[Huawei Developers] Freemind R:
Lots of apps these days are finding it difficult to maintain user growth. The main reasons for this are that the demographic dividend for users has gradually subsided, user bases and growth rates are decreasing, and some apps even have negative user growth. Also, competition in app categories such as e-commerce, lifestyle, and gaming is on the rise, while the retention rate of new users is dropping. Low user acquisition and retention rates have been long-standing challenges for app operators.

[IMG="https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021033917.54781273677604277503448906884550:50511103012853:2800:92B26522F13BA0E261DFDFB0834A93712980EF52C16881C0E342AE85194FF524.png"]https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021033917.54781273677604277503448906884550:50511103012853:2800:92B26522F13BA0E261DFDFB0834A93712980EF52C16881C0E342AE85194FF524.png[/IMG]​

So how do app operators resolve this pressing issue?

One obvious solution is to leverage data to search for growth opportunities in the entire user lifecycle, and to maintain growth through refined operations.

The first step of refined operations is to divide users into different phases of the user lifecycle. With HUAWEI Analytics Kit, the lifecycle of a user can be divided into the following phases: beginner, growing, mature, inactive, and lost.

For beginner users, growth plans should be made based on maximizing return on investment (ROI) and user activation to ensure that you can quickly acquire the users you want and then convert them into growing users.

For growing and mature users, the key areas of focus is to improve retention and conversion rates. It is very important to maximize the value of these users and make them more active and stable.

For inactive and lost users, prevent user churn, try to win back lost users, analyze the causes of churn, and optimize user activation promotion plans.

[IMG="https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021033941.96091055113260212408597385979354:50511103012853:2800:0050B474D41E2847DB0DD66FD5BDE57782D9659AA7C9E98E6E405DFCB1E47E9B.png"]https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021033941.96091055113260212408597385979354:50511103012853:2800:0050B474D41E2847DB0DD66FD5BDE57782D9659AA7C9E98E6E405DFCB1E47E9B.png[/IMG]​

For Beginner Users: Reduce the User Acquisition Cost, and Promote User Activation and Growth

[IMG="https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034012.48046802190561249526470323352962:50511103012853:2800:838EADF4F736CCBDE9A0B0768A212BF339837448AE34B2167B007CD8E78A2241.png"]https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034012.48046802190561249526470323352962:50511103012853:2800:838EADF4F736CCBDE9A0B0768A212BF339837448AE34B2167B007CD8E78A2241.png[/IMG]​

How can HUAWEI Analytics Kit help you reduce user acquisition costs in the beginner phase? It does so by providing you with various analysis capabilities such as event analysis and comparison analysis, which allow you to view event trends and the distribution of event-generating device models and operating systems. Then, we can use the filter to perform comparison tests of different types of events and select the optimal channel to place services.

How to promote activation and growth of beginner users? This can be done by guiding users to complete key operations based on their interests. For example, for video apps, guide users to watch videos for a certain period of time or purchase membership; for game apps, help users pass early levels; and for e-commerce apps, enable users to place the first order within a short period of time. Then, perform funnel analysis and attribution analysis to analyze the conversion rates of users based on their behavior at key nodes when using apps, so that you can optimize the process, improve the provisioning mode, and UI design of individual nodes.

Case Study: Operations Practice of a Short Video App to Reduce the User Acquisition Cost

[IMG="https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034141.38763206471656862434873795351060:50511103012853:2800:333F6F0C8FB826304E175960D04F2F586001BCE8C8E58C43A091B5D2832E7C95.png"]https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034141.38763206471656862434873795351060:50511103012853:2800:333F6F0C8FB826304E175960D04F2F586001BCE8C8E58C43A091B5D2832E7C95.png[/IMG]​

The app delivered ads to acquire new users in various ways. However, the problem was that the contribution rate of each channel cannot be accurately calculated, and the user churn rate was high.

By using the attribution analysis model of HUAWEI Analytics Kit, operations personnel of the app defined the target conversion event as "new download and use", and the to-be-attributed event as "ad clicks on each channel". According to the generated report, Vigo Video had the highest contribution rate, while Weibo had the lowest. So they shifted their marketing budget from Weibo to Vigo Video. After three months of optimization, their user acquisition costs decreased by 26% and the new user retention rate increased by 15%.

For Growing and Mature Users: Promote User Activation and Retention, and Increase the User Conversion Rate

[IMG="https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034241.18886411401382421285797387946132:50511103012853:2800:55F252ABAC42E40A3F453D65C839DF43A41DFD31B6DFC38C70B8DA643E5F1F7C.png"]https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034241.18886411401382421285797387946132:50511103012853:2800:55F252ABAC42E40A3F453D65C839DF43A41DFD31B6DFC38C70B8DA643E5F1F7C.png[/IMG]​

In addition to user activation and retention, we must also focus on the user conversion rate. How to retain and convert users is a common challenge for most apps. For retention, perform path analysis to detect the actual behavioral paths of users when they are using apps, then check whether the paths are different from the designed ones. If not, we can guide users to the designed paths by operational means. In addition, funnel analysis can also be performed which will give you an intuitive picture of the conversion rate and churn rate of each phase, which facilitates app optimization. For conversion, use both the filter and behavior analysis model to analyze users by segment, and to know the behavior characteristics of different users. After that, use audience analysis model to segment users for precise services based on the analysis result, thereby increasing the user conversion rate.

