Web3 is expanding rapidly and projects are experiencing customer acquisition growth. Currently, projects typically measure user growth data by active users, which refers to the number of wallets that interact with contracts.
However, it is evident that active wallets are not the most core users, as there are many robots and multi-wallet situations on the chain that can interfere with this data. If we take the same measures for all users, it will not only waste resources but also lead to problems such as decreased user satisfaction due to the inability to serve core users well.
Web2 experience tells us that segmentation of user groups and achieving refined user operations are successful measures that consider both costs and effectiveness.
Fortunately, Web3 has also presented excellent solutions to address such issues.
Footprint Growth Analytics has specifically introduced the cohort feature to solve these problems.
Develop clusters based on business with Cohorts
We have defined some common cohorts to start with:
Active User Categories
We used the past six months’ active user cohort from a specific project as a sample for analysis. The chart shows that the project is maintaining stable growth, as evidenced by the high proportion of new users. However, there is a clear and significant signal of 20% of users churning and another 10.83% at risk of churning.
Cohort analysis: Understanding the deeper reasons behind the data
Grouping charts provide an overview, but merely counting the number of users in each group doesn’t help us understand why users churn or where new users come from. It’s important to analyze retention rates, stability of the NFT holder group, and the growth of diamond hands.
To gain deeper insights, we should examine user profiles. For example, we can observe the tags of churned users:
From the labels, we can see that some of these users are event participants who may not be loyal. They were attracted by the benefits of the event but did not stay after it ended. We can also see that many users participated in other NFT trading markets, indicating that other products may be more attractive to them. You may need to conduct user or market research to improve the product. Of course, in practice, you also need to conduct correlation analysis with the product and business to get more meaningful answers.
Need help?
After segmenting users, it’s important to analyze their performance and optimize product or market strategies based on their characteristics. By nurturing and developing each group around the OODA loop (Observe, Orient, Decide, Act), the project can thrive.
If you are still lost and struggling with a bunch of mixed addresses, or identifying and expanding more quality user groups, book a demo now and get a user profile!