2. User Retention
Users retention for the Sellers.
What is the User Retention (or Retention analysis)?
Retention is a measure of how many users return to your product over time.
Retention analysis helps product teams answer the question “How many of our new users come back to the product?”.
Retention is the percentage of users who are active at any time after X days of signing up or installing your app. It's the percentage of user growth that turns into a recurring base of customers.
Why you need to care about user Retention
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.
To run a useful retention analysis you need to ask yourself:
What's the ideal frequency at which your 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.
If you haven’t figured out this question we listed a handful of real-life products examples here.
Once you figure this out you should pick the frequency to read your analysis in an efficient way.
How to use it
If you make Retention one of your core metrics, you can change the trajectory of your company.
Increasing user retention and minimizing churn are keys to building a base of loyal users to grow.
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.
Shifting the retention curve up
Optimize your first-time user experience and prove your product’s core value to new users.
Flattening the curve
Increase your baseline level of users by delivering a solid product experience.
Depending on the product you are building, your customers should log in at various cadences. Below are commonly used set up:
📚 Resource: Andrew Chen, Y Combinator, more here