As operations costs continue to rise, we are paying more attention to retained users in our app than to churned users. From the operations angle, churned users include both inactive users and users who have uninstalled an app. Although the retention rate reveals the user churn status, it cannot tell us why users chose to uninstall an app.
Currently, few data analysis platforms on the market are capable of collecting uninstallation data. Even if a platform is able to do so, it depends on the push capability to find out whether a user has indeed uninstalled an app, which affects analysis timeliness and accuracy.
But now, with uninstallation analysis of Analytics Kit 5.3.1, capturing app uninstallation events is no longer a daunting task thanks to the system-level broadcast capability. This feature shows you the uninstallation status, characteristics of users' pre-uninstallation behavior, and attributes of these users. On the basis of this information, you'll be able to formulate targeted strategies to reduce user churn, win back more users, and then enhance ROI.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
Uninstallation Analysis
After a user uninstalls your app, HMS Core (APK) will report the uninstallation information to the cloud platform.
* Principles
App Uninstallation Status
The uninstallation analysis report encompasses app uninstallation trends, as well as versions, channels, operating systems, and device models of users who have uninstalled the app, helping you locate uninstallation causes.
For example, if the uninstallation rate of users who use Android 7.0 is higher than that on other Android versions, you may conclude that this app delivers a poor performance on phones with an earlier version. To deal with this, you can optimize your app to reduce the churn of users who use your app on less high-performing phones.
* For reference only
Uninstallation Causes
If you discover abnormalities in app uninstallation trends, or find it difficult to locate the churn causes, you can analyze characteristics of the users who uninstalled the app. With such a rich array of data, you'll be able to design targeted optimization measures to encourage users to use your app more frequently and reduce user churn.
The pre-uninstallation behavior analysis card provides insights on user behavior, thereby helping to pinpoint the churn causes.
Pre-uninstallation Analysis
The top 10 pre-uninstallation events and top 10 pre-uninstallation session paths give you a sense of what users did within the app and why they uninstalled the app.
Using an e-commerce app as an example, if the Start payment event occurred more frequently among users who uninstalled the app, it possibly means that the user experience was affected due to defects in the payment step.
* For reference only
User Insights
A key step to reduce app uninstallation is to discover user groups that are more likely to uninstall the app by analyzing user attributes. With this data at hand, you can then design measures in advance to attract users to use the app more often.
Uninstallation analysis presents you the attributes of these users, such as the first launch time, last interaction time, app versions, operating systems, device brands, and acquisition channels. These attributes paint a picture about users who uninstalled the app. Based on such data, you can leverage audience analysis, another feature of Analytics Kit, to select target users and take measures in advance.
* For reference only
Uninstallation analysis provided by Analytics Kit shows you the app uninstallation trends, and allows you to gain insights into user behavior and basic attributes so that you can take measures in advance to reduce user churn, improve the user retention rate, and improve overall ROI.
Analytics Kit is dedicated to providing intelligent, easy-to-use, and secure data analysis services. In the future, Huawei will continue to leverage advantages of the 1+8+N ecosystem and deliver more practical functions with a better experience in a wider range of scenarios.
For details about integrating Analytics Kit, please go to the HUAWEI Developers website, where you will find the integration guide and other resources for reference. You can also contact us by submitting a ticket online.
For more information, please visit:
Our official website
Our demo
Development documents:
Android
iOS
Web
Quick App
can i track custom events?
ProManojKumar said:
can i track custom events?
Click to expand...
Click to collapse
Hello，
Yes you can track events like Automatically Collected Events, Predefined Events or Custom Events via Uninstalls Analysis. You can check our official site for the detailed information.
Document
developer.huawei.com
Related
Precise targeting of users is very important when you release new product features or organize marketing activities. Precise targeting, however, is not a simple process. For example, how do you push messages that users are interested in without disturbing them, divide users into groups and push messages accordingly, and trigger message sending based on users' behavior and interests?
HUAWEI Analytics Kit, along with App Messaging, can help answer these questions.
What are HUAWEI Analytics Kit and App Messaging?
HUAWEI Analytics Kit is a free-to-use data analysis service for app operations personnel to track how users behave in apps and facilitate precise data-driven operations. Applicable to multiple platforms such as Android, iOS, and web, and various types of devices such as mobile phones and tablets, it can automatically generate more than 10 types of analysis reports based on users' behavior events.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
App Messaging triggers in-app messages in specific scenarios according to users' behavior events. It provides a large selection of templates for message display, including pop-ups, banners, and images, and supports custom formats with a variety of configurable message elements, encompassing images, colors, content, buttons, and redirections.
Message recipients vary according to dimensions, including the app version, system version, language, country or region, audience generated by HUAWEI Analytics Kit, and user attribute. App Messaging can help you enhance user loyalty for sustainable growth.
Examples of scenarios where HUAWEI Analytics Kit and App Messaging are applicable
Example 1: The funnel analysis function of HUAWEI Analytics Kit was used for a game app, and it was discovered that the pass rate of the fourth level of the game was far lower than that of previous ones. To prevent users from churning, the operations team decided to push in-app messages about gift packs that could help pass the fourth level to players who failed to pass this level more than twice, so as to encourage the players to continue trying and therefore reducing the churn rate.
In addition, when new players complete a task designed for beginners, a message about gift packs for new players can be pushed to them to help enhance their interest in the game and improve user retention.
Example 2: Through HUAWEI Analytics Kit's retention analysis and audience analysis functions, the operations team of an online education app found that users who added courses to favorites were more likely to be retained than others. Therefore, to enhance the user retention rate, the operations team decided to push a message that encouraged users to add the course they have joined to favorites.
Moreover, for e-commerce apps, messages about discounts and stock shortage can also be automatically pushed to users after they add a product to the shopping cart but have not paid, in order to improve the payment rate.
It takes you only 5 minutes to integrate HUAWEI Analytics Kit, which helps you achieve scenario-specific precise targeting and improve the conversion rate of active users.
Integration guide:
Android
iOS
Web
Sample code:
Android
iOS
Web
If you encounter any problems during the integration, you can submit a ticket online.
We look forward to your participation!
As mobile Internet continues to grow while user attention spans continue to shrink, it is becoming increasingly important for app developers and operations personnel to find effective ways to direct users to specific in-app content or pages with a single tap.
A real-life example of this would be delivering app install ads to various channels, and then redirecting users to an in-app activity page when users open the app for the first time after downloading it so as to improve the activity participation rate.
Another example would be sending SMS messages containing links that direct users to a specific page in the app if they have installed the app. However, if a user has not installed the app, the download page will be displayed instead, and once the user downloads and installs the app, they will be taken to the specified activity page when they open the app for the first time. This function helps increase user acquisition and activation, enhance conversion, and reduce user churn.
Analytics Kit, together with App Linking, not only makes the preceding examples a reality, but also collects and analyzes a wide range of ad performance data for all channels, thereby helping you maximize user acquisition, activity, and retention.
What are Analytics Kit and App Linking?
Analytics Kit is a one-stop industry-based solution for products such as Android apps, iOS apps, web apps, and quick apps. Applicable to a wide range of devices, it offers scenario-specific data collection, management, analysis, and usage, helping enterprises achieve effective user acquisition, product optimization, precise operations, and business growth.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
App Linking is a cross-platform service provided by HMS Core, and allows you to create deep links which take users to specific pages in your app. It is applicable to scenarios such as ad delivery, targeted user activation, and content sharing.
The following gives you some examples about how Analytics Kit can be used synergistically with App Linking.
l Analyzing app installation sources and measuring user acquisition performance of each channel
You can take advantage of Analytics Kit to track app installation sources, and use App Linking to enable one-tap access to specific pages in your app. In this way, you'll be able to know which channels outperform others in terms of user acquisition, which helps enhance your acquisition and conversion rates.
* Data for reference only
To be more specific, you can use App Linking to create an app promotional link containing UTM parameters, and deliver the link to various marketing channels. When users download your app through the link and open the app for the first time, the Analytics SDK will automatically collect the link tap events and generate an analysis report. You can then adjust your ad delivery strategy according to data contained in the report, such as the real-time number of link taps, number of users who tap the link, and number of new users acquired by each marketing channel and task.
l Efficiently winning back users
Acquiring new users is often a challenge, and you may find that shifting to a strategy of winning back inactive users provides much better results.
