Retention is a Report which shows how different Cohorts of users (defined by Starting Events on a Date) retain over time. This type of analysis is typically used to understand the difference between users who retain and those that don't and how to adapt user journeys in order to make products more sticky.

The main inputs are:

In Kubit you first select the Cohort of users who performed the 'Starting Event' during a specific period of time. The report will "bucket" these users based on the first day they did the starting event within the window of time you've selected as well as the interval of time.

Intervals of time determine how your data will be represented

If you select Daily/Weekly/Monthly etc. the user had to perform the Starting Event within the interval of time and the results will be separated by each day/week/month etc. within the time period.

All Time will group all users into a single cohort and they will all have the same Day 1, the day following the last date of the cohort time period.

There are multiple ways to measure retention, so in this section we'll cover the ones supported by Kubit. There is, however, something common concepts between all retention types - the Retention chart displays each cohort as a row identified by Cohort Date, followed by how many unique users exist on that initial date, then how many of them came back/churned on the following dates.

In a nutshell - for each starting cohort, for each remaining date of the time period, count all users who are part of the starting cohort and have at least one returning event on that date.

It is useful when users are expected to return regularly over relatively short time periods.

Rolling retention is called Rolling, because we not only require a user to have been a part of the starting cohort and have a returning event on a particular date X in order to count them on that date, but we also want them to have a returning event on each date between the starting cohort date and X. We expect the user to have been continuously returning, hence - Rolling.

Useful when high stickiness is expected.

In some businesses users return less regularly, e.g. once a month to pay a bill or fill in supplies. When that's the case Unbounded Retention can help get a more accurate number of retained users, because users will be counted not only on their return date, but also on all dates prior to it.

Retention shows how different Cohorts of users (defined by Starting Events on a Date) retain over time. This is the most common and default mode of measuring Retention Rates.

Retention over time plots the trending retention rate for users retained on each interval of time i.e. Day 1, 3, 7. This is great to understand if your Day 1 retention rate is improving over each cohort of subjects.

You'll notice with this mode the lines plotted are the Daily Retention points vs. the cohort dates like in the default Retention mode.

Usage Interval shows the typical Usage Interval of a product feature, e.g what % of users have generated the returning event by Day X. Typically you'll use Usage Interval to understand what a good Retention timeframe may be, when the line crosses a 50% threshold that's when half of your Users typically perform the returning event.

In the above example, 50% of users return on Day 4 to perform the return event which means our Retention report should use 4 day intervals to be consistent with user behavior.

Usage Interval Over Time shows you how the Usage Interval of an event changes over time. Similar to Retention Over Time you can see if the interval of time Usage Interval of each cohort is higher/lower over time.

Churn is essentially the inverse metric of Retention. If Retention tells us how many users are returning, Churn measures how many users are not coming back. Switching from Retention to Churn is done by a switch and does not require re-execution of existing analyses.

In retention there are typically 2 ways to think of a day:

1. A Calendar Day that starts at 00:00 and ends at 23:59.

2. A 24h Window between the "Starting" and "Returning Event(s)" that is individual for each user.

Let's assume the following sequence of events for a user:

1. Start event - 2023-07-01 10:00:00

2. Return event - 2023-07-01 11:00:00

3. Return event - 2023-07-02 09:00:00

4. Return event - 2023-07-02 12:00:00

Strict calendar date window computes the difference between start and return events based on calendar dates. Let's go through the example above.

Strict calendar date windows are not equal because in this case the start date makes the window shorter compared to Day 1, 2 etc. In the example above Day 0 starts at 2023-07-01 10am and ends at 2022-07-01 11:59pm while Day 2 and 3 have exactly 24 hours length starting from 00:00:00 and ending at 11:59pm.

24 hour windows defines each day as equal interval as exactly 24 hours after each start event timestamps. Let's go through the example above again but this time using 24 hour windows calculation window.

The example above shows that each day is computed with exactly 24 hours after the start event. Day 0 starts at 2022-07-01 10:00:00 and ends at 2022-07-02 09:59:59.

That's why the returned event at 2022-07-02 09:00:00 is computed as Day 0 but not Day 1 as it will be with Strict Calendar Date window.

Whenever a Breakdown is specified in Retention you can use the 'Average' toggle to display the average count for all breakdown groups as a separate group. The 'Average' group is displayed with the other date groups.

There are a few advanced parameters which can be used to fine-tune the analysis: