【Prediction】How Payment Prediction Can Help Make Your Operations Ultra-Precise - Huawei Developers

1. Why do I need to predict user payment potential?
Payment prediction refers to using machine learning to determine audiences that demonstrate low, medium, and high payment potential among active users from the previous week. These predictions are based on in-app user behavior and user attributes.
Successful products are those that consistently garner revenue, and thus, boosting revenue is a major priority for any product operations team. But in an era with a fully mature Internet, it can be costly to acquire new users. Therefore, extracting value from existing users is crucial for achieving sustainable success. For audiences generated by payment prediction, you can formulate targeted marketing strategies that address relevant users, for example, offering discounted plans, to boost payment conversions.
2. How can I encourage user payments?
Due to the high cost of acquiring new users, maximizing the value of all existing users throughout the entire lifecycle has been a major priority for enterprises, as well as guiding users toward paying for products or services. A user who is more willing to pay is also more loyal to your products. Loyal users can help you find more potential users, setting a virtuous cycle in motion.
A broad range of apps have pursued in-depth product operations strategies, in order to determine the conversion model that best suits them. This is especially true for e-commerce and game apps.
(1) E-commerce apps
In order to encourage reluctant users to make payments, e-commerce apps often launch promotional activities, such as time-limited discounts and flash sales that appear and vanish in an instant.
(2) Games
Most payment conversions for games are from item purchases. In addition to providing item purchase preferences, games can also push payment-relevant notifications at key moments to encourage payment conversions.
3. How can I leverage payment prediction to grow my revenue?
Profit models vary widely depending on the app category. This article will use game apps as an example to show you how payment prediction can help boost product revenue.
As mentioned earlier, games earn most of their revenue from item purchases. Video ad clicks in games are also an important revenue source. We will talk about how payment prediction works under these two scenarios.
How does payment prediction work?
Simply put, payment prediction finds the various probabilities of players paying over the next week, based on their in-game behavior and attributes, such as the app version and number of sessions over the previous week.
The system predicts three audiences by default, those with high (>70%), medium ((20%, 70%]), and low (≤20%) probabilities of paying over the next week. Of course, you can also customize probability ranges based on the characteristics of your game and players. For example, you can define the medium-high probability range as 50% to 70%.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
* Payment prediction details page
After the audiences are generated, you can conduct refined operations using AppGallery Connect's grow services.
Focus on in-app revenue for the high-probability audience
Users in the high-probability audience are more likely to top up in games than any other audiences. Messages related to time-limited top-up discounts and limited items should be pushed to them. In these messages, keywords such as "discounts" can help encourage hesitant users to pay. Keywords such as "time-limited" and "limited" are also highly useful, as they create a sense of urgency, helping promote payment conversions.
Currently, the high-probability paying user audience generated by Prediction can be directly applied in HUAWEI Push Kit and App Messaging. For example, when a user views the details of a card drawing activity, you can use App Messaging to show a pop-up to notify this audience of a time-limited promotion for drawing rare items. Since the users in this audience are more willing to pay, the time-limited activity is likely to lead to more payments.
Push Kit can also be utilized to send notifications about top-up discounts to targeted users.
Message pushing by audience is quite easy to use and cost-effective. Audiences can be directly selected when messages are sent.
* Available audiences for Push Kit
Focus on ad click revenue for low-probability audiences
It is difficult to get low-probability users to pay, and thus it's necessary to adjust your profit model to extract value from them. The most common way to earn revenue from such users is through in-app ads.
Many game developers are concerned that an overabundance of ads can undermine the playing experience, and that revenue generated in this manner may even fail to compensate for lost users who are turned off by the number of ads. Consider the following scenario: A user is about to complete a game level, but finds that they are about to run out of credits. This user is unwilling to purchase an item to extend the game, but when he or she sees a message that offers credits in return for viewing a 15s ad, they are likely to be enthusiastic rather than annoyed. This is correct way to place ads in games.
Ad delivery should take player psychology into account. Users with low payment probabilities should be encouraged to watch rewarded ads with keywords such as "gold coins" and "credits", to boost the ad click-through rate and earn more revenue.
You can apply the low-probability audience as a filtering condition in Remote Configuration to display specific ads only to this audience, without affecting the gaming experience of your other users.
* Prediction filtering conditions page in Remote Configuration
In addition to the usage cases described above, you can also leverage the payment prediction function to formulate more targeted and versatile operations strategies that meet your full range of needs.
Welcome to try out our Prediction service. For more information, please visit our website.
To learn more about DTM, click here.
For more details, you can go to:
Reddit to join our developer discussion
GitHub to download demos and sample codes
Stack Overflow to solve any integration problems
Original Source

