How to quantify product market fit

Product market fit has been entrenched in tech lexicon for a few years now. Marc Andreesen has been credited with bringing the term into mainstream lexicon in 2007 (read his original post here).

“Being in a good market with a product that can satisfy that market” — Marc Andreesen

Let’s put the ‘good market’ part of the definition to the side for now. Having lots of potential customers has more to do with the VC investibility of a business rather than the current usage of the term — how well a product satisfies the market.

The whole point of achieving product market fit is that it allows a business to enter its growth phase. If you know a customer will like what you’re selling, you can move on to getting it in front of as many customers as possible.

Passing the product market fit phase can be measured by a singular metric — repeat usage.

Repeat usage

Repeat usage can easily be defined as the number of times a customer uses your product over a given time period. To measure, take all the users who used your product for the first time in a given month, and track how many users came back in subsequent months. You want to know how many came back and how long it took them to do so.

For subscription businesses, it’s defined as retention or churn and you would track a cohort over at least 6 months to see what % are still using your product. Businesses selling consumables define it as returning customers and might simply measure the % that made a repeat purchase in 6 months.

There are 2 common mistakes businesses make when measuring repeat usage.

Firstly, they measure each cohort by revenue — this regularly leaves your data distorted by power users. Measure distinct customers.

Secondly, businesses measure the % of monthly revenue that has come from returning customers — this metric is heavily affected by your rate of customer acquisition and cohorts get mixed together over time. Measure each cohort separately.

Why is the growth phase dependent on repeat usage?

Let’s take a monthly subscription business as an example (the example can also easily be applied to businesses with high volume, returning customers such as media sites).

Early stage businesses often see revenues like this:

Growth is consistent, looking at this graph everything seems fine. What’s actually happening under the hood, are 2 things:

• Customer acquisition is increasing by 10% each month — the benefits of marketing, SEO improvements, increased sales staff etc.
• 70% of each cohort of new users has been retained in each subsequent month — typically, businesses have aggressive churn in early months as customers try, then discard the product; this tapers off over time.

Here’s that same graph split by cohort:

It’s becoming more apparent that growth is being driven by new monthly users.

Now if we extrapolate this trend, but new monthly users don’t increase after June, we get a clear plateau in monthly users:

However, if that same business had healthier levels of churn (eg. the same levels of aggressive churn in early months but keep, at a minimum, 40% of the cohort) we get the below graph:

40% long term retention gives us 50% more users within 10 months compared to no floor for retention. And the business only needs approximately 50 new users in October to match September’s monthly users.

Going into a growth phase, a business needs to know that the customers it acquires are going to be repeat users. It amplifies the growth phase giving businesses ‘hockey stick growth’.

When a business goes past the growth phase and reaches its mature phase, the rate of acquisition of new users slows down. Without a repetitive base, monthly active users will steadily decline, undoing all the successes of the growth phase.

Back to product delivery

Put simply, repeat usage is proof that the product was delivered well enough the first time that the customer is willing to pay for it again. Implicitly, you have offered, sold and delivered your product offering; to a matching customer niche; and all the supporting parts of your product (customer onboarding, customer support, post-purchase communications, etc.) have performed well enough to retain the customer.

One of my colleagues has described the situation as:

If proof of concept is Minimum Viable Product, then product market fit is Minimum Lovable Product.

So before you shift the majority of a company’s resources towards customer acquisition, ask yourself, what is the chance that each customer I acquire is going to stay?

PS: I recommend reading First Round Capital’s interview with Brian Rothenberg (VP Growth and Acquisition Marketing at Eventbrite) — his comments on ‘scaling yourself out of product market fit’ brought my attention to this phenomenon and helped shape my opinions.