As competition among mobile apps gets more intense, converting users is more challenging than ever. You need to understand how people are responding to your apps, so you can identify any issues as soon as they arise, and deal with them promptly.
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Funnel analysis enables you to do that precisely. You can use this service to monitor conversion rates at each stage of the customer journey, identify the stages where your conversion rate is low, and optimize your approach accordingly.
How the Funnel Model Drives Your Business
1. Identify weak points in the customer journey
The first step to boosting conversion rate is to design a funnel which measures behavior throughout the user journey. This starts from when the user registers an ID, then encompasses every subsequent touch point: when they sign in to your app, level up, spend virtual currency, and finally, make a payment. By looking over the data for each of these stages, you can see where your conversion rate is low, and think of a strategy to address this. In this way, you can improve your app's user experience and retain users.
HUAWEI Analytics provides three pre-defined funnel models: the registration conversion funnel, e-commerce conversion funnel, and search conversion funnel. You can also design your own funnel.
2. Find root causes with drill-down analysis
Different users have different requirements, which means the only way to understand why a certain stage has a low conversion rate is by segmenting users. Let's say your conversion rate drops at the member payment stage. To understand why this is, you need to segment users by region, age, member level, gender, and the app version they are using, and then compare data for these groups.
With HUAWEI Analytics' funnel models, you can filter by platform, app, user attribute, and audience, and perform drill-down comparison analysis to find the root causes of low conversion rate.
3. Create audiences with one click
To increase your app's conversion rate, you need to do more than simply focus on a percentage. HUAWEI Analytics' funnel model allows you to save users who have converted or churned at each stage of the journey as an audience. You can then analyze the behavior and attributes of these audiences using a behavior analysis model.
For example, by saving the users who churned at a certain stage as an audience, you can analyze their distribution in terms of events, the page of your app they prefer, app startup time, session paths, and activeness. This helps you gain deeper insight into your churned users, so you can find ways to prevent this churn.
How to Use a Funnel Model
There are four steps to use a funnel model:
1. Design or choose a funnel model and determine the conversion path.
2. Monitor the conversion rate at each stage and identify the reasons behind a low conversion rate.
3. Develop improvement measures.
4. Implement these measures and continue to check whether the conversion rate in the funnel increases.
When it comes to analyzing root causes in the second step, there are a few things to consider:
l Segment users and analyze their behavior: In the funnel analysis report, you can save churned users and converted users as audiences, and then analyze their characteristics with event analysis, page analysis, session path analysis, launch analysis, and retention analysis.
l Perform drill-down analysis: Compare conversion rates of different audiences using filters and comparison analysis, and find areas where your app can be improved.
l Optimize user experience: By looking at conversion rates, you can see where users are dropping off and find ways to improve the user experience.
l Take a broad perspective: Consider your app's overall design, rather than paying too much attention to small details, like the style of a button, or the color of a page. You can use other services like Push Kit, A/B Testing, Remote Configuration, and App Messaging together with HUAWEI Analytics to analyze your app more comprehensively.
Once you have found possible reasons for user churn, you can think of ways to address these. This is the third step of using a funnel model. In this step, you need to sort the factors that may be affecting the conversion rate by priority, and optimize key factors.
During the fourth step, you will check whether the conversion rate increases once you have implemented the countermeasures in step three.
By going through these four steps, you can find the causes of user churn and improve your conversion rate with data-driven solutions.
Typical Case
A travel app used HUAWEI Analytics to design a funnel which measured user behavior throughout the user journey, including when the user opened the app, browsed the travel guides on the home page, added a travel guide to favorites, tapped to share a travel guide, and finally successfully shared the guide. By monitoring the conversion rate at each stage, the developers found that the churn rate between the time where the user tapped to share a travel guide, and the time when they actually shared the guide, was very high. To find the problem, they saved users churned between the two stages as audiences, and analyzed their behavior using the path analysis function. When they did this, they discovered that among these users, most of those who have added a travel guide to favorites had taken a screenshot of the travel guide page.
Based on this discovery, the operations team made an assumption: when users share content to social media, they take screenshots, combine multiple screenshots into one image, beautify the image, then post. If this was true, the team could help their users by adding the option to generate a long image of the current page and share it to social media.
To verify this, the team conducted an online survey which found that 72% of users wanted to be able to save travel guides or notes as long images, while 51% would prefer to save content in the form of a post card.
Having established this, the team added two functions to the content sharing page: generating a long image, and generating a post card. These functions were immediately popular among users, and helped double the app's content sharing rate.
Easy Integration
Today, more than 6000 apps use HUAWEI Analytics around the globe. When you integrate this service, you can use data-driven insights to increase your apps' conversion rates. What's more, integrating HUAWEI Analytics only takes about five minutes!
☞ HUAWEI Analytics Integration Guide:
Android iOS Web
☞ Sample Code:
Android iOS Web
If you have any questions during integration, you can contact us by submitting a ticket online.