Related

Eager to boost user engagement? AppGallery Connect's Grow service portfolio Helps

You've probably wondered how to keep your users engaged over the long haul, in the fickle mobile app market. Fortunately help is on the way!
AppGallery Connect represents a plethora of premium services, such as A/B Testing, Push Kit, App Messaging, App Linking, and leaderboard, which can help you retain and activate users. AppGallery Connect released a set of refined operations tools designed with the goal of enhancing user experience, helping attract and engage users in new and exciting ways and markedly improve business prospects.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
Gain Crucial Insight into User Preferences
A/B Testing provides for informed, data-driven decision making, facilitating more effective operations. It allows you to conduct group experiments to determine user preferences with regard to UI design, copywriting, and product functions. A/B Testing does not boost user activity on its own, but it sheds light on user priorities via high-level data, giving you the tools to boost your DAU and MAU.
For instance, an app development team had designed multiple update variants for the app's home page, and had hoped to determine the optimal option. It used A/B Testing to distribute these variants to targeted user groups, with the goal of soliciting feedback. The test revealed that one specific variant facilitated a staggering 25% increase in user activity. Unsurprisingly, the team opted to apply the variant as the app's new home page.
In another example, an app development team conducted a test involving two user groups before unveiling a new function that they anticipated could facilitate greater user engagement. The function was only available to one of the user groups. Two weeks later, the daily active duration of users in the treatment group was half an hour longer than that of users in the control group. Considering this highly-revealing data, the team opted to release the function, and it proved to be a right choice as the app's daily active use time rose by an impressive 15%.
Targeted Push Notifications
Push Kit establishes multiple cloud-device messaging channels that push messages to users under their permissions with seamless efficiency, achieving a mind-blowing 99% delivery rate. Push Kit allows you to select targeted audiences, and send messages with pinpoint precision, freeing users from the distracting, and undiscriminating traditional marketing campaigns. The Kit offers diverse messaging formats, including short text, long text, and images, enabling you to tailor messages to best attract users.
Users often expect to be informed when a favorite product is released or on sale. Push Kit helps you push specific messages to targeted audiences in real time when users allow for it, making premium content and highly-relevant information accessible in an instant, which contributes to soaring user engagement. A good example of this phenomenon is a video app that recently integrated Push Kit and boosted its DAU by a remarkable 20%.
Context-Specific App Messaging
Irrelevant messages can be distracting, and cause user backlash, often leading to app uninstallations. App Messaging helps you avoid this pitfall, equipping you to send highly relevant messages that enhance user retention and engagement, rather than detract from it. Furthermore, you can define events to trigger messages in specific scenarios, to send messages that reach users at just the right moment, such as those instructing users to perform anticipated operations.
A call screen theme customization app used this service to great effect recently, after having released a new theme. The theme was largely ignored by users initially, and to attract more users to the theme, the development team integrated App Messaging, which enabled the app to send new theme release notifications to specific users. After these users signed in, App Messaging would allow the app to recommend themes according to the users' preferences, causing more users to view and select themes. Just three weeks in, the app saw its usage increase by 11%.
Moreover, App Messaging has the following features which encourage users to use your key app functions.
Precise targeting: Triggers targeted messages to account for wide-ranging user behaviors and services in all conceivable scenarios.
Diverse formats: Displays messages in pop-up, banner, image, and custom formats with a variety of configurable message elements, encompassing images, colors, text, buttons, and redirection.
Message data: Collects data related to message displays and taps, and utilizes it to conduct high-level conversion funnel analysis.
Large-Scale User Mobilization, with Deep Linking to In-App Content
With App Linking, you can create links to specified in-app content. It allows you to create cross-platform links, both long and short, that still work even when your app has not been installed by a user. When the user taps a link created in App Linking, they are redirected to the specified in-app content. In the event that the user has not installed the app, App Linking will direct them to AppGallery for download. After download and installation are complete, the app will launch automatically under users' permissions, with the in-app content displayed for the user. App Linking streamlines the redirection process, saving users time, and allows you to engage greater numbers of users by providing content of interest.
One effective way to utilize App Linking is to send deep links by SMS or email (when authorized by users) that allow users to claim unconditional coupons. Once the user opens the link, the relevant in-app content will pop up for them, allowing them to claim the coupon, or if they do not yet have the app, they will be directed to AppGallery for app download, before proceeding. App Linking helps you transform potential users into active ones in a wide-ranging manner.
In addition, App Linking boosts views of specific pages by waking up inactive users and converts mobile website users into native app users. It also helps you analyze the link performance of each traffic source based on the tracing parameters, so that you can find the platform that can achieve the best promotion effect for your app.
Operations Services, a Literal Game-Changer in User Engagement Boosting
Operations services in HUAWEI AppGallery Connect offers a diverse array of functions, including gift packages, game achievements, and leaderboards. Leaderboards and achievements are effective ways to keep users plugged in to a game. The presence of a leaderboard encourages game players to compare scores and levels with those of other players. Real-time player leaderboards, either offered in a floating window or through the game app itself, can install a highly-competitive and loyal user base. Similarly, achievements can inspire players to dive into the game and commit to honing their skills. You can opt to add achievements on a regular basis to keep your game fresh, and attract a passionate cadre of hardcore gamers.
HUAWEI AppGallery supports versatile global app distribution to address all conceivable usage scenarios, and to all major device types, reaching 700 million Huawei device users. The platform's innovation-conducive growing and operations service portfolio gives developers the tools to attract and retain users with newfound ease, thus winning over abundant support from users for you.
To learn more details, please visit https://developer.huawei.com/consumer/en/service/josp/agc/index.html

