As enterprises have placed higher requirements on data monitoring and analysis, marketing campaigns have changed at a breakneck speed. This phenomenon has posed a challenge for operations personnel, who find it difficult to make informed decisions based on data that's being updated on an hourly or T+1 basis.
This is especially true when a new marketing campaign is launched, a new version is released, or when ads are delivered in different periods and through multiple channels. Under these scenarios, product management and operations personnel need the access to minute-by-minute fluctuations in the number of new users and number of users who have updated the app, as well as the real-time data on how users are engaging with the promotional campaign. Armed with this timely data and real-time decision-making capabilities, the personnel are able to ensure that the results from a promotion, update, or launch meet expectations.
Analytics Kit leverages Huawei's formidable data processing and computing capabilities, offering a reconstructed real-time overview function that's based on ClickHouse. It makes operations more seamless than ever, by showing data such as the number of new users and active users in the last 30 minutes and current day, channels that acquire users, app version distribution, and app usage metrics.
Data Provided by Real-Time Overview1. Data Fluctuations from the Last 30 Minutes and 48 HoursThis section provides the access to the numbers of new users, active users, and events over the last 30 minutes, as well as comparisons between the numbers of new users, active users, and events by minute or hour from the current day or day before. You can also filter the data to meet your needs, by specifying an acquisition channel, app version, country/region, or app to perform more thorough analysis.
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* Example of a real-time overview report
2. Real-Time Information about Events and User AttributesThis part provides user- and event-related data, including their parameters and attributes. When using it in conjunction with data from the previous section, you can get a highly detailed picture of app usage, getting crucial insights into key indicators for your app.
* Example of a real-time overview report
* Example of a real-time overview report
3. User DistributionHere you'll get in-depth insights into how your app is being used, thanks to a broad range of real-time data related to the distribution of users by channel, country/region, device model, and app.
* Example of a real-time overview report
When Should I Use Real-Time Overview1. To Identify Unexpected TrafficOnce you have delivered ads through various channels, you'll be able to view how many users have been acquired through each of these channels via the real-time overview report, and then allocate a larger share of the ad budget to channels that have performed well.
If you find that the number of new users acquired through a channel is significantly higher than average, or that there is a sudden surge from a specific channel, the report can tell you the device models and location distributions of these new users, as well as how active they are after installing the app, helping you determine whether there are any fake users. If there are, you can take necessary actions, such as reducing the ad budget for the channel in question.
You can also check the change in the number of users acquired by each channel during different time segments from the last 48 hours. Doing so allows you to compare the performance of different promotional assets and channels. Those that fail to bring about expected results can simply be replaced.
Take an MMO game as an example, whose operations personnel use the real-time overview function to determine changes in new user growth following the release of different promotional assets. They found that the number of new users increased significantly when an asset is delivered at 10 o'clock. During the lunch break, however, the new user growth rate was far lower than expectations. The team then changed the asset, and was happy to find that the number of new users exploded during the evening, meeting their initial target.
2. For Real-Time Insights on User Engagement with a Promotional CampaignOnce you've launched a marketing campaign, you'll be able to monitor it in real time by tracking such metrics as changes in the number of participants, geographic distribution of participants, and the numbers of participants who have completed or shared the campaign. Such data makes it easy to determine how effective a campaign has been at attracting and converting users, as well as to detect and handle exceptions in a timely manner.
For example, an e-commerce app rewards users for participating in a sales event. It used the real-time overview report to determine that the number of participants from a certain place as well as the app sharing rate of participants from this place were both lower than expected. The team pushed a coupon and sent an SMS message to users who had not yet participated in the campaign, and saw the participation rate skyrocket.
3. To Avoid Poor Reviews During Version UpdatesWhen you release a new app version for crowdtesting or canary deployment, the real-time overview report will show you the percentage of users who have updated their app to the new version, the crash rate of the new version, as well as the distribution of users who have performed the update by location, device model, and acquisition channel. If an exception is identified, you can make changes to your update strategy in a timely manner to ensure that the new version will be better appreciated by users.
Furthermore, if the update includes new features, the report will show you the real-time performance of the new features, in addition to any relevant user feedback, helping you identify, analyze, and solve problems and optimize operations strategies before it's too late.
Timely response to user feedback and adjustments to operations strategies can help boost your edge in a ruthlessly competitive market.
That's it for our introduction and guide to the real-time overview 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.
Thanks for sharing.
