some people also use the term usage churn, but I personally prefer to say “decline in engagement”, or “inactivity”, or “dormancy”, to avoid further confusion.

P.P.S. We relate the term “churn” to paying customers, or at least to activated freemium customers. When we refer to users who are not active and haven’t even reached the Aha! moment, we should call them what they are — unactivated users, and not factor them into churn , retention, or engagement analysis. This may sound obvious, but in reality I constantly see this confusion happening.

Do You Have a Churn Problem?

Having churn doesn't necessarily mean you actually have a problem with it. I know it's confusing, but let me explain.

Consider Your Product Lifecycle Stage

This is something I rarely see discussed, but it often confuses founders. Here's how I think about it.

Early-stage startups typically have low churn: they have mostly customers who are either not paying or paying little, very short feedback loops and tight relationships, and customers often get features they request. What's not to like? But it doesn’t yet mean you have a strong Product-Market Fit, only that your natural churn problems haven’t even started yet.

Middle-stage: As the product builds up and gains traction, it becomes less and less customized. The product roadmap now relies not on anecdotal data and feature requests from early customers, but on aggregate usage data, market signals, and feedback from the entire customer base. It now becomes more “average” to fit their needs. Early customers may feel it doesn’t fit their need as it used to, close relationships weaken, and their requests are no longer priorities. They start leaving. It’s natural evolution, expect it.

Late stage: With growing traction the product inevitably attracts non-target audiences. The product grows, and so does the variance and experimentation in positioning and messaging to attract growth from the target segment and test adjacent ones. Churn starts becoming higher and happens more often in earlier terms of the customer lifecycle. 10-20% annual churn often becomes the norm.

This is the time to start gathering proper churn data and analyze its nature, and launch the first “low-hanging fruits” churn prevention initiatives.

Scaling PMF: This is time to hit gas on growth, optimize many product surface areas and course-correct the strategy. With these plans product can manage to lower churn to sensible numbers (below) and keep it at bay.

Mature companies often experience PMF erosion due to market or technology shifts, or competitive threats, and may see increased churn rates again, especially if they expand into new products/markets and start the whole PMF cycle again.

Not All Churn Deserves Your Attention

Don't leave your churn unsegmented!

Care for your ICP first. You may notice that your ICP churn isn't as high compared to total customer churn — that's a great sign!

Building everything for everyone is the fastest path to slow death. Many companies say they have multiple ICPs, but even linguistically this doesn't make sense — there's a reason it's called “ideal”! It is a segment with the highest chance of success with your product, that “stays, pays, and prays for your product”.

I get it, sometimes defining a single ICP can be challenging despite best intentions, especially for very horizontal products. But that doesn't mean there should be multiple ICPs. There can be audiences that exhibit similar characteristics to ICP, but are different in others. You can mark them as “Lookalikes”, “ICP-ish” or whatever you want.

ICP isn't just a qualitative profile — it's defined with data! Data shows you which customers purchase fastest with least pain for everyone, engage best, grow fast, refer your product, etc. Yes, before you have enough data, you'll operate on qualitative assumptions. But by the time you actually need to systematically address churn, you must have data that tells you who your ICP is.

In the majority of cases, churn is an indication of value or positioning problems. And it is the very reason to have an ICP — to build and deliver value that relates to and resonates with someone, not everyone.

Then explore “lookalikes” if you like, but don’t invest into non-ICPs — it's a waste of time and effort. They'll come, try, and leave no matter what. Let them be. Double down on your strengths instead of chasing every single weakness.

Track When Churn Happens

In its simplest form, you need to understand the ratio of first-term churn (customer churns before their first renewal) vs. later cycles.

Benchmarks say that 25% or less churn rate for first-term renewal is normal. This doesn't mean you should ignore your first-term churn, but understand that it's not the end of the world. Most likely, it signals problems in your positioning, GTM, perhaps targeting, and maybe activation inefficiencies.

Later-term churn patterns reveal entirely different problems, mostly associated with either objective factors (e.g., macroeconomics) or delivering insufficient value to your ICP over time.

How Much Churn is Bad?

As we enter the benchmarks zone, mind that they should be supporting, not primary evidence. I mean, we don't even have a repeatable PMF playbook for startups because each is unique in market, customers, and problems. So why would benchmarks be universal?

Here's a famous benchmarks comparison from Lenny:

But mind two things:

1. This is aggregate average data, which can differ significantly by domains/industries. Even “enterprise” segment has very different players.

2. This is monthly churn rates benchmarks. It’s easy to get tricked by seemingly small numbers. But monthly churn compounds into sometimes staggering annual churn rates of 50%+ of the entire customer base!!!

How to convert Monthly churn rate into Annual churn rate:

ANNUAL CHURN RATE = 1 - (1-MONTHLY CHURN RATE)¹²

or, for your convenience:

So a rather "innocent" 4% monthly churn rate, if unaddressed, can result in a 38.7% annual churn rate!

Here are average B2B SaaS industry benchmarks from Tom Tunguz:

Three Churn Reason Categories

At a high level, I categorize churn reasons into three main buckets:

1. Value Problems: Churn is the “silent feedback” from unsatisfied customers

2. Objective Factors: Churn is a consequence of macro or micro environment

3. Leaks: Churn happens because of product experience or purchasing gaps

Your biggest portion of meaningful churn will likely be the first one. But let's examine the other two buckets first.

"Objective" Churn

Churn isn't only, always, or entirely about you and your product.

There are also:

Macro reasons: Worsening overall economic climate, new laws or regulations, industry or technology shifts. There's a high chance you already know about this. And you need to adjust!

Example: I joined UXCam in 2022, at the onset of economic recession. At the time, the product positioning was heavily skewed toward behavioral analytics targeting primarily UX designers. BEcause of this, when companies started to revise their toolstack to save costs, UXCam turned out to be a non-mission-critical software.

We understood that quickly, added a few features, and repositioned the product as an analytics suite for mobile apps, offering behavioral, product, and issues analytics for entire cross-functional mobile teams (=> mission-critical!)

Micro reasons: Other times, companies go out of business, downsize, or pivot drastically. Even vice versa - they can grow so fast that they simply outgrow your solution 🤷‍♀️

You can’ do much about it at the moment, but it’s always a good idea to support good communication to stay on top-of-mind for them if their need re-emerges, or even to foster viral potential (the customer may actually like your product but not need it, and they can recommend it).

The "Leaks": Where Customers Fall Through The Cracks

These are cases when your customers actually think that your product is valuable, but something prevents them from paying for it.

Payment Failures (aka Involuntary Churn)

Card expiration or blockage, transaction failures due to bank new regulations, insufficient funds, any kind of payment errors, and so on. The customer may not even know about this!

Make sure your billing system is set up to make further attempts, and notify your teams so that they proactively contact the account admin (in-app, email, even phone) to alert them about the issue.

Naturally, the chance of payment data becoming invalid is higher in annual subscriptions and can constitute as much as 40% of all renewal cancellations!

Low Pricing Optionality

Micro-factors can cause a need to downgrade. But if the price jumps between plans are too large, they create artificial churn cliffs: budget-conscious customers may downgrade to free plan or churn entirely because they can't find acceptable middle ground between what they want to use and what they should pay for. Remember, one of the 5 core Jobs-to-be-Done of your pricing model is to engage customers: if they don't use what they pay for, they won't keep paying for it 🤷‍♀️

Wrong Value Metric

The reverse is also true — the higher the engagement with the product, the more customers should have to pay. And your value metric should be aligned with the growing product consumption. Otherwise, you can expect some of your customers to “hack the system”, resulting in revenue churn. For example:

1. You may charge per seat, but customers can use the “shared email” hack to downgrade their subscription. Rule of thumb: is your product doesn’t offer personalized value to each user in the client account - pick another metric.

2. If you charge per workspace or project, they can game your system and move all their work into one workspace: their usage won’t decline, but their ARPC will!

I’m not saying one value metric is better than another. It should only be relevant and fair — not only for your users, but also for your business.

Understanding Why Churn Happens

Now that you know who churns, when, and where, it's time to understand why.

First, you need to understand, what kind of churn do you have (value problems, objective factors, leaks), how much of each, and their ratio.

Churn Surveys

During cancellation, you should ask customers for a reason. If you don't yet — this should be your first initiative on the "Fight Churn" action list.

Churn survey questions are usually variations of:

• It's too expensive

• I don't need it anymore

• I'm not using it enough

• I found another solution

• The product needs improvements

• Missing features

• Not sure how to use it

+ offer (but don’t force) a text field for them to provide explanation.

You should use these questions to your advantage. For example, you can add answers like “Poor customer support” if it makes sense for you. Or, provide more granular “improvements” reasons, like “Technical issues” and/or “Usability issues”.

The point is not in the precise set of answers, but to understand on a high level whether churn is out of your control, or within it.

The survey won’t give you precise answers, but it will tell you where to look. For example, if the majority of answers are "It's too expensive", then it’s time to pay attention to your pricing model — understand your customers’ willingness to pay, revise value metrics, plans structure, etc.

A few notes here:

• Align your survey data with data on high-ACV clients churn that sits with your Account or Customer Success Managers. You need to have a single source of churn truth that can be segmented by customer size, ARPC, tenure, etc.

• Churn surveys don’t give insight into downgrades (revenue churn). That’s a different investigation, often performed on a customer-by-customer basis.

• You can use various retention techniques in the cancellation process, such as offering time-limited discounts or downgrades, offering to pause subscriptions, increasing FOMO with loss aversion, or suggesting they talk to Customer Support. Read more about such tactics in Aakash’s Anticonversions Guide.

Talk to Customers

You didn't think you could avoid this, did you? Well, you were right.

But you can't talk to everyone, so you need to pick your battles. My suggestion is to form hypotheses via churn data & survey, then talk to relevant churned customers to get qualitative insights on their circumstances, motivations, and reasoning to enrich, refine, or reject your hypotheses.

Focus on recently churned ICP customers who used the product for a reasonable time period first. Then, if needed, go backwars in time or expand into the “lookalikes”. Just don’t forget to segment your insights.

Feedback Loops from "High-Touch" Sources

Establish clear communication channels with Sales, Customer Success, and Account Managers. Ask them to document every high-value churn with detailed context — these customers represent not just significant revenue, but also ample learning opportunities, especially in the ICP segment.

Analyze First-Term Churn

This is a more advanced analysis and requires all the data foundations. Do this after you’ve dealt with later-term churn, of if your first-term churn is significant.

Remember the “25%” benchmark for the First-term churn? While these numbers are normal, they still represent customer potential or can give insights into your development and go-to-market strategy.

There are a few high-level reasons for this:

1. Your activation process is subpar and didn’t manage to create stong habit loops in real life (that can be truly hard in complex B2B products, and many teams try to “hack” their numbers by changing the definition of Aha and Habit moments to something more convenient…)

2. Your onboarding is okay, but with further engagement customers realize the product value is insufficient in relation to the price tag.

