In Search of a Better way to Measure Product/Market fit "Product people - Product managers, product designers, UX designers, UX researchers, Business analysts, developers, makers & entrepreneurs October 10 2020 True Kpis, marc andreessen, Product management, Product Management Skills, Product/Market Fit, sean ellis, Mind the Product Mind the Product Ltd 1339 Product Management 5.356

In Search of a Better way to Measure Product/Market fit


Tech billionaire Marc Andreessen has been credited with bringing the term “product/market fit” into the mainstream lexicon in 2007. During my dealings with investors and product veterans, I’ve often heard that you can always feel when product/market fit is happening.

Andreessen too gives us a vivid illustration of what product/market fit feels like in his post:
You can always feel when product/market fit isn’t happening. The customers aren’t quite getting value out of the product, word of mouth isn’t spreading, usage isn’t growing that fast, press reviews are kind of “blah”, the sales cycle takes too long, and lots of deals never close. And you can always feel product/market fit when it’s happening. The customers are buying the product just as fast as you can make it – or usage is growing just as fast as you can add more servers. Money from customers is piling up in your company checking account. You’re hiring sales and customer support staff as fast as you can. Reporters are calling because they’ve heard about your hot new thing and they want to talk to you about it.”

While I agree that you can always feel whether product/market fit is happening or not, I have always wondered about measuring product/market fit. If you could measure product/market fit, then maybe you could optimize it. And then maybe you could systematically increase product/market fit until you achieved it.

Usual Benchmarks to Measure Product/Market Fit

This led me to research how others were measuring product/market fit. Some of the benchmarks and KPIs that I found people associating with product/market fit include:

1. Do your Customers Recommend you to Friends?

This includes metrics like Net Promoter Score (NPS)

2. How Many Customers Leave and How Soon?

Some define product/market fit based on churn and user retention

3. Repeat Usage and Engagement

Others validate product/market fit based on repeat usage which can easily be defined as the number of times a customer uses your product over a given time period.

These product/market fit definitions, though vivid and compelling, are lagging indicators. These are great to validate that you have product/market fit, but not so much as to guide you through the mazes as you go about establishing product/market fit.

Sean Ellis’ Approach to Product/Market Fit

The approach I found closest to a leading indicator was proposed by Sean Ellis of “growth hacker” fame. Ellis says product/market fit can be boiled down to a simple survey question: “How would you feel if you could no longer use [product]?” The users who respond “Very Disappointed” (vs. Somewhat Disappointed, Not Disappointed, or Don’t Use It Anymore) represent your target market (despite the fact that lots of other people gave it a try).

After benchmarking nearly 100 startups in his customer development survey, Ellis found the magic number to be 40% – that is companies that struggled to find growth almost always had under 40% of users respond “very disappointed”.  Companies with strong traction almost always exceeded that threshold.

A helpful example comes from Hiten Shah, who posed Ellis’ question to 731 Slack users in a 2015 open research project: 51% of these users responded that they would be very disappointed without Slack, revealing that the product had indeed reached product/market fit when it had around half a million paying users. Today, given Slack’s legendary success story, this isn’t too surprising.

Finding Product/Market Fit in Practice

I put this into practice in my previous venture, and it helped us to navigate our way to the right market and product, and enjoy the sublime feeling of “product/market fit”.

We were working on a SaaS product, and a few months after our launch, we were doing pilots with about 100 businesses (I have rounded the numbers for easier understanding). We contacted these customers with Sean Ellis’ survey questions. Close to 18% said they’d be Very Disappointed if the product stopped working. (A small caveat here is that it is important to mention that the intention of the question is to understand your feelings about the product. You do not want to convey to customers that you are killing the product.)

It was clear that we had not reached product/market fit. However, the way we looked at it was that these 18 teams represented a snapshot of our target market. We deep-dived into the personas of these 18 businesses, and discovered interesting insights – for example many of these teams belonged to a particular function.

We were also mindful that there is always a difference between what people will say and what people will actually do. So we tried to harmonise intent and action by supporting survey responses with data about what respondents were doing, and often getting in touch with them for a more detailed chat. This allowed us to learn more about the motivations behind their choices and to analyse their in-app usage data to try and identify problem areas and sticking points. It helped us to understand the core product functionality that our customers absolutely must have, while testing our assumptions and learning the characteristics of our market segment.

These insights led us to almost a mini pivot of our product. We removed features that didn’t make sense to these personas and added to our roadmap those that made sense to them. We revamped our marketing accordingly.

We did the survey again about six months later. And this time the number was almost close to 25%. Still not product/market fit, but at least we knew we were going in the right direction.

The process helped us focus on that core group of dedicated users to understand what they love and what would make the product better, and also to address what was holding back the on-the-fence users who also fell into our identified personas.

By surveying our users, segmenting our supporters, learning what users loved and what held them back, and then dividing a roadmap between the two, we found a methodology to increase product/market fit. And we were not done — the product/market fit score is something that we continued to track, especially as we expanded the target audiences to new segments, especially those who had indicated “Somewhat Disappointed” in the surveys.

A note of caution here: Ellis’ testing revealed that a score of 40% or higher correlates with product/market fit – but correlation does not equal causation. A positive score on this test strongly indicates product/market fit, but it doesn’t guarantee it. The market for your product is going to be nuanced and complicated, and it’s entirely possible for a product to excel at this test, only to crash and burn during the attempt to scale.


Product/market fit rarely happens holistically or by accident. Companies constantly tweak their products, their target markets, and their sales and marketing tactics to hone in on the perfect combination of needs, demands, and how to satisfy them. As a product leader, this ultimately is informed—if not driven—by your knowledge, research, and efforts.

Like most things, it’s also an ever-evolving journey to uncover new demands, modify the market you’re targeting, and adjust the messaging and communication tactics your company uses to reach new buyers. Reassessing your product/market fit and relying on benchmarks such as the Rule of 40 ensure your company doesn’t become complacent and is continually adapting to reach your growth targets and defend your position from competitors.

Finally, as LinkedIn founder Reid Hoffman notes: “Product/market fit requires you to figure out the earliest tells.” Using an analogy to poker is appropriate since the process of finding product/market fit is an art rather than a science. Product/market fit emerges from experiments conducted by the entrepreneurs. Through a series of build-measure-learn iterations, it is discovered and developed during a process rather than a single Eureka moment. A-ha moments of inspiration do happen, but product/market fit is not created that way.