Telling early traction apart from product-market fit

The Founders work group argues over the tests, from pausing all outreach to tracking the skeptics as a cohort.

Updated October 13, 2026

Traction responds to something the founder did. Fit compounds through the product's own loop, and it rarely arrives as one moment.

Launch spikes fade. Emmett Shear's Kiko got 90,000 signups over a year and a half after a TechCrunch feature, and growth stopped when the press did. Twitch grew 25 to 30% a month for almost nine months against a bar set before the data came in.
Name the loop, then test it. Reid Hoffman asks founders for their theory of compounding loops and suggests shutting off your own pushing for a few weeks. Shishir Mehrotra would run that test on champions instead, since a product that changes how a team works can stall without help even when its users love it. Emmett, who never stopped pushing at Twitch, came round to Reid's return-on-effort version: whether each push pays back more as the network fills in.
Check that the love repeats. Ben Silbermann ran Pin It Forward with about 300 bloggers and started believing once strangers who matched his early fans loved Pinterest at the same rate. He would set any advance bar on depth: how many people write in when it breaks, and how often they come back. Emmett agreed for a new product.
Watch how new users arrive. About 99% of Notion's growth came from word of mouth when Notion had a team of 25, so Ivan Zhao could read growth as pull. He would count how many new team workspaces start with someone who was already a personal user.

Group chat with Emmett, Ben, Ivan, Reid, Shishir — the Founders work group

Question:

How do you tell early traction apart from real product-market fit?

Ben SilbermannFounder and Chairman of Pinterest. Founder of How We Feel

I think Pinterest is a useful case because by the usual measures we had no traction at all. We launched in late 2009 and emailed everyone we knew. A few months later we had about 3,000 signups, and those were accounts, which tells you nothing about who was still using it. Friends were polite: "Oh, looks interesting." That politeness is the signal I'd discount most.

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What kept us going was a different signal. A small group of people really loved it, and when the site went down they wrote in asking why it was down and sent us suggestions. My cell phone was on the support emails, so I heard about it right away.

YouTube So my first test is simple: does anyone get upset when your product breaks?

My second test is whether that love repeats. The people who cared were mostly in the Midwest and used Pinterest for ordinary parts of life: planning a home, deciding what to cook. Instead of changing the product, we asked where people like them gathered. I went to a bloggers' conference in Utah, and then we ran Pin It Forward with about 300 bloggers who each introduced the service to their readers.

YouTube When strangers who matched our early fans loved it at the same rate, I started believing we had something. Early traction that only comes from your own network, or from a launch spike, hasn't passed that test yet.

The last lesson is about diagnosis. Our core idea was simple: collect things you like and look at other people's collections. So I bet we hadn't found the right people yet, rather than that the concept was broken. Looking back, "We didn't have an engineering problem. We had a design and community problem."

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Ivan ZhaoFounder of Notion

So, for us product-market fit never arrived as one moment. It came as a slow ramp: first some people cared about the thing we made, then strangers started reaching out, then they paid. Even now Notion feels small compared to where it could be. I'd be careful with any test that promises a clean before and after.

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The signals I'd still watch, from our early years:

Read the support queue as product feedback. I answered every support ticket myself for the first couple of years. Traction looks like people asking how to do the thing you designed. Fit looks like people pushing the product into uses you didn't plan and asking you to support them. You have to decide which requests to listen to, and that judgment is part of the work.Apple Podcasts
Check whether people build and share things without being asked. Our community started on its own. We didn't engineer it. People made their own setups and templates and showed them to others, and the consultants and ambassadors came out of that. When we launched 1.0 on Product Hunt, users we knew only through support tickets showed up to comment and upvote. Paid acquisition can't produce that behavior.Apple Podcasts+1
Watch whether personal use turns into team use. Everyone who works somewhere also has a personal life. When people who used Notion for their own notes started bringing it into their companies, and we had to set up a space for those internal champions, the pull was real.Apple Podcasts

We never had a formula for engineering fit, and some of it was luck. Organic pull showed up across countries we never marketed to, and that told me more than our growth chart did.

Shishir MehrotraCEO of Superhuman (formerly Grammarly). Co-founder of Coda and former Chief Product Officer of YouTube

Ben's "does anyone get upset when it breaks" test is close to how I measure it, and I'd add the step that comes after it. The test I trust most is whether your customers have become your evangelists. Coda is a team product, so we felt fit when it spread on its own: someone on one team picked it up and carried it into a different team. A group that set out to run a workflow in your product and is now running it there counts as traction. Fit shows up when existing customers keep trying to tell other people about it.

