Product market fit is not reached at a defined point. Brian Balfour suggests thinking of it as a “series of tests and checkpoints that increase in difficulty, but also decisiveness.” It’s important to know where you are on this path before switching to a focus on growth.
Below are the checkpoints and to the left are the types of tests to conduct to see where you are the PMF path.
Leading indicator surveys
Net Promoter Score (NPS)
Sean Ellis’ “Must-have” Score
Leading engagement data
Acquisition, Activation, Engagement
Measures the % of active users over time by cohort
All three at once
Net Promoter Score is a method to measure customer happiness that has been used by many companies as an indicator of growth. NPS is calculated based on responses to a single question:
How likely is it that you would recommend our company/product/service to a friend or colleague?
The scoring for this answer is most often based on a 0 to 10 scale.
Scores of 9 to 10 are called Promoters, and are likely to exhibit value-creating behaviors
0 to 6 are labeled Detractors, and they are likely to exhibit the value-detracting behaviors.
7 and 8 are labeled Passives, and their behavior falls in the middle of Promoters and Detractors.
NPS is calculated by subtracting the percentage of customers who are Detractors from the percentage of customers who are Promoters.
The Product Market Fit Survey
Sean Ellis’ of Survey.io, developed the Product Market Fit Survey, also known as the “Must-Have” score, in collaboration with KISSmetrics. It gives you an objective metric that removes emotion from the scaling decision while also giving you other important qualitative information. The key question on the survey is:
How would you feel if you could no longer use [product]?
Not disappointed (it isn’t really that useful)
N/A – I no longer use [product]
If you find that over 40 percent of your users would be “very disappointed” without your product, there is a great chance you can build sustainable, scalable customer acquisition growth. This 40 percent benchmark was determined by comparing results across hundreds of startups. Those that were above 40 percent are generally able to sustainably scale their businesses; those significantly below 40 percent always seem to struggle.
Read How to Measure Product Market Fit with Survey.io, by VentureHacks
The “must-have” score tells you what people say they would do (i.e., be very disappointed if the product disappeared). Now you need data about what they are actually doing. Leading indicator engagement data (typically at a small scale) tells you whether the user is getting meaningful value out of the product. This data must reflect events or actions, not views or downloads, aligned the core purpose of the product.
At this stage you are no longer examining individual interactions but looking at statistical results from a large group.
Metrics to consider
Retained engagement - does a high proportion of new customers start using your product and then never stop? Does your retention curve flatten to a solid percent of users?
Percent of users driving revenue - Of your total users, what percent are converting to provide you with revenue? If you’re solving a worthwhile problem, people will pay.
Organic growth - Are you seeing growth happen organically by happy users telling everyone who will listen about it? Is the value proposition so strong that people convert with little to no effort, and are your target customers so in need of a solution that they seek you out in droves?
7.5 Engagement - customer research tips
Are you tailoring to the right market segments? Do different customer groups/segments behave differently?
Can customers find out about your offering? What about the time and effort required to find and use your product?
Are you aware of specific language, ethnic, status, age, gender, or religious nuances that may pose barriers to enrollment?
Are there hidden costs or stressors to gather the required documentation to sign up? Are there ways you could incentivize first use? Is your enrollment process too time-consuming?
Who are your new customers? Through which channels did they come to purchase your products?
What is your cost to acquire new customers?
How can you improve the acquisition process?
What are preferred transaction and servicing channels for customers?
Do customers read or respond to various communications?
Which customers use multiple products and features?
How well do you understand the context in which customers use your products and services?
What incentives do you have in place to increase use? How may the context be changing?
Are there key pain points customers face that you may not be aware of?
What incentives do you have in place to increase uptake?
Who has the most influence on decision-making behaviors of your customers?
Do you know why this could be happening?
Read more1. Make your pirate metrics actionable, by Amplitude
2. The Ultimate Guide to Customer Acquisition (Kissmetrics)
Retention is a key metric for PMF, and is measured through a retention rates and tracked with a retention curve. A retention curve plots the percentage of returning users on the Y axis and time since first use on the X axis.
The curve shows the percentage of customers active over time for each cohort, a group of customers that share a common characteristic, such month of purchase
You can expect to see an “initial drop off” of customers who try it and don’t come back
If the curve becomes flat at a certain value, that is the percentage of customers retained from that cohort
The stronger the product-market-fit, the lower the initial drop and the higher the percentage of customers who try the product and continue to use it in the long run
Consider what/who is the retained market, and how big the percentage is? Identify key characteristics of those retained vs those who left.
How do I compare curves across cohorts?
First, plot the raw data for cohorts in a matrix with the cohorts as columns and time as rows.
Next, calculate the retention rate for each cell, the number of active users (for that cohort and timeframe) divided by the initial number of customers in the cohort.
Finally, plot the data points on a line graph as in the example below. If PMF is improving, the cohort retention curves should move up and become flatter over time.
What if I experience problems with retention?
Answer the questions below to determine where you might retain customers and where they churn.
What are the biggest technical barriers to ongoing use? Does the customer understand the product value ? Where can you respond to customers’ changing needs through better, more consistent dialogue?
Trust and loyalty - Are you giving them sufficient choice, respect, and control drivers?
How actively do customers use your products?
What are the inactivity or dormancy rates?
Are you getting significant referral business?
Which customers use a service once and then never use it again?
Which customer/ customer groups are terminating? Why are they terminating?
Refer to Mastering Retention, by Amplitude
“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.” - Marc Andreessen
Metrics from Product Market Fit, by Marty Cagan (SVPG)
For a B2B product, I consider the product to have demonstrated Product Market Fit once you have achieved a threshold number (I advocate at least 6) of live, referenceable customers for a given vertical market.
For platform products, the idea is similar but we’re looking for a threshold number of applications to have been successfully built and deployed using your API or platform services.
For consumer products, especially those that are free services, we’re looking for an indicator of serious engagement. I like Sean Ellis’ 40% benchmark but we often need to craft the test for Product Market Fit based on the unique situation
Common product market fit challenges
Not separating the problem from solution, or jumping into the solution too quickly before you really understand the customer and their pain points.
Starting with a technology and focusing on how you're going to sell it. Start with the customer experience and work backwards to the technology.
Being too responsive to a few MVP customers. The first people who use the product are likely to be non-representative of the larger market, so you need to know what the problem is you're trying to solve, who you're designing a solution for, and what your vision is. Blindly following customer feedback just because it's coming in can be risky.
Fooling yourself into believing you've reached PMF by only focusing on the user groups that are responding well to early versions of the product.
Declaring victory too quickly, or thinking revenue traction is PMF. It's tempting to declare that PMF is done and spend a lot of money trying to scale when the product is not yet ready.