Case Study: Operations Practice of an E-commerce App to Improve User Retention and Conversion

[IMG="https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034312.61448886471062217048309187788947:50511103012853:2800:11E55C8FAE8D3B650750344F38D2836F1A681D40735B20AB89E305221E43A866.png"]https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034312.61448886471062217048309187788947:50511103012853:2800:11E55C8FAE8D3B650750344F38D2836F1A681D40735B20AB89E305221E43A866.png[/IMG]​

The operations personnel of the app found that the user retention rate and purchase conversion rate in the last two months had decreased. How did they quickly solve the problem? Operations personnel first segmented users based on user attributes (such as the gender, age, region, and phone brand) and user behavior (such as browsing products, adding to cart, and purchasing). Then they figured out different behavior characteristics through path analysis and funnel analysis models according to the segmented users. It was then discovered that the churn rate from submitting an order to make a payment was the highest. Moreover, inactive users commonly made fewer than three purchases, and functional areas on the homepage were not clearly divided. Based on these findings, the app's operations personnel formulated an optimization solution to improve the purchase conversion rate.

For Inactive and Lost Users: Prevent User Churn, Wake Up Inactive Users, and Summarize Experience

[IMG="https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034503.32057707065953234368768206715577:50511103012853:2800:91083B803943B9D085243C8A15D391588C43B5F00B82A8A703CAA687D1CC6C7F.png"]https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034503.32057707065953234368768206715577:50511103012853:2800:91083B803943B9D085243C8A15D391588C43B5F00B82A8A703CAA687D1CC6C7F.png[/IMG]​

Inactive and lost users operations personnel want to see.

For inactive users, the key is to prevent user churn and precisely choose which inactive users to wake up. We can formulate operations policies in advance to avoid user churn based on the predicted user groups that had churn risks or winback potential in each phase based on the user lifecycle analysis model of HUAWEI Analytics Kit. In addition, behavior analysis can help to detect users whether they have the value and possibility to be woken up, as well as to send them messages to try to wake them up. For lost users, it is more difficult to win them back than to acquire new users. Therefore, it is recommended that we focus more on summarizing experience and optimization to avoid churn of active users. For example, figure out the characteristics of lost users, enhance the detection capability before the churn, use data from lost users to help optimize the promotion of current users, and increase the activation and loyalty of active users to avoid user churn by improving operations policies based on funnel analysis, behavior analysis, and comparison analysis.

Case Study: Operations Practice of a Game App to Wake Up Inactive Users

[IMG="https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034539.74542395168444955206524640522984:50511103012853:2800:BDEE5D805F52B02B5F83477D0A563EF3B18AE4740A91A95A9708AA64BA8FCF54.png"]https://communityfile-dre.op.hicloud.com/FileServer/getFile/cmtybbs/005/796/051/2640852000005796051.20201021034539.74542395168444955206524640522984:50511103012853:2800:BDEE5D805F52B02B5F83477D0A563EF3B18AE4740A91A95A9708AA64BA8FCF54.png[/IMG]​

Let's take a look at how the operations personnel of this game app woke up inactive users. The operations personnel first selected the inactive users who were worthy of and likely to be waken up by performing behavior and audience analysis. It was determined that such users were users who had made at least three in-app payments and passed more than five in-game levels. The operations personnel then designed specific plans for waking up inactive users. The user lifecycle analysis model provided the predicted user groups that had churn risks. Therefore, they avoided user churn by improving user experience and giving users benefits through in-app messaging or push messages. Also, a detailed analysis of the behavioral attributes of churned users was carried out. It turned out that such users had less than 5 friends in-game, and most have complained about the game freezing. The operations personnel then tested and determined the causes of user churn, and optimized their app and operation policies accordingly. Such in-app improvements included providing a multi-channel account sign-in feature, a one-touch friend adding feature, and optimizations to the interaction logic. After optimization, the app successfully decreased the inactive user rate by 12%, and the user churn rate by nearly 8%.

Lastly, let's recap on how to increase the number of users of the entire user lifecycle through the use of the HUAWEI Analytics Kit.

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Once you integrate the HMS Core Analytics SDK (Android, iOS, and JavaScript), you can upload user attributes and behavior data, so that the actual behavior of users at a specific time can be displayed, giving you the basis of data analysis. HUAWEI Analytics Kit supports the automatic collection of 11 user attributes and 27 events, as well as customized user attributes and 500 customized events, making your optimizations easier and providing more data for refined operations.

Moreover, it provides abundant analysis models based on the atomic data, such as events, behavior, funnels, audience, lifecycle, and attribution, enabling you to learn about user growth, user behavior, and product functions. Backed by these models, the filter can be used to perform segmentation analysis of app types, user attributes, and audiences. More importantly, it supports various types of apps, including iOS, Android and Web apps to meet your cross-platform analysis needs. You can complete the integration and release your apps in half a day. HUAWEI Analytics Kit has become one of the most popular services globally due to its quick development speed and powerful analysis capabilities. Integrate HUAWEI Analytics Kit today, and explore its myriad of enriching features.

References

Official website of Huawei Developers

Development Guide

HMS Core official community on Reddit

Demo and sample code

Discussions on Stack Overflow