3. Or your product doesn’t deliver on the promise altogether: its positioning is misaligned with real value, and/or messaging is targeted at a non-relevant segments. It’s not always clear right away and may take time for the customer to discover.

To understand who is who, first look at their profiling data and filter out everyone who isn't your ICP.

Then examine their engagement patterns and where/when in the journey they go inactive — the closer churn happens to the renewal date, the more likely they haven’t found sufficient value in your product. Early cancellation indicates potential onboarding problems or mistargeting. And don't forget to compare this with cancellation reasons from their churn survey to see patterns.

Preventing Churn Proactively

We’ll talk churn management strategies in the next post, but I still want to suggest a few tactics to deal with symptoms and keep your hand on the pulse.

Close Every Involuntary Leak

Implement comprehensive payment retry logic, proactive card update campaigns, and multi-channel failure communication. These are the easiest churn prevention wins — pure operational improvements that directly impact the bottom line.

Subscription Cancellation Options

• For prosumers or SMBs, consider offering to pause subscriptions for seasonal or temporary reduced needs. Better to retain the customer relationship than force complete cancellation.

• Offer temporary discounts or free months if they pick the “It’s too expensive” reason.

• Induce FOMO by showing what they are going to lose

All of those have worked on me personally, and more than once :)

According to the pricing guru Patrick Campbell, such preventive measures can lower your cancellations by 10%-25%!

Push Annual Subscriptions

Offer substantial discounts or free months for annual payments in your pricing plan, but also gently nudge your multi-month customers to opt into annual subscriptions. This isn't about postponing the inevitable — annual subscribers indeed tend to stay longer, as churning now becomes friction in itself for them.

At-Risk Scoring System

Create a qualification system to rank each account. Include characteristics like account size changes (i.e., consistent reduction in seats), engagement decline trends, support tickets and conversations, technical problems, and renewal date proximity. Use these signals for targeted intervention like personal outreach, engagement campaigns, or temporary discounts.

In the next article of the B2B Churn 101 series, we'll discuss strategic churn improvements, such as product engagement, innovation, differentiation, pricing, organizational alignment, and even aesthetics. Stay tuned :)

AI is changing the startup playbook as well as the growth one. The same tools that let you build faster also let your competitors catch up faster. And when competitors are already knocking at your door and eating away your customer base, speed may not save you. Built-in defensive mechanisms will.

To understand, why defensibility becomes ever more important, let’s first briefly take a look at the typical growth funnel, and how AI changes it.

How AI Changes Growth

In a lot of ways, AI flips the growth playbook on its head. It doesn't just change how and what we build — it changes customer behavior, go-to-market, and the business model.

So, what the hell is going on?

Acquisition is getting weird

For starters, for AI products, TOFU isn’t that big of a problem — the AI hype does the job for you. While curiosity doesn’t equal actual intent, it still contributes to virality. And as some say “any interest is good interest”.

Traditional channels are losing effectiveness. SEO and UGC are being partially replaced with LLMs usage. Paid ads become less predictable and more costly, (although AI does enable some personalization).

On the flip side, authenticity and human-generated content matter more than ever (you don’t miss the water till the well runs dry 🤷‍♀️). When every product demo looks like magic, people seek authentic voices, rawness, and truth.

Founder content, building in public, open sharing of mistakes, and AI-free community discussions are the new channels that aren't easy to hack — and this is where their value comes from.

Clay grew fast by demoing magic moments on LinkedIn. They didn't just launch quietly — they showed what B2B outreach could look like with AI. People didn't just sign up. They shared it.

Lovable turned short-form content into demand long before the product was polished. They shipped in public, built community, and positioned themselves as the brand to root for.

Today, early adoption lives at the intersection of content, product demos, and founder voice and is won long before the product is perfect.

Activation: the good, the bad, and the ugly

AI products can provide value sometimes even before the signup happens, and with as low investment as just one prompt. How cool is that?!

For companies, though, giving away immediate value isn’t free anymore. While there is an unprecedented opportunity to shorten time-to-value, it comes with a catch:

1. Defining what value really means, because with LLMs it’s getting way more subjective. Sometimes a simple text output is enough, other times a complex interactions doesn’t get to value. Traditional activation metrics no longer predict success definitively.

2. AI hype has created “AI tourism” when a user visits a product, uses the free 5 tokens to see what it’s about, then leaves to never remember about it.

So predictability of activation is diminishing, while the costs to service the “try-before-you-buy” model are getting quite tangible.

Retention is the real challenge 🥲

Many AI products excel at solving one Job-to-be-done incredibly well. This makes sense given the extreme competition and complexity of training models. But this can easily jeopardize retention.

First, the question of recurring value becomes acute when your product delivers its entire value in a single(ish) interaction. Take an AI logo maker, or a document scanner, or a professional headshots creator. For the majority of non-professional users, such a need is very infrequent, and it’s easy to forget the product.

Second, without adjacent value it may be hard to create habit loops to keep people coming back.

For example, you may discover Photoroom to help you remove and swap backgrounds for your headshot in a social network. You don’t need this often. But you might continue using it more frequently for its great selection of templates and relevant editing tools for Instagram stories, LinkedIn posts, or other social formats.

Monetization is one big experiment

We’re figuring out new pricing models that can balance value and cost scalability in real-time.

Frankly, Julia and I are not too concerned about monetization — we're in the "business" business, after all, so pricing will be one of the things we'll figure out sooner rather than later. We just need a bit more time and data.

However, there are some interesting shifts happening:

The Perceived Value Shift: Customers now compare complex solutions to commoditized LLMs that feel like they provide similar value for a fraction of the price, even if it needs a little bit more work.

The Value Anchoring Puzzle: Sometimes it's not even clear (for businesses and users alike) how much the product should charge, less or more? We tapped into new problem and solution domains, creating entirely new value that may be hard to anchor to anything.

The Revenue Protection Push: There’s a more prominent shift towards annualized payments to fund retention, incentivized by substantial discounts.

Defensibility isn’t an afterthought anymore

But overcoming these hurdles is not the end!

As soon as you’ve found even a hint of Product-Market Fit, the clones arrive. Fast. The same speed that helped you build will help them clone.

AI has radically compressed the time it takes to go from idea to working product. You can stitch together a working MVP using:

• Pre-trained APIs from OpenAI or Anthropic

• Open-source models

• No-code tools and deployment platforms like Vercel, LangChain, or Replit

Which means protecting what you've built isn't something you think about at Series B. It's something you start designing for from day one, because "good value" isn't enough when everyone can deliver good value quickly.

Inspired by a metaphor from NFX, Julia finds the Bailey and Motte framework useful for thinking about how AI startups balance fast execution with long-term defensibility.

Here’s how it applies:

The Bailey and Motte Model

In medieval settlements, the Bailey was the outer courtyard — fast to build, where daily life happened. The Motte was the stone tower on a hill — hard to build, but also hard to take and destroy.

Startups operate just like these medieval settlements:

• Early wins happen in the Bailey: fast distribution, rapid growth, strong initial traction.

• But the companies that last don’t just defend the courtyard. They invest in the stone tower, the Motte, while no one’s attacking.

Bailey: Win the Moment

Your Bailey is where value gets created, delivered, and exchanged.

It's tactical, "everyday" type of defensibility — the moves that help you capture and hold territory in the short term.

But the AI-native Bailey requires specific superchargers:

Speed as a competitive advantage

Speed has always mattered, but now it's existential.

For many companies, the race to integrate AI isn’t optional — it's about avoiding disruption.

Take product analytics tools: traditional manual and reactive analysis is being replaced by AI-first platforms that proactively surface insights and suggest explanations. This may be an extreme example, but it perfectly illustrates speed as a defensibility factor.

Companies that move slowly risk being left behind entirely. If not today, then tomorrow.

Brand as a tactical amplifier

Brand used to be purely a long-term play. Now it's becoming tactical— an amplifier to your distribution through emotional engagement and human connection.

In a sea of AI tools that mostly do the same thing, users ask: "Which one gets me?" and "Which one do I trust to show up the way I need?"

Each finds what resonates better with their personality:

• ChatGPT feels calm, polished, helpful — a neutral assistant.

• Claude feels more empathetic, thoughtful — a helpful coworker.

• Perplexity feels like a straight-to-the-point researcher with receipts.

These aren't just different outputs; they're emotional signals, the "vibes" that create preference when the value is similar. Your tone, aesthetics, onboarding, human connection — all contribute to this immediate brand differentiation.

Founder's open sharings, real values, strong stance on some ideas, or building in public are brand elements that create a sense of belonging and unity (something the entire population is massively lacking these days).

Now more than ever we see that features don't win loyalty — vibes do. And when everything else looks the same, brand becomes the shortcut to trust.

Growth momentum creates unfair advantages

AI-native products can scale incredibly fast, and early momentum compounds into real advantages:

• Better investors

• Stronger hiring pipelines

• Faster feedback loops

• Often better pricing deals with model providers

‼ But speed without structure doesn't last. Which brings us to the Motte.

Motte: Moats That Compound

Your Motte represents long-term defensibility — the strategic advantages that compound over time and become harder to replicate.

Traditional moats still matter!

Network Effects

They come in two main forms:

• User network effects: the more users are using the product, the better it becomes for eveyone. Social networks are a vivid example.

• Data network effects: more users per se do not make the product better, but the data they bring with them does. Waze uses each user’s GPS and driving data to create a more accurate, up-to-date mao of current road conditions. Google Search improves search results as the database of queries, click patterns, and outcomes grows.

This way, products that provide better outcomes for all users the more they are used, have natural defensibility against competitors.

Data network effects are sort of inherent for AI tools. ChatGPT improves with every conversation, because every user and interaction trains it and tunes the model for future ChatGPT users, as well as all other products built on top of it.

But user network effects become even more important.

Gumloop, a multiplayer workspace for prototyping AI workflows, becomes exponentially more powerful when adopted across a team, because:

• It learns from shared prompt patterns and execution history

• It stores context across experiments and collaborators.

This way, Gumloop becomes a living memory of your team’s AI problem-solving — something a new tool can’t easily replicate. If one person leaves Gumloop, they lose some convenience. If an entire team leaves, they lose accumulated logic, shared iterations, and co-created workflows.

Character.ai creates AI-powered chatbots ("characters") with distinct personalities, which are then shared within the wider community. More users bring the diversity of characters.

Most popular chatbots get featured and promoted, which creates incentives for users to create higher quality or more unique characters, amplifying engagement and retention.

Suno makes user-generated AI songs discoverable, which in turn improves overall recommendation and sampling algorithm as the volume and variety of songs increases. The more unique data the user base generates, the more novel or relevant output the AI can create or recommend in the future.

Switching costs

Thinking of defensibility, it is important to ask yourself not only how easy it is to replicate your product, but also "If users were leaving my product, is there any value they would be losing?"