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Getting upset when the product breaks is roughly what a disappointment score captures, and I track it next to NPS because the two can split apart. Disappointment score asks how disappointed you'd be if you couldn't use the product anymore (I learned it from Rahul Vohra). NPS asks whether you'd recommend it (credit to Fred Reichheld). Early Coda had sky-high disappointment and low NPS. Once people worked the Coda way they couldn't go back, but recommending it meant asking a team to change how it works. Superhuman Mail had the reverse pattern, since Gmail was always one click away. When Reichheld and I talked about it, he described the gap as a sign of market maturity. Products in a new category show up first in disappointment scores, and NPS matters more as products start to look alike.

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Traction can also vanish overnight. Our first big design partner was Noam's team at Uber, and one day their usage dropped to zero. The team had told him they'd quit if they had to keep using it, and they handed us a list of about 30 fixes. We didn't build that list right away. They had only seen the tables piece of Coda, so building to their list would have overfit us to a small part of the product.

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I agree with Ivan that fit never arrives as one clean moment, and that holds even at huge scale. YouTube passed a billion users, and our exec retreats still argued that we hadn't found true fit with older audiences.

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Reid HoffmanCo-founder of LinkedIn, Inflection AI and Manas AI. Partner at Greylock

I treat product-market fit as two stages, and early traction tells you about the first stage only. Initial PMF is getting on the beach: some set of users clearly wants the thing. Scale PMF is winning the country, which is a different game with different competitors, and you have to learn it fresh. A lot of founders read beach-level traction as proof they can win the country.

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My main test is to ask for the founder's theory of the compounding loops of growth and engagement, then check whether the data shows those loops starting to turn.

Apple Podcasts A launch spike, press coverage, or founder hustle produces traction that stops when you stop pushing. With real fit, each new user makes the next one cheaper to acquire or more likely to stay.

Airbnb is the case that keeps me from trusting topline numbers in either direction. When I wanted to lead the Series A at Greylock, David Sze told me every VC has to have a deal they fail on, and Airbnb could be mine. Six months later the numbers hadn't changed, and he came back and said I'd been right.

Apple Podcasts The flat chart hid a two-sided loop that was already working, with hosts bringing travelers and travelers bringing hosts. Ben's 3,000 Pinterest signups fit the same pattern: the topline chart was weak while the loop underneath was starting to work.

At LinkedIn, early reactions ran from neutral to negative. The question we asked was whether people's views would change once they saw the service in action.

Apple Podcasts The signal we tracked was re-engagement driven by social interactions. Every new member asks "who else is here?", so we showed them people from their own network who had already joined, and we watched whether those connections brought them back.

So the concrete test: shut off your own pushing for a few weeks. If usage keeps growing through the product's loop, you have fit. If it drops, you have traction.

Emmett ShearCEO of Softmax. Founder of Justin.tv and former CEO of Twitch

Traction is a response to something you did. Product-market fit is growth that keeps compounding after you stop doing it.

Kiko, our Gmail-style calendar, is my example of traction without fit. TechCrunch featured us, which was the big launch moment for a startup then, and we "grew a tiny bit and then not anymore."

Spotify Over a year and a half we got 90,000 signups, and most of them obviously didn't keep using it.

Spotify A signup count that size looks like traction. It came from press, and when the press stopped, so did the growth.

Twitch went the other way, and three tests came out of it:

1- Set the bar before you see the data: When Justin.tv split into skunk works projects, we gave each one three months and a number to hit in advance. Gaming had to reach 20 to 25% monthly usage growth.

Spotify Once you have a hypothesis, you start reading every data point as confirmation, so the threshold has to come first.

2- Duration: We grew 25 to 30% a month for almost nine months before we launched the Twitch domain at all. By launch day it was "fully validated."

Apple Podcasts One good month counts as traction. Nine months of compounding is evidence of fit.

3- A loop you can name: Every streamer we recruited posted about it and brought their audience, and those viewers found more streamers on the site.

Spotify The streamers' complaints became our feature list, and each fix made the next recruit easier.

Apple Podcasts If you can't say why this month's users bring next month's users, your growth is coming from your own effort.

The qualitative signal matched. For gamers the product was self-evident: "this is what I've been waiting for."

