Retention is used to answer the fundamental questions: “Are people using my product over time?” or “Are they coming back to my platform?” Retention helps you analyze how your product has grown over time and what is making your customers come back to your platform.
To access this in Pendo, navigate to the Retention analytics section.
Choose how you would like your data to be filtered with the following options:
- Source: Choose which apps you want to include in your data set.
- Cohort Type: Review your results in visitors or accounts.
- Cohort Size: Choose if you want to divide your user base by "Month" or "Week" based on their first visit date. In retention analytics, a month is defined as 30 days and a week is defined as 7 days.
- Dates: Based on the selected cohort size, data is displayed for the last 6, 9, 12 months/weeks.
- Segments: The default segment selected will be for everyone who visited your product or application. You will be able to choose your desired target group as needed.
- App: This dropdown allows you to filter to a specific group (all web or all mobile apps) or single application.
The default view will show you all of your unique visitors in the last 6 months using a “1 Month” cohort size for any activity across all your applications.
Each row is referred to a “cohort” which is why “retention analytics” is also known as “cohort analytics.”
To help illustrate the chart interpretation, use the following assumptions while interpreting the sample retention cohort:
- Today’s date is October 15th 2018
- User A’s first visit recorded by Pendo is on April 15th 2018
- User B’s first visit recorded by Pendo is April 30th 2018
Both Users A and B will be included in the April 2018 cohort since their first visit date is in April.
This means each individual visitor or account will have it’s own 30-day or 7-day timeframe that will roll up into a cohort. Using the example assumption above, learn how to interpret parts of the chart:
Here is how each user’s time frame is calculated when using a “1 Month” cohort size (30-day increments):
Similarly, here is how each user’s time frame is calculated when using a “1 Week” cohort size (7-day increments):
Within a row, each cell will tell you the retention of the cohort - meaning how many users came back to your product or application after time (months/weeks) has passed.
For example, in May 2018 you had a total of 1,521 visitors. For Month 1, 93% of your users returned. This means about 1,414 visitors out of 1,521 visitors used your product or application again within a 30-day period.
All cells are independent of each other, so there might be times when you notice an increase in percentage meaning more people visited your application in that time frame.
Cells with Asterisk
You might see an
* symbol next to the percentage. This means that the number is subject to change because there is still time remaining for the month (30-day increment) for that cohort.
Using the previous example users, if you continued the individual timeframe breakdown, Month 5 and Month 6 are still incomplete for both Users A and B. Future timeframes are highlighted in pink to illustrate why Month 5 and Month 6 will have an asterisk in the cohort:
So your chart might look like this:
The shade of color in the chart is based on the following percentages:
Interacting with the chart
If you click on a cell within the chart, a panel on the right-hand side will open and display the list of the visitors that are included in the calculation. The list will display the total number of users forming that particular cohort percentage and up to 250 Visitor IDs (this number may increase in the future). Clicking on a Visitor IDs will take you to that visitor’s detail page.
similarly, you can click on the "Dropped Visitors" tab to see everyone (up to 250 visitors) who were present in the previous period but have not returned in this period.
Towards the bottom, you will see an to .csv option. Click on the option to download all the Visitor or Account IDs from the selected cohort with any additional metadata values you wish to include in your .csv.