There are a few factors that make it harder for customers to leave your product:

• Context: the tool already knows your personal or team preferences

• History: past chats, drafts, actions that guide new ones

• Outputs: documents, summaries, decisions — all stored and referenced

• Team knowledge: shared memory across the company

• Integrations: tools connected and workflows are set up and running

• Trust: users know what to expect and what not to

For example, it can be easy for you to switch from one AI meeting notetaker to another if you are only using it to share recordings with people you meet.

But if you rely on a meeting assistant like Fireflies.ai to extract client data from sales calls and feed it into your CRM, or to extract insights from user research calls, you may find it hard to just switch to another solution. While it may be not as hard to move raw data due to data regulations, you’ll also have to move all your insights, context, and model fine-tuning for your org needs.

Switching costs are higher for teams, especially enterprise ones. The sheer volume of data as well as the investments in the setup, training, and integrations will not merit switching to a new software and going through all the hoops of buying, training, and adoption for companies.

But even for prosumer software switching costs matter.

The simplest example is ChatGPT or Claude that allow you to create projects with content and instructions — basically a “second brain” with memory. This creates a reliable lock-in with a model of your choice, as moving all this context can be a ton of work.

Or take Reforge AI Assistant. If you've ever written PRDs or GTM plans inside Reforge's AI, you won't be switching a tool — you'll be giving up your entire strategic context, even if you're the only one in your organization using it.

👉 But there are also AI-native defensibility factors!

Data quality and uniqueness

The source and quality of your training data can create unbeatable advantages! Proprietary and well-curated data can deliver results that commoditized models simply can't match, in the following ways:

1) Your data helps you deliver outstanding outputs compared to the quality bar from commoditized products. This is especially relevant in verticalized domains, where generic outputs simply aren't good enough.

EvenUp, a Legal AI for healthcare claims, drafts legal demand letters by using deep proprietary knowledge of how different hospitals generate their medical bills. Their system improves as it ingests more real-world, domain-specific data and edge cases — something a generic model or public data source cannot compete with in accuracy or relevance for this niche.

2) Proprietary data isn't easily replicable and accessible, so it creates a lasting differentiation. Any product built on public data can be disrupted tomorrow by a product based on the same public data.

Databook aggregates public company data and augments it with proprietary strategic account data, technographics, and custom signals from customers themselves. The result is richer, more actionable sales intelligence that competitors can't easily re-create — giving Databook a data-driven advantage in enterprise sales enablement.

3) Your data isn't just used to produce outputs (e.g., summaries, answers) but to personalize user experiences based on behavioral signals and personal preferences. This is truly hard to replicate.

A B2B mobile analytics and marketing platform, Localytics leverages behavioral, in-app engagement data from each client to tailor mobile messaging, optimize campaigns, and provide market-specific benchmarking. As more clients use the platform and generate proprietary engagement data, both their own recommendations and industry benchmarks improve, creating a data network effect.

Workflow embedding

While many AI tools may provide impressive results, oftentimes they are just add-ons to the core user workflow.

This leads to a potential risk of being disrupted by the core workflow owner. Even if they don’t provide exceptional results, they give a breadth of solutions within the problem space. This poses a risk for the exceptional point solution to lose a huge portion of their customer base overnight.

Because let’s be honest, who loves using (and paying for!) a myriad of tools with constant context switching? Remember that perceived value often means more in the heads of users than actual value.

Products that embed themselves into essential workflows become much harder to replicate. When your product becomes a core part of someone's workflow, they don't “use” it — they depend on it. This transforms standalone features into mission-critical tools that heighten switching costs.

Consider Gong, which embedded itself in sales workflows by analyzing calls and surfacing deal insights — automatically, directly in CRM tools. Replacing Gong isn’t about swapping software — it’s about retraining sales reps, losing deal history, and breaking forecasting pipelines.

Or take a very illustrative example of Grammarly acquiring Coda to provide end-to-end documentation and collaboration workflows to its customers.

Workflow embedding is probably one of the hardest to build defensibility factors as it may require significant investments into partnerships, acquisitions, or even product development.

Workflow adoption can also be taxing for users, so growing an entirely new workspaces may be a huge challenge. Take Fibery.io — they’ve grown to $1.8M revenue and 2,500 customers with just 10% churn rate. Yet their CEO openly admits the adoption struggle: "Fibery shines in more complex processes, but this makes it harder to adopt. Fibery power unfolds with time and with 20+ people using it." It took them 8 years to reach this success. Imagine how many workflow-centric platforms never made it past the adoption hurdle.

Business model

For the first time in SaaS, we're dealing with real marginal costs at scale.

It’s a pretty rapid fall from 80%+ margins to just 30%-40% for AI-powered software. Depending on the nature of your AI product’s tasks and usage intensity, you need to undersand how to intelligently balance compute costs with value scalability. This may determine whether you have enough $ fuel to keep innovating

We'll be writing more on AI-native pricing models and their implications for products and growth, so stay tuned :)

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Smart unit economics becomes a competitive moat while AI startups are burning through runways on unsustainable pricing.

Building fast is easy, but sponsoring and protecting your growth isn't anymore.

Building Both Layers Simultaneously

AI gives you speed. But it gives your competitors speed too 🤓

The companies that will dominate AI-native markets won't just build fast — they'll build smart. They'll shift from getting attention to earning retention, from shipping cool features to building strategic advantages.

The key insight: you can't build Bailey first, then Motte later.

You need to be thinking about both layers from the moment you ship your first version. The earlier you start that shift, the harder it will be to catch you.

Which layer are you focusing on right now? And how soon can you start building the next?

Good luck!

Today I want to explore what these shifts mean for the skills Product Managers need to develop - skills that are no longer optional.

Shift to consumerized work software

This shift brings B2B PMs closer to individual users and demands enhanced customer knowledge (buyer and user level), data-driven behavior analysis, understanding growth pathways, and self-serve journeys. Sounds duh, but collectively we’re still not great at this.

Shift to efficiency-first mindset

Simply put, PMs must now justify their employment with numbers sitting as close to profit as possible. Every initiative should indicate how it contributes to business health and prove estimated impact.

Shift to AI-enhanced workflows

This has direct and indirect effects:

• Direct: Non-critical tasks get automated. AI speeds PMs through information processing, project management, and mundane tasks across the value chain. As Ravi Mehta noted in his recent article, "Product Managers will do more true product management." But as tactical work disappears from calendars, critical skill gaps become visible.

• Indirect: Delivery is faster. AI made engineers way faster, but they're now blocked by PM decision speed.
  Competition is fiercer too - you build faster, but so do competitors. Meaningful innovation, clear differentiation, and seamless experience are required just to stay afloat.

This creates a new-age PM who must lean into their original mandate as business owner while becoming more effective in practice. PMs need to step up their game in innovation, impact, velocity, and leadership. For some of us this will mean lots of new skills to learn, for others less.

Here I want to share a mental model of how to think about upskilling. So to say, teach you how to fish. Here are four principles for new-age PMs:

1. Become the true mini-CEO of your product (yes, finally!)

2. Become antifragile in times of high uncertainty

3. Don't become a bottleneck

4. Own your game

Let's dive in!

Become the Mini-CEO

The product community has long rolled their eyes at the "mini-CEO" concept (usually out of bitterness). But the table is turning, and the time is coming. Companies are finally feeling the need for PMs who can actually operate at this level. Time to stop laughing and start learning.

If you want to understand what "mini-CEO" actually means, just observe your real CEO. They know their product, market, technology, and customers intimately. They make decisions quickly. They understand how different functions contribute to the whole. They're responsible for outcomes even when they don't directly control all the inputs.

To help you become that, it is crucial to develop a few skills though.

Commercial fluency

I’m not saying become a CFO overnight and own financial forecasts. But you must know your business profit and revenue structure, key commercial and business health metrics, and how your work connects to dollars.

Every initiative should pull at least one of the profit levers:

• Increase revenue

• Protect revenue

• Save costs

• Avoid costs

This isn’t nice-to-have knowledge. It changes how you communicate, plan, prioritize, and structure your work. Before it was authority, guts, and random data. Now you have a unified language to talk about opportunities whether they come from CEO, Sales, customer interviews, or your crumbling unit economics.

It also gives you a new perspective on your and your team’s work: now you won't spend weeks and thousands on poorly structured research just because “this is just how we work”.

And when VC playbooks are breaking and uncertainty is through the roof, it isn’t just helpful - it’s protection: execs keep PMs who drive business value, not just ship features.

There's a sad-but-true article from Leah explaining why no one is actually safe in this environment: The 250k per Employee cultural KPI causing layoffs in tech

I’ll be writing more both on commercial skills for PMs, and on running and sizing discovery projects, so make sure to subscribe to not miss these posts :)

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Growth & GTM fluency

Growth is everyone’s business. Sure, there are specialized Growth PMs chasing metrics performance, but that doesn't mean the rest of us get to ignore it. Every PM needs to become growth-fluent to some degree.

Here’s what this means:

GTM basics: How does your product's value actually reach customers? What are the implications for your planning and delivery?

Build Value Ecosystems: Shipping features is not your job. Your job is to ship value and make sure customers find it, adopt, engage with it, and pay for it. And this should be baked into your planning and development process.

Understand pricing: Most PMs have zero clue how pricing models impact growth or influence value development (I got you! 5 JTBD of your pricing model). This matters even more now with usage-based pricing on the rise and AI products that actually cost money to run.

Know your buyers: You might know how users engage with your features, but do you understand how purchasing happens? What economic buyers care about? How do sales and procurement cycles work?

End-to-end customer perspective

The bigger the company, the more PMs get trapped in their isolated feature bubbles. They become experts at their corner but have no clue what happens before or after their handoff.

This is backwards. CEOs don't run companies by only knowing their department — they understand how everything connects, even if not deeply everywhere.

PMs need the same breadth. You need to understand how they navigate the entire product, what touchpoints they hit, where they transition between flows, where they fail, customer account dynamics, and how this all varies across customer segments.

Why? Because your customers don't have “engineering problems” or “design problems” or “billing problems”— they have problems with your product. Their struggles can surface anywhere in their lifecycle and journey, and if you're wearing blinders, you'll miss the opportunities in the gaps between teams and touchpoints.

Cross-functional collaboration, not just facilitation

I am triggered by the word “facilitate” because often it means just organizing meetings and leading workshops. Real collaboration doesn’t happen in your calendar.

As I just mentioned, opportunities live in cross-functional gaps. Customer and market knowledge sits in sales, marketing, CS, even legal. To find and capture these opportunities, you need to work with these people as actual partners. And you need to understand how they work, their goals, processes, reasons, challenges, etc. You can’t collaborate with people you don’t understand. Maybe it’s just me, but I’ve often sensed some implicit war between “tech” and “business” departments. This is unproductive and kills innovation.

True cross-functional collaboration means working together (not just with each other) toward joint objectives — not only meetings where you review Jira tickets together.

