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.
Related
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.
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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.
For Beginner Users: Reduce the User Acquisition Cost, and Promote User Activation and Growth
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
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
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
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
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
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.
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
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.
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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.
1．For Beginner Users: Reduce the User Acquisition Cost, and Promote User Activation and Growth
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
*Source: Developer feedback
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, channel A had the highest contribution rate, while channel B had the lowest. So they shifted their marketing budget from channel B to channel A. After three months of optimization, their user acquisition costs decreased by 26% and the new user retention rate increased by 15%.
2.For Growing and Mature Users: Promote User Activation and Retention, and Increase the User Conversion Rate
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
*Source: Developer feedback
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.
3.For Inactive and Lost Users: Prevent User Churn, Wake Up Inactive Users, and Summarize Experience
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
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%.(*Source: Developer feedback)
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.
Huawei Analytics Kit will track custom events from fragment. what is the best advantage when you compare to others.
Interesting and helpful.
Why is the user churn rate so high?
How can I prevent user churn?
How can I maximize the value of retained users? All are common challenges faced by app operations teams.
This means that increasing the user retention rate by just 5%, can lead to a staggering 95% increase in revenue. Therefore, operations work is largely dedicated to retaining users and maximizing their value.
How to detect user churn risks in a timely manner and formulate a targeted user operations strategy? As user acquisition costs have increased, many enterprises have adjusted their operations strategy from extensive traffic diversion to refined operations. However, this new paradigm has led to new challenges related to user retention, and "bottleneck" effects that can hinder payment conversion rates. That's where HUAWEI Prediction comes into the picture.
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What Is HUAWEI Prediction?
HUAWEI Prediction anticipates the precise target audiences, by utilizing machine leaning technologies that harness the data-driven user behavior and attributes analysis in HUAWEI Analytics. The service can accurately predict churned users, paying users, and return users, providing invaluable insight on your app's user base.
1. Predicting Churned Users
For many companies, user operations are simply a repetitive cycle, which consists of: defining users who have not signed in or made any purchases over a specific period of time as churned users, strategizing to win them back, and pushing messages or sending SMS messages to reach them. These actions can be reckless, as the causes of user churn are not yet clear, and simply delivering coupons or specific messages is insufficient, and can even backfire. Users have grown accustomed to ignoring messages that they receive from apps.
Effective strategies for retaining users
Rather than dedicating painstaking effort to win back churned users, it would be far better to predict user churn in advance, so that you could take proactive measures to retain users who are at risk of being lost. For example, if a user has been active over the past week but is predicted to be inactive or to uninstall the app, they will be defined as having high churn risk. Then it's a matter of identifying common attributes for such likely-to-churn users (device model, location, etc), as well as identifying metrics (recent app usage, total page views, etc.). HUAWEI Prediction mines the vast array of available data for you,and applies its in-depth insights into likely-to-churn user behavioral characteristics, so that you can adjust your operations strategy in a proactive manner, to reach them more effectively.
2. Predicting Paying Users
2. Predicting Paying Users
Product monetization capabilities are important in determining whether a product will be successful in the long run. In recent years, apps have tried a wide range of promotional activities, such as free service trials, membership benefits, coupons, joint membership models, and even online & offline promotions. The ultimate goal of all of these costly operations is to get users to pay for services.
Methods for boosting the payment conversion rate
First, it's important to target the audience that will make payments in the future. For example, you can use user payment data from the previous two weeks to build a model, and use the model to predict the probability that active users from the past week will pay fees in the following week. This enables you to conduct refined operations that target these specific users, such as optimizing the product purchasing experience and sending discount coupons.
HUAWEI Prediction is designed to do just this. It obtains insight into user behavior to predict audiences that demonstrate a high payment probability, and identifies the detailed attributes of the audience, such as the geographic and device model distributions. You can then use this high-level analysis to allocate resources in an optimal manner, thereby ensuring that the payment conversion rate is maximized.
3. Predicting Return Users
Due to high costs of acquiring new users, extracting full value from all existing users throughout the entire lifecycle, and winning back former users are all key to turning a profit. A satisfied user will repeatedly use your service, and conduct new transactions on a regular basis. A higher return rate indicates greater user loyalty, and loyal users can help bring in new users.
How to attract users to return for purchases?
Just like with predicting paying users, predicting return users can help you boost payment conversions from paying users on a continual basis, with targeted operations actions. Users who have been more recently active are naturally more likely to make payments. You can thus set a condition for users with a high return possibility, as historical paying users who have been active over the most recent week.
HUAWEI Prediction can help you make accurate predictions, which enables you to formulate precise marketing strategies to target specific users, and then see these strategies through, whether this involves pushing greetings to existing users, or configuring discount packages for members. Relying on such data-driven operations can lead to outsized benefits, in terms of both payment conversion and user loyalty.
How can I enable HUAWEI Prediction?
To enable the Prediction service, simply click Enable now on the service page.
HUAWEI Prediction is dependent on the user behavioral data and attributes reported by HUAWEI Analytics Kit. Therefore, before enabling the Prediction service, you'll need to enable HUAWEI Analytics and integrate the Analytics SDK, to ensure that enough events are reported to support the execution of prediction tasks.
For details about the integration procedure, please refer to the following documents:
Android:
https://developer.huawei.com/consum...tegrating-sdk-0000001050161876?ha_source=hms1
iOS:
https://developer.huawei.com/consum...tegrating-sdk-0000001050168479?ha_source=hms1
Web:
https://developer.huawei.com/consum...tegrating-sdk-0000001051065743?ha_source=hms1
HUAWEI Prediction helps anticipate potential user behavior in advance, providing in-depth insight into target users, and facilitating the efficient allocation of resources, to create maximum value.
For more details about HUAWEI Prediction, and how to get started, please refer to our online materials.
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
Products must fulfill wide-ranging user preferences and requirements. To enhance user retention, it is important to design targeted strategies to achieve precise operations and satisfy varying demands for different users. User segmentation is the most common method of achieving this and does so by placing users with the same or similar characteristics in terms of user attributes or behavior into a user segment. In this way, operations personnel can formulate differentiated operations strategies targeted at users in each segment to improve user retention and conversion.
Application ScenariosIn app operations, we often encounter the following problems:
1. The overall user retention rate is decreasing. How do I find out which users I'm losing?
2. Some users claim coupons or bonus points every day but do not use them. How can I identify these users and prompt them to use the bonuses as soon as possible?
3. How do I segment users by location, device model, age, or consumption level?
4. How do I trigger scenario-specific messages based on user behavior and interests?
5. Can I prompt users using older versions of my app to update the app without having to release a new version?
...
The audience creation function of Analytics Kit together with other services like Push Kit, A/B Testing, Remote Configuration, and App Messaging helps address these issues.
Flexibly Create an AudienceWith Analytics Kit, you can flexibly create an audience in three ways:
1. Define audiences based on behavior events and user labels.
User events refer to user behavior when users use a product, including how they interact with the product.
Examples include signing in with an account, leveling up in a game, tapping an in-app message, adding a product to the shopping cart, and performing in-app purchases.
User labels describe user attributes and preferences, such as consumption behavior, device attributes, user locations, activity, and payment.
User events and labels allow you to know which users are doing what at a specific point in time.
Examples of audiences you can create include Huawei phone users who have made more than three in-app purchases in the last 14 days, new users who have not signed in to your app in the last three days, and users who have not renewed their membership.
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2. Create audiences through the intersection, union, or difference of existing audiences.
Let's look at an example. If you set Create audience by to Audience, and exclude churned users from all users, then a new audience containing only non-churned users will be generated.
Here is another example. On the basis of three existing audiences – HUAWEI Mate 40 users, male users, and users whose ages are greater than 30 – you can create an audience containing only male users who use HUAWEI Mate 40 and are younger than 30.
