[Huawei Developers] Mike-mei:
In the information age, the external market environment is constantly changing and enterprises are accelerating their digital marketing transformation. Breaking data silos and fine-grained user operations allow developers to grow their services.

In this post, I will show you how to use the prediction capabilities of HMS Core Analytics Kit in different scenarios in conjunction with diverse user engagement modes, such as message pushing, in-app messaging, and remote configuration, to further service growth.

Scenario 1: scenario-based engagement of predicted user groups for higher operations efficiency

Preparation and prevention are always better than the cure and this is the case for user operations. With the help of AI algorithms, you are able to predict the probability of a user performing a key action, such as churning or making a payment, giving you room to adjust operational policies that specifically target such users.

For example, with the payment prediction model, you can select a group of users who were active in the last seven days and most likely to make a payment over the next week. When these users browse specific pages, such as the membership introduction page and prop display page, you can send in-app messages like a time-limited discount message to these users, which in conjunction with users' original payment willingness and proper timing can effectively promote user payment conversion.

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* The figure shows the page for creating an in-app message for users with a high payment probability.

Scenario 2: differentiated operations for predicted user groups to drive service growth

When your app enters the maturity stage, retaining users using the traditional one-style-fits-all operational approach is challenging, let alone explore new payment points of users to boost growth. As mentioned above, user behavior prediction can help you learn about users' behavior willingness in advance. This then allows you to perform differentiated operations for predicted user groups to help explore more growth points.

For example, a puzzle and casual game generates revenue from in-app purchases and in-game ads. With a wide range of similar apps hitting the market, how to balance gaming experience and ad revenue growth has become a major pain point for the game's daily operations.

Thanks to the payment prediction model, the game can classify active users from the previous week into user groups with different payment probabilities. Then, game operations personnel can use the remote configuration function to differentiate the game failure page displayed for users with different payment probabilities, for example, displaying the resurrection prop page for users with a high payment probability and displaying the rewarded ad page for users with a low payment probability. This can guarantee optimal gaming experience for potential game whales, as well as increase the in-app ad clicks to boost ad revenue.

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* The figure shows the page for adding remote configuration conditions for users with a high payment probability.

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Scenario 3: diverse analysis of predicted user groups to explore root causes for user behavior differences

There is usually an inactive period before a user churns, and this is critical for retaining users. You can analyze the common features and preferences of these users, and formulate targeted strategies to retain such users.

For example, with the user churn prediction model, a game app can classify users into user groups with different churn probabilities over the next week. Analysis showed that users with a high churn probability mainly use the new version of the app.

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* The figure shows version distribution of users with a high churn probability.

The analysis shows that the churn rate is higher for users using the new version, which could be because users are unfamiliar with the updated gameplay mechanics of the new version. So, what we can do is get the app to send messages introducing some of new gameplay tips and tricks to users with a high churn probability, which will hopefully boost their engagement with the app.

Of course, in-depth user behavior analysis can be performed based on user groups to explore the root cause for high user churn probability. For example, if users with a high churn probability generally use the new version, the app operations team can create a user group containing all users using the new version, and then obtain the intersection between the user group with a high churn probability and the user group containing users using the new version. The intersection is a combined user group comprising users who use the new version and have a high churn probability.

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* The figure shows the page for creating a combined user group through HUAWEI Analytics.

The created user group can be used as a filter for analyzing behavior features of users in the user group in conjunction with other analysis reports. For example, the operations team can filter the user group in the page path analysis report to view the user behavior path features. Similarly, the operations team can view the app launch time distribution of the user group in the app launch analysis report, helping operations team gain in-depth insights into in-app behavior of users tending to churn.

And that's how the prediction capability of Analytics Kit can simplify fine-grained user operations. I believe that scenario-based, differentiated, and diverse user engagement modes will help you massively boost your app's operations efficiency.

Want to learn more details? Click here to see the official development guide of Analytics Kit.