Product Analytics. Part 2 Calculating D1-D30 retention curve. Part 1

191. Calculating D1-D30 retention curve. Part 1

Calculating a single retention rate (D7 retention rate for example) could be useful when you compare funnels. Imagine you test several onboarding flows. To compare them you need to calculate multiple metrics for each user cohort (in this case a cohort is all users who had a certain onboarding flow):

  • signup rate
  • activation rate
  • unit economics (like ARPU)
  • D7 retention rate

We’ll implement a similar analysis ☝ in the chapter about AB-tests. For now, it’s just an example when a single retention rate could be useful.

Let’s calculate it using our new...

“well worth the money”
Sign up and check out 36 free lessons and exercises.

Anatoli Makarevich, author of SQL Habit About SQL Habit

Hi, it’s Anatoli, the author of SQL Habit. 👋

SQL Habit is a course (or, as some of the students say, “business simulator”). It’s based on a story of a fictional startup called Bindle. You’ll play a role of their Data Analyst 📊 and solve real-life challenges from Business, Marketing, and Product Management.

SQL Habit course is made of bite-sized lessons (you’re looking at one atm) and exercises. They always have a real-life setting and detailed explanations. You can immediately apply everything you’ve learned at work. 🚀

“well worth the money”

Fluent in SQL in a month

Master Data Analysis with SQL with real life examples from Product Management, Marketing, Finance and more.
-- Type your query here, for example this one -- lists all records from users table: SELECT * FROM users
Loading chart... ⏳
Previous lesson: Aggregating analytics events into daily activity
Next exercise: Calculating D1-D30 retention curve. Part 2