3. Create audiences intelligently by using analysis models.
In addition to the preceding two methods, you can also generate an audience with just a click using the funnel analysis, retention analysis, and user lifecycle models of Analytics Kit.
For example, in a funnel analysis report under the Explore menu, you can save users who flow in and out of the funnel in a certain process as an audience with one click.
In a retention analysis report, you can click the number of users on a specific day to save, for example, day-1 or day-7 retained users, as an audience.
A user lifecycle report allows you to save all users, high-risk users, or high-potential users at each phase, such as the beginner, growing, mature, or inactive phase, as an audience.
How to Apply Audiences1. Analyze audience behavior and attribute characteristics to facilitate precise operations.
More specifically, you can compare the distributions of events, system versions, device models, and locations of different audiences. For example, you can analyze whether users who paid more than US$1000 in the last 14 days differ significantly from those who paid less than US$1000 in the last 14 days in terms of their behavior events and device models.
Also, you can use other analysis reports to dive deeper into audience behavior characteristics.
For example, a filter is available in the path analysis report that can be used to search for an audience consisting of new users in the last 30 days and view the characteristics of their behavior paths. Similarly, you can check the launch analysis report to track the time segments when users from this audience launch an app, as well as view their favorite pages, through the page analysis report.
With user segmentation, you can classify users into core, active, inactive, and churned users based on their frequency of using core functions, or classify them by location into users who live in first-, second-, and third-tier cities to provide a basis for targeted and differentiated operations.
For example, to increase the number of paying users, you are advised to focus your operations on core users because it is relatively difficult to convert inactive and low-potential users. By contrast, to stimulate user activity, you are advised to provide incentives for inactive users, and offer guidance and gift packs to new users.
2. User segmentation also makes targeted advertising and precise operations easier.
User segmentation is an excellent tool for precisely attracting new users. For example, you can save loyal users as an audience and, using a wide range of analysis reports provided by Analytics Kit, you can analyze the behavior and attributes of these users from multiple dimensions, such as how the users were acquired, their ages, frequency of using core functions, and behavior path characteristics, helping you determine how to attract more users.
In addition, other services such as Push Kit, A/B Testing, Remote Configuration, and App Messaging can be used in conjunction with audiences created via Analytics Kit, facilitating precise operations. Let's take a look at some examples.
Push Kit allows you to reach target users precisely. For instance, you can send push notifications about coupons to users who are more likely to churn according to predictions made by the user lifecycle model, and send push notifications to users who have churned in the payment phase.
Applicable to the audiences created via Analytics Kit, A/B Testing helps you discover which changes to the app UI, text, functions, or marketing activities best satisfy the requirements of different audiences. You can then apply the best solution for each audience.
As for App Messaging, it contributes to improving active users' payment conversion rate. You can create an audience of active users through Analytics Kit, and then send in-app messages to these users. For example, you can send notifications to users who have added products to the shopping cart but have not paid.
What about Remote Configuration? With this service, you can tailor app content, appearances, and styles for users depending on their attributes, such as genders and interests, or prompt users using an earlier app version to update to the latest version.
That concludes our look at the audience analysis model of Analytics Kit, as well as the role it plays in promoting precise operations.
Once you have integrated the Analytics SDK, you can gain access to user attributes and behavior data after obtaining user consent, to figure out what users do in different time segments. Analytics Kit also provides a wide selection of analysis models, helping paint a picture of user growth, behavior characteristics, and how product functions are used. What's more, the filters enable you to perform targeted operations with the support of drill-down analysis. It is worth mentioning that the Analytics SDK supports various platforms, including Android, iOS, and web, and you can complete integration and release your app in just half a day.
Sounds tempting, right? To learn more, check out:
Official website of Analytics Kit
Development documents for Android, iOS, web, quick apps, HarmonyOS, WeChat mini-programs, and quick games