[Huawei Developers] John Wo:
With the drying up of Internet traffic sources, successful user payment conversion is more essential than ever to monetizing products.

This requires meticulously data-driven operations. Current business growth solutions mine historical user behavior for valuable clues, for example, performing attribution to grasp user payment trends. Still, it would be much better to be able to proactively predict user attributes and behavioral preferences in advance, and then use these predictions to make optimal decisions that could increase payment conversion. That's where HUAWEI Prediction enters the picture…

1. Which types of users tend to make payments?

User payments don't usually come out of nowhere, as they are usually preceded by a series of actions, such as viewing an ad, experiencing a product, or comparative shopping activities. Therefore, the goal of payment prediction is to find those users who demonstrate a high payment potential, among all recently active users. In-app purchase events are the direct result of payment behavior.

HUAWEI Prediction's payment prediction task trains a model that takes user payment data from the most recent two weeks into account, to predict the probability that app users from the previous week will make a payment over the next week. Naturally, since the basic data and model training are highly dependent on the in-app purchase events reported by your app, predictions are only accurate when there is sufficient, high-quality reported data.

2. What are the characteristics of users who demonstrate high payment potential?

Users can perform transactions for any number of reasons, for example, being drawn to a product description, or for believing that a product offers excellent value. To perform targeted marketing and operations strategies, it's important to be able to identify common attributes and behavioral characteristics of users with high payment potential.

For your app's prediction analysis, you can add metrics, such as user acquisition, total page views, and time of last use, to analyze your app's audience metrics in a thoroughly in-depth manner, as seen below.

In the example below, the high payment potential users clearly share common attributes and behavioral characteristics. Users in this audience tend to be those who have frequently used the app for a relatively lengthy period of time, and have also recently used the app on a frequent basis. From this information, we can surmise that such users have a high degree of familiarity with the product, and a strong desire to purchase, but are deterred for various reasons, such as that the price is slightly too high, or that the product is not their primary necessity. They are thus, in the process of waiting for a discount, or still comparing the product against other similar products.

In this case, a promotion such as a time-limited discount, or other form of incentive, will attract a large number of users who are still considering the purchase. However, if the promotion is pushed to all app users, the operations costs would also soar, and may even exceed the amount earned during the promotion. Next, we'll walk you through how HUAWEI Prediction can help you limit the promotion to target users alone, and keep costs under control.

3. How can I promote user payment conversions?

Now that established the high payment potential audience profile, we'll need to maximize the value of this group. This can be achieved by implementing a successful promotion.

Audiences identified by HUAWEI Prediction can be applied to a range of other AppGallery Connect services. For example, we can use Remote Configuration to carry out the promotion.

To do so, go to the Condition management page of Remote Configuration, and add the Prediction filter, as well as all corresponding parameters for the promotion. This ensures that you will send the promotion only to those users in the predicted audience, thereby minimizing operations costs.

After the promotion is complete, you can refer to the payment details for confirmation that it worked as intended, and proved more effective than previous promotions.

You can also apply the predicted audience to other services, for example, by utilizing A/B Testing to test how the predicted audience responds to different payment growth solutions.

To learn more about HUAWEI Prediction, feel free to check out this document.