[Huawei Developers] Jack Wu Shenzhen:
AARRR — short for acquisition, activation, retention, referral, and revenue — is a key operations model, where acquisition, as the very start, greatly affects how users will be converted. You may have tried different methods to improve the acquisition effect, user engagement, and user retention, but to no avail. So, what else can you do?

With the payment analysis report in Analytics Kit 6.0.0, you can analyze the behavior of your users by referring to data such as their payment frequency and preference. By combining this function with other analytical models in the kit, you'll have an array of data to work and plan from for higher revenue.

Enticing Users to Pay Quickly​

The first payment made by a user is the most significant as it implies they are satisfied with the app — but it is a process that can take some time.

This process inevitably varies app by app, so we can only touch on how to guide quick user payments in general.

Identifying common events that lead to the first payment

Sign in to AppGallery Connect. Find your project and app, and go to HUAWEI Analytics > Audience analysis. Create an audience of users who made the first payment. Then, check the report for this audience to identify the functions they frequently use. Let's say for an education app, most users tend to search for or share a course before making their first payment.

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Go to Payment analysis. Under Add filter, select the audience just created. Then, the report will present data about this audience, allowing us to optimize our operations strategies.

Leading non-paying users to frequently used or core functions

As mentioned above, the course searching and sharing functions most likely lead users to make their first payments. We can therefore guide users to use these functions more often. Or, we can send non-paying users push notifications that introduce the functions in detail, to guide such users to use them.

Increasing the ARPU & Payment Rate​

Increasing the average revenue per user (ARPU) and payment rate is important for boosting total user payment. To this end, we need to implement different operations strategies for different audiences, which can be created using the RFM model. The reason is simple: user payments vary by their payment abilities and preferences.

Determining users' paying habits

Go to Payment analysis. The report here shows changes in the paying users and the amount they pay. Using the filter and comparison analysis functions, we can easily locate the paying habits of different audiences.

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If we find that most high-paying users are active users in Beijing, we can specifically target them with campaigns to make recurring payments.

Making audience-specific strategies

We can first segment users into different audiences by using the RFM model.

R: Recency, indicating the last consumption users made before the data collection date. It can be used to measure the user consumption period.

F: Frequency, indicating the consumption times of users in a given period

M: Money, indicating the consumption amount of users in a given period

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After creating audiences, we can send them coupons or different push notifications with content that interests them, such as membership-related campaigns and promotions including price-break discounts.

In short, targeted operations based on analysis of how different audiences make payments in the app can help improve payment-related indicators and ROI.

To learn more, click here to get the free trial for the demo, or visit our official website to access the development documents for Android, iOS, Web, and Quick App.