Your app's new users take one of two paths. Some uninstall or ignore your app after a few days, while others – retained users – continue to use it. The standard definition of user retention varies across different types of apps. For an e-commerce app, this depends on their repurchase rate; for a news app, the main indicator is news views; and for a social app, content creators are considered loyal. Therefore, let's first identify the key retention indicator for your app.
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*Retained users as presented in the retention analysis report (reference data only)
Once an app is released, its creator tends to put a lot of effort into user acquisition and activation. This is a period of increased number of new and engaged users. However, such users don't always stay as active or present, meaning that the app is neither attractive nor profitable. This is why fast user growth or high user engagement within a short period of time are not reflective of your app's competitiveness. Only retained users create constant value.
How then do we improve user retention?
Ø Find out where your retained users come from.
Let's say you advertised your short video app on major app stores. The app did acquire some new users due to the ads, but after a period of time, many of them were lost. How can you tell which channel was responsible for acquiring the ones that remain?
To evaluate core indicators like the number of new users and retained users acquired from each channel, you'll need quick access to such data.
HUAWEI Analytics Kit offers analysis reports on user data on demand. With this retention analysis feature, filter user growth or retention data by channel and time period. This gives you a birds-eye view of the acquisition channels that end up with more retained users. Focus on these channels when developing marketing strategies.
Ø Find your Aha moment.
An app's Aha moment is the key to user conversion — the point when users realize that the app satisfies their needs. A photo app user might like the photo they took with the app filter and decide to share it on social media, while a video app user might give a clip they like the thumbs up. This is when the user decides the app is of value and will continue to use it. Since the Aha moment is critical for retaining new users, how do you identify it?
The two steps are: grouping users into audiences, and analyzing behavior by specific audience.
l Save retained user in the retention analysis report as an audience in one click.
l Analyze user attributes and behavior in the audience analysis report. After configuring event tracking for your app, view the distribution of events where new users are retained and find the most popular feature among users.
Once you've found that Aha moment, promote it to your users through notifications or push messages. Prompting them to experience the feature will improve retention.
Ø Find out more about your retained users.
How do you reduce the churn that takes them away? The new and active user retention reports give you the number of daily retained users. Take immediate action if that number is declining.
Save the retained users at a specific day, week, or month as an audience with a single click. Then, view the user distribution of the audience you just created by event, system version, device model, and region. Finally, with the help of Push Kit, you'll be able to reach the audience precisely and incentivize them with gifts or coupons to make purchases.
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HUAWEI Analytics Kit, our one-stop analytics platform, provides developers with intelligent, convenient, and powerful analytics capabilities, so you can optimize your apps' performance and identify effective marketing channels. With the newest version, Analytics Kit 5.0.5, we've added new functions like e-commerce analysis, gaming industry analysis, marketing attribution analysis, and install referrer analysis, to meet the data analysis requirements of developers across a huge range of industries. Let's take a closer look at these updates.
Streamline your data with e-commerce industry analysis
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We’ve added two new sections to reports, giving you useful data such as core sales indicators and sales conversion rates:
Product sales analysis: Shows the GMV, order quantity, number of users who made purchases, details page views, payment conversion rate, and refund data. You can also filter sales data by time segment, app, user attribute, and audience.
Product category analysis: Shows the total purchases, number of users who made purchases, and sales for each product category. You can also add additional filters.
Understand players' behavior with gaming industry analysis
Gaming industry analysis provides you with data such as core revenue indicators and user analysis, so you can measure your game's overall performance and identify opportunities for growth.
Game top-up analysis: Shows changes in indicators such as average revenue per paying user, average revenue per active user, top-up payment rate, top-up user level, and top-up user retention rate.
Virtual currency analysis: Shows the number of new users consuming virtual currency and the consumption of virtual currency. You can also drill down by app, user attribute, and audience.
Levels and items: Shows data about leveling up and usage of items by user level.
User analysis: Shows the number of users and total consumption by consumption range, as well as play time and payments for new users and active users.
See which marketing channels work with marketing attribution analysis
The marketing attribution report measures the degree to which a push message contributes to a target conversion event, and helps you optimize your push messages.
See where your users come from with install referrer analysis
By configuring matching and parsing rules for an install referrer, you can obtain its attribution report, which tells you where your newly subscribed users have come from. You can then tailor your approach for users from different sources.
We're always finding ways to provide you with intelligent, convenient, and secure data analytics services, and are now exploring specific scenarios based on Huawei's "1+8+N" ecosystem, to help you develop apps according to what users want.
Want to find out more about Analytics Kit? Detailed guides are available on the HUAWEI Developers website. If you have any questions during the integration process, you can submit a service ticket online to consult our technical personnel.
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Precise targeting of users is very important when you release new product features or organize marketing activities. Precise targeting, however, is not a simple process. For example, how do you push messages that users are interested in without disturbing them, divide users into groups and push messages accordingly, and trigger message sending based on users' behavior and interests?
HUAWEI Analytics Kit, along with App Messaging, can help answer these questions.
What are HUAWEI Analytics Kit and App Messaging?
HUAWEI Analytics Kit is a free-to-use data analysis service for app operations personnel to track how users behave in apps and facilitate precise data-driven operations. Applicable to multiple platforms such as Android, iOS, and web, and various types of devices such as mobile phones and tablets, it can automatically generate more than 10 types of analysis reports based on users' behavior events.
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App Messaging triggers in-app messages in specific scenarios according to users' behavior events. It provides a large selection of templates for message display, including pop-ups, banners, and images, and supports custom formats with a variety of configurable message elements, encompassing images, colors, content, buttons, and redirections.
Message recipients vary according to dimensions, including the app version, system version, language, country or region, audience generated by HUAWEI Analytics Kit, and user attribute. App Messaging can help you enhance user loyalty for sustainable growth.
Examples of scenarios where HUAWEI Analytics Kit and App Messaging are applicable
Example 1: The funnel analysis function of HUAWEI Analytics Kit was used for a game app, and it was discovered that the pass rate of the fourth level of the game was far lower than that of previous ones. To prevent users from churning, the operations team decided to push in-app messages about gift packs that could help pass the fourth level to players who failed to pass this level more than twice, so as to encourage the players to continue trying and therefore reducing the churn rate.
In addition, when new players complete a task designed for beginners, a message about gift packs for new players can be pushed to them to help enhance their interest in the game and improve user retention.
Example 2: Through HUAWEI Analytics Kit's retention analysis and audience analysis functions, the operations team of an online education app found that users who added courses to favorites were more likely to be retained than others. Therