Discover which features drive retention and growth, including the window of time you have to guide users to realize 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 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 end date
- Metric: You can use different dimensions as a base for your retention analysis
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 greater understanding and perspective of either the percentage or the absolute numbers you're using. You can also view the data as time-series or as a bar-chart.
How do I discover Product Growth Opportunities?
When you have multiple customer segments and/or types of users in your product, you’ll want to review the user retention for each segment as they will have different usage patterns. In the example below, the orange line indicates all users from your Enterprise accounts compared to the green line indicating all other users. The users within Enterprise accounts are retained at a much higher rate and show the increasing engagement over time.
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’re in trouble if you cannot deliver value and keep users coming back.
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 April 2nd and April 8th have the highest retention over time.
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 want to know three things:
- what features are they using,
- do they have common characteristics (demo/firmographic) and
- what was their 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.
Understanding the Report
Assume that it’s 14 Feb 2020 today. In the above image, the Retention Analysis report started on 27 Jan 2020 and has Week as the Cohort distribution. So, for the first week of Jan 27 - Feb 2, 893 Users signed up to the application. You can see that Week 0 shows 100%. Next week (Week 1) Feb 3 - Feb 9, 54.65% of the 893 users who signed up in the previous week logged back into the application. In Week 3, Feb 10 - Feb 16 (ongoing week, since it’s Feb 14 today), 48.26% of the 893 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 Feb 3 - Feb 9, 1046 Users signed up to the application (this does not include any of the 893 users who signed up during the week of Jan 27 - Feb 2). For these Users, Feb 10 - Feb 16 is their Week 1 and 47.61% 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 (since it’s Feb 14 today).
For the Week 1 column, the total percentile value (50.85%) is the average of all it’s retention values across different cohorts (in this case, an average of 54.65% and 47.81%). Similarly, 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 which 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:
- Click the Analytics menu from the left pane.
- Click the Retention Analysis report from the Audience section.
- Click the Advanced option.
- Select the required event types in the First Event (here Sign-up date) and Return Event (here Dashboard page) fields.
- Click Apply.
- Scroll down to view the report.
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 calculation, only if this check box is selected. This enhancement provides you with more flexibility in the calculation of the Retention analysis report.