Having Trouble Winning Back Lost Users? HUAWEI Prediction Can Help You Prevent User Loss, and Maximize Value

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.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
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.

Predicting User Loss, for Enhanced User Retention

An app's ability to retain users has a major impact on its profitability. Retaining users has always been a major challenge for developers, given the presence of competitor apps, and the willingness of today's users to shop around to find the best possible experience.
Therefore, developers have needed to invest more in marketing in order to acquire users. Furthermore, paid traffic has proved even harder to retain, as such users do not tend to install apps solely based on their original needs. Fortunately, there are effective ways to prevent user loss and maximize the value of paid traffic.
This article will highlight a few case studies which illustrate how the Prediction service can help you predict user loss and retain users.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
The figure above shows how a game app was able to improve its user retention, by following just a few steps in the Prediction service. The app's original retention rate was low, and thus its developers enabled the Churn prediction model in Prediction to identify users who demonstrated a high churn probability. They then utilized Prediction to gain actionable insights into the characteristics and behavior of these users, saved them as an audience, and then pushed tailored messages to engage with them on a more targeted basis. This process can be broken into several different stages, and we'll address the key stages for the Prediction service.
1. Precise user targeting
If a user is about to churn, they will likely become less active. The user's activity can be measured based on behavioral data, such as the daily launch rate, launch frequency, and use duration.
Let's take ARPG games as an example. Typical active players of such games tend not to launch the game many times a day, but will stay in the game for relatively lengthy periods of time after each launch. For games in this genre, we regard players who launch the game 2-3 times and play about 5 rounds a day as active players. If an active player has not launched the game very often over the course of a week, or even failed to sign in for two consecutive days, the Prediction service, based on the churn prediction model trained via machine learning, will determine that the player has a high churn probability in the next week.
The Prediction service uses the active user data over the most recent two weeks to train the churn prediction model, which is used to predict the probability that active users of the app in the past week will be lost during the next week. It's important to note that users who are inactive the next week, or uninstall the app are considered churned users.
The Prediction service does not require any high-level algorithm expertise to implement user targeting. Instead, prediction tasks are automatically executed in the background on a daily basis.
2. In-depth insights into audiences
For a targeted audience, the attributes and behavioral characteristics are analyzed, in order to implement a more actionable operations plan for better user retention.
*The data provided is for your reference only.
*The data provided is for your reference only.
This figure is an example of the background data in the Prediction service. It shows that users with high churn probabilities still have game sessions from the past seven days, though these are infrequent. In addition, Prediction enables you to query such users by device model, making it easier to reach them via periodic message pushing, in HUAWEI Push Kit, which users their most recent usage times.
3. Reaching users more effectively
For a targeted audience whose characteristics have been learned, you'll need to implement the right measures to retain its users. HUAWEI Push Kit, mentioned above, is the most handy method for doing so.
An audience generated by the Prediction service can be directly applied by HUAWEI Push Kit to target users. In the case of retention, push messages are sent specifically to users who demonstrate high churn probabilities. Common examples of this include the pushing of time-limited game gift packages, new game characters skills, and even simple greetings.
Prediction tasks incorporate data that is updated on a daily basis. As a result, push messages can be sent periodically to continuously engage recent users with high churn probabilities.
Acquiring traffic is more challenging than ever, but thanks to intelligent data technologies, you can now conduct refined operations that help you extract the most value from existing users. The Prediction service and Push Kit are indispensable for helping enterprises effectively improve ARPU, while also promoting digitization and intelligence across the board.
To learn more about Prediction, please visit our official website.
For more details, you can go to:
l Our official website
l Our Development Documentation page, to find the documents you need
l Reddit to join our developer discussion
l GitHub to download demos and sample codes
l Stack Overflow to solve any integration problems
Original Source