Organizational leadership & influence

Most PMs are terrible at executive communication. They get stuck in feature-level conversations while leadership cares about impact and risks.

The new reality demands more senior PMs — not in titles (those will likely deflate), but in capability. PMs who can co-create strategy, rally cross-functional peers around shared goals, and get buy-in by being genuine business partners who understand what the company, leadership, and market actually need.

The Five Pillars of PM Influence

Speak their language: Execs think in dollars, not features. They care about customer impact, revenue impact, and vision impact. Learn the commercials.

Master alignment: The more you understand your peers' goals, incentives, and daily struggles, the more you can craft proposals that help them win too. When people see you're solving their problems, not just pushing your agenda, influence becomes natural.

Build trust: Trust has two components: intent and reliability. Intent means people believe you're genuinely trying to help the business, not just advance your career. Reliability means you keep people in the loop, follow through on commitments, and if things go sideways — you say so.

Establish credibility through data: A leader's most important job is building conviction. Charisma helps, but solid data is the universal trust driver. Do your homework. Gather evidence. Present clear pointers that prove both your impact potential and the credibility of your ideas. Half-baked proposals with gut feelings don't cut it at the exec level. I was that person, so I know.

Perfect your narrative: Build clear, logical storytelling that resonates with stakeholders and gets them excited to help you. Your narrative should connect the dots between customer problems, business opportunities, and proposed solutions in a way that makes their buy-in a no brainer.

Executive Communication: It’s the other way

Most PMs communicate like they're writing a novel: setup, struggle, journey, resolution. Execs want the exact opposite.

Lead with the essence: problem, insight, recommendation, necessary resources. Dive into details on request (but have them handy!) CEOs don't care about your research methodology or every customer quote — they need enough context to guide their questioning and decision-making.

Think executive summaries, not hero journeys. Get comfortable with the discomfort of cutting straight to the point.

Expectations Management & Responsibility

Keeping in touch with reality is a universally underappreciated skill

We're chronically bad at this. We overpromise on timelines and impact not from malice, but from bias. We want to impress, so we throw around great-looking numbers that feel good in the moment but destroy trust over time.

Here's a financial planning trick that I stole. Create three scenarios: pessimistic, realistic, optimistic. You don't need to present all three, but thinking across them keeps you grounded and demonstrates thoughtfulness.

And let's talk responsibility. Yes, you rarely own outcomes entirely — that's organizational reality. But you absolutely own the outputs that contribute to those outcomes. Responsibility means owning your shit. That’s how we learn and gain trust.

Remove Yourself from the Equation

I spent years trying to figure out what made certain execs exceptional. Then in one performance review, my manager said: "Alex, learn to remove yourself from the problem-solving equation. There is no “I” there".

That was it. These leaders provided exact solutions because they didn't filter decisions through "how does this affect me?" The problem existed, the solution was clear, so it got done.

When your ego enters the equation, stakes get personal. Personal stakes cloud judgment. You optimize for looking good instead of doing what’s right. You avoid uncomfortable conversations. You delay decisions that might reflect poorly on past choices.

Self-removal is rare because it's unnatural. But it generates unbiased solutions and, paradoxically, makes you more influential. People trust leaders who prioritize the right answer over their own comfort.

Two practical approaches:

Get an outside perspective. Find a coach or mentor who cares about your success but doesn't share your stakes. They can see risks you're blind to when you're inside the problem.

The "Frank" method. Write the situation factually — no adjectives, no emotional language. Assign it a random name: "So Frank faces situation XYZ. Here's what I'd tell Frank to do." This reframing creates enough distance to see clearly.

TL;DR: The nasty part of me would say “it’s time to grow up”. The professional one says “get real and learn to partner with your leaders, even if it is uncomfortable and even seems risky”.

Become Antifragile

Today, uncertainty is unprecedented. Pandemics, wars, economic upheaval on one side, and overwhelming pace of innovation and change on the other. There's barely any footing anymore. Old ways are crumbling, new ways are in the making, and predictability has become an illusion.

Becoming antifragile means embracing this uncertainty and actually thriving because of it. At its core lies incrementality — the ability to make small, reversible moves that compound into strength over time. While big bets can break you when they're wrong, incremental progress lets you adapt, learn, and actually benefit from volatility.

Today, this isn't just philosophy — it has to be operationalized.

Adaptive Planning

I heard this term recently and loved it because it reflects our reality. While not foreign to product management, it was always discussed in "should" terms but rarely practiced. Today it must be the status quo.

Outcome-driven planning

If you're still shipping features, you're confusing means with ends. The job is to build value ecosystems, and value isn't some static unit like a user story — it’s measured by impact on customer and business outcomes. So why not focus on them? Be rigid on outcomes, flexible on how to reach them.

You may be rolling your eyes "what are we, 2020?" If so — good for you. But lots of teams create annual feature-centric roadmaps without much regard for the outcomes they intend to bring. Why? Organizational legacy, resistance, perception of the product team’s job to “just focus on customers and ship features”, and even their inability to impact-size maintenance or tech-debt work.

We're not in the "invest money to build" business anymore — we're in the "invest money to make money" business.

Incremental success

Launches rarely succeed overnight. They need time and optimization to reach potential, and this time must be accounted for. Plan for iterative development, even though it's uncomfortable — you can't see far ahead and must rely on continuous learning to steer forward. The mantra is "smaller, faster, tighter".

Your business still may be planning in quarters, but you need to build your cycles on much shorter timelines: monthly, bi-weekly, or even weekly.

Built-in accountability

Customer and economic outcomes should be built into team charters and org design. This doesn't mean only one team owns a metric — it means every team has clear accountability for specific outcomes that ladder up to company goals.

But when teams are accountable for outcomes (not outputs), they need to work together differently. Everyone's success becomes interdependent. The team owning the "AOV" metric might need another team's support to change core product functionality that sits in their surface area.

This creates operational “chaos” as teams must collaborate across boundaries and factor partnerships into their planning. This gets exponentially harder when every team has a dozen OKRs. The solution: shift to smaller, more focused goals and give teams autonomy to figure out how to work together to reach them through adaptive, incremental development.

Experimentation

In this setting, you must reduce uncertainty with minimal time and effort.

PMs are no strangers to “discovery”, but very often it ends with forming hypotheses from customer interviews and data. This does not reduce uncertainty — the Build-Measure-Learn loop does. This is experimentation, and it should be baked into your discovery process.

Experimentation is often equated with A/B testing only. In reality, it includes prototyping, smoke tests, surveys, pre-sales, and more. When teams lack traffic, fast setup, or clear data, they often resort to nothing. But every day a hypothesis remains untested has an opportunity cost.

The objective is finding the best way to sense-check your solution with the market, appropriate for your development stage and resources. And it doesn’t have to be perfect or definitive — it has to be directive. So the question isn’t "how do we run an A/B test in our B2B product that lacks traffic?" It's "what's the fastest valid experiment we can run right now?"

With today's rapid prototyping tools, it's about adopting the mindset and operationalizing it into your process, not running isolated experiments to showcase best practices. Experimentation is an operating system of innovation.

TL;DR: Uncertainty isn't going away: make small bets, test fast, fail cheap, and build capabilities that thrive on chaos instead of crumble under it. This is yet another way in which product teams need to work like growth teams.

Don’t Be The Bottleneck

Previously, squads had 4-7 engineers, a designer, and a PM. It was fair — building took forever, and PMs had time to plan their next work. No more.

AI has made engineers way faster. The bottleneck shifted from building and coding to making decisions about what to build next. It’s a new unexpected challenge for PMs: match this engineering velocity or become the constraint that slows everything down. So no, I don't buy the "AI will replace PMs" hype. AI can't replace nuanced judgment, empathy, and creativity — but it demands we work differently.

Proactive Opportunity Management

PMs need to stay ahead of the curve in generating and assessing opportunities. This means working with insights proactively. There are multiple sources for these insights: impact assessment of releases, data analysis, costs and unit economics improvements, customer feedback, market research, and more.

We've always done this work. The difference? It was reactive:

• Manual investigation when conversion rate drops

• Customer interviews because we're planning feature XYZ for Q3

• Digging through customer request backlogs to validate ideas

This won't cut it anymore. You need to get ahead with:

Proactive data analysis: Slice and dice product and behavior data to find correlations, outliers, inconsistencies. Product analysts partnering with PMs, designers, and engineers to proactively hunt for insights and co-own improvements isn't optional anymore.

Continuous customer conversations: Talk to customers for no reason at all. Some of my best insights came from general conversations that weren't part of formal user research.

Pulse monitoring: Keep tabs on other teams' releases, cross-functional reports, market trends, competitor moves, industry experiments.

Signal collection: Create channels where employees can send insights and proposals. Make it easy for the organization to feed you intelligence.

But insight generation is only half the equation. You also need to get better at continuous impact-sizing and business-casing ideas, then feeding them into your experimentation pipeline.

The main shift: this work used to happen in the context of specific initiatives to prepare for delivery cycles. Now it should be decoupled from roadmap initiatives and done continuously. By the time engineers finish their current work, you need a backlog not of ideas, but of validated opportunities worth investing in.

Operational Velocity

I'm deliberately not creating the "AI skills" category. We're still early in this timeline — things are swirling and forming, and it'll be a few years before we can look back and understand what really mattered. Some PMs work on AI features and need technical depth. Others don't.

Everyone wants to seem like they've figured it out, but I see far more discussion about running companies with 50 AI agents than companies actually doing it. The pressure to try everything, build endless automations, and completely reimagine your workflow is FOMO at this point.

Yes, increased engineering velocity is real. But it's not always dramatic, and organizational inertia takes time — especially if you're not a shiny new AI-native startup (and if you are, you don't need my advice).

If you're naturally curious about AI, experiment away. But if you're feeling outside pressure to build agents for everything while juggling a full workload? Take it slow. Don't let anyone shame you for not being obsessed with every new tool. Your natural interests don't have to go there, and that's completely normal. Here, I said it.

I'm not saying do nothing. I'm saying if you're coping with your current workflow, or if your organization is stuck in old ways, there's time to learn without anxiety. Start with simple improvements you probably already make in your life and work. Focus on what you actually need today, not imaginary agents for problems you don't have.

For specific ways to improve PM workflows with AI, I recommend a brilliant recent article from Ravi Mehta How AI is supercharging every phase of the PM lifecycle. Let that be your guide.

Decision Making

Decision-making isn't about having all the information — it's about understanding what's at stake: impact and (ir)reversibility.

Strategy requires high conviction because you're committing to one direction long enough to go through cycles of failure and learning before it works. Everyone needs to believe in it and channel their energy into it. So be robust here — gather data, analyze carefully, make this one thing impeccable.

Tactical decisions are different. They're about finding the right path to make your strategy work, fast. Here, the cost of indecision usually exceeds the cost of being wrong. Every day you delay is an opportunity cost, and a lack of decision is still a decision btw. Your job is to lower the stakes wherever possible: make decisions reversible, reduce blast radius (remember: small, fast, tight). And normalize failure to learn.

