Use behavioral personas for product growth

Business Benefits

Increase customer retention by understanding and testing customer behavior.


Research your current users to understand the value they get from your product, and which behavioral patterns lead to high customer retention.

Hiten Shah, a SaaS expert and a master of customer development, asks the following survey questions to gauge why a particular product is successful:

  • How would you feel if you could no longer use [product]?
  • How did you first use [product]?
  • What would you likely use as an alternative if [product] were no longer available?
  • How could [product] be improved to better meet your needs?
  • What is the primary benefit that you have received from [product]?
  • What type of person do you think would benefit most from [product]?

Use some of these answers to segment your customers and study their behavior. For example, Shah uses answers to the first question, “How would you feel if you could no longer use [product]?” to segment committed, very disappointed, lukewarm, somewhat disappointed, and indifferent, not disappointed customers, to understand how people behave on a platform with different levels of investment.

Create relevant behavioral personas for your product and turn them into customer cohorts to answer questions about your product’s core value.

If you were analyzing a Gmail extension that checks the spelling and grammar of all a user’s emails, then you could imagine a few simple behavioral personas relating to usage right away:

  • High-volume emailers: users who process many more emails than the median user.
  • Low-volume emailers: users who process fewer emails than the median user.

You could expand this out to include people who write their emails very quickly, those who spend more time on them, the degree of broadness of the networks people have, whether they email many different people or just a few, and so on; behavioral personas are a very flexible tool.

Compare cohorts to identify potential retention drivers.

For example, a mobile gaming site with a social feature is retaining an average percentage of its user base. Yet the team is not 100% sure what the core value of the site really is; is it the gaming aspect, or is it the fact that there’s a social layer accessible as well? What keeps users coming back? You could have three basic personas, each of which is going to exist in some proportion and have different retention numbers:

  • High social only: Users who heavily use social features, but don’t play many games.
  • High gameplay only: Users who mainly play games, but don’t use social features.
  • High gameplay + high social: Users who both play games and use the social features actively.

To do that, we could segment these three groups by how likely they are to come back to the site. That will tell us whether it’s the most social, the most gameplaying, or the most social and gameplaying users that tend to come back.

Use pivot tables to easily compare your data. You could export this from a view you’re interested in exploring in Google Analytics, for example.

Google Analytics

For whatever events you choose, like clicks or pageviews, you export the number of times that event was performed on a daily, weekly, or monthly basis, along with individual user IDs. We might pick events like play game, open chat, and invite friend, to capture data on both user gameplay and social activity.

You may have to manually reorganize or transpose the data until you get something that looks like the following, for a single user:

Single User Data

Or the following, for many users:

Multi-User Data

To create a pivot table in your spreadsheet, go to Data > Pivot table if you’re using Google Sheets, and then add your user IDs as your rows, and your columns as Values.

Pivot Table Configuration

When you’ve done that, you’ll have a finished pivot table that looks something like this:

Pivot Table Complete

Brainstorm questions for further analysis from your comparisons, and start to build hypotheses from your data.

As you move into more complex analysis, you should consider key questions, such as:

  • What are the key actions that you identified as drivers of habit formation? What are some methods you can test to get more new users to cross those thresholds?
  • How are your least and most engaged users different?
  • Did your behavioral persona analysis reveal any use cases you didn’t expect or didn’t think were very important?
  • Are some of your personas more important for your main business objective, like revenue?

For example, if your main objective is revenue, you could easily compare how much different groups of users are spending. But if you see people who add three friends on their first visit to a site are better retained, you would need to investigate how users within that persona emerged. Maybe you find that 75% of those adding three friends on the site in their first session, were themselves invited to the site by a friend.

Test your hypotheses about user retention and behavior.

Reaching a juncture like this is the perfect place to start testing hypotheses to improve user retention. What you have is correlation, not causation, so you need to experiment to find out if your insight into user behavior is real.

For example, you might start by setting up an A/B test where half of your new users who sign up go through an additional onboarding step where they’re encouraged to add three friends, and half do not.

A/B Test

This is dependent on having the right sample size of users, so you may have to run the test for longer if your audience is smaller. Through these sorts of experiments, you can start to uncover the actionable insights that will allow you to drive better retention.