“AARRR”Model is one of the important theories of enterprise operation. As an important part of the model, La Xin has deeply affected the subsequent user transformation. In order to improve the quality of innovation, user activity and retention, we did everything possible, but the income is not satisfactory, and there is no way to improve the income.
The “paid user analysis” report is newly added to Huawei analysis service version 6.0.0, which has an in-depth insight into the payment situation from the aspects of paying users’ purchase behavior, purchase frequency and purchase habits, and combined with other analysis models of Huawei analysis service to jointly help enterprises improve product operation revenue.
1. Guide users to quickly generate consumption intention
The user’s first payment is an important signal that the user affirms the value of the app. Usually, users need to experience for a period of time before they find the core values of the app and are willing to pay for the app.
Different apps attract different users, which leads to great differences in the first payment time of different app users. So how to guide users to quickly generate consumption intention?
- Identify high frequency events for first-time paying users
Enter the “audience analysis” report page, create a new first-time payer, view the first-time payer report, and determine the high-frequency use ability of first-time payers. Take an education app as an example, as shown in the figure below, the most frequent events among the first-time payers are search courses and shared courses.
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Enter the “payment analysis” report page, filter the “first-time payers” through the “filter”, view the payment law of the first-time payers, and formulate the operation strategy.
- For unpaid users, guide them to reach the high frequency / core functions in advance
Through the analysis of first purchase users, it is found that when users use the function of searching courses or sharing courses, they are more likely to pay for the first time. Therefore, we can strengthen the guidance of users’ ability to use search and share courses in the app, or guide users’ use and experience by pushing the detailed introduction of this function for unpaid users.
2. Improve the average payment amount (ARPU) and payment rate of users
Increasing the average payment amount (ARPU) and payment rate of users is the main goal of improving the overall payment of APP users. Due to the different payment capabilities and payment preferences of different users, different users have different payment contributions to the app. It is necessary to layer users through the RFM model and adopt different operation strategies for different types of users to improve the payment indicators.
- Determine user payment habits
Click to enter the “payment analysis” report page to view the change trend of current app users, paying users and payment amount, and determine the payment habits of different groups according to the change of trend, combined with the ability of filter and comparative analysis.
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As shown in the figure above, for example, the app’s paying users are active users with high payment, which are concentrated in Beijing. Therefore, the app can continuously guide and transform the active users in this region.
- Formulate payment strategies for different types of users
Layered users through RFM model
R stands for recency(last consumption): the user’s latest consumption from the retrieval date. It can measure the consumption cycle of users.
F stands for frequency(user consumption frequency): the number of times users spend in a specified period of time.
M stands for money(consumption amount): the consumption amount of the user within the specified time period.
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For different types of users, you can personalize the corresponding content, such as annual fee member customization, full reduction activities, preferential activities to stimulate consumption, such as coupon distribution, etc.
Through the analysis of the payment situation of different types of APP users, hierarchical operation is carried out, so as to improve the overall payment index and ROI.
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