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DL
David Lieb
08/30/24
@ Y Combinator
If your cohort retention curves are not improving, it's crucial to engage with your users to understand their needs and make necessary adjustments.
Video
YC
How To Keep Your Users | Startup School
@ Y Combinator
08/30/24
Related Takeaways
DL
David Lieb
08/30/24
@ Y Combinator
If your cohort retention curves do not flatten out, it suggests that you haven't yet created a product that users want to keep using.
DL
David Lieb
08/30/24
@ Y Combinator
Visualizing cohort retention trends helps you understand how user engagement changes over time relative to the initial cohort size, allowing for better analysis of user retention. Cohort retention curves are essential for understanding user engagement over time, as they track the percentage of new users who return to use the product repeatedly.
DL
David Lieb
08/30/24
@ Y Combinator
The ideal cohort retention curve not only flattens but also increases over time, indicating that users are becoming more engaged with your product.
DL
David Lieb
08/30/24
@ Y Combinator
It's important to build your own cohort retention curves using your logs or Google Sheets to develop a better understanding of your data.
DL
David Lieb
08/30/24
@ Y Combinator
The key insight from cohort retention curves is that the shape of the curve matters more than the absolute number of users; a flat curve indicates potential for user accumulation over time.
DL
David Lieb
08/30/24
@ Y Combinator
Improving your product by adding new use cases or simplifying user flows can positively impact your cohort retention curves.
DL
David Lieb
08/30/24
@ Y Combinator
Acquiring better users is essential; targeting the right customer can significantly improve your cohort retention rates.
DL
David Lieb
08/30/24
@ Y Combinator
You don't need to check cohort retention curves multiple times a day; refreshing them weekly or bi-weekly is sufficient to catch significant changes.
DL
David Lieb
08/30/24
@ Y Combinator
When evaluating cohort retention, focus on the overall shape of the retention curve rather than isolated metrics from specific time points. Be cautious when using analytics tools for measuring cohort retention, as they may not accurately reflect what you think they do.