[Huawei Developers] Freemind R:
"The purpose of business is to create and keep a customer." - Peter Drucker

As traffic sources dry up, enterprises have found it increasingly difficult to acquire new users, and face potentially enormous losses caused by user churn. Therefore, determining which users are at risk of churn in advance, and taking proactive steps to retain them, is critical to achieving success on the market.

Products that feature a high user retention rate, not only benefit from the increased revenue, but are also easier to promote, as long-term users are more likely to recommend the product to other users.

HUAWEI Prediction offers a groundbreaking new model for user retention that can give your app the fortitude to withstand even the most volatile market.

With regard to predicting user churn, four questions immediately spring to mind:

How can I determine whether a user is at risk of churning?

Are the predictions accurate?

What are the characteristics of users who are at risk of churning?

What can the predictions be used for?

Let's address these questions one at a time.

1. How can I determine whether a user is at risk of churning?

First, we need to determine how long it takes for an inactive user to be regarded as a churned user to be won back. Determining the correct length of time is critical. If the time period is too short, the predicted audience will inevitably include a number of retained users as well, which can cause the cost of winning this group of users back to soar unnecessarily. But if the time period is too long, the user group will include those users who have been permanently lost, for whom there is no possibility of winning back.

The churn prediction task preset by HUAWEI Prediction obtains insight into industry-specific user lifecycle attributes, by taking a massive amount of experimental data into account. The task uses the active user data from the previous two weeks to train the model, which then predicts the probability that active users of the app from the previous week will be lost over the next week. Users who are inactive in the next week, or those who uninstall the app, are regarded as churned users.

2. Are the predictions accurate?

The audience of churned users will change with time, as users' behavior tends to vary on a daily basis. That's exactly why predictions are based on the most recent data. HUAWEI Prediction provides two indicators to evaluate the accuracy of its predictions, the true positive rate and false positive rate. In a churn prediction task, the true positive rate refers to the ratio between the number of users correctly predicted to have been churned, to the total number of churned users; the false positive rate refers to the ratio between the number of users falsely predicted to be churned, to the total number of users who are not churned. You've likely noticed that a higher true positive rate indicates a lower false positive rate, as well as a higher prediction accuracy.

You can also check the predictions from the most recent 7 days to evaluate the quality of the data reported by your app. Sufficient and quality data are essential for accurate predictions, as the prediction model is trained according to historical data.

3. What are the characteristics of users who are at risk of churning?

The service has preset three probability ranges: high, medium, and low, and also gives you the freedom to customize a probability range according to your needs. Thus, each prediction will generate four audiences. You can then perform in-depth analysis into user attributes, user behavior, and other audience characteristics.

Let's use an audience with a high churn probability as an example. The prediction details page displays the number of users in this audience. You can customize the metrics of interest by selecting user attributes and behavior cards. For example, for predicted high-probability churned users, you can select the User acquisition and Total sessions cards. According to the customized metrics, most users in this audience have a lengthy usage history, and a high number of total sessions.

In this case, we can infer that the users are likely to churn, not because they have not been targeted by your activities, or think that the product design is poor, but rather, because they are already in the inactive stage of the user lifecycle. Therefore, targeted operations should be planned with the goal of reactivating them.

This leads to the question of "how"?

4. What can the predictions be used for?

You're likely familiar with some commonly used user activation and winback activities, for example, discounts and message pushing. Most of the time these activities target users who have already churned, but the rate of success can be disheartening.

The following is a real case study. A game had been hampered by a low user retention rate, and had tried a number of marketing activities, including time-limited gift packages, with the goal of winning back users. However, the data revealed that almost all users who claimed the packages were active users, rather than the real target of the promotion, which was churned users. This imposed high costs with little reward.

The game's operations team then turned to HUAWEI Prediction and applied the predicted audience in Remote Configuration. They sent the gift package to only the identified audience, via Remote Configuration. A remarkable 80% of target users were re-activated, demonstrating the unmatched prowess of HUAWEI Prediction in helping apps retain users in the most cost-effective manner possible.

To reach users who will churn in the near future precisely and retain them in time is the best cost-effective operations strategy. That's why we should use predictions.

To learn more about HUAWEI Prediction, check the document.