Driving Product Growth With Behavioral Personas

When your core product experience brings your users back time and time again, you gain some incredible advantages. You spend less on acquisition, learn from your users faster, and start building a community through word of mouth and referrals.
If you can’t retain your users, you’re going to spend more on acquisition while not growing as quickly. You’ll spread distaste for your product, not delight. Soon, you’ll find that there are no more users out there to churn through.
Retention is a simple phenomenon—users choosing to come back to your product—but it can be elusive.
It’s Not Luck—It’s About Understanding Your Users
The difference between a thriving product and a zombie one is that users want to keep using the former. The difference between a thriving product and a world-changing one is that everyone wants to use the latter all of the time—virtually everyone is a power user.
You can use analytics to go from a non-sticky site to a sticky one by delving into how your users behave and what they value.
Introducing Behavioral Personas
The primary route we use to make sense of user behavior is behavioral personas. You may be familiar with cohorts based on things like date—all those users who signed up in January 2017, for instance. A behavioral persona is similar, but segments users out by patterns in how they use a product.
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.
Current User Diagnostic
Once you have behavioral personas, you want to see how each of them corresponds with user stickiness. The two big goals of studying your current users are:
Understanding the value users get from your product, and
Understanding the patterns that lead to retention
Why current users? Too often marketers want to look at just their new users — or just their churning users. They want to identify whether or not their acquisition is working, or they want to figure out ways to bring back users who are churning or are on the brink of churning.
Figure out what your current users like about your product, however, start building on that, and you’re going to make your site better for everyone.
Example: Mobile Gaming Site with a Social Feature
Let’s say it’s doing okay, 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.
The point of studying current users is to build on the core experience that users value and make it even better.
To do that, we’re going to segment these three groups by how likely they are to come back to our 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.
To understand how these three groups differ in terms of their engagement, we’re going to export some raw event data and then analyze it with respect to retention.
Creating a Pivot Table
Hubspot’s Dan Wolchonok illustrates how to do basic analytics with little more than some pivot tables as you can find in Google Sheets or Excel: the first step is getting your raw data.
You could export this from a view you’re interested in exploring in Google Analytics, for example.