Product Market-Fit: anything you don’t know
The goal of any product is to attract as large audience as possible, who agree to spend money on the proposed solution to their problem.
Often, product managers are faced with the following problems: advertisers did the best, pumped out ads, a lot of people come, but something goes wrong and a conversion rate lowers.
There is a main indicator of product success called Product Market-Fit (PMF).
Product Market-Fit describes if a product meets expectations of its target audience; a harmonious combination with the market, which allows to increase conversion rate and revenue.
The concept created in 2007 by Mark Anderson and defined in his blog as: “In a good market, a product can be used to satisfy this market.”
Sean Ellis, the author of ‘Growth Hacking’, sees PMF as the most crucial stage in a development phase of a company.
Dan Olsen, a product management trainer, means by PMF a final product that already meets customer needs and is in pole position in comparison with analogues on the market.
In theory, it’s quite simple: Product Market-Fit is to be reached → business will benefit. The real problem lies in a lack of clarity about exactly how to find it.
How to achieve Product Market-Fit
There are three criteria by which PMF would be measured:
- Product Market-Fit survey by Sean Ellis;
- User engagement / User Retention;
- Unit economics.
Product Market-Fit survey by Sean Ellis
A leading indicator of PMF according to Sean Ellis: ask users, how would they feel if they could no longer use the product, and measure the percent who answer “very disappointed.” There are four possible answers:
1. Very disappointed
2. Somewhat disappointed
3. Not disappointed (it isn’t really that useful)
4. N/A — I no longer use [product]
It is considered that 40% of “very disappointed” people is enough to achieve PMF. This number gets to nowhere: there is no studies have been conducted except empirical way.
After the hype article about Superhuman startup, there is a new type of ‘interview’ conducted by Sean Ellis and GoPractice I use.
The second question will help to understand the audience (users usually describe themselves). The third will help to double-check the product value you promote. The fourth is to collect (random) feedback.
For test purposes, it’s better to ask different users on different cases. I usually ask the following cohorts:
- people who paid;
- people who experienced the core value of product. Most often, this means people that used the product several times.
- people who used the core feature.
There is an interesting approach that measures user engagement. We call it “the product of the product”.
A good example:
- Our product is an image editing and sharing app.
- The main purpose is to help users get professional-like photos in a minute.
- What metric are we tracking? We need to track how many likes a user gets after having edited a photo in the app. Likes is a product of the product.
- The Nord Star metric is the number of photos that get 30% more likes.
Let’s say, your advertising brought you a whole bunch of different people. There is only one way out: to build cohorts and see whose retention is better.
That is, even it is only 5% retention it’s already good. It is much worse if this number is zero. Andrew Chen says that a good retention for B2C products is 25–35%, and the churn rate for B2B is 2–5%.
Brian Balfort gives a very good example of bounce curve.
With a stable, albeit low retention rate, we can already take these users as a base and try to figure out what to fix and how to get more users. History is replete with many examples where growth with poor retention rate was followed by total collapse. Even for big companies like Fab and Homejoy.