1) I was a bit surprised there was no mention to the Reforge approach to identifying your retention metric. Each product might have multiple use cases (eg. renting and hosting for Airbnb) which have different frequencies (daily, weekly, monthly). This might land teams to monitor different retention metrics for different customers.
2) Once retention curves are visualized, it might be helpful to segment them by criteria like: geo, device, UTM parameters, etc to deepen the insights from the retention metric
3) Appreciate the detailed SQL guide and reference to the usual suspects of product analytics. Lately, I’ve been using June.so which directly visualizes retention cohorts and segments based on Segment events. It’s so easy and delightful!
Great stuff, thanks for writing this both!
Couple of additions:
1) I was a bit surprised there was no mention to the Reforge approach to identifying your retention metric. Each product might have multiple use cases (eg. renting and hosting for Airbnb) which have different frequencies (daily, weekly, monthly). This might land teams to monitor different retention metrics for different customers.
2) Once retention curves are visualized, it might be helpful to segment them by criteria like: geo, device, UTM parameters, etc to deepen the insights from the retention metric
3) Appreciate the detailed SQL guide and reference to the usual suspects of product analytics. Lately, I’ve been using June.so which directly visualizes retention cohorts and segments based on Segment events. It’s so easy and delightful!
Hope this helps, keep up the great work! 🤝
Oh my god this is exactly what I needed. Thanks, Olga and Lenny!
This is some next level content. Thank you Olga and Lenny!
Thank you for the fantastic guide. What does the snapshot_date refer to?
Hi Olga, is this query for unbounded on X-day retention?
Is there any value in looking at company retention rather than user retention? E.g., anyone from a certain company active makes that company active.