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# Enable offline access so that you can refresh an access token without
# re-prompting the user for permission. Recommended for web server apps.
access_type='offline',
# Enable incremental authorization. Recommended as a best practice.
include_granted_scopes='true')
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A lot of apps these days are struggling to grow their user base, while traffic is also getting increasingly expensive. Although some apps have acquired a large number of new users, the corresponding churn rate increases sharply. So, it is important that you can win over a number of retained users that are particularly loyal to your app.
What is retention?
Let's take mobile apps as an example. After downloading the app, some users just take a cursory glance, some will never come back after they've claimed the coupons, some may even uninstall the app without ever using it. It is only users who continuously use and benefit the app that can be regarded as retained users.
The retention rate generally refers to the rate of repeated behaviors of a user over a given period of time, and is usually measured by the retention rate after 1 day, 7 days, and 30 days.
Generally speaking, there are three types of retention curves: the smiling curve, flattening curve, and declining curve.
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Smiling curve: ideal curve for all app managers. It shows an increasing trend in a certain period of time. For an e-commerce app, for example, it indicates that users keep coming back to purchase your products.
Flattening curve: indicates that the app has attracted some new users, and these users like the app and continue to use it. However, not all flattening curves are good. You want the curve to flatten as high as possible on the graph, as this indicates a better long-term retention rate.
Declining curve: indicates that the app has not achieved a great deal of approval among users. A declining retention rate is a sign that you need to act now to optimize your app, and make it more appealing to your target users. Otherwise, its future prospects do not look good.
How is user retention analysis implemented?
User retention analysis is implemented by segmenting your users. You can segment your users through Analytics by phone model, device type, gender, age, and region. Then, you can think about the reasons why users are retained or churned by looking at these segmented user groups. For example, if you find that the retention rate of new users is much lower than existing ones, you might want to optimize your tutorials, or provide a quick activation service for new users. If you have an education app, and you find that users retained for longer are those who add more courses to their favorites, you can prompt users to add courses to favorites by sending them in-app notifications, or rewarding them with bonus points. If you see that the majority of churned users were using earlier versions of your app, you can prompt users to upgrade in different scenarios and through multiple channels.
How is the retention rate improved?
A retention curve can be broken down into three stages: the onboarding stage, the nurturing stage, and the attrition stage. Through this report, we can see that the best way to improve user retention is either by shortening the onboarding stage and converting new users into loyal users as quickly as possible, or by pushing the curve up as high as possible.
Let's first look at how to shorten the onboarding stage.
The key is to improve the retention rate of new users. Here is a case.
Case: HUAWEI Analytics helped a social e-commerce app improve its retention rate by 15.3%.
The app, which features a range of social functions, faces competition from many similar apps. To increase its Daily Active Users (DAU) by improving its user retention rate, the app took a series of actions: First, Analytics was used to design a funnel measuring behavior throughout the user journey, from when the user first installs the app, to when they register an account, sign in to the app, browse products, add an item to their cart, place an order, and finally make a payment. By monitoring the conversion rate at each stage, it was found that the churn rate between the adding a product to cart and placing an order stages was as high as 39%. This led them to set a 15 minute time limit for the cart, prompting users to place their order quickly. In addition, users were segmented by channel. When doing this, it was found that the retention rate of active users from channel A was much lower than the overall retention rate. So, it stopped trying to attract new users from channel A and increased their investment in other channels. By implementing these strategies, the app was able to increase its user retention rate by 15.3% within just two months.
As the above example shows, you can do all of this, and boost your retention rate, by using HUAWEI Analytics. Once you integrate the HMS Core Analytics SDK, 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 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 has become one of the most popular services globally due to its quick development and powerful analysis capabilities.
HUAWEI Analytics has been used by more than 5000 apps globally. Be sure to check it out!
For more details, you can visit:
Our official website
Our Development Documentation page, to find the documents you need:
Android SDK
iOS SDK
Web SDK
Quick APP SDK
We’re looking forward to seeing what you can achieve with HUAWEI Analytics!
Thank you for those useful information
Kylie Harris said:
Thank you for those useful information
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I am not able to see analytics event on Huawei dashboard(AGC) in realtime. What might be the issue?
An app's ability to retain users has a major impact on its profitability. Retaining users has always been a major challenge for developers, given the presence of competitor apps, and the willingness of today's users to shop around to find the best possible experience.
Therefore, developers have needed to invest more in marketing in order to acquire users. Furthermore, paid traffic has proved even harder to retain, as such users do not tend to install apps solely based on their original needs. Fortunately, there are effective ways to prevent user loss and maximize the value of paid traffic.