Shishir MehrotraCEO of Superhuman (formerly Grammarly). Co-founder of Coda and former Chief Product Officer of YouTube

I'd be careful running Reid's "stop pushing" test on a product that asks people to change how they work. Coda's early disappointment scores were sky high while NPS stayed low, because recommending Coda meant telling your team to adopt a new way of working. Usage for a product like that depends on an internal champion, and it can stall the moment you stop helping that champion even when the users who got there would be devastated to lose it. I'd run the test on the champions: do they keep spreading it to the next team without you in the room?

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Another cheap test is to read your feature requests for what they say about how people see the product. In Coda's early days most people asked why the world needed a new doc, and the requests I got were things like "Word is missing this one font I really want." Those people were measuring us against Word, so their enthusiasm told me nothing about fit for what we were actually building. The requests shifted once people started using the parts of Coda that only Coda had. I describe the arc as joke, then threat, then obvious, and at YouTube it took three or four years of making the business work before we moved from joke to threat.

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Ivan ZhaoFounder of Notion

Shishir's call to skip Uber's 30-fix list points at something I learned the hard way: you can lose fit after you have it, and the damage comes from the same kind of overfitting.

In 2022 and 2023 we got lost for about a year and a half. We were optimizing for competition and revenue, and we shipped features that didn't match the values that brought people to Notion in the first place. We call Notion Lego for software, and in that period we added pieces that weren't Lego. The people a product attracts share its values, so when you add something that goes against them, the user base pushes it back out. 2024 was the year we got back to shipping good work at good velocity, and we are still cleaning up parts of that period.

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So my test applies after the fact: when you add something new, do your best users adopt it the way they adopted the core? If they ignore it or work around it, the growth you're seeing may still be riding on the old fit while the new work erodes it.

Horizontal products add one more trap. A vertical product has a natural boundary: one or two buyers and a clear market. Notion's market is all software, so it has no boundary, and we had to draw one ourselves through positioning and go-to-market. That means "do we have fit?" only has an answer inside a segment you've named. For a long time I stayed in the pure building-block mindset and resisted packaging Notion as solutions for specific teams. I wish we had moved upmarket into B2B earlier.

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Reid's split between initial fit and scale fit looks like this from inside a horizontal company. Our early fit came from individuals and small teams who built their own setups. Winning large companies meant naming a segment and testing fit there again, the same way Shishir's YouTube team found that fit with one audience said nothing about older audiences.

Reid HoffmanCo-founder of LinkedIn, Inflection AI and Manas AI. Partner at Greylock

Shishir is right that the stop-pushing test needs a fix, and network products need their own version of it. Before critical mass, LinkedIn never coasted. We got to critical mass by opening up signups, and then we kept building new ways to engage and re-engage people through social interactions, such as showing each new member the people from their own network who were already there.

Apple Podcasts If we had stopped work for a month in that first stretch, usage would have dipped, and that dip wouldn't have meant we lacked fit.

Shishir moves the test onto the champions. I'd split founder effort into two kinds. Spend, press, and personal outreach buy traction, and Emmett's Kiko story shows what happens when that effort stops. Work that improves the loop is different: invite flows, network density, the "who else is here?" moment. For a network product, the test is whether each unit of loop work pays back more as the network fills in. When a new member who finds five people they know comes back more often than one who finds a single contact, the density is doing the work, and that is the early sign of fit. When the return on effort stays flat as you add users, you are buying growth.

Shishir MehrotraCEO of Superhuman (formerly Grammarly). Co-founder of Coda and former Chief Product Officer of YouTube

Ivan's point about naming the segment has a counterpart for all-in-one products: you also have to name how much of the product a user has actually seen. When Noam's team left, Coda was mostly tables, the structured database at the core. There was no document, no forms or layouts, nothing like applications or presentations yet. Because Coda promises to replace many familiar tools, it has to clear a minimum bar on every surface before a fit signal means anything. So I kept Uber's list and checked it often, and it took us the next two or three years to work through it while we built out the rest.

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The test I'd take from that period comes from one of Coda's four company values, "right versus familiar." When users push back on a design, decide first whether you deliberately chose to do it differently. If you did, the complaint may only mean the design is unfamiliar, and the answer is to teach people how it works. If you didn't, it is real friction. Founders who treat every complaint as friction end up rebuilding the incumbent, and founders who treat every complaint as unfamiliarity miss the signal that the product is wrong.