Analytics Kit, in conjunction with App Linking, can help you do this in three easy steps:
1. First, use the user revisit report to identify the turning point when users change from inactive users to lost users, and reasonably define inactive and lost users.
2. Second, use session path analysis, audience analysis, channel analysis, and user lifecycle analysis to create a profile for inactive and lost users. This reveals the behavioral characteristics of users before they became inactive or churned, and whether they tend to come from specific channels.
3. Lastly, use this information to determine which group of users should be won back first, select appropriate winback strategies and activities, and use other services such as Push Kit and SMS to target these users by sending them deep links created using App Linking.
l Adopting viral marketing to facilitate user activation and retention
Viral marketing can be used to improve both user activation and new user acquisition by leveraging the power of social media.
For example, you can design a coupon activity which encourages existing app users to forward the activity to social media in order to obtain coupons. Non-users who tap the activity link will then be redirected via deep linking to the app activity page or download page. From here, they can perform operations such as downloading the app, helping existing users obtain the coupon, forwarding the link to others, or making in-app purchases. This can facilitate user activation and at the same time attract new users.
After that, you can utilize Analytics Kit's retention analysis, page analysis, and session path analysis features to compare the changes in retention before and after the activity was conducted, and analyze the characteristics of users' behavior paths in your app, laying a foundation for subsequent review and optimization.
For more details, you can go to:
l Our official website
l Our Development Documentation page, to find the documents you need
l Reddit to join our developer discussion
l GitHub to download demos and sample codes
l Stack Overflow to solve any integration problems
lDemo of Analytics Kit
lAndroid SDK integration documentation
liOS SDK integration documentation
lWeb SDK integration documentation
lQuick app SDK integration document
Original Source
Sports and health apps are thriving as more and more people place greater value on their health and exercise. This has in turn increased traffic and brought more complex demands. To seize this opportunity and retain long-term users, what is needed is to perform precise operations.
To do so, a practical, industry-specific event tracking system is vital. It is the very start of data analysis and paves the way for data-based operations. In light of this, Analytics Kit offers the event tracking template for the sports and health apps, which provides E2E event tracking management, simplifying app development. Analytics Kit thereby facilitates event tracking and maximizes data value, driving sports and health apps towards digital transformation.
Intelligent Event Tracking1. Selecting a TemplateSelect Health of Sports and Health. The page displayed shows four templates: Behavior analysis, Account Analysis, Consumption Analysis, and Services and Other.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
* Template for sports and health apps
2. Configuring Event TrackingAnalytics Kit supports event tracking either by coding or through visual event tracking. Tracking by coding can be implemented by copying the sample code, downloading the report, or using the tool provided by Analytics Kit. Tracking by coding is relatively stable, and can collect and report complex data. Visual event tracking comes with lower costs and technical requirements, and allows the visual event to be modified and added after the app release. To use visual event tracking, you need to integrate Dynamic Tag Manager (DTM) first. You can then synchronize the app screen to a web-based UI and click relevant components to add events or event parameters.
* Configuring event tracking
3. Verifying the Tracking ConfigurationYou can use the verification function to quickly identify incorrect and incomplete configurations, as well as other exceptions in events and parameters once event tracking is configured for a specific template. With this function, you can configure event tracking more accurately and mitigate business risks.
* Verifying the tracking configuration
4. Managing Event TrackingThe management page presents the event verifications and registrations, the proportion of verified events to its maximum, as well as the proportion of registered parameters to its maximum. Such information serves as a one-stop management solution, giving you a clear understanding of event tracking progress and the structure of tracking configurations.
* Managing event tracking
CaseReleased in February, 2016, Now: Meditation has become a leading app dedicated to meditation and mental health in China. Its user retention rate and payment conversion have significantly improved since it utilized the event tracking template for sports and health apps provided by Analytics Kit. The template provides analysis reports containing various indicators and comparison analysis by different dimensions, giving an insight into how users used the app, identifying unusual payment conversion rates, and showing conversion rates of different channels. All of these can illustrate what attracts users to the app and what can be done to encourage users to make payments.