We hope you enjoy using HUAWEI Analytics.
It's quite worth trying
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.
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
Click to expand...
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I am not able to see analytics event on Huawei dashboard(AGC) in realtime. What might be the issue?
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
Defining the Session Path Analysis ModelThe session path analysis model provides an intuitive way of displaying how an app is used. With this model, developers can clearly understand the changes in user behavior, frequently visited paths, steps where users churned, and steps with an unexpected churn rate. Such information helps developers enhance the app, user experience, and user conversion.
When to Implement Session Path Analysis
1. Guiding Updates by Showing How Users Actually Use the AppTo maximize the benefits of app optimization and updates, it's important to identify the differences between an app's purpose and its actual usage.
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* Example of a session path analysis report
Let's take Now: Meditation, a leading meditation app in China, as an example of Analytics Kit being used effectively. Now: Meditation provides five types of online courses (pressure relief, emotional management, personal development, sleeping, and focus enhancement) and its revenue is mainly generated from member payments. Initially, the product team thought that pressure relief and emotional management courses would be the most popular, but in reality, the session path analysis shows that sleeping-related courses were the ones drawing most user payments.
In light of this discovery, the product team decided to adjust the display level of the sleeping-related courses so that users would spend less time making a choice. Now: Meditation has reaped the rewards of this change, which has boosted the next-day retention rate of new users by 15%, the number of daily active users by 17%, and the payment conversion rate by 20%.
2. Locating App Flaws by Comparing the Preferred Session Paths of Different UsersThe filter function enables us to compare the path preferences of different audiences. For example, we can check whether new users share the same path during different time segments, and learn which paths are preferred by loyal users. You can segment and further analyze different audiences, and determine the rule of each session path. In this way, session path analysis lets you best optimize your app.
Let's say that we have a lifestyle and social app that allows users to share excerpts of their life using short videos, images, and text, as well as follow people and like others' posts. The operations team found that recently the overall user retention rate dropped significantly, so they were eager to learn about the behavior characteristics of loyal users. Such information could be used to help them design operations campaigns to lead new users and those about to churn to have such characteristics. In this way, the team could improve overall user loyalty for the app.
* Example of a session path analysis report
The app's operations team utilized the session path analysis function. They selected Active users in the filter and set App launch as the start event, with the goal of analyzing the behavioral path of such users. They found that over 70% of active users launched the app three times per day, and were more likely to browse content and follow other users. With this information, they were able to implement two measures to improve user retention by improving the rates of push notification tapping and of users following each other:
First, by enhancing the push notification sending mechanism, drafting better push notification content according to the A/B testing result, and sending audience-specific push notifications. And second, by displaying a message to prompt the user to follow another user when the user browses the latter's home page for more than 3 seconds. After just one month since these two measures were implemented, the retention rate skyrocketed.
3. Finding the Path with the Highest Payment Conversion RateAn app tends to have different banners, icons, and content, designed to guide VIP members toward making payments. But which path best gets users to make payments? What is the difference in churn rate for each step per path? Which path has the best conversion effect? And which paths are worthy of more in-app traffic?
Session path analysis has the answers. First, we select only the events related to app launch and payment completion, and then set payment completion as the end event. Session path analysis will then automatically display the traffic resulting from each path to the payment completion event, helping us compare their payment conversion rates.
* Example of a session path analysis report
4. Specifying the Start Event, and Exploring a Diverse Range of PathsThe product management or operations team often pre-design a path for a function or campaign, which they expect users to follow. However, not all users will follow this path, and a certain number of users will churn during each step. This leads to some important questions, such as, what do users do after they churned? What attracted them in the first place?
Let's say for an e-commerce app, many users churned in the steps between order creation and payment completion. The operations team wanted to analyze why users abandoned payment. Was it because users browsed other products? Did they leave to compare prices? Did they set another order because they had provided a wrong address or ordered too little/much? To answer these questions, the team can set order creation as the start event, and checked what users did after this event.
* Example of a session path analysis report
5. Checking the Path with Unexpected Churn to Determine the ReasonWhen using session path analysis, if you find unexpected churn during certain steps, you can save these steps as a funnel with just one click. Then, you can use the funnel analysis function to identify the reasons behind user churn.
For example, if the session path analysis report has revealed that most new users churned from adding a product to favorites to placing an order and making payment, the events of this process can be saved as a funnel. You can then use the funnel analysis function to determine the reasons behind user churn, and analyze the conversion effects of the funnel.
* Example of a session path analysis report
Funnel analysis provides the filter and comparison analysis functions, which allow you to check data according to such conditions as app version, active users, new users, and download channel. By analyzing such data, you'll be able to locate the root causes leading to user churn, and take measures to optimize your app accordingly.
* Example of a funnel analysis report
These are about the major scenarios where session path analysis can play its role. 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.
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Thanks for sharing!!