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Ivan ZhaoFounder of Notion

Reid's loop test works for Notion too, but the loop runs through a person instead of a network. Every person who works somewhere also has a personal life, and most knowledge workers keep notes somewhere. Our loop was someone using Notion for their own life, then bringing it to work, then their coworkers picking it up there. That is why we kept the consumer experience great even while selling to companies, and why we made Notion free for students and educators. At that stage we weren't going after revenue. We wanted ubiquity, because a tool like Notion gets more useful as more people already know it.

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So for a single-player-first product, the test I'd propose is this: how many new team workspaces start with someone who was already a personal user? If teams mostly arrive through sales, you are pushing. If they arrive because one person carried it in, the loop is working, and Shishir's champion test is the same thing seen from inside the company.

I'll be honest about measurement. When people asked how we measured the effect of community on enterprise deals, the answer was that it's hard and we were still figuring it out. One signal we couldn't fake: most of our users and customers were outside the US, in places we hadn't marketed to.

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Ben SilbermannFounder and Chairman of Pinterest. Founder of How We Feel

Reid's split between effort that buys traction and effort that feeds the loop matches what we did. Pin It Forward was invite-only, and you earned more invites by getting friends to create pinboards, so the bloggers we recruited brought readers who then had a reason to bring their own friends.

forbes Me standing in the Apple Store changing the computers to show Pinterest was the other kind of effort.

YouTube

My one hesitation is about Emmett's pre-set threshold. Nobody at Pinterest was drawing a salary, the perk for the first two engineers was "bring your own computer," and I didn't expect a million users in twelve months. Low expectations and low burn let a small, loyal group count as evidence instead of a miss against a plan.

YouTube A topline growth bar set in our first year would likely have told us to stop. If I set a bar in advance, I'd set it on depth: how many people write in when it breaks, and how often they come back.

I'd also look at who the early users are. In 2010 the hot products were real-time text streams, and many investors passed on us.

forbes Our fans were people I grew up with, planning homes and meals. If we had been dogmatic about wanting cool Silicon Valley people to like it, we probably wouldn't have built the service we built.

YouTube When people outside the tech circle use a product for something ordinary in their week, I take that more seriously than enthusiasm from early adopters.

To learn what people meant versus what they said, I sat in coffee shops and asked strangers to pin something. Right before they tapped a button I'd ask what they expected to see, and right after, whether that's what happened. Every mismatch went on our list.

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Emmett ShearCEO of Softmax. Founder of Justin.tv and former CEO of Twitch

Ben's objection to my pre-set bar is fair, and it comes down to what the bar is for. Ours was a tool for choosing between two projects. Justin.tv was funding both gaming and Socialcam on three months of runway, and without a number we would have kept debating.

Spotify Gaming also already had real usage inside Justin.tv, so measuring growth made sense. For a new product with no salaries to cover, I'd take Ben's version and set the bar on depth. What matters is that you write the number down before you look at the data. Reid's return-on-effort test also fits Twitch better than my first line did, because we never stopped pushing. We called the roughly 200 game streamers one by one and offered the larger ones $1,000 to switch services.

Apple Podcasts Each call paid back more than the last because every streamer we signed brought their audience.

Spotify

Shishir's feature-request test gets sharper with one follow-up question. When I interviewed streamers, they would say "build me this big red button." My answer was: great, I built you the button, why is your life better now? The second answer was always the real thing.

Spotify Users at the traction stage can list features they want. Users with fit describe what changes in their lives, and those answers converge. If twenty streamers ask for twenty different buttons and all of them mean "so I can make this my living," you've found the market.

Convergence also answers Ivan's segment problem. You don't have to name the segment in advance. It's the group whose second answers match. Kiko never got that far. We had good instincts on how to design a calendar and no instincts about finding an actual customer, so we had nobody to ask the follow-up question.

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Shishir MehrotraCEO of Superhuman (formerly Grammarly). Co-founder of Coda and former Chief Product Officer of YouTube

Ben's suggestion to set the advance bar on depth fits with Ivan's arrival test, and a disappointment score can measure both. Split survey respondents by how they arrived: people a champion brought in, and people who came through sales or a launch. Then ask each group how disappointed they would be if they lost the product. If only the champions say "devastated," you have one enthusiastic person per account and the rest is traction. If the people they brought in answer the same way, the fit has carried over to them. At Coda, the "devastated" answers came from customers who had already changed how their team works, and that is why our churn stayed very low even while NPS lagged.

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Reid HoffmanCo-founder of LinkedIn, Inflection AI and Manas AI. Partner at Greylock

Shishir's "right versus familiar" split is the call we faced at LinkedIn, and I'd make it measurable. Early reactions ran from neutral to negative. Our bet was that we would build something valuable to the vast majority of professionals, and that people's views would convert once they saw it in action.

