A Guide to Retention Charts

June 30, 2020
Early this year I ventured into Reforge’s retention course. Why? Isn’t my job just to worry about the pixels and interactions. With data analytics becoming more accessible to product teams, designers have an opportunity to dive into the data to get answers and to also be a strategic partner. Retention is the holy grail that either makes or breaks a company.

We live in times of uncertainty where people are choosing more carefully where to spend their time and resources. Now more than ever we need to build products that actually create value for people and aren't just copycats of the latest trends.

If you're curious to learn how to read the numbers from different perspectives to understand the full picture of retention, here’s a quick cheat sheet:

The Lifecycle Bar Chart

Shows the flow in and out of various user states within a certain period of time

This quick flow helps you see what’s contributing to the growth

Though this gives you an overall health check, it leaves unanswered questions: Where is the loss coming from? Are we getting better or worse? Why are we losing users? And why are we keeping them? How are these groups trending over time?

Cohort Chart

Shows a pool of users who have signed up in a specific time period and counts how many (what percentage) of them are left at a later period of time

Absolute vs Percentage

Another way to understand users is by looking at how many (what percentage) of users went dormant in a certain time period

Absolute Relative vs Percentage Relative

And finally a common way to get pulse check relative to a baseline is to compare an individual cohort to the overall average

Relative to Average
Note: Remember when segmenting these cohorts by persona, geography or behavior, the data is not the answer but a direction that you still need to dig deeper into with qualitative research

The Retention Curve

Shows cohorts of a segment so you can easily see how your retention is changing over time (is it getting better? worse? or staying the same?)

When the slope goes towards the x-axis, you will lose 100% of users while a flat line means that you’ll retain a certain % of every cohort. However, in some cases it’s not bad to have a bad retention curve (Zillow - buying a home).

Engagement Distribution

One of the critical components of retention is engagement which measures how active users are

A distribution shows the current status

Engagement Groups

This snapshot presents the percentage in each level over time and allows you to compare them to the active user base

This comparison to the user base immediately allows you to see negative signals (Total active user is growing but engagement is stagnant)

Closing Thought

The biggest lesson I learned from Reforge is not to try to improve retention as a whole but rather break it into pieces and tie it back to the user’s mindset at every state stage. The following helps me remember the goals of each pillar of retention.

During activation setup the user to successfully understand the core value prop
Next, get them to experience the core value prop as quickly as possible
Re-wire the user’s behavior and establish a habit around the core value prop
Help users deepen his/her engagement and invest in your product to make it harder to switch
Not all is lost until you try to resurrect the people - dormant (users who stopped using the product) or churned (users who became disengaged)

Happy analyzing 🙌🏾