How to find product-market fit 🧭
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Should I care about PMF? 📈
In startup culture, a huge amount of emphasis is placed on product market fit, so much so that Marc Andreessen (of a16z fame) once famously said:
“Do whatever is required to get to product/market fit. Including changing out people, rewriting your product, moving into a different market, telling customers no when you don’t want to, telling customers yes when you don’t want to, raising that fourth round of highly dilutive venture capital—whatever is required.”
So obviously PMF is important, but the question remains, what actually is product market fit, how can you quantify it, and how can you work towards it?
How do I know if I have PMF? 🤔
Lenny Rachitsky actually has a great twitter thread, where he explains that product market fit isn’t a necessarily a binary thing. It can be better described as a spectrum of confidence
...as a spectrum of confidence that changes over time. On one end of the spectrum, you are 100% confident you have PMF. On the other, you definitely do not. The more signs of PMF you see, and the more intense each sign, the more confidence you should have.
— Lenny Rachitsky (@lennysan)
Aug 9, 2022
Because of this spectrum, identifying whether a product has achieved PMF can be challenging. This is where indicators come in handy. In this thread, Lenny suggests some key indicators for PMF that can help startups evaluate the success of their product:
1️⃣ Retention Curves
There are a few ways to analyze retention, but in this case the most useful way is to look at cohort based retention. This lets you see how user retention changes with each change to the product. If retention gets better, great you are moving towards PMF. If not, consider reverting changes or altering the new feature being tested.
The key thing to look for is where the curve flattens out, the higher the better, but the level at which it indicates PMF varies slightly depending on your product.
For a social product a 25% retention indicates PMF, for a marketplace a 30% retention indicates PMF and for a consumer subscription a 40% retention indicates PMF.
2️⃣ Explosive growth through word of mouth
This is an easy one to understand, the more people that are being referred by word of mouth, the more likely you are to have PMF.
In order to achieve viral growth, your average customer to refer just over one more customer to indicate PMF.
3️⃣ The Sean Ellis survey
You can get a great indication of how close you are to PMF by giving users a survey asking: “How disappointed would you be if you couldn't use our product any more?”
You give the user 3 options: "Very disappointed", "Somewhat Disappointed" and "Not disappointed "
Based on research, Sean identified that the vast majority of startups that have 40% or more people answering “very disappointed”, are very likely to have PMF.
So you know what PMF is, but how do you get there? 🗺️
First round review has a great article about the Superhuman “PMF Engine” which details how they extended the Sean Ellis survey to get to PMF. Here is an overview of the framework they used, which you can easily implement for your own product.
Step 1️⃣ : Workout who to survey
Sean Ellis recommends that to get the best results, you need to survey people who have used the product at least twice in the last two weeks. Superhuman started this process at a time when they had 100-200 people to poll, but you can actually get directionally correct results with only 40 users using this method.
Another thing to consider is you need to know a little about each user. This lets you assign each user a persona, in order to identify which personas are getting the most value out of your product.
Step 2️⃣ : The poll
Superhuman actually extended the Sean Ellis survey in order to get more actionable data from their users, not just an indication of how close they are to PMF. The extended survey is shown below.
Each of these questions allows you to get feedback that directly translates to action items for your product roadmap
Step 3️⃣: Segment
The first thing to do is to look at the answer breakdown for the first question. You should look at the personas that answered “very disappointed”, and then filter the whole dataset to only show these personas. This allows you to instantly get a large part of the way towards the coveted 40%.
To build up a picture of your High Expectation customers, you can look at the list of people who answered “very disappointed” to question one, then analyse their answers to question two. This is useful because happy customers will almost always describe themselves.
Once you have an idea of who your high expectation customers are, this can be a great tool for your ongoing decision making, at every point you should ask: "how does this help my high expectation customer?"
Step 4️⃣: Analyse feedback to convert on the fence customers
In this step you look into what users love about the product, by analysing their responses for question three. A great way to visualise this and analyse the different reasons people like your product, is to convert this into a “word cloud” diagram.
It is important to recognise that you should not include feedback of the users who answered "not disappointed" in the word cloud. They should not influence your product strategy because their feedback may be distracting and not aligned with your product goals. Acting on their feedback may lead you away from achieving product/market fit. The word cloud for superhuman is shown below.
From this you can identify that the primary value users are getting from superhuman is the speed.
Now you know the main value proposition of your product (according to users) you can segment again to identify the users who will be most easy to convert, and use the improvements they suggested in the answers to question four to improve your road map.
To work out what to add to your roadmap, look at the users who answered somewhat disappointed, and filter them down to the ones who recognised your main value prop in their answer to question three.
In the case of superhuman, the somewhat disappointed users who recognised speed as the primary benefit had a lot of suggestions for improvements. These can be put into a word cloud to identify the highest leverage improvements.
By looking at the above word cloud, it is obvious to see that the biggest things holding users back from being fanatics is the lack of mobile app, and the lack of integrations.
Step 5️⃣: Build your roadmap
You only need to do two key things to get closer to PMF according to this framework: double down on your key value proposition, and address what is holding others back. In this case, superhuman built features to make the app even faster, and built a mobile app and integrations to convert the "on the fence" customers.
Step 6️⃣: Rinse and Repeat
If you repeat these steps (being careful to only survey new customers) you should be able to iterate towards PMF. Each time all your focus should be on increasing the hugely important, “very disappointed” figure. It is worth mentioning that even after you hit PMF, tracking this figure is still important, early adopters tend to be more forgiving, but as you grow, more and more users will expect feature parity with your competitors
What do you want to explore next?