Use Interana’s Retention analysis to measure how long new users engage with your service after registration; track this engagement per minute, hour, day, week, month, or year. Interana’s Retention analysis reveals how long a user is spending with your product or service, when the user stops using, and when the user re-engages. This helps you learn which features are sticky, which features are turning away users, and the level of customer loyalty. Often, this is explored by tracking a rate across different entity groups.
You can determine the number and percentage of your users who return after an initial event (for example, a sign-up event) to perform other actions over time.
Interana’s Retention view allows you to identify who is staying and who is straying and helps you develop new product strategies, ways to attract new customers, ways to increase engagement with current users, and ways to prevent users from straying.
Then you can drill down into the data to find out why. Use retention analysis to answer questions like:
- How many users are coming back during the week or week after week?
- How many users registered and stayed active a week after the product launched?
- Did that new feature you added increase or decrease retention?
- Do users stick around for a few months and then leave? Or, do users return intermittently?
Start by creating a metric for the retention analysis
First, you need to create a per-actor metric to use for the retention analysis. Click the Metrics icon in the left-side navigation bar to open the Metrics tab.
Then click the New Metric button in the Per-Actor Metrics section.
In the New Metric dialog, enter a descriptive, unique name for the metric. In this example, we named the metric RetentionDemo.
Select the data source that you want to use in the Dataset field, and the key in the For Each field.
In this example, we are using the
music dataset and the
In the Measure field, select
Minimum. Then select the primary time column for the event. This captures the first time an event was recorded for a user. We recommend that you use a non-repeating event – account creation is typically a good choice (see note below).
Optionally, add filters to the metric. For example, you can filter for account creation events. For this example, we are filtering by users registering for the service.
The New Metric dialog will show you a description of the metric, confirming that it is a retention metric: “Variants of this metric...will be available for use in retention view”
Save the metric. In the Per-Actor Metrics list, click the Explore icon next to the metric you just created to use the new metric in an analysis.
Repeatable vs. non-repeatable retention events
Any event that can occur multiple times (a repeatable event) will have multiple entries in the time column. This will affect the measurement and results. For example, if you are tracking account creation per user and your users can create multiple accounts, this can give you incorrect retention measurements.
Next, explore the retention analysis graph
Each row shows the number of users who triggered the measured event during the time period (under the date in the first column), and then the number of users who were active over each day. The initial measuring date (Day 0) will always show 100%.
Each row looks at only the users in that row. Users in one row are excluded from all subsequent rows.
Day 0 represents the initial measuring day which determines the number of users measured in that period. Subsequent days show the number of users who were active on that day. The analysis shows any type of activity from those users.
Using the chart controls to adjust the graph
Use the Table alignment setting to switch between relative time (the default setting) or absolute time displays. Relative time shows the days relative to the starting date in each row, plotting days relative to the starting date as columns. Absolute time switches the chart to show dates along both axes.
Use the Cell value setting to configure whether the cells show the percentage of the initial value for each day (across the x-axis) or the numeric value for each day.
Using filters with retention analysis
There are two ways to filter your retention analysis:
Add a filter to the metric: Filter the initial anchor event to determine which event Interana uses as the basis for the retention measurement.
Add a filter to the query: Filter the query by follow-on activity. For example, to analyze when your users take other actions after the initial sign on event.