Understanding your product’s engagement is important because of the broad impact that user engagement in your product has on your business. The more engaged your users are, the more likely they are to find value in your product, retain for longer periods of time, encounter monetization opportunities, and refer additional users.
Since engagement is so vital to success, understanding it is critical. But how should we look at engagement?
When you measure engagement, what you want to know is how active your user base is over a given time period. Contrary to conversion analysis, where we want to understand if a user has a) converted or b) dropped off, and retention, where we want to see if a user has a) retained or b) gone dormant, engagement is not a binary metric. Engagement of a user base lies on a spectrum. That means, to effectively measure engagement, we need to break up the user base into different buckets along this usage spectrum and examine the different groupings along this stretch. This is because your goal is to move users from a less engaged state to a more engaged state, and by breaking the engagement spectrum off into buckets, you are creating a tangible way of measuring progress to and from each sub-state.
But that brings us to another important question: how should you create these buckets?
One common way of doing this is plotting a distribution of users with activity in your app, and then craft three engagement groupings (power, core, and casual) based on a fixed percentage.
- For example, if I worked on a music streaming service called MusicFinder, I would plot users doing any activity. The bottom 10% of users are classified as casual, the middle tier as my core users, and the top 10% as my power users.
While this seems logical, it falls short of the mark for a few reasons:
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Any activity does not necessarily exemplify good activity for your business. It’s important to choose an event that aligns with value creation. Otherwise, you’re adding a lot of noise.
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In the case of the streaming service, I want to make sure that I’m looking at events like starting to play a song or creating a playlist because they’re correlated with my value proposition—listing to music. If a user foregrounds the app but does nothing of substance, that should not be considered an engaged user.
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The current state of the engagement distribution of the user base does not necessarily reflect the ideal distribution.
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If the majority of MusicFinder’s user base plays a song once a quarter, that doesn’t mean the core base should be on a quarterly cadence. That probably means we have a big problem to solve.
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Rather, choosing the right core engagement bucket relies on critical thinking about your product. Ask yourself two questions:
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What problem does my product solve?
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This dictates the event behind the value for your core user persona. This is the event (or events if doing analysis for multiple personas) whereby we should be measuring engagement.
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How often does my target audience encounter this problem?
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This dictates how often the core user group should be coming back. If users solve this problem at a monthly rate, the core engagement group should be monthly.
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Now that we have the core user bucket, we can create hypotheses around what the power and casual user buckets could be—with the casual bucket being a step down and power being a step up.
- For example, if MusicFinder is meant to entertain users at a weekly cadence, the power group would use biweekly/daily, and casual users would be on more of a monthly basis.
The last step is to confirm our analysis with data and make sure our qualitative hypotheses line up. To do this, hop into your Signal report and run a correlation analysis between your engagement event and retention.
Here, I’ve run a query to see how my Starting Play event correlates with 2nd Week Retention
We can see that starting to play a song optimally occurs 4 times within 7 days, and done by the most engaged minority of our users. This fits nicely with our hypothesized power user bucket at a biweekly/daily cadence.
Now that you have your user engagement buckets that are aligned with your product and business objectives, you can start digging into ways you can influence movement between these engagement states. Stay tuned for next week’s article to learn how.
