#SOL价格预测# #GUSD双重收益# #DOGE ETF上市# I have been monitoring the retention curve data for over 15 years.

I have seen thousands of retention curves, and this is one of the first metrics I ask to see when evaluating startups. I have gone through thousands of databases and analyzed retention curves broken down by different segmentation dimensions. As a product builder, I have also observed this metric from another perspective. I have run hundreds of A/B tests, drafted countless versions of user onboarding guides and notification emails, trying to change the shape of the retention curve.

A/B testing (also known as split testing or bucket testing) is a random experimental method used to compare two versions of a product (Version A and Version B). Its core purpose is to determine which version performs better in achieving predefined goals by collecting data and analyzing user behavior.

From the results, there are some patterns here.

Just like the laws of physics, it is strange that over time, there are always certain deterministic patterns that keep emerging. Here are a few examples I want to share:

You cannot improve a poor user retention rate. Yes, adding more notification features will not improve your retention curve. You cannot achieve a good user retention rate through A/B testing.
Retention rates will only decline and will not rise. Interestingly, its decay rate does follow a predictable half-life pattern. Early retention rates can predict later retention performance.
Revenue retention expands, while usage retention decreases. The good news is: although users may gradually churn, the users that remain sometimes spend more!
The retention rate is closely related to your product category. There are both inherent reasons and those cultivated later. Unfortunately, you are destined to be unable to make the hotel booking app a daily use product.
As user expansion and growth occur, retention rates tend to decrease. The highest quality users come from early and organic growth, while users acquired later tend to perform the worst.
User churn is asymmetric; losing a user is far easier than winning them back.
Calculating retention rates is very difficult. Seasonal factors do exist, and newly launched test versions can interfere with the data; system vulnerabilities also occur from time to time. Although D365 is a real indicator, we cannot rely solely on this result.