Boost Business Growth for Your Automotive App with Analytics Kit

The shift from acquiring new users to retaining existing users in the automotive industry means that automakers need to innovate their brands and improve their relationship with users, to boost growth and realize their digitalization strategies.
By aligning with this shift, Analytics Kit 6.2.0 has just recently provided reports, event tracking templates, and sample code for the automotive industry. The kit provides reports covering vehicle services, vehicle sales, and community and after-sales, offering an array of industry-wide data. With this data, companies in this industry can enhance user experience and stay competitive.
Indicator-Laden Reports, for Better Operations
1. Data Overview: Offers Key Operations Indicators
The Data overview report presents data concerning basic operations indicators such as the number of users that are registered, new, active, and paying, giving a broad view of app operations. This report also displays data related to mall revenue and vehicle models. They enable the operations team to understand the revenue and distribution of bound vehicle models, and thereby can adjust services and operations strategies for major models.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
* Data overview
2. Reports of Vehicle Services & Vehicle Sales: Provide Insights into User Needs
To remain competitive, an automaker needs to go beyond just selling vehicles. It should be oriented by users, furnishing them with one-stop services that encompass vehicle selection, purchase, driving, upgrade, and maintenance. Data related to these can be found in the vehicle sales and service reports, helping automakers understand clearly what users need by presenting user data concerning their characteristics, driving habits, and model preferences.
The Vehicle services report presents data on the distribution of vehicle models bound by app users, service usage, maintenance reservations, and vehicle loss reports. With such information at their disposal, automakers can improve their maintenance services and personalize service recommendations to users.
* Vehicle services report
The Vehicle sales report illustrates the vehicle buying preferences of users, with data covering the sales volumes of vehicle models, vehicle sales volumes in different locations, number of vehicle purchase orders, distribution of users who request and do not request a financial solution, and distribution of selected financial products. With such data available, automakers can make informed decisions when adjusting the product mix of their malls. They can also realize precision marketing by recommending different vehicle models to users according to their locations and model preferences.
* Vehicle sales report
3. Report of Community and After-Sales: Helps Strengthen User Loyalty
A dedicated app community is a positive step to retaining users. A community allows users with similar needs or preferences to interact with each other. They will likely use the app for longer periods and are more likely to enjoy extra app features.
The Community and after-sales report focuses on data related to user engagement and feedback. Data on voucher recipients and voucher users highlights how many users interact with promotion campaigns, helping identify price-sensitive users. The trends of community members are evident through data that analyzes active community users, active users in each community section, distribution of sections that new posts belong to, post sharing channels, and the average number of times each user contacts customer service. Using this report, automakers can locate what their users' are really concerned about for better user-oriented services.
* Community and after-sales report
Out-of-the-Box Templates
The reports for the automotive industry come with event tracking templates, and the events and their parameters can be chosen as required, allowing you to add your own custom events and parameters. Report previews and sample code are also available, which are updated in real time. In a word, you can configure event tracking according to your actual needs. By easing integration and event tracking configuration, verification, and management, Analytics Kit boosts the efficiency and accuracy of event tracking.
* Configuring event tracking
What sits at the heart of turning traffic into value is paying attention to and then satisfying users' needs. Automobile manufacturers and dealers can use analytical models in Analytics Kit to fully understand data at hand for precise operations, improved user loyalty, and increased business performance. For example, they can refer to payment analysis and audience analysis to understand what events or scenarios most frequently lead to payments. They can then take measures to interact with users under appropriate scenarios to improve the payment conversion rate.
To learn more, click here to get your free trial of the demo, or visit our official website to access the development documents for Android, iOS, Web, and Quick App.
Does it support a Hybrid applications?
Basavaraj.navi said:
Does it support a Hybrid applications?
Click to expand...
Click to collapse
Hi, Hybrid Apps are supported. kindly check this link for Calling Device APIs on an HTML5 Page Using JavaScript in Hybrid Mode, thank you.
Document
developer.huawei.com
Thanks for sharing
Why does the server return error code 402, when data fails to be reported?
ReboLangos said:
For what type of businesses these analytic kits are good?
Click to expand...
Click to collapse
Hi, you can use HMS Core Analytics Kit as long as your app or business requires data analysis. Check out more at https://developer.huawei.com/consumer/en/hms/huawei-analyticskit?ha_source=hmsxds.
Have a nice day!