This article will highlight a few case studies which illustrate how the Prediction service can help you predict user loss and retain users.
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The figure above shows how a game app was able to improve its user retention, by following just a few steps in the Prediction service. The app's original retention rate was low, and thus its developers enabled the Churn prediction model in Prediction to identify users who demonstrated a high churn probability. They then utilized Prediction to gain actionable insights into the characteristics and behavior of these users, saved them as an audience, and then pushed tailored messages to engage with them on a more targeted basis. This process can be broken into several different stages, and we'll address the key stages for the Prediction service.
1. Precise user targeting
If a user is about to churn, they will likely become less active. The user's activity can be measured based on behavioral data, such as the daily launch rate, launch frequency, and use duration.
Let's take ARPG games as an example. Typical active players of such games tend not to launch the game many times a day, but will stay in the game for relatively lengthy periods of time after each launch. For games in this genre, we regard players who launch the game 2-3 times and play about 5 rounds a day as active players. If an active player has not launched the game very often over the course of a week, or even failed to sign in for two consecutive days, the Prediction service, based on the churn prediction model trained via machine learning, will determine that the player has a high churn probability in the next week.
The Prediction service uses the active user data over the most recent two weeks to train the churn prediction model, which is used to predict the probability that active users of the app in the past week will be lost during the next week. It's important to note that users who are inactive the next week, or uninstall the app are considered churned users.
The Prediction service does not require any high-level algorithm expertise to implement user targeting. Instead, prediction tasks are automatically executed in the background on a daily basis.
2. In-depth insights into audiences
For a targeted audience, the attributes and behavioral characteristics are analyzed, in order to implement a more actionable operations plan for better user retention.
*The data provided is for your reference only.
*The data provided is for your reference only.
This figure is an example of the background data in the Prediction service. It shows that users with high churn probabilities still have game sessions from the past seven days, though these are infrequent. In addition, Prediction enables you to query such users by device model, making it easier to reach them via periodic message pushing, in HUAWEI Push Kit, which users their most recent usage times.
3. Reaching users more effectively
For a targeted audience whose characteristics have been learned, you'll need to implement the right measures to retain its users. HUAWEI Push Kit, mentioned above, is the most handy method for doing so.
An audience generated by the Prediction service can be directly applied by HUAWEI Push Kit to target users. In the case of retention, push messages are sent specifically to users who demonstrate high churn probabilities. Common examples of this include the pushing of time-limited game gift packages, new game characters skills, and even simple greetings.
Prediction tasks incorporate data that is updated on a daily basis. As a result, push messages can be sent periodically to continuously engage recent users with high churn probabilities.
Acquiring traffic is more challenging than ever, but thanks to intelligent data technologies, you can now conduct refined operations that help you extract the most value from existing users. The Prediction service and Push Kit are indispensable for helping enterprises effectively improve ARPU, while also promoting digitization and intelligence across the board.
To learn more about Prediction, please visit our official website.
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
Original Source
When your app user base reaches a certain threshold, you may find it harder to acquire new users continually. In this case, rather than struggling to attract new users, it's better to try to activate your inactive users. Let's take a look at how HUAWEI Analytics Kit can help you activate and win back inactive/lost users.
1. Defining Inactive and Lost UsersTo win back inactive and lost users, the first thing you'll need to do is reasonably define them. To do this, you'll need to take your business characteristics into consideration, and quantify key user behaviors.
For example:
If you have a game app, you can define these users by the number of consecutive days that they do not sign in to your app.
If you have an e-commerce app, you can define these users by the number of consecutive days that they do not place an order in your app.
If you have a video app, you can define these users by the number consecutive days that they do not watch a video in your app.
Analytical models in Analytics Kit are the tools you need to define inactive and lost users accurately and quickly.
Identifying turning points that lead to user churn
Let's use the demo app for Analytics Kit as an example. Its revisit user report indicates that the first turning point appears in day 31–90. This means that there is a small possibility that users who have been inactive for 30 days become active again. The second turning point appears in day 91–180. According to this information, it would be wise to define users whose consecutive days with no app use are greater than or equal to 30 days but equal to or smaller than 90 days as the inactive users, and define users whose consecutive days with no app use are greater than 90 days as the lost users.
Analytics Kit also offers the user lifecycle model, which allows you to customize the statistical scale for each phase of your app's users.
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* This figure shows the user lifecycle analysis report for Analytics Kit with virtual data.