Teach your team judgment and create decision frameworks so they stop escalating everything.

Watch for biases that cloud thinking under pressure - confirmation bias, sunk cost fallacy, anchoring.

And remove yourself from the equation when stakes get personal.

TL;DR: Engineering velocity skyrocketed. Product decision-making didn't. Close the gap with being proactive, improving your efficiency, and learning to make fast decisions.

Own Your Game

At the end of the day, it's all about you. The only person who will define your success is looking at you in the mirror.

It's hard times, a hard world, and a hard job — and you need to be up for it. I'm not talking the hustle-bro-neostoic culture. I'm talking about setting yourself up for long-term sustainable success through inevitable highs and lows.

Vertical expertise and positioning

In fierce competition, niching becomes your differentiation. For businesses and for the workforce. Your niche can be big, but make it concrete. Rally your focus toward building this expertise while maintaining general fluency in other areas.

Most often this takes the form of domain specialization. Although industry knowledge doesn't necessarily translate into great functional performance, great functional performance supplemented by domain expertise wins in this market. Or go functional — Technical PM, Growth PM, AI PM, Marketplace PM…

Learn to market yourself

I’m not necessarily talking about personal branding, though it helps. But more importantly, market yourself internally. You don’t have to be a shameless self-promoter or detached from reality. But if you don't tell people about your achievements, you won't get deserved recognition. It's your job to communicate your impact and value, not anyone else's.

Be strategic about everything

From product initiatives to career to life choices. You don't need to live through spreadsheets, but be deliberate and keep the main things the main things. Early in your career, flowing with life is normal. But after a few years, take ownership of direction, or a crisis is guaranteed.

Carve out "forest time" — strategically assess goals, progress, direction, and course-correct. Weekly or bi-weekly for work, quarterly for life.

Learn to restore

I don't believe in work-life balance. Not for those seeking mastery. Mastery is sacrifice. But I recently realized how crucial true restoration is — not just travel, dinner with friends, or Netflix.

This pace of change and information processing has profound implications on our nervous system. We weren't built for constant fight-or-flight mode. No wonder mental health deteriorates despite medical breakthroughs. Anxiety, irritability, focus difficulty, inability to relax, overwhelm — these aren't normal.

An evening off isn't enough, and weekends tend to drown in errands. We need time-tested tools: detachment, digital detox, mental space, exercise, breathing, connecting with nature. Make time for this.

Understand yourself

Sooner or later you'll understand that being happy means being yourself — nothing less, and nothing more!

Compromising your identity, values, goals, principles to fit someone else's image of how a PM (or whoever) should be, is ridiculous and not worth it. What gives you fulfillment is leveraging your individual traits and strengths, and following your interests — not numbers on someone’s spreadsheet.

Learn about yourself early. What follows is a long period of integrating this knowledge into life and work, reconciling who you are with what the world wants you to be.

Build your support circle

In the end, you're alone — more so in this job.

For some, connecting is easy. For others, not so much. If you're in the second group, find a support circle of friends, professionals, mentors who are or were in your shoes. To learn, share, vent. Even if you don't think you need it, try it — it feels good. There's no decree that you must keep everything inside. Let it out, cleanse a little. You'll be lighter.

TL;DR: Niche down, speak up about your wins, stay strategic, and learn to truly detach. This job is hard and lonely, so build the personal foundation that sustains you through the neverending cycle of highs and lows.

What about leadership?

It's not entirely clear to me yet. I've spoken to quite a few product leaders in the past months, and while there's general agreement that "the core of the job stays the same," there's significant volatility around what new skills and approaches they should master. Remains to be seen.

Overall, I think the job will skew in the directions described above, just at a higher level:

• Product strategy will align more tightly with growth and business strategy.

• Product leaders will increasingly own P&L, team efficiency, and participate in annual financial forecasts.

• They'll spearhead cultural and operational changes around antifragility, growth mindset, new collaboration models, autonomy, and AI-enabled workflows.

• And of course, even more emphasis on coaching — both unchanging fundamentals (more critical than ever) and new skills and mindsets.

Good luck!

Pricing model is one of the most significant growth levers for a business, but it also impacts Product-Market Fit, Go-to-Market Fit, scaling and innovation. But too often I see functional leaders or founders looking at it from just one angle: "Are we charging the right amount?"

Pricing model jobs-to-be-done (JTBD) concept is my idea to show that the model influences the entire growth strategy, and highlight key elements to be truly understood in order to build a meaningful, scalable, and bullet-proof pricing system.

But before we dive in, I also want to emphasize that pricing model should focus on long-term revenue. It’s a system where each element needs to play well with others, as they’re all working towards the same goal: sustainable long-term growth. And if you are in a situation where you have to prioritize short-term revenue for a while, use tactical adjustments like time-bound promotions or efficiency optimization. Don’t take it out on your pricing model — it’s not easy to build in the first place, and it’s even harder to change in many organizations without unintended consequences for customer trust, internal operations, or long-term value.

5 Jobs-to-be-Done

Let’s go through each job-to-be-done and explore it.

JTBD #1: Convert

It's hard to argue that pricing plays a huge role in a customer’s purchasing decision. Even more, it influences their decision to sign up for a free trial: if the price is way out of their budget, this may play against even just trying it out.

If the pricing is within their affordable range, then what they’re looking for, at a high level, can be boiled down to clarity about:

• The NOW: Is it clear what they are going to pay for and why?

• The FUTURE: How will product value and price scale with their growth and engagement?

To help potential customers make their purchasing decision easier, your pricing model needs to consider 3 crucial input factors:

1. ICP knowledge. You have to know your customer’s characteristics, needs, use cases, problems, and company dynamics inside and out. This is where focusing on your Ideal Customer Profile (ICP) really pays off: the better you know your customer, the more meaningful and aligned your pricing model will be with their needs. A "spray and pray" tactic won’t help here.

2. Value metric. This metric represents your idea of how the core value of your product scales with more customer engagement and usage. For example, the number of events can be a value metric for an analytics product, and the number of projects for collaboration software, or storage volume for cloud storage. Of course, this is just a proxy for the key value your product delivers. By looking at the pricing model and seeing the value metric (e.g., “# of…”), the customer can understand if your idea of value scaling aligns with theirs, and if it’s a good deal for their money.

3. Willingness to Pay (WTP) is the prospect’s subjective feeling about your product’s worth. This may change during evaluation. For instance, they might initially think it’s too high, but after reviewing competitors, realize this price range is the norm for the category. Or their subjective feeling might change after an actual trial (in both directions). The most known method for researching market WTP and defining your pricing point is the Van Westendorp’s Price Sensitivity Survey.

   1. In self-serve purchasing scenarios, it’s also crucial to understand your customer’s permission to pay — how much can they pay on their own? What’s the budget threshold that doesn’t require the company’s procurement process?

JTBD #2: Engage

The design of your pricing tiers should support customers in adopting and using your product. Engagement is the premise of customer retention and expansion, so everything — even your pricing system — should work to foster and strengthen it.

I’m sure we’ve all encountered products that put "spokes in the wheels" with usage limitations and paywalls. These felt unfair because they blocked us from getting the core value we hoped for.

While as an industry we’re shifting towards usage-based pricing, making this system truly work for the customer is hard. It might seem fairer as a concept, but aligning it with real customer value isn't always easy, because the value metric is just a proxy for actual product value (and in B2B products, it’s almost never straightforward).

Personal story: I spent mucho dinero trying to make a mini-app for myself with Replit. As a non-developer, debugging constant problems was a challenge, especially when Replit congratulated me that my app worked when it didn’t (but the credits were used…). Even if the problem was me, I still didn’t perceive this approach as entirely fair.

But isn’t there a risk of giving away too much for the price?

Here’s the thing: if your customers don’t use everything included in their plan (features and usage quotas), they’ll start questioning if it’s reasonable to pay that money for something they don’t use. If there’s a downgrade option, they’ll probably take it. If not, they might start looking for a cheaper solution, or for alternatives that, while perhaps less smooth or effective, are "good enough" for their money.

So your pricing model should be well-aligned with and supportive of:

• Use cases, needs, and engagement trends of your ICP: Does the value included in their chosen plan align well with their needs? Does it provide the value they’re paying for? Are we giving too little, unnecessarily prompting them to upgrade just to unlock the value they were looking for, but coming with a shower of unnecessary features? Or, conversely, do they not use much of the stuff in their plan, which makes them potentially look for a solution with a more reasonable price?

• Almost the same goes for your value metric (if you have one). The difference is that value metric quotas measure the volume of usage and engagement, while specific features in the plan cater to specific customer needs, pains, and goals.

JTBD #3: Retain / Renew

The third job-to-be-done is ensuring the pricing model actively supports customer renewals: after a billing cycle, are they satisfied with the value derived for the money?

This is all about subjective perception, of course. The equation looks like this:

Price < (Perceived value - Friction).

To promote renewals, you need to understand how your ICP perceives value, uses your product, how hard it is for them to overcome or put up with friction, and what stands in their way of renewing their payment (if anything). Especially look at the gap between engagement and renewals: if the plan and usage quotas are well-designed for their needs and context, is there anything else holding them back?

There are other, more technical contributing factors to this that I’ll discuss in the Churn 101 series.

JTBD #4: Upsell

When it comes to customer expansion, your pricing system should actively promote it. Sound product engagement is the foundation of your customer’s growth within your product. But if your pricing doesn’t make sense for their scaling, it can be a serious roadblock to an upsell decision, at minimum. At worst, it can cause churn.

This is where your customer knowledge is truly stress-tested:

• Do you understand how customer needs, problems, and organizational dynamics scale with size (e.g., a growing team or even several departments) and more complex use cases?

• Do you understand what the economic buyer cares about? And what concerns their purchasing committee might have (legal, compliance, security, integrations, billing, etc.)?

• Especially in high-growth scenarios, do you see how your value metric supports or, conversely, potentially hinders their growth?

• Lastly, how does an upgrade fit into their allocated budget? How is the decision made? Does each new feature correlate with what they perceive as viable for a price bump?

JTBD #5: Protect

Finally, you need to factor in cost scalability for your organization. Supporting high usage isn't always free (actually, it's never free). You need to understand your unit economics to design pricing scaling scenarios that don't violate them.

Of course, this is where AI costs come into play, as this is already our reality. If you have such a product, you need to understand where the computational differences lie. What user actions can sharply increase your costs?

This will depend on the type of solution you're offering, more specifically: what kind of tasks AI performs (is there a lot of variance in computational costs for each task type?), and what’s the average number of tasks performed by a user segment (this matters for certain task types or multi-purpose products).

I’m planning an article on understanding the pricing structure for AI features/products, so stay tuned! 😃

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Pricing IS Product Value

The main thing I’m trying to convey here is that most people I talk to think of the pricing model as just a business asset. While true, I urge you to think about this in a dual mode: it’s also a form of value your product offers to your customers. Almost like a feature. And to make it work, it has to align with your customers’ idea of value.