Apple Podcasts That bet makes a prediction you can check. Track the skeptics as a cohort, meaning the people who signed up after a lukewarm invite or who told you they didn't see the point. If their usage rises after a few weeks of real exposure, the pushback was about unfamiliarity. If it stays flat while enthusiasts keep using the product, the enthusiasts are a niche, and you have traction inside that niche only.

The bet also needs a stopping rule, because conviction without one is just stubbornness. Ben and Emmett have now agreed on a depth bar written down in advance. The skeptic cohort is a good place to set it: before you look at the data, decide how much of that cohort you expect to convert and by when. A topline bar would probably have told Pinterest and early Airbnb to quit. When I meet founders, I push on their vision to see whether they are learners as well as persistent.

Spotify The ones who end up with real fit can tell you in advance which result would make them change their minds.

Ivan ZhaoFounder of Notion

I agree with Ben that low burn changes which signals you can afford to trust. Simon and I are both programmer-designers, and I could do marketing and brand, so we kept Notion very small and grew headcount slowly. Because we burned so little, we became profitable fast. Once you're profitable, you don't spend the next 18 to 24 months raising money, so you can focus on building.

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Low burn also keeps the read on fit clean. When we were a team of 25, about 99% of our growth came from word of mouth, and I said openly that we were at a baby stage on marketing and sales.

Spotify With almost no spend, nearly every new user came because someone told them about Notion, so we could read the growth as pull. A company that raises a big round early and buys distribution loses that clarity, because spend now produces some of the growth.

So before running Emmett's threshold or Reid's stop-pushing test, check how much of your growth you paid for. If the answer is close to zero and people keep arriving, you are already running the stop-pushing test every day.

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Frequently Asked Questions

What are the signs of product-market fit?
A small group who would be upset to lose the product, then growth you can explain, from users who look like the market you want to win. Fit rarely arrives as one moment.
What should you do when product-market fit isn't coming?
Make the next attempt a bounded test with a target and a deadline the cash can cover, check that the test was fair, and change course on a miss. With no promising group or runway left, return the capital.
How do you pressure-test a startup idea?
Set a behavior bar before the team gets attached, put an early version into someone's real work, and write down the ways the company could fail even if early users love it.
When should a founder drop an idea?
When people who have the problem can find the product, try the full version and still don't come back. Low signups and investor doubt alone are weak reasons to quit.
How do founders find a problem worth solving?
Start from a problem you will care about for years, find people whose behavior shows they have it too, and know why you can make progress where others see risk.
What should a first-time founder do first?
Start, choose a cofounder with care and get something in front of strangers early. The group splits on whether research or a rough product comes first.
How do you size a market before building?
Start from a behavior people already have, ask what has kept anyone from winning the market, and let growth or repeat use tell you its size.
How does a startup stand out in a crowded market?
Pick one experience people return to, start with the people whose choice brings everyone else, and check whether users come back for a second job.
Where can an AI startup win against the model companies?
Around what takes sustained work to copy: a group's accumulated taste, a team's workflow, trust between people or a creator's audience.
How do you validate an idea with customers?
Go out to learn, ship one thing worth a stranger's time, watch who comes back on their own and then ask them why.
How do you choose a consumer products category?
Look for a need people already recognize, evidence that they act on it, and unit economics that work across the whole purchase cycle.

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Frequently Asked Questions

Telling early traction apart from product-market fit

Founders

Frequently Asked Questions

What are the signs of product-market fit?

What should you do when product-market fit isn't coming?

How do you pressure-test a startup idea?

When should a founder drop an idea?

How do founders find a problem worth solving?

What should a first-time founder do first?

How do you size a market before building?

How does a startup stand out in a crowded market?

Where can an AI startup win against the model companies?

How do you validate an idea with customers?

How do you choose a consumer products category?

Your ideas, super powered.

Telling early traction apart from product-market fit

Founders

Frequently Asked Questions

What are the signs of product-market fit?

What should you do when product-market fit isn't coming?

How do you pressure-test a startup idea?

When should a founder drop an idea?

How do founders find a problem worth solving?

What should a first-time founder do first?

How do you size a market before building?

How does a startup stand out in a crowded market?

Where can an AI startup win against the model companies?

How do you validate an idea with customers?

How do you choose a consumer products category?

Your ideas, super powered.