Understanding churn and retention is essential for any product or subscription business. Cohort analysis helps you see how long users stay active, how different groups behave over time, and where you might have drop-offs 📉
In this video, I walk you through a simple and reusable workflow:
extract your data with SQL
create clean cohort and activity fields
export the results
and build a clear retention table in Google Sheets 📊
This is the exact approach I use in real analytics work — clean, reproducible, and easy to iterate on later.
If you want to follow along, here are all the resources 👇
🔥 Substack-article with code and text:
📄 Google Sheet (retention table)
🧾 SQL Query
📁 Sample Dataset
If you have questions or ideas for the next videos, feel free to drop a comment. In the next episode, we’ll take this further and look at Customer Lifetime Value (LTV) 🔥
0:00 - Why total churn is a vanity metric
1:10 - Setting up the BigQuery environment
3:55 - The MIN(date) window function explained
6:35 - Calculating Payment Age with DATE_DIFF
9:15 - Exporting results to Google Sheets
12:20 - Building the Retention Heatmap (Conditional Formatting)
#productanalytics #dataanalytics #cohortanalysis #retention #churn
#sqltutorial #googlesheets #analyticsworkflow #datascience #saasmetrics #ltv #customeranalytics