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Gainsight Inc.

Retention Analytics

This article outlines the procedure to configure and use Retention Reports in Gainsight PX.

Overview

Discover which features drive retention and growth, including the window of time you have to guide users to realize the value of your product.

  • Know how well your product retains its users by establishing a baseline and measuring over time.
  • Identify the window of time you have to guide users to realize the early value of your product.
  • Validate your hypothesis on which features and user attributes drive retention and growth

Your ideal user retention graph should look like a smile (curves back up) as customers increase their usage over time. This means that over time, you’re giving your users more reason to come back and adopt your product. This can come from introducing new product capabilities that users want and executing specific re-engagement efforts outside of your product to help them realize the value of your product.

Retention Analytics Settings

  • Cohort size: represents how you divide your users based on signup date daily/weekly/monthly
  • Date range: you can configure the date range you wish to cover based on the end date
  • Metric: You can use different dimensions as a base for your retention analysis

Retention Filters.png

Benchmark Different Cohorts

Using the filter, you can measure different user and account segments as well as feature usage and compare them side by side to gain a greater understanding and perspective of either the percentage or the absolute numbers you are using. You can also view the data as time-series or as a bar-chart.

Retention Analysis.png

How do I discover Product Growth Opportunities?

When you have multiple customer segments and/or types of users in your product, you may need to review the user retention for each segment as they will have different usage patterns.Ideal User Segment.png

Has My Product Achieved Product Market Fit?

When your product has achieved product-market fit, your user retention will flatten out over time. If the line trends towards zero, users are not realizing value in your offering and not returning back to your product. This trend line down to 0 is also described as a having leaky bucket. No matter how good your customer acquisition is, ultimately you are in trouble if you cannot deliver value and keep users coming back.

Flattened Retention.png

How Can I Increase My User Retention?

The first step to increasing your retention is to understand who are the users with the best retention rate and what are they doing in your product. For example, per the detailed cohort analysis below, we can see that users who signed up between August 24th and August 30th have the highest retention over time.

Best Retention Cohort.png

The smile effect in your user retention report is what you strive for. This indicates a thriving customer base that is returning to use your product more and more over time.

For all users within this cohort, you need to know three factors:

  • The features are they using
  • Do they have common characteristics (demo/firmographic)
  • The users' signup source

With these three characteristics, you can gain a complete picture of your ideal user profile and their motivation for using your product. Armed with this information, you now can introduce personalized product experiences to guide all users to adopt the “aha” moments within your product.

For more information about all frequently asked questions, refer to Analytics FAQs article from the Additional Resources section.

Understanding the Report

Assume that it’s 01 Nov 2020 today. In the above image, the Retention Analysis report started on 10 Aug 2020 and has Week as the Cohort distribution. So, for the first week of Aug 10 - Aug 16, 51.98% of users signed up for the application. You can see that Week 0 shows 100%. Next week (Week 1) Aug 17 - Aug 23, 47.49% of the total number of users who signed up in the previous week logged back into the application. In Week 3, Aug 24 - Aug 30  51.18% of the total 756 users have logged in again. This is an incomplete cohort and the value may go up by the end of the week.

Similarly, in the second week Aug 17 - Aug 23, 716 Users signed up for the application (this does not include any of the 756 users who signed up during the week of Aug 10 - Aug 16). For these Users, Aug 17 - Aug 23 is their Week 1 and 52.79% of the Users logged back during Week 1, so far. This is an incomplete Cohort and the number may go up by the end of the week. Week 2 for these Users is yet to begin. 

For the Week 1 column, the total percentile value (52.44%) is the average of all its retention values across different cohorts. Similarly, the overall percentage is calculated for all the other Week columns. As few Weeks still have incomplete Cohorts, they are excluded from the calculation of overall percentage, until the end of the week. This skews the overall percentage and brings the value down. 

Advanced Filters in Retention Analysis

Gainsight PX also allows you to compare two events, in a Retention Analysis report. The two events to be compared are known as the First Event and Return Event and these drop-down menus are located under the new Advanced section. These drop-down menus contain event types that can be used as filters.   

  • First Event: When you select an event type in this field, the report filters the number of Users or Accounts who have clicked the event type during a given time frame. The event types supported are features, custom events, page view events, session events, user attributes (sign up, custom attributes of multiple data types), account attributes ( create date, custom attributes of multiple date types).
  • Return Event: When you select an event type in this field, the report filters the number of Users or Accounts who have performed the action selected in the First Event field. The event types supported in the First Event field are supported in this field also. Apart from those event types, User and Account attributes are also supported in this field.

The report is generated in Cohort size selected (day, Week, Month). 

Business Use case: You can calculate the number of users who signed up in your application (first event) and then compare this value (number of users who signed up) with the number of users who accessed the purchase page (or any other page), during a specific time period.  

To compare two events: 

  1. Click the Analytics from the left pane.
  2. Click the Retention Analysis report from the Audience section
  3. Click the Advanced option. 
  4. Select the required event types in the First Event and Return Event fields. 
  5. Click Apply.
  6. Scroll down to view the report. 

Advanced Retention.png

Apart from the advanced filters, you can apply regular filters and filter the data based on Accounts, Users, and Engagements. 

Include or Exclude Incomplete Time Frames

You can include or exclude the incomplete time frame of the most recent week or month, in the calculation of the Overall Week’s Percentage. The Include recent timeframe check box can be used to accomplish this task. The incomplete time frames are now included in the calculation, only if this check box is selected. This enhancement provides you with more flexibility in the calculation of the Retention analysis report.

Include Recent Frame.png

Additional Resources

Analytics FAQs

 

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