Every founder is told the same thing: find product-market fit. It is the milestone that separates a company that survives from one that does not, and the proof most investors want before a Series A.
Ask what it actually is, though, and the answers scatter. Most founders cannot define it, because most have never reached it. The ones who can, successful founders and investors, say that “ you will know it when you feel it.” Product-market fit is described as a vibe: something you sense, but not something you can point to, measure, or set a target for. The problem is, a founder can’t decide what they should build with only eight months of runway left based on vibes.
At a startup, every important decision is expensive and potentially fatal. A seed round buys the company 18 to 24 months of runway. It pivots several times, with each pivot burning months of cash building on a guess that turns out to be wrong. Three or four pivots in, the company runs out of money before it can demonstrate any meaningful traction.
The best minds in the field have circled the challenge of product-market fit for years. Andy Rachleff coined the term and Marc Andreessen made it famous. Steve Blank and Eric Ries built customer development and the lean startup out of it. Several more practitioners and operators have since tried to decrypt the “PMF” black box. Each contribution was valuable, but a unified method for reaching fit systematically remained elusive and, critically, no one figured out how to measure it. That is the gap one founder set out to close.
Yann Goarin is a former product marketer who led more than 20 product launches over a decade at Google and YouTube. He left in 2023 to start Zag Labs, an advisory firm helping early-stage startups go to market. Seeing his clients struggle to find product-market fit, he went looking for answers, sifting through every book, article and podcast on the subject. Yet he couldn’t find a clear definition or a repeatable method to get there. So he pulled together the best of what already existed, and built on top of it to fill in what was missing.
The “PMF System”, Goarin claims, is the first comprehensive and cohesive model that maps the whole path to product-market fit, from idea to scale. It is organized around five core dimensions every company needs to focus on: problem, customer, value, solution, and timing. It shows a founder where they are, where they are going, how to get there, and how to track progress over time.
The path to product-market fit has two phases, each measured by different metrics.
The first phase is problem-solution fit, before a company has users or customers. The goal is to test whether a prospect has a problem pervasive enough they’re willing to pay to solve it. Goarin scores it across three buckets: interest, preference, and purchase intent. Interest can be tracked through response rate to outreach and meeting acceptance rate. Preference can be inferred by asking prospects to compare the new approach against what they use now, or against doing nothing. Purchase intent is the hardest test: whether they’ll commit before they’ve used the product, through a waitlist, a pilot, a signed letter of intent, a deposit. The threshold is concrete: at 40 percent of qualified prospects showing genuine intent to pay, the company has strong signals of problem-solution fit and can build confidently.
The second phase is product-market fit proper, once people are using the product. A new set of instruments takes over: satisfaction, demand, and efficiency. Satisfaction can be tracked through retention, NRR, and the Sean Ellis test, where users respond if they’d be very disappointed to lose the product. Demand can be measured through organic growth, referral rate, and K-factor, in other words, how much new usage comes without paid acquisition. Efficiency is tracked through unit economics: CAC, LTV, the ratio between the two, payback period, and margins. Here too the threshold is tangible: at 40 percent of users saying they’d be very disappointed to lose the product, a company has strong evidence of product-market fit and can start scaling.
What makes this more than just another set of disparate frameworks is that it is one unified and highly practical process. It applies the scientific method to help founders make decisions they would otherwise make on intuition: form the hypothesis, define the experiment, run the test, read the results, decide, repeat.
When Goarin first applied the system to Leanear, a data-security startup stuck in a cycle of pivots, it signed a $750,000 enterprise contract with the defense group Thales within six months. He took fintech startup ZorroFi from initial research to UC Berkeley’s SkyDeck accelerator and a $200,000 pre-seed in under three months. He has since worked with over a dozen startups, and results suggest the system works across industries and stages.
It does not always return good news. Applied honestly, the system sometimes tells a founder the idea does not work. But that is the point. Founders waste years and funding on ideas the evidence rejected long ago, because nothing gives them a reason to pivot or stop. Showing clear, quantifiable evidence that there is no demand pull finally gives them one.
Goarin now wants to spread the PMF System to the accelerators, academic programs, and VC firms that shape startups early on. He just completed a pilot with Techstars LA, where he mentored 10 startups going through the accelerator’s Spring 2025 cohort. He is productizing the system into an agentic solution that supports founders day-to-day. He’s thinking of writing a book. The premise is clear: measuring product-market fit is not only possible, it’s a skill that can be taught and that dramatically improves outcomes for early-stage companies.
While it seems like Yann Goarin has largely solved the mystery of measuring and quantifying product-market fit, something that has eluded Silicon Valley for over twenty years, it remains to be seen how quickly the startup world catches on. It’s worth asking what that could mean at scale. Nine out of ten funded startups fail, some due to bad market conditions, but many because of mistakes founders could have avoided if they’d known what to look for. One wonders how many of those failed startups might have become unicorns with the right guidance.