Boost Continuous Service Growth with Prediction

In the information age, the external market environment is constantly changing and enterprises are accelerating their digital marketing transformation. Breaking data silos and fine-grained user operations allow developers to grow their services.
In this post, I will show you how to use the prediction capabilities of HMS Core Analytics Kit in different scenarios in conjunction with diverse user engagement modes, such as message pushing, in-app messaging, and remote configuration, to further service growth.
Scenario 1: scenario-based engagement of predicted user groups for higher operations efficiency
Preparation and prevention are always better than the cure and this is the case for user operations. With the help of AI algorithms, you are able to predict the probability of a user performing a key action, such as churning or making a payment, giving you room to adjust operational policies that specifically target such users.
For example, with the payment prediction model, you can select a group of users who were active in the last seven days and most likely to make a payment over the next week. When these users browse specific pages, such as the membership introduction page and prop display page, you can send in-app messages like a time-limited discount message to these users, which in conjunction with users' original payment willingness and proper timing can effectively promote user payment conversion.
{
"lightbox_close": "Close",
"lightbox_next": "Next",
"lightbox_previous": "Previous",
"lightbox_error": "The requested content cannot be loaded. Please try again later.",
"lightbox_start_slideshow": "Start slideshow",
"lightbox_stop_slideshow": "Stop slideshow",
"lightbox_full_screen": "Full screen",
"lightbox_thumbnails": "Thumbnails",
"lightbox_download": "Download",
"lightbox_share": "Share",
"lightbox_zoom": "Zoom",
"lightbox_new_window": "New window",
"lightbox_toggle_sidebar": "Toggle sidebar"
}
* The figure shows the page for creating an in-app message for users with a high payment probability.
Scenario 2: differentiated operations for predicted user groups to drive service growth
When your app enters the maturity stage, retaining users using the traditional one-style-fits-all operational approach is challenging, let alone explore new payment points of users to boost growth. As mentioned above, user behavior prediction can help you learn about users' behavior willingness in advance. This then allows you to perform differentiated operations for predicted user groups to help explore more growth points.
For example, a puzzle and casual game generates revenue from in-app purchases and in-game ads. With a wide range of similar apps hitting the market, how to balance gaming experience and ad revenue growth has become a major pain point for the game's daily operations.
Thanks to the payment prediction model, the game can classify active users from the previous week into user groups with different payment probabilities. Then, game operations personnel can use the remote configuration function to differentiate the game failure page displayed for users with different payment probabilities, for example, displaying the resurrection prop page for users with a high payment probability and displaying the rewarded ad page for users with a low payment probability. This can guarantee optimal gaming experience for potential game whales, as well as increase the in-app ad clicks to boost ad revenue.
* The figure shows the page for adding remote configuration conditions for users with a high payment probability.
Scenario 3: diverse analysis of predicted user groups to explore root causes for user behavior differences
There is usually an inactive period before a user churns, and this is critical for retaining users. You can analyze the common features and preferences of these users, and formulate targeted strategies to retain such users.
For example, with the user churn prediction model, a game app can classify users into user groups with different churn probabilities over the next week. Analysis showed that users with a high churn probability mainly use the new version of the app.
* The figure shows version distribution of users with a high churn probability.
The analysis shows that the churn rate is higher for users using the new version, which could be because users are unfamiliar with the updated gameplay mechanics of the new version. So, what we can do is get the app to send messages introducing some of new gameplay tips and tricks to users with a high churn probability, which will hopefully boost their engagement with the app.
Of course, in-depth user behavior analysis can be performed based on user groups to explore the root cause for high user churn probability. For example, if users with a high churn probability generally use the new version, the app operations team can create a user group containing all users using the new version, and then obtain the intersection between the user group with a high churn probability and the user group containing users using the new version. The intersection is a combined user group comprising users who use the new version and have a high churn probability.
* The figure shows the page for creating a combined user group through HUAWEI Analytics.
The created user group can be used as a filter for analyzing behavior features of users in the user group in conjunction with other analysis reports. For example, the operations team can filter the user group in the page path analysis report to view the user behavior path features. Similarly, the operations team can view the app launch time distribution of the user group in the app launch analysis report, helping operations team gain in-depth insights into in-app behavior of users tending to churn.
And that's how the prediction capability of Analytics Kit can simplify fine-grained user operations. I believe that scenario-based, differentiated, and diverse user engagement modes will help you massively boost your app's operations efficiency.
Want to learn more details? Click here to see the official development guide of Analytics Kit.

Categories

Resources