Houston, do we have Product/Market fit?

Before growth can happen and new markets can be conquered, they need to be discovered and understood. Generally, we would refer to this as product/market fit, that stage of your venture when your product satisfies its first strong market demand and things can be taken to the next level, facilitating growth.

But how do you know when all the necessary steps have been taken? What methods of obtaining data are there? Are there any possible mistakes in estimating the ability of your product to satisfy markets that can be avoided?

In short, how do we know when product/market fit has been achieved?

Perhaps it can help to break the product/market fit analysis down into smaller steps. Can we come up with a well-planned and concise approach to finding a market for your product or business in four simple steps?

Let’s break it down:

1. The Leading Indicator Survey
2. Engagement Data
3. Retention
4. Fit

Let’s have a closer look at each individual point now.

The Leading Indicator Survey

Surveys can be good indicators of how your product meets the demands of your market. The following two types of surveys are undoubtedly the most widespread ways to test your venture’s product/market fit at the earliest stage.

1. Product/Market Fit Survey
2. The NPS (Net Promoter Score)

The Product/Market Fit Survey is the simplest and most straightforward method to get an idea of your product’s performance, created by Sean Ellis. In a nutshell, the survey asks how customers would feel if your product was no longer around. The 40% Rule states that a successful result would be if at least 40% of those questioned responded with “very disappointed”, which would imply a good product/market fit.

The NPS — a customer loyalty metric — on the other hand, measures your users’ happiness/loyalty directly and is often used to predict growth.

There is a reason why surveys are only the first key to product/market fit. While it is undoubtedly useful to get an understanding of what customers are thinking, in the end it is actions/data that counts. Thus, do take advantage of the simplicity and quick turnout of surveys, but take them with a grain of salt, as they can generate false promises and rarely show how big a market actually is. Nevertheless, it’s an effective tool for estimating interest in whatever you have to offer.

Engagement Data

Now that we know what users think about your product, let’s have a look at what they are actually doing with it. We’ll start with having a glance at actual engagement data to see how customers interact with whatever it is that you have to offer.

For example, if you’re selling an eBook, then your engagement data would reflect the number of purchases and downloads, whereas if you’re running a music streaming service you might want to look at song plays, songs shared per day, subscriptions sold, and so on. Each business has its own set of engagement data and need to define and monitor them on regular basis.

Whilst certainly useful, this data can, again, be a bit limited. Engagement data tends to be restricted to a smaller scale or timeframe. For example, you could be looking at how often a song is played per day.

Furthermore, it is important to remain objective when looking at this data, as it can easily distort (or encourage you to distort) the reality of the performance of your product. You might be selling a lot of subscriptions on some days, but not on others — focusing exclusively on the successful days would provide you with an unrealistic estimate of your performance.

Retention

Key number three, retention, will start to paint a much more realistic picture of how close we are getting to product/market fit. It can easily be visualized graphically in a curve by plotting the percentage of retained customers over time.

Two different situations are plotted on the graph above. Situation A flattens, indicating a product/market fit for a given market, whereas situation B is in decline and does therefore not indicate a product/market fit yet.

This is a Retention Curve — also check out retention cohorts and other visualizations. Intercom has a great article about this.

Keep in mind that certain types of businesses work with high retention and others with low. Also retention could be low on daily and high on monthly depending on the usage of your product.

Now that we have established that there is a market on Situation A, the next step will be to define it by asking key questions about it. Who exactly is that market? What are their demographics? How big is it? You do this by identifying the traits of those that retained and those that did not. Doing this will help you realize what you’re doing right and where improvement might be needed.

One way to tell the difference between those who stick around and those who don’t is by splitting the retention curve up into segments. You can split it into key demographics like age, sex, location, and so on, or even time. This will help you realize what recurring users have in common, allowing you to put even more emphasis on that particular segment. Which exact segments you choose of course depends entirely on the nature of your business or your product.

Alternatively, you could run a few qualitative surveys to narrow down the difference between the retained and the not retained.

Retention data and curves go hand in hand with surveys, but provide much more specific and relevant data to work with. On the other hand, it can take quite a bit of time to collect all the engagement data, especially compared to the simpler surveys. That, however, is only a small sacrifice compared to the benefits. If user acquisition is important — retention is paramount.

Fit

Here’s the ideal situation: Take, for example, a music streaming service. Imagine that, word of mouth has taken off and business is booming. Say we got 100,000 daily active users, listening to 1,000,000 songs per day and we have 300,000 subscriptions so far. If that isn’t product/market fit, then I don’t know what is.

We can isolate three key points here:

1. Top-Line Growth (i.e. 300k subscriptions)
2. Retention (i.e. 50% of users were active on daily basis)
3. Significant Usage (i.e. besides coming back to use our product, they interacted with it significantly by playing and sharing songs)

A result like this needs a lot of work and nurturing and rarely happens overnight as it happens with some incredibly successful apps and products.

The key is to use all the tools you have at the relevant time. There is no point in launching a product and just hoping that it will take off like the example above.

If you use all your tools at the right time, you will see what’s best about your strategy and thus be able to multiply it. Experiment. See what works, and what doesn’t. Retain what is good, eliminate what’s bad, move on, repeat.

Why repeat?

Product/Market fit never ends, as markets move, your product needs to move as well. The continued realization of value by your users is key to customer retention. It’s about ensuring they continue to get value from their use of your service so they’ll actually keep using it. A great UX and fancy UI only goes so far.