What is a Retention Curve?
A retention curve visually illustrates how effectively your app retains users over time. It depicts the percentage of users who continue using your app after a certain action, usually defined by first interactions with your app. This provides valuable insight into user engagement and drop-off patterns.
Understanding the graph
By default, once you have created a report, you will see one retention curve that represents the overall average retention rate for the entire time period selected. You can hover over the curve to see the average retention rate for each day, week, month, or quarter, depending on the return interval you have selected.
💡 Note » When analyzing averages in the retention curve, make sure you use percentages instead of count.
Visualize and compare specific cohorts or segments (groupings) in your retention graph
Select specific cohorts within the table under the start action date column and visualize how their retention compares to each other and the overall average retention.
For instance, you can compare the average retention with retention from a single cohort e.g. users who signed up on the week of August 26th to September 3rd. This helps you easily identify which cohorts have better retention rates and how they compare to the overall retention.
This visual comparison helps you identify trends and anomalies.
You can also group your report by user or device properties and compare the averages among those groups. E.g. by app version or platform.
Cohort Analysis with the Retention Table
The table below the graph is referred to as the retention table. This table offers a more detailed breakdown of your retention data.
The first row of the table outlined in red shows the overall average retention for each day/week/month/quarter, depending on the return interval selected.
- Each row with the blue arrow represents a cohort of users grouped by the start action date.
- Each column represents the time window from the start event, defined by the return interval.
In the example below, we created a retention report to see the retention of users who have signed up on our delivery app and have completed a purchase. We have grouped cohorts by week (represented in rows) and have defined the return interval as each week (represented in columns) as we think users should order weekly on our app. We're looking at retention on a rolling basis.
How to read the table?
- In the < Week 1 bucket, there were 13 users who did "Sign Up" at any point of the week of Aug 26 - Aug 31, and returned to complete a purchase within 7 days.
- In the Week 1 bucket, there were 12 users who did "Sign Up" in the same week of Aug 26 - Aug 31, but returned to complete a purchase 7-14 days after signing up.
💡 Note » Count vs. Percentage: You can switch between count and percentage views in the table.
Retained vs dropped-out users
Hover over any cell to see the number and % of retained users and the same for dropped out users.
Cohort analysis helps you uncover patterns in your retention data. The use of colors in the cohort table helps you quickly identify retention patterns. The blue shading gets darker the higher the retention percentage. It's important to note that the scale is relative to each cohort row.
- For example, if you observe that one cohort has an 80% retention rate while others are much lower, you can investigate what distinguishes that particular cohort. Did you run specific campaigns, launch new features, or target a different audience for that cohort?
In summary, the table view in retention analytics provides a detailed breakdown of user retention over time, with cohort analysis allowing you to identify patterns and compare different groups of users based on their initial actions. Color coding and visual cues make it easier to spot trends and areas for improvement in your product's user retention.
What are the types of retention curves?
- Flattening Curve: Flattening curves indicate that some users who initially engaged with a product continue to find value in it over time. It's important to note that not all flattening curves are the same. The higher the point at which the curve flattens, the better the long-term retention.
- Declining Curve: When a product lacks desirability or presents significant user friction, the retention curve continually declines, eventually dwindling to very few or zero users. In such cases, it becomes crucial to reevaluate and modify the product to offer a more compelling value proposition for a core group of users before expanding to a broader audience.
- Smiling Curve: Exceptional products can exhibit smiling curves, where the retention curve actually ascends during a hyper-growth phase. This occurs because network effects drive previously churned users to return. As switching costs rise for these users, they find it more beneficial to return, contributing to the upward curve.
Depending on your strategy, product, and report created, you can expect different curves.
As a norm, you would like to get more users to the point where the curve starts flattening and you would want the curve to flatten at a higher point. Users that reach this point are retained better in the long term.
You also want to consider alternatives to resurrect churned/inactive users so that you can get that nice smiling effect in your retention curve.
Comparing the behavior between well-retained cohorts vs cohorts that drop out early in the journey is key to optimizing your retention strategy.
✧ Coming soon
Save retained and drop-out cohorts to analyze and compare their experience.