A business that retains its users becomes profitable faster than one that does not. That’s why understanding retention is the key to long-term success.
All you need to start optimizing your retention is a base of users, no matter how small.
How to define Feature Retention
Similar to the general User Retention, it’s the percentage of users engaged with a specific feature at any time after X days of signing up or installing your app.
To run a useful retention analysis you need to ask yourself:
What's the ideal frequency at which users should use my product?
You need to figure out what makes the most sense for your business.
Usual answers are: daily, weekly, monthly or yearly.
Once you figure this out you should pick the frequency to read your analysis in an efficient way.
To calculate your feature retention you only need two more conditions:
an entry condition (usually sign up, app download etc)
a returning condition (the event corresponding to the feature for which you want to measure retention )
Then it's all about computing the % of users who engage with the feature after having started using the product.
This template provides the most common retention curve - also known as “N-Day” Retention.
“N-Day” retention is the proportion of users who come back on the “Nth” day after first use.
It’s a line graph depicting the average percentage of active users for each day.
When measuring N-Day Retention, Day 0 refers to the day on which a new user first uses the product. First-use can encompass anything from signing up to completing a specific action.
Retention on Day N is the proportion of users who started on Day 0, who also returned and were active N days later.
The most common way to visualize acquisition cohorts is to use a table based on when users signed up.
The rows state a timeline and the number of customers you acquired at each time interval.
Each column is the amount of time that has elapsed since the users subscribed.
Every cell has the % of the original acquisition number that retained at that period in time. Using acquisition cohorts, you can find out when users tend to drop off.
Mind that the aggregated retention is a weighted average of the acquisition cohorts.
Why Feature Retention analysis?
Feature Retention analysis helps product teams answer the question “How many of our users come back to the product for this feature?”.
Retention impacts every important business metric that you care about.
Without retention, your product is a leaky bucket. You can pour in as many dollars as you like into marketing and still wind up with no long-term users.
If you make Retention one of your core metrics, you can change the trajectory of your company.
How to use the Feature Retention template
To discover how many users come back for a single specific feature you have to:
pick an event corresponding to the entry condition ( first event advised)
pick the event that maps the feature you want to analyze
select the time-frequency
Go to the report to see the retention of your feature!
Make your colleagues aware of how your product is performing by sending the graph in your Slack channel.
Here you can find a complete Playbook about user retention: a must-read!