2. Creating Lost User ProfilesA comprehensive understanding of your users makes it much easier for you to build the best possible app. Implementing an effective winback strategy requires that you create detailed lost user profiles that are based on a complete understanding of past behavior characteristics of inactive or lost users.
To create these profiles, you can refer to:
The number and trends for inactive/lost users
The user lifecycle report offers an intuitive glimpse at the number and trend of users in the inactive and lost phases.
* This figure shows the user lifecycle analysis report in Analytics Kit with virtual data.
Phases when users become inactive/lost
The user lifecycle report displays the ratios of inactive users converted from beginner users, growing users, and mature users to all inactive users.
From the figure below, we can roughly divide inactive users into two categories: those converted from the users in the beginner and growing phases, and those converted from the users in the mature phase. The ratio of the first category is much higher than that of the second — this indicates that the more dependent a user is on your app, the less likely it is that he/she becomes inactive or lost.
* The figure shows the user lifecycle analysis report in Analytics Kit with virtual data.
Users in the first category became inactive before they have fully experienced your app. This might have been due to a cumbersome user experience, undesirable product design, or failure to deliver the "Aha!" moment that hooks users to your product. Users in the second category became inactive or lost after they fully experienced your app. This is likely because the product failed to bring them the experience better than their expectation.
Whether most inactive/lost users have similarities
On the user lifecycle report page, you can save inactive/lost users as an audience with just one click on the number. You can then go to the audience analysis report to check whether most inactive/lost users have similarities in aspects such as the model, location, event, system version, or download channel.
* This figure shows the audience analysis report in Analytics Kit with virtual data.
* This figure shows the audience analysis report in Analytics Kit with virtual data.
Behavioral characteristics of inactive/lost users
Behavior analysis provides a filter function, in which you can select inactive/lost users. The session path analysis report will then tell you the behavior path of inactive/lost users before they became inactive/lost, and the session step where this occurred.
* This figure shows the session path analysis report in Analytics Kit with virtual data.
You can save nodes related to user churn as the funnel, and then pinpoint causes of churn by checking the funnel analysis report.
* This figure shows the session path analysis report in Analytics Kit with virtual data.
Analysis of the value inactive/lost users contributed in the past
It's a good idea to analyze the value inactive/lost users have contributed to your app in the past. By doing so, you can separate them into different groups and formulate winback strategies that have a greater chance of success.
The event analysis function enables you to identify previously paying users among inactive/lost users, and learn more information about them like total top-up amounts, gross merchandise volume (GMV), and top-up frequency. Thanks to this information, you can divide inactive/lost users into different groups according to the priority and difficulty of winning them back.
3. Specifying Winback Strategies
Determining which group should be won back first
In the previous step, we created inactive/lost user profiles, and separated them into different groups according to the difficulty of winning them back, through such indicators as the previous behavior and value of lost users, whether they have uninstalled the app, and whether they can be reached now. In principle, the users to win back first are those who have become inactive, but have not yet uninstalled the app.
Determining the focus of your winback strategy
Different winback strategies have different focuses, including:
Benefits: For example, you can send coupons to inactive/lost users, or remind them of existing coupons or virtual currencies that will soon expire.
User interests: Let's use a video app as an example. Some of the inactive users are interested in animation. To win them back, you can send them notifications about upcoming new animation series, or about activities related to animations they are interested in.
Emotions: Let's say you have a life simulation game app which features virtual pets. To entice users to open your app to take care of their pets, you can send them notifications about their pets' health or emotional status.
Selecting a winback channel
Ads: In addition to attracting new users, ads can also play a role in activating or winning back users. When you use ads for this purpose, you're likely to get better than expected results.
Push notifications: If you choose this channel, make sure that the time and frequency for pushing notifications are reasonable. Also remember to check the percentages of users who uninstall your app and disable the push notification after they receive the notification.
SMS messages: You should use this channel to reach only target users, since costs associated with SMS messages are a little higher. To achieve better winback results, you can tailor the content of messages sent out to different user groups.
Emails: This is one of the most common channels for reaching users. You'll need to consider how to best impress your inactive/lost users in a short email, in order to win them back.
Those are the three steps for targeting and wining back lost users with Analytics Kit. Though Analytics Kit makes it easier than ever to win back users, it's still better to create mechanisms that warn you about which users may become inactive, and take proactive measures to retain users who have been won back. This will ensure you to keep improving engagement and loyalty of your users.
About Analytics Kit:
Analytics Kit is a one-stop user behavior analysis platform for products such as mobile apps, web apps, and quick apps. It offers scenario-specific data management, analysis, and usage, helping enterprises achieve effective user acquisition, product optimization, precise operations, and business growth.