1. Understanding App Usage to Guide Product OptimizationAfter event-related data was reported, operations personnel of Now: Meditation checked them in the session path analysis report. The report clearly showed how users behaved in the app, what they did following each step, the steps where users churned, and the steps with an unexpected churn rate.
This powerful function enabled the personnel to find that most active users tended to check content related to improving sleep. Operations personnel concluded that this type of content was most popular among users. Consequently, in the updated version of the app, the product team adjusted the display level for this particular content. They also optimized push notifications by using A/B Testing and sent targeted notifications to audiences. One month after these measures were taken, the retention rate skyrocketed.
* Example of a session path analysis report
2. Analyzing the Reasons Behind UninstallationsAnalyzing why users uninstall an app has become a must. Few data analysis platforms, however, could perfectly meet this demand. Luckily, with the uninstallation analysis function in Analytics Kit, capturing app uninstallation events is no longer a daunting task thanks to the system-level broadcast capability. This function shows the uninstallation status and characteristics of users' pre-uninstallation behavior. With this information at your disposal, you'll be capable of finding the root reason behind uninstallation and better optimizing operations campaigns and the product.
The uninstallation analysis report of Now: Meditation clearly showed that before users uninstalled the app, they tended to engage in three events: tapping push notification, ad display, and performing searches. Operations personnel believed the reasons why users uninstalled the app were due to inappropriate frequency, timing, and incorrect audience for push notifications and ads. Other reasons include lack of informative course content and wrong course recommendations. Based on this assumption, the product team decided to send push notifications and ads less frequently, opting to send different notifications and ads to different audiences by using A/B Testing, and in accordance to user attributes. On top of this, the team also improved the course recommendation mechanism. These small changes have delivered a sense of personalization to users, which in turn has led to a significant drop in the uninstallation rate.
* Example of an uninstallation analysis report
3. Attributing Contribution Rates of Slots to Convert More UsersAn app tends to have different banners, icons, and content, designed to induce VIP members toward making purchases. This leads to some questions: what is the difference in how much each slot and marketing campaign contributes to payment conversion? How to optimize the combination of slots? And how to allocate resources to them more reasonably?
In order to answer these questions, the operations personnel of Now: Meditation used the event attribution analysis and marketing attribution analysis models in Analytics Kit. With these models, they were able to evaluate the user attraction and conversion effects of slots by week and month and check how push notifications contributed to user conversion. Let's take the analysis of the home screen slots as an example. The operations personnel used event attribution analysis to conduct an analysis of the slots. They chose Payment completed as Target conversion event and selected Push notification tapping, Pop-up window tapping, Splash ad tapping, Banner tapping, Searching, Checking exclusive content, and Checking popular courses as To-be-attributed event. They then chose Last event attribution as Attribution model. The following day, the report showed how much each slot contributed to the target conversion event. With this information, the personnel then adjusted how traffic and marketing campaigns were allocated to slots. As a result, they could effectively plan resource allocation.
* Example of attribution analysis
4. Establishing a Churn Warning System to Win back Inactive and Lost UsersWhat's the top concern of apps now? Undoubtedly, it's how to retain users. In the case of Now: Meditation, operations personnel used retention analysis and revisit users analysis to reveal the causes behind user churn. The personnel then used the user lifecycle analysis function to save inactive and lost users as an audience. Once this audience was created, they analyzed the scale of such users, their ratio in all users, their behavior characteristics, the phase from which they turned, and whether they came from a specific channel. With such information, the personnel prioritized which audiences they should attempt to win back. Then, they tried to engage users through ads, push notifications, SMS messages, and e-mails according to users' interest, benefits, and emotions. By the end of this, they established a complete churn warning and user winback system.
* Example of a user lifecycle analysis report
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.
Does it support the gaming application as well?
how we can check uninstallation.
Basavaraj.navi said:
Does it support the gaming application as well?
Click to expand...