These foundational pieces:

• ICP knowledge

• Value metric as a representation of your product's value consumption

• Willingness-to-pay and permission-to-pay

• Unit economics as a business and customer concept (after all, customers ultimately cover the cost-to-serve; poor unit economics can lead to unsustainably high prices, directly impacting the customer’s ability to get meaningful value)

represent the principles behind your model and pricing system design.

They are the direct inputs that determine your pricing model's ability to successfully execute its 5 Jobs-to-be-Done. Without a deep, continuous understanding of these elements, your pricing model risks failing to convert the right customers, adequately engage them with relevant value, foster lasting retention, facilitate expansion, or protect your business's long-term viability.

Pricing is as much your product’s value as your features are, and these four pillars ensure it's built on a meaningful, fair, and sustainable foundation for your customers and your business. And if it is so — that, in turn, will accelerate your business.

Pricing Evolution

Another crucial idea is that your pricing system evolves with your own growth.

It may look something like this:

• Early stage: Here you may not even have a formal pricing model. Your main goal is to acquire customers to gather necessary data and feedback (your top priority) to pave your way toward PMF. Your pricing is flexible and typically very low because your priority isn't money per se right now.

• Pre-PMF: Now things start to come together. You understand your customers and their usage much better, you have some traction, and your product works. Now you need to find a product-model fit. You design your first "real" pricing system and raise prices (often 50%+).

• Post-PMF growth: This is where pricing becomes your growth lever. You experiment with tiers, value metrics, the model itself, and continuously revise your pricing points.

• Maturing: You expand your pricing experiments beyond just the model: pricing localization, product bundling and unbundling, discount systems, price flattening, etc.

Along this journey, your ICP also evolves: who they are, their needs and use cases, willingness-to-pay, and usage patterns.

They evolve almost in unison with your pricing and serve as stepping stones for each other. This dynamic interplay underscores why your understanding of the foundational pillars — ICP, value metric, WTP, and unit economics — must continuously deepen and adapt alongside your product and market.

Your pricing model's evolution is inherently a reflection of your evolving insights into these core elements, ensuring it remains aligned and effective at every stage of growth.

Bonus: Ownership

Because pricing is seen only as a business asset, it’s often "owned" by the C-level suite, with the CFO implicitly appointed as head. Most times, this means pricing is a "no-go" territory. I’ve rarely seen pricing experiments, and even when they happened, it was a point of anxiety and control from the CEO.

But when a team is responsible for revenue targets (product & growth teams increasingly own parts of product revenue), how are they supposed to work with this lever if they’re not allowed to touch it?

So products evolve, customers evolve, but pricing either doesn’t, or does so in a non-experimental way — just rolling out a new pricing plan out of the blue with a big announcement. No wonder it results in problems.

Again, this mindset centers around the thought that pricing only impacts revenue. But it also influences operations and efficiency, and of course, customer experience and behavior. It’s only fair that working on pricing should be a cross-functional effort.

But even if you have a cross-functional Monetization team, they’re rarely allowed to go beyond incremental improvements like pricing page design, added payment methods, in-product monetization awareness, etc.

They rarely can just drive structural changes, like pricing points, plan design, value metric, local pricing, etc. This makes sense as these change the structure of company’s revenue and require robust exploration, alignment, and sanity checks. And leaders must be involved here.

I’m a big fan of Programs, and in this case, I advocate for a Monetization Program that oversees pricing evolution. Here, a tight-knit group of cross-functional leaders makes decisions and owns the pricing evolution.

This is becoming even more important now, as AI pricing must change to account for new usage patterns and unit economics.

Good luck!

Of course, it’s not some divine revelation to say that retention matters a lot, that it’s king, that churn is a silent killer. But few act on this. It becomes just a word with a negative connotation.

Why churn is rarely deliberately addressed?

80% of founders and leaders I talk to, while concerned about churn, tend to steer growth conversations elsewhere, because tackling churn feels like a massive undertaking, without a straightforward path, requiring investment (financial, focus, time, cognitive tax), and often with unclear ROI.

I've observed three common reasons why churn tends to be left to chance:

1. Lack of awareness

In Unpacking B2B Product Growth I showed growth as a high-level system of levers:

This reveals three main levers for B2B revenue growth:

• Grow the customer base and get new revenue (Acquisition).

• Retain existing customers to prevent revenue loss (Retention).

• Expand existing customer value by selling more to them (Expansion).

Revenue from retained customers compounds year over year, and as a business matures, a larger share of total revenue should come from these customers. This is why with time churn starts to matter more.

Conversely, even a small decrease in churn can yield massive revenue uplifts. Bain & Company research highlights that a 5% increase in customer retention can boost profits by 25% to 95%. So even 1% gain can help a lot.

Customer vs. Revenue churn

A key characteristic of B2B businesses is that their customer base can vary significantly in ARPC, leading to uneven revenue distribution. At a previous company, 12% of our customers generated over 60% of ARR. It’s common. Now imagine if one of them churns. Your customer churn rate might still look impressive, but your annual revenue would suffer serious blood loss.

Or, say, your customers stay, but more and more downgrade to cheaper plans, perhaps due to an economic recession. This is also revenue churn, even if customer retention remains intact.

So at all times you need to track and correlate both.

…

While Sales and Customer Success teams somewhat manage revenue churn, customer churn largely remains unaddressed holistically and consistently.

It's not that customer churn consequences are hard to grasp, but their impact often doesn't resonate with SaaS startup founders and leaders (I quite arbitrarily consider startups as companies below the $10M ARR mark).

Partly due to complacency, as some churn is simply unavoidable.

Partly because these effects are delayed. In our uncertain times, many can't afford to think in terms of 1+ year initiative timelines.

Mostly? In my personal opinion, it is because of the collective addiction to the acquisition-first growth mindset.

2. Acquisition-first mindset

Maybe it's just me and I’m in my own bubble, but most SaaS growth conversations I encounter are about acquisition. Growth is equated to a #leads problem. While I appreciate its importance, especially for early-stage startups, this mindset is dangerous both for individual companies and the industry as a whole.

Of course, improving marketing channels, sales pipeline and conversion is important for B2B. It’s just that pouring tons of acquisition money into a leaky bucket of churn doesn’t make much sense. But I see it all the time…

And… getting new customers feels good. We love new stuff! It brings freshness and, even better, hope. We don't like fixing old stuff, especially when fixes bring unglamorous decimal percentage improvements on delayed timelines.

I call this viscious cycle The Acquisition Trap, and it goes like this:

• Customers are leaving.

• Product teams hustle to build more features, hoping they will magically solve churn, or/and create custom features for specific customers, disrupting existing plans.

• Product becomes a "patchwork blanket" of half-baked MVPs; value proposition gets diluted.

• Other customers notice the lack of innovation, irrelevant features, and awkward UX, and leave.

• Acquisition teams now compensate for losses, but acquiring and selling becomes harder as value prop and ICP become unclear.

• Marketing expands their focus into adjacent segments without product alignment, and Sales aggressively closes leads.

• Product prioritizes features for prospects and customizes for new customers. Innovation stops. Product Value prop and brand continues to diminish.

• More pressure on Acquisition… and the cycle repeats until the company runs out of money.

This is the danger of the acquisition-first mindset, and it's real. I don't know if it can or needs to be fought; perhaps market punishment is enough. The only question is when it will reach its culmination. I think soon.

3. No Playbook

Churn manifests in various ways, each originating from multiple underlying causes. In a way, churn is the (unsaid) customer feedback for your product.

Solving churn is not a framework, and there are no silver bullets and templated solutions. It’s a methodology that requires robust understanding and tracking.

1. Churn is an outcome of a myriad of interconected reasons. Your team needs to deeply understand why it happens. It’s a lot of work.

2. To track and improve it, there has to be a detailed Churn Driver Tree, and PMs are collectively still not great at it as, as connecting and correlating low-level customer behavior signals with retention is hard and confusing.

3. Because of churn being a high-level outcome metric, correlating specific initiative results with overall retention can be tricky, so motivation (or honesty) can go out of the window.

4. Churn is a lagging indicator. It’s not easy to see and compare results from initiatives on the annual timelines (typical for B2B). Proxies help, but you still can’t always be sure of the aggregate effect.

5. User onboarding and time-to-value has its peak moment, and its great! It’s truly a high-leverage and high-priority tactic. But it’s not enough. And if the company maintains an acquisition-first mindset, even great onboarding will lose to churn inertia.

6. It requires some company maturity to invest into robust planning and tracking systems. The unlucky flip side is that the later a company tries to fix churn, the more costly, overwhelming, and less effective the endeavor becomes.

Bonus: Unclear ownership

Churn should be everyone's problem:

• Marketing should bring relevant leads.

• Sales should focus on ICPs.

• Product should builds features that drive adoption and engagement.

• Growth should effectivelly and seamlessly connect users and customer to value (via a free model, activation, pricing, engagement strategies).

• Design should optimize for intuitive and clear UX.

• Engineering should builds technically performing, scalable products, and fast.

• Customer Success should enable users and anticipate and triangulate problems.

But when everyone’s responsible, in real life it often means that no one is, if there is no unified company strategy and objectives to adress churn.

Why today retention is more important than ever?

In 3 Seismic Shifts in Product Management I wrote:

Today, a business can launch and validate faster than ever with as little as one person. Competition is intense, and growth is getting harder and more expensive, while willingness-to-pay is lowering due to the economic climate. Strengthening engagement and retention is not optional anymore — it's a question of survival.

Let me explain.

We now have a lower barrier to market entry, so many new, small, niche products have emerged, often built by a single person. So, increased competition. But how likely are these new apps to become B2B users? Not very. They can create their own solutions for their own smaller problems. Thus, the share of B2B customers remains roughly the same, while competition grows.

So, Alex, isn't this an acquisition problem?

Well, it is, especially if you’re earlier stage, certainly.

But the thing is, that for companies with existing customers and revenue, acquisition becomes a symptom for failed retention. They are leaking customers and revenue and try to offset this loss with new revenue.

Problem is, in the acquisition-first mindset, new revenue is perceived to be the main solution for growth. And it operates on a weird assumption that there’s some magical pool where customers just spawn, and you just need to find a new tactic to 'fish them out.' Erm, no such magical pool exists. Most customers are taken. Switching solutions costs money, time, brainpower, and organizational effort.

They leave in two cases:

• They've found a better solution for a similar price.

• They've found a similar or better solution for a better price.

The first case is something we in the SaaS industry have learned to work with.

Now, we're increasingly facing the second situation: a swarm of new competitors. They may be imperfect, but they're specialized and precise (because in this competitive market you must niche down to win) and offer a better price. It may not necessarily be cheap, but their pricing structure clearly correlates with product value, its scaling, and the customer's long-term willingness to pay.

