Using Retention Curves to Understand PMF

Outlining a simple strategy to evaluate feature market fit.

In a previous article, I walked through the importance of retention curves for sustainable growth in a SaaS business. I also touched on the different shapes of those curves and what it can signify in terms of business strength and product-market fit (PMF). As a next step, let's dive into how you can leverage these curves to compare features within your product to feature-level PMF.

The Value of Retention Curves for Product Features

The beauty of retention curves doesn't end with analyzing overall user retention and business strength. Retention curves are also a powerful tool for evaluating the performance of individual features within your product. Why is this important? Discovering which features retain users the best helps you to better understand your users, decide where to invest resources, and choose what to iterate on.

If a feature demonstrates a healthy retention curve, it's safe to say that the feature has found product-market fit. Users consider it valuable and continue to engage with it over time, meaning it effectively solves a problem for them. On the other hand, if a feature's retention curve trends toward zero, it has not yet found its PMF. This doesn't necessarily mean you should discard the feature, but it does indicate that it needs further refinement to bring value to users.

To illustrate this, let's say your product has three key features: A, B, and C. By plotting separate retention curves for each feature, you can see how well they keep users engaged over time. For instance, Feature A might show a 'Healthy Retention Curve' as we defined in our previous article, while Feature B has a 'Slow Bleed' curve, and Feature C resembles the 'Freefalling' pattern. See the image below as an example:

This analysis allows you to identify that Feature A is keeping users engaged and is likely a major value proposition for your product, while Features B and C need improvement. For Feature B with the slow bleed curve, you should work to iteratively test feature improvements aimed at strengthening PMF. For Feature C with the freefalling retention curve, it’s likely this feature is missing the mark and not solving the problem you initially intended. It may be time to rethink your approach to that problem and identify whether there are larger pivots that you could make that may bring your users more value.

Using Retention Curves When Testing New Features

When introducing a new feature, you can use retention curves to help you understand that feature’s performance relative to the existing features in your product. This comparison will help you identify early signs of whether this feature has PMF with your users and is bringing them value.

The great thing about feature-specific retention curves is that they help tune out some of the noise that other engagement metrics have. For example, looking at a feature usage as a percentage of daily active users may understate how much value a particular feature has. A feature that is lower on overall engagement but has a strong retention curve is still a value driver.

Let’s use the Garmin Forerunner’s weightlifting feature as an example. The Forerunner is primarily tailored to runners, but a subset of that user base also likes to do weightlifting workouts. The weightlifting feature is likely used by fewer users relative to some of their other running-oriented features, but that same weightlifting feature could have a strong retention curve because the people that do use it absolutely love it. Even though the majority of the user base may not engage with it, the stickiness of the feature with the group that does could make the difference in preventing them from switching to an Apple Watch or other wearable.


In summary, retention curves can be more granular than just an indicator of overall user engagement and churn. They provide valuable insights into feature performance and feature PMF and can be an asset in strategic decision-making during product development. By diving deeper into these curves, you can optimize features to drive better user retention and, ultimately, sustainable growth for your business.

Remember, every curve tells a story. Listening to these stories will help you make informed decisions that drive your business forward.