Click to collapse
Hi, HMS Core Analytics Kit provides Game Industry Analysis Reports for the gaming applications. You can check this article published early: https://forum.xda-developers.com/t/...ry-analysis-reports-in-analytics-kit.4320363/
You can also access our official site for more details: https://developer.huawei.com/consumer/en/hms/huawei-analyticskit?ha_source=hmsxds
lokeshsuryan said:
how we can check uninstallation.
Click to expand...
Click to collapse
Hi, there's Uninstallation Analysis provided by HMS Core Analytics Kit. You can check this article published early: https://forum.xda-developers.com/t/...lation-analysis-to-reduce-user-churn.4305551/
You can also access our official site for more details: https://developer.huawei.com/consumer/en/hms/huawei-analyticskit?ha_source=hmsxds
During daily operations, it is a top priority for marketers to quickly obtain operations data and send it to the analytics and attribution platforms. HUAWEI Dynamic Tag Manager (DTM) empowers operations and development personnel to quickly obtain and distribute data by configuring rules or adding visual events, helping substantially improve work efficiency. Today, I will explain the advantages of using DTM from an SDK integration perspective.
I. Pain Points for Traditional SDK Integration
During app operations, operations personnel usually need to check and analyze operations data. To do so, they often need to connect to multiple data analytics platforms or ad attribution platforms, which causes three major pain points to occur.
Pain point 1: high development cost and resource wastage
For an enterprise app, enterprise personnel may care more about different data based on their role in the enterprise. Take a shopping app as an example. The product manager may care most about the sales volume of a product, operations personnel may want to count the app launch times and new users, development personnel will care about how users are using the app, and marketing personnel will definitely want to view the benefits that their ads are bringing. To meet all these requirements, the enterprise app will need to integrate the SDKs of various third-party platforms, causing high development costs and a long development period. Even worse, this may increase the app size and make it hard to maintain the app.
Pain point 2: high security risks
Recently, the Ministry of Industry and Information Technology released a notice on removing apps that infringe upon user rights. The notice named and shamed five enterprise apps that have significant issues in this regard. The issues include collecting personal information without authorization, forcing users to use the targeted push function, and requesting excessive permissions frequently and without good reason. Upon further investigation, it was discovered that the issues were mainly caused by third-party SDKs. It is all too common that third-party SDKs illegally collect user device information, which is why integrating SDKs of multiple third-party platforms has the potential to pose significant security risks to enterprises.
Pain point 3: low work efficiency due to complex operations
For personnel unfamiliar with SDK integration, integrating SDKs can be a daunting process. For personnel familiar with SDK integration, having to integrate dozens of SDKs can become a repetitive and unrewarding task.
II. What Are the Advantages of Integrating the HUAWEI DTM SDK?
With HUAWEI DTM, you only need to integrate the DTM SDK to quickly obtain and distribute data, freeing you from the hassle of integrating multiple third-party SDKs.
Advantage 1: quick integration without the need to release an app update
The figure below compares typical operations scenarios with and without the DTM SDK integrated.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
Without DTM, you'll need to integrate the SDKs of all the analytics platforms you want to use into the app, which will increase the app package size. In addition, events will be tracked and reported separately by each SDK, which increases the complexity of the app unnecessarily. If you want to use a new analytics platform, you'll need to integrate the SDK of the platform into the app.
With DTM, you only need to integrate the DTM SDK to send data to multiple analytics platforms. You can dynamically and flexibly adjust the configuration policy for the app on the DTM portal to decide which data to report to analytics platforms, without having to modify the app code or release an app update.
Advantage 2: high security and reliability, ensuring data security
The DTM SDK is integrated into the app during app packaging. It starts when the app is launched and stops as soon as the app is closed, without performing any operations in the background.
1. The DTM SDK only provides capability APIs, and will not collect and store any personal data from users.
2. The DTM SDK only reports data to analytics platforms specified by the operations or development personnel.
3. If malicious or illegal data is detected, the setAnalyticsEnabled method of HUAWEI Analytics will be called to disable data reporting.