Importantly, the betterness of the solution*price combo is subjective. Customers leave $500/month SaaS solutions for $20/month ChatGPT. It might not be necessarily better or easier or as streamlined, but it's okay for the price. And for another $20/month with n8n, they can close most painful workflow gaps.

In many ways, people simply don't need generic SaaS solutions anymore when anyone can build anything for their specific problem with a little time.

What this means is:

1. If your solution leaks customers and revenue, you're at exponentially increasing risk of losing to cheap and imperfect indirect competitors. Better leads and processes won't save you much (though they might make you feel better for a short while🕺).

2. Your acquisition fishing rod is only necessary to catch ready-to-switch customers at the right moment IF you're certain you won't lose them. Otherwise, you'll fall into The Acquisition Trap.

3. Today, customers expect far more value per dollar spent on a product than before. And they have a choice. So even good products with polished UX and performing funnels are at risk.

When your product delivers outstanding perceived value for a meaningful price, has a great user experience, and in-product mechanisms that prevent the leaks, your acquisition will no longer be a major concern by design.

In the next part of the Churn 101 series, I'll discuss how to identify and size churn correctly, and learn why it happens, to create meaningful opportunities for improvement.

PLG isn't just a rigid framework with a specific set of steps; it's a flexible system adaptable to your unique needs. Implementing it all at once isn't feasible or reasonable, as there's no universal "silver-bullet recipe" for every product and company. Instead, you need to act, learn, and iterate to see if your ideas move the needle.

But what "needle"? If you don't understand how PLG benefits your business, how will you measure its success? How will you know what works and what doesn't? PLG isn't a "one-and-done" system; it's a dynamic process which adapts to evolving goals.

Yet, many still perceive PLG as merely a tactic. When I discuss PLG with curious founders, I start by helping them unpack what it truly means for their context. What exactly do they hope to achieve? What is their most pressing pain point, and why now?

I’ve frequently observed a lack of clarity. Founders often voice frustration with investing big dollars in acquisition that doesn't yield sustainable revenue growth, failing to offset churn or significantly improve customer expansion. Or, leaders tasked with "building PLG" may lack full company context. They might initially believe PLG will strengthen their market presence, only to later realize its intended purpose (by execs) is to cut costs and scale more efficiently.

This confusion often stems from a general misunderstanding of "growth" itself, still misinterpreted by founders, marketers, product professionals, and sales teams alike. I talked about this in my previous article “Unpacking B2B Product Growth”.

Below, I propose two mental models to understand why to add PLG to your sales motion (struggling or successful):

Approach 1: Fix What’s Broken

This is, in my opinion, the most common scenario, especially for products that have been 5+ years in the business.

It’s a frequent case for companies that prioritize sales over product. And there are a lot of them. As my boss (a CFO) once told me, "Alex, in Germany, if you're running a B2B SaaS business, the most important thing is to have a sales pipeline. The rest is a nice-to-have".

This mindset often leads companies to pursue the widest possible customer segments, often forcing them into cabal contracts, and prioritizing sales requests to drive short-term revenue (and sales🤷‍♀️) goals, and leaving product and customer experience to chance. This results in poor engagement, churn, customer dissatisfaction, and missed expansion opportunities.

The product org becomes a feature factory with diminished motivation and alignment, being a service organization to business teams who often lack empathy for customers and dismiss behavioral data.

This dynamic transforms the product into a leaky bucket of customers and revenue. Sales cycles lengthen and become more expensive, while willingness-to-pay lowers. This is where PLG curiosity starts, believing that it will save them by lowering CAC. 3xhaha.

The reality is, these companies have a retention problem, not an acquisition one.

Moreover, market competition is continuously increasing, even if the overall market size isn't necessarily expanding. New entrants often target easier-to-capture SMB and prosumer segments, building strong products for end-users and establishing mechanisms to convert them into customers and help them evolve into high-spend accounts. Over time, these newcomers expand their presence and brand, leading your existing customers to question if they can achieve the same results with a typically cheaper new product.

So not only does acquiring new customers become harder, but if your switching costs aren't very high, your existing customers will likely leave. This isn't just about price — it's often because competitors' products simply offer a better experience for your customers' employees.

In this situation, Product-Led Growth is about building a strong foundational product, not just polished homepages. It's about providing a clear, coherent, and engaging user experience that makes customers want to stay, even if switching costs are low.

A self-serve product delivers real users, data, and continuous feedback, leading to a clear understanding of what works and what doesn't. Budget-conscious customers are unforgiving: they won't accept "your feature request was added to the backlog 14 months ago, stay tuned!".

The comforting fact is that you don't need to implement the entire PLG motion to achieve this. You can apply specific tactics, such as self-serve activation and purchasing, allowing your product team to fix retention leaks instead of merely cramming their backlog with new features (more likely unpolished MVPs).

Free or small-budget users can help you develop a product that your higher-tier customers will love. In fact, with time, these users can evolve into higher-budget accounts if you support their growth.

This approach helps you defend your existing market by making your product compelling enough for your customers to stay. We can call this the Defense mode.

When the biggest leaks are plugged, the company can move into the Offense mode (read below). Or not, if they wish to continue to grow through sales and other strategies.

TL;DR:

The Defense mode of adding Product-Led Growth is about protecting your existing market from customer and revenue leaks and competitors encroaching from "the bottom". This can be addressed by applying product-led tactics to fix leaks and expand your customer base.

Approach 2: Fuel What’s Working

If your sales-driven product already demonstrates strong engagement indicators, promising retention and upsell signals, then consider creating a full Product-Led Growth motion to drive self-serve customer acquisition, retention, and monetization, and enjoy the scalability and predictability perks.

In this scenario, your crucial task is to identify a meaningful, often heavily reduced, version of your product that can deliver and sell its value to customers without human intervention. This is a prerequisite, as it's challenging for a product that isn't easily and quickly understood to sell itself. Don’t cram all of your features into the self-serve product — only most important for attracting and serving lower-tier customer segments with less complex use cases. Your other features, as much as they're dear to your heart, can be introduced as customers grow in size, complexity, and the variety of their use cases.

Of course, this isn't always possible. For instance, I spoke with the founder of a data product whose core value could only be realized with large organizational datasets. This product simply didn't make sense for smaller customers. While they could use some product-led tactics to improve product engagement and customer expansion, PLG as a concept for serving smaller organizations wasn't suitable for them.

I've found that this Offense mode is more applicable for products that have recently achieved strong PMF indicators. In this case, Product-Led Growth serves as a PMF expansion strategy, helping businesses decrease costs, reduce risks, and improve revenue predictability and market presence. This approach leads to a more comprehensive growth strategy for building a self-sustaining and efficient motion, requiring a more strategic approach to where to start, what to learn, and how to proceed.

TL;DR:

The Offense mode of adding Product-Led Growth is a way for already rather strong products (as per acquisition, retention, and conversions/monetization signals) to win new markets and expand & strengthen their PMF.

The PLG Spectrum

I want to reiterate that PLG is not a binary "yes" or "no" system. Everyone can benefit from it to some degree. This fact is almost entirely overlooked — even when founders no longer believe PLG is just "viral acquisition," they still tend to think of it as 0% or 100%.

In reality, it's a spectrum, and the 0-100% scale applies more specifically to the degree of human support involved. If we consider the most common formula for "Growth," we can visualize it like this:

Here, each growth lever (Acquisition, Activation, Engagement, Monetization) can involve a varying degree of human support, from 0% to 100%.

This makes the growth spectrum look something like this:

This means there are various combinations and approaches.

Your acquisition might be entirely product-led, entirely sales-led, or a combination of both in various proportions.

The same applies to Activation. When I led the analytics SaaS (a complex product by definition), it was clear that some users had the technical ability to onboard themselves, while others did not and required human support, especially with setup. Both were viable first account users within our ICP. Thus, our activation wasn't entirely product-led, although we certainly aimed to minimize Customer Success involvement for lower-tier accounts and scale human onboarding through recurring group webinars, onboarding videos, and so on.

TL;DR:

With PLG, you actually have a choice in how you add it to your business. It is highly contextual to the product and business stage, the product itself, the market.

The main takeaway is that you don't need to pursue "pure," full-blown PLG to be product-led. However, being product-led to some degree is no longer optional. In this era of abundant competition (and stagnant market size and willingness-to-pay), you need at least a great product to remain competitive.

And because I seem to evangelize Product Growth, I felt the need to clarify the term. The heck is it? A shorthand for product-led growth? PM-led growth? A skill? A function? A team? A title? An outcome? It depends. Joking. It’s a bit of everything, frankly.

But to get there I suggest to take a step back and outline the B2B growth “system of coordinates” to keep us on the same page.

B2B Growth: What Are We Really Talking About?

I think I was so much in my own buble, that only after quite a number of conversations and research with PMs, marketers, founders, etc, it finally struck me that everyone mean their own thing by “growth”:

• For 80+% of people, growth primarily means new customer acquisition. In SaaS, most discussions about growth revolve around marketing (especially performance marketing) and sales. C’est la vie.

• Others define it as revenue growth.

• A select few, typically in executive roles, referred to profit growth.

Let’s make this clear: Profit growth > Revenue growth > Customer growth.

So new customers acquisition is just one of the several growth levers, yet 80+% of conversations orbit this single point, like a black hole.

I totally get why it’s top of mind for startups. But for many it stays a priority forever, like other levers (retention, expansion, efficiency optimization) don’t count. And they miss out.

Well, sure, everyone’s aware of churn, but not everyone fully appreciates the long-term revenue impact of even small churn improvements. And even when they do — it’s kinda hard to do it and unclear where to even start. Acquisition feels more straightforward and predictable. And expansion? Mostly just a luck game.

(B2B) Product-Led Growth

Moving on to Product-Led Growth. Fundamentally, it is a go-to-market strategy where the product itself serves as the primary vehicle for customer acquisition, activation, retention, and monetization (conversions and expansion).

It’s a company-wide strategy that is inherently cross-functional:

• Marketing leverages the product to generate demand flywheels.

• Sales qualifies high-intent prospects through product usage data.

• Customer Success enables self-serve customers to achieve success without extensive human support.

• Design crafts intuitive, clear, and coherent experiences, speeding the customer through their lifecycle with minimal friction.

• Engineering builds a product with high time-to-value and robust technical performance.

• Product Managers identify the best opportunities to improve this motion and achieve desired growth outcomes.

Each company department contributes to this strategy, while maintaining its “core” functions, e.g. new product value creation for core product teams, reaching and closing high-ACV deals with buyers for core sales teams, etc.

Core and growth teams have to be separate as they have distinct strategies, targets, priorities, skillsets, and operational systems. Maybe in the future they will merge back into a single function (and I believe they will!), but not just yet.

The PLG Spectrum

One of the most common (and dangerous) myths about PLG is that it is incompatible with upmarket-focused products.

