Software Growth

PMF (Product-market fit)

Product-market fit means you have built something a real market wants badly enough that demand pulls the product along, and customers keep using and paying for it.

Product-market fit (PMF) describes how well a product satisfies demand in a real market. Stronger fit shows up in customers continuing to use and pay for it, recommending it, and finding lasting value. It develops by degrees and can vary across customer segments. For a founder, the practical question is how much evidence of demand and retention you have before committing more money to growth.

Andreessen's definition

Product/market fit means being in a good market with a product that can satisfy that market.

Marc Andreessen / The Only Thing That Matters (June 25, 2007)

The term was popularized by Marc Andreessen in "The Only Thing That Matters." He describes the feel: customers buy as fast as you can make the product or usage grows as fast as you can add servers, and money piles up in your bank account. He credits Andy Rachleff with the idea that lack of market is the top company-killer, and argues that market matters more than team or product.

It is a good definition and a bad measurement tool. It tells you what it feels like but nothing you can put on a dashboard. The methods below fill that gap.

The Sean Ellis 40% test

Sean Ellis proposed asking users one question: "How would you feel if you could no longer use this product?" If 40% or more answer "very disappointed," that is an encouraging signal of fit, rather than proof on its own. He arrived at this by comparing nearly 100 startups, and those below 40% struggled to gain traction. Practical notes:

  • Survey people who have actually used the core product, not fresh signups. A common guideline is 40 to 50 qualified responses.
  • The question measures dependence, not satisfaction. Polite users say they like things.
  • Rob Walling has described his own product Drip scoring around 43 to 46% on this survey.

Worked example: you survey 50 active users of your scheduling tool. 17 say "very disappointed."

That is under 40%. Look at the 17, find what they have in common, and sharpen the product and marketing toward them.

But ultimately real user behavior is much more important than what users say in a survey.

Sean Ellis / Is Product/Market Fit Hiding in Your User Base? (September 9, 2026)

Ellis now stresses that a strong survey score needs a behavioral check. Survey recently active users who have experienced the core value, then compare retention for that same segment. If the score is high but retention keeps falling, investigate sampling bias, the use case, and the activation event used to start the cohort.

The retention curve

Your retention curve is the best proof.

Brian Balfour / The Never Ending Road To Product Market Fit (December 11, 2013)

The behavioral test is a retention curve. Plot the share of each signup cohort still active over time. If the curve flattens at some level, a group of users has found lasting value. If it keeps sliding toward zero, investigate whether you are measuring the right customer segment and a meaningful starting event. Brian Balfour of Reforge has long advocated reading fit this way. Research by Lenny Rachitsky and Casey Winters suggests six-month retention of around 60% (good) to 80% (great) for SMB and mid-market SaaS, measured as paying companies still active. For consumer SaaS, their 40% to 70% range measures users who started a paid subscription. These are comparison points, not universal PMF thresholds.

Rob Walling's five stages of fit

Product-market fit is a spectrum.

Rob Walling / January 17, 2026

Walling's newer framework names five stages: pre-product-market fit, weak, emerging, strong and mature. The emphasis shifts from discovering a useful product and customer segment toward improving retention, establishing repeatable acquisition, building a team and defending the business. He warns that revenue alone can disguise poor fit, particularly when a few large contracts account for much of it. His stage ranges are operating heuristics, not universal thresholds or a validated scoring system.

Consider a fictional SaaS with $20,000 MRR from two contracts whose users rarely return. Its revenue does not by itself justify scaling. Compare usage and retention across customer cohorts, and investigate whether the product solves a recurring need.

Jason Cohen's view: demand, retention, and critical mass

Jason Cohen, co-founder of WP Engine and author of A Smart Bear, takes a stricter view. In his Product/Market Fit: Experience & Data essay, the turning point is when serving demand becomes the hard part.

If every day it’s a struggle to keep up with demand, it’s working.

Jason Cohen / Product/Market Fit: Experience & Data (November 5, 2023)

He looks for three signals together:

  • A sustained increase in growth. Customer demand becomes easier to attract, and growth continues at a faster pace. His examples often grow linearly at a higher rate; exponential growth is not required.
  • Low customer cancellation. His thresholds are below 3% per month for B2B and 5% for B2C. Strong acquisition cannot indefinitely compensate for customers leaving.
  • Enough scale to trust the signal. He proposes at least $20,000 in monthly revenue or an increase of 200 weekly active users per month. One large customer or a tiny starting base can distort the picture.

These are Cohen's criteria, drawn from his experience and selected company examples. Walling's spectrum leaves room for weaker or emerging fit before this point. For your SaaS, use both perspectives to ask what evidence you have and what still needs to improve.

For small SaaS

This level of retention is not required for product-market fit or to build a sustainable business.

Lenny Rachitsky / What is good retention? (June 9, 2020)

Rachitsky makes this qualification about benchmarks drawn from venture-scale businesses. A smaller SaaS can be sustainable with lower retention if its acquisition costs and margins support it. A few dozen paying customers, repeat usage, and referrals give you evidence to keep testing growth. They do not automatically meet Cohen's stricter definition. Check whether new cohorts retain, customers renew, and acquisition pays back before increasing spending. Fit can differ by segment and weaken over time, so keep measuring.

Sources

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