Advantage 3: easy to use, even for personnel without coding experience
Currently, DTM supports dozens of third-party analytics platforms. Its codeless tag management capabilities can be easily used by personnel without a coding background. DTM allows you to implement marketing data tracking as needed without requiring the services of development personnel. This allows you to effectively reduce development costs as well as inter-departmental communication costs.
To learn more about DTM, please visit:
>> DTM official website
>> DTM development guide
>> DTM codelab
In the information age, the external market environment is constantly changing and enterprises are accelerating their digital marketing transformation. Breaking data silos and fine-grained user operations allow developers to grow their services.
In this post, I will show you how to use the prediction capabilities of HMS Core Analytics Kit in different scenarios in conjunction with diverse user engagement modes, such as message pushing, in-app messaging, and remote configuration, to further service growth.
Scenario 1: scenario-based engagement of predicted user groups for higher operations efficiency
Preparation and prevention are always better than the cure and this is the case for user operations. With the help of AI algorithms, you are able to predict the probability of a user performing a key action, such as churning or making a payment, giving you room to adjust operational policies that specifically target such users.
For example, with the payment prediction model, you can select a group of users who were active in the last seven days and most likely to make a payment over the next week. When these users browse specific pages, such as the membership introduction page and prop display page, you can send in-app messages like a time-limited discount message to these users, which in conjunction with users' original payment willingness and proper timing can effectively promote user payment conversion.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
* The figure shows the page for creating an in-app message for users with a high payment probability.
Scenario 2: differentiated operations for predicted user groups to drive service growth
When your app enters the maturity stage, retaining users using the traditional one-style-fits-all operational approach is challenging, let alone explore new payment points of users to boost growth. As mentioned above, user behavior prediction can help you learn about users' behavior willingness in advance. This then allows you to perform differentiated operations for predicted user groups to help explore more growth points.
For example, a puzzle and casual game generates revenue from in-app purchases and in-game ads. With a wide range of similar apps hitting the market, how to balance gaming experience and ad revenue growth has become a major pain point for the game's daily operations.
Thanks to the payment prediction model, the game can classify active users from the previous week into user groups with different payment probabilities. Then, game operations personnel can use the remote configuration function to differentiate the game failure page displayed for users with different payment probabilities, for example, displaying the resurrection prop page for users with a high payment probability and displaying the rewarded ad page for users with a low payment probability. This can guarantee optimal gaming experience for potential game whales, as well as increase the in-app ad clicks to boost ad revenue.
* The figure shows the page for adding remote configuration conditions for users with a high payment probability.
Scenario 3: diverse analysis of predicted user groups to explore root causes for user behavior differences
There is usually an inactive period before a user churns, and this is critical for retaining users. You can analyze the common features and preferences of these users, and formulate targeted strategies to retain such users.
For example, with the user churn prediction model, a game app can classify users into user groups with different churn probabilities over the next week. Analysis showed that users with a high churn probability mainly use the new version of the app.
* The figure shows version distribution of users with a high churn probability.
The analysis shows that the churn rate is higher for users using the new version, which could be because users are unfamiliar with the updated gameplay mechanics of the new version. So, what we can do is get the app to send messages introducing some of new gameplay tips and tricks to users with a high churn probability, which will hopefully boost their engagement with the app.
Of course, in-depth user behavior analysis can be performed based on user groups to explore the root cause for high user churn probability. For example, if users with a high churn probability generally use the new version, the app operations team can create a user group containing all users using the new version, and then obtain the intersection between the user group with a high churn probability and the user group containing users using the new version. The intersection is a combined user group comprising users who use the new version and have a high churn probability.
* The figure shows the page for creating a combined user group through HUAWEI Analytics.
The created user group can be used as a filter for analyzing behavior features of users in the user group in conjunction with other analysis reports. For example, the operations team can filter the user group in the page path analysis report to view the user behavior path features. Similarly, the operations team can view the app launch time distribution of the user group in the app launch analysis report, helping operations team gain in-depth insights into in-app behavior of users tending to churn.
And that's how the prediction capability of Analytics Kit can simplify fine-grained user operations. I believe that scenario-based, differentiated, and diverse user engagement modes will help you massively boost your app's operations efficiency.
Want to learn more details? Click here to see the official development guide of Analytics Kit.