PLG is not binary — it's a spectrum. It can range from minimal application, like offering a free trial, to a 100% product-led company. This means that even enterprise-focused products can strategically implement specific product-led tactics to help their growth.

Real people at real companies share the same narrative — their enterprise products struggle. With customer retention, expansion, and, inevitably, new customer acquisition. It leaves them vulnerable, and basically invites the competitors to eat away their market share by starting in lower market segments, growing their brand, and gradually moving upmarket.

I think that today, product-led growth strategy is non-negotiable for B2B SaaS to at least remain competitive and attractive through a strong, modern product — no amount of human touch can save a flawed one. The question is to find the right place on this spectrum.

B2B Product Growth 🧐

We’re narrowing down. I have to admit, the terminology here is muddled. At this time, I don’t have a better term in mind (share if you do!) So bear with me.

Product Growth as a Specialized Function

In practical terms, Product Growth refers to the specialized sub-org or team within a company’s Product Organization, that is tasked with executing product-led growth strategy.

These are Growth PMs who lead Growth teams. They optimize for key growth outcomes through rapid experimentation and data-driven insights. They have less product innovation and maintenance work, which allows them to swiftly uncover growth levers and bottlenecks and work with them. They possess specialized skills, resources, and a distinct operational rhythm.

Such team(s) are crucial to driving your company’s PLG success, even if it is just targeted implementation of product-led tactics for market defensibility.

Organizational Setups

Here things start to get really interesting. Org design varies from one company to another, and evolves with the company’s maturity and commitment to PLG.

For the startups and scaleups I work with, I typically see these pathways:

Scenario 1: Growth teams within Product Org

Very often, PLG originates in the Product Org as a single initiative driven by a small, often temporary, cross-functional “tiger team.” With proven success, it may grow into the first “official” growth team that moves swiftly across various product surface areas, trying to plug glaring gaps in the self-serve product.

With time and some success, the single team can split into several, where each focuses on their owned area (e.g. Activation team, Acquisition team, etc.) This leads to Product Growth “sub-department” with its own Head/Lead who oversees Growth PMs. This Head works with other Growth Heads (e.g. Marketing, Analytics, Design) on executing PLG strategy.

Scenario 2: Growth teams within Marketing Org

When acquisition is the primary company’s focus, PLG may originate and develop in the marketing department — starting with the Acquisition team, then expanding into activation, monetization, engagement.

This may be a tricky setup, as it’s not entirely clear who Growth PM(s) should report to — VP Marketing? VP Product? I’ve encountered both cases. Not without problems, but not entirely disfunctional. IMO - if it works, it works. In reality, I don’t believe there’s an ideal org setup — each has disadvantages and trade-offs.

Scenario 3: Dedicated Growth Organization

When PLG starts scaling, a company may choose to create a separate Growth Organization led by a Head/VP of Growth. Typically, they lead Heads of Growth fuunctions such as Product, Marketing, Design, Analytics Growth.

Reporting lines may also be not entirely straightforward here. I know a Head of Growth with marketing background, and in their team, Growth PMs and Designers report to VP Product, while this Head of Growth leads Growth Marketers and Analysts🤯

Growth setups are complex, often rooted in organizational legacy, executive mindset, and actual company needs. And this only adds to the general confusion surrounding Growth, PLG, and Product Growth. It is what it is.

Product Growth as a Functional Imperative for the Product Org

But what about core (and platform) product teams? Wouldn’t it be odd if a product-led company would just let core product teams operate in a traditional “feature-factory” way? I say it would.

Core and Growth teams must work together. Core teams solidify insights from growth experiments into foundational product experiences, and embed long-term growth mechanisms into user experience and lifecycle. Their efforts must amplify and supplement each other, ensuring the entire product ecosystem drives the company's strategic growth.

In fact, I believe that even if there are no dedicated growth teams in a company, the Product Organization should still act as a growth- and business-focused function (due to the 3 seismic shifts I’ve explored earlier). Any product team — core, platform, AI, whatever else — should become a growth team. Not in their operational approach (though certainly some things can be borrowed), but in that they deliberately drive business growth and success through product value.

To do so, Product teams must go beyond their traditional imperative of "just build new value" and expand it into a wider Value Ecosystem:

Build new value: Innovation remains crucial. Product teams find the best opportunities to solve their customers' problems and translate this intelligence into relevant solutions.

Connect users to value: It’s not enough to build it — teams must ensure that users can discover, understand, and successfully adopt new product features. And consider this feature’s wider fit with conboarding, conversion pathways, pricing structure, marketing content, and support. Leaving this to chance will almost always result in lackluster impact on product KPIs.

Capture business value: This is where the rubber meets the road. The Product organization must be accountable for important business outcomes (e.g., Retention, LTV, ARPC, costs, etc.), and its teams should understand, plan for, and prove their contribution to these outcomes (whether direct or mediated).

In this sense, core product teams are also growth teams, even though they will always focus deeper on innovation, foundational architecture, and keeping-the-lights-on work.

Final Thoughts

While in a more common sense Product Growth relates to the Growth PMs sub-department leading Growth teams, to me it means a growth-focused Product Organization imperative, because the old "Product-should-only-care-about-users-and-just-ship" one just doesn’t cut it anymore.

I hope that we do talk more about this in the B2B environment, because clarifying this concept illuminates the strategic role of modern product management in driving revenue and profit, and outlines the necessary evolution for product teams to remain relevant and impactful.

But the world is changing right beneath our feet. And for product management to remain relevant, we must go back to our “roots“ and re-claim the business mandate.

In this article I go over the three main “seismic shifts” I see in the business software market and analyze how exactly they demand transformation to the “traditional” approach.

Shift #1: Evolving Customer Expectations

Today, the model of “top-down” B2B sales is crumbling.

We live in a world of intuitive, self-serve consumer apps. We’ve become smarter, faster, more capable. We’ve seen what “good” looks like, and expect the same from our work software. Spending time talking to people to understand, try, or purchase products feels very 2010. Even economic buyers prefer to let their employees organically adopt software and only pay for proven usage.

Yet, most B2B software remains a mere collection of features, stitched together like a patchwork blanket, leaving customers confused, frustrated, and forced to ask for help — a direct path to churn.

I meet enough founders who genuinely believe that new features will magically solve their churn and NRR problems. But features don't drive mass engagement; deliberately crafted experiences do. Who should craft them? The same teams that build these features.

But when teams are focused only on their piece of the puzzle and pressured to just ship value, connecting users to that value is often left to chance. In a self-serve world, that's a losing game. Enterprise B2B may not feel it just yet. They will.

Shift #2: Efficiency-First Business Mindset

Layoffs are ongoing since 2022. Metrics like ARR per FTE have made it to the top of business health KPIs. The "growth at all costs" motto is dead, every hire is scrutinized, and companies can’t afford to scale by headcount only.

An unpleasant fact is that payroll is typically the highest cost on a company's balance sheet, making it a viable profit growth lever. Employees with incremental impact are at risk.

What makes a high-impact product team? Their ability to drive outsized business results with the same production bandwidth.

In this efficiency-first business environment, product is no longer just about building. It's about enabling businesses to scale and drive customer and revenue growth via product, not via people.

Shift #3: The AI Revolution

Here I want to highlight two fundamental ways AI is redefining B2B product management.

The AI-Fueled Hyper-Competition

Today, a business can launch and validate faster than ever with as little as one person. Competition is intense, and growth is getting harder and more expensive, while willingness-to-pay is lowering due to the economic climate.

Strengthening engagement and retention is not optional anymore — it’s a question of survival. The emphasis shifts from building more to building stronger. Because more value doesn’t automatically translate into business impact.

Features must become mini-ecosystems that provide proper discovery paths, seamless onboarding and continuous engagement strategies, pricing alignment that drives adoption, and robust support mechanisms.

If your product is a “patchwork planket” of disparate functionality and unpolished MVPs, you’re diluting core value, confusing customers, and leaking revenue.

But if done well, B2B products can leverage their unique advantage — the opportunity to sell more to existing customers who are growing with you and increasing their usage and spend.

This is a significant growth lever where product teams must contribute with workflows that allow customers to discover, understand, and get hooked on additional value for their entire organization. In reality, this responsibility is often relayed to Customer Success teams (the unsung heroes of B2B) — and this doesn't scale.

AI as an Impact Accelerator

AI is reshaping day-to-day product development and is increasingly assisting in even critical tasks. It is speeding us through execution that previously required hours. This efficiency will only grow.

Another profound implication is that lines between traditional roles are blurring. Designers and Engineers, powered by AI, can now accomplish increasingly more of what was recently the exclusive domain of Product Managers.

PMs’ role as facilitators is diminishing. Their worth now is measured by their product, analytical, commercial, and organizational sense. PMs and leaders have to actively cultivate often underdeveloped capabilities like:

• Ability to find unique, differentiated customer & market insights.

• Crafting strategies that demonstrably contribute to the company’s top and bottom lines.

• Thinking in feature portfolios that strengthen adoption, engagement, and monetization.

• Rallying cross-functional leaders to execute focused strategy as a united front.

Many product people possess developed analytical, product, or strategic sense. Most have a profound lack of commercial sense. They know their metrics and OKRs but have never seen their company’s balance sheet. They don't understand how their business makes money, what drives profitability, or what the scaling costs are.

Let me ask you: How can you make an impact if you don't understand how impact is made? How can you build value if you don't understand how business value is captured? How can you be strategic if you don't understand the structure of company growth?

We’ve barely moved from “output-driven” to “outcome-driven” paradigm, but now it’s time to take the next step and move from top product outcomes to financial impact. 🤷‍♀️

So What?

These three shifts are not going anywhere, meaning a fundamental recalibration of priorities and operational models:

• From outputs to financial impact: Product must demonstrably contribute to revenue and operational efficiency.

• From human- to product-centric scale: Scaling efficiency relies on automating internal processes and customer workflows to reduce reliance on costly human resources.

• From acquisition-only to retention & expansion-focused growth: Sustainable growth comes from solid product engagement as the foundation for revenue retention and expansion.

• From features to ecosystems: Features are no longer standalone units. They must be designed as interconnected ecosystems that facilitate growth outcomes within the product itself.

However, the product management function, in its majority, isn't well equipped to handle these shifts. This reveals critical gaps that hinder true strategic growth:

• Organizational perception of product teams as feature owners without explicit accountability for their downstream business effects.

• A deficit in understanding fundamental business and financial concepts, and how product drives value capture.

• Fragmented customer journey and user experience ownership, leading to weak product engagement.

• Underdeveloped organizational leadership skills, leaving PMs perceived as facilitators rather than strategic business leaders.

The new playbook isn't about more work – it's about a different kind of work. The product organization must transform from a perceived cost center into a direct driver of top- and bottom-line impact. This is the new imperative.

In this blog, I’ll be writing about what this imperative entails, necessary transformation for the product function, and Product Growth. Sign up to not miss my next post :)

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