For more details, you can go to:
Our official website
Demo of Analytics Kit
Android SDK integration documentation
iOS SDK integration documentation
Web SDK integration documentation
Quick app SDK integration documentation
The shift from acquiring new users to retaining existing users in the automotive industry means that automakers need to innovate their brands and improve their relationship with users, to boost growth and realize their digitalization strategies.
By aligning with this shift, Analytics Kit 6.2.0 has just recently provided reports, event tracking templates, and sample code for the automotive industry. The kit provides reports covering vehicle services, vehicle sales, and community and after-sales, offering an array of industry-wide data. With this data, companies in this industry can enhance user experience and stay competitive.
Indicator-Laden Reports, for Better Operations
1. Data Overview: Offers Key Operations Indicators
The Data overview report presents data concerning basic operations indicators such as the number of users that are registered, new, active, and paying, giving a broad view of app operations. This report also displays data related to mall revenue and vehicle models. They enable the operations team to understand the revenue and distribution of bound vehicle models, and thereby can adjust services and operations strategies for major models.
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* Data overview
2. Reports of Vehicle Services & Vehicle Sales: Provide Insights into User Needs
To remain competitive, an automaker needs to go beyond just selling vehicles. It should be oriented by users, furnishing them with one-stop services that encompass vehicle selection, purchase, driving, upgrade, and maintenance. Data related to these can be found in the vehicle sales and service reports, helping automakers understand clearly what users need by presenting user data concerning their characteristics, driving habits, and model preferences.
The Vehicle services report presents data on the distribution of vehicle models bound by app users, service usage, maintenance reservations, and vehicle loss reports. With such information at their disposal, automakers can improve their maintenance services and personalize service recommendations to users.
* Vehicle services report
The Vehicle sales report illustrates the vehicle buying preferences of users, with data covering the sales volumes of vehicle models, vehicle sales volumes in different locations, number of vehicle purchase orders, distribution of users who request and do not request a financial solution, and distribution of selected financial products. With such data available, automakers can make informed decisions when adjusting the product mix of their malls. They can also realize precision marketing by recommending different vehicle models to users according to their locations and model preferences.
* Vehicle sales report
3. Report of Community and After-Sales: Helps Strengthen User Loyalty
A dedicated app community is a positive step to retaining users. A community allows users with similar needs or preferences to interact with each other. They will likely use the app for longer periods and are more likely to enjoy extra app features.
The Community and after-sales report focuses on data related to user engagement and feedback. Data on voucher recipients and voucher users highlights how many users interact with promotion campaigns, helping identify price-sensitive users. The trends of community members are evident through data that analyzes active community users, active users in each community section, distribution of sections that new posts belong to, post sharing channels, and the average number of times each user contacts customer service. Using this report, automakers can locate what their users' are really concerned about for better user-oriented services.
* Community and after-sales report
Out-of-the-Box Templates
The reports for the automotive industry come with event tracking templates, and the events and their parameters can be chosen as required, allowing you to add your own custom events and parameters. Report previews and sample code are also available, which are updated in real time. In a word, you can configure event tracking according to your actual needs. By easing integration and event tracking configuration, verification, and management, Analytics Kit boosts the efficiency and accuracy of event tracking.
* Configuring event tracking
What sits at the heart of turning traffic into value is paying attention to and then satisfying users' needs. Automobile manufacturers and dealers can use analytical models in Analytics Kit to fully understand data at hand for precise operations, improved user loyalty, and increased business performance. For example, they can refer to payment analysis and audience analysis to understand what events or scenarios most frequently lead to payments. They can then take measures to interact with users under appropriate scenarios to improve the payment conversion rate.
To learn more, click here to get your free trial of the demo, or visit our official website to access the development documents for Android, iOS, Web, and Quick App.
Does it support a Hybrid applications?
Basavaraj.navi said:
Does it support a Hybrid applications?
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Hi, Hybrid Apps are supported. kindly check this link for Calling Device APIs on an HTML5 Page Using JavaScript in Hybrid Mode, thank you.
Document
developer.huawei.com
Thanks for sharing
Why does the server return error code 402, when data fails to be reported?
ReboLangos said:
For what type of businesses these analytic kits are good?
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Hi, you can use HMS Core Analytics Kit as long as your app or business requires data analysis. Check out more at https://developer.huawei.com/consumer/en/hms/huawei-analyticskit?ha_source=hmsxds.
Have a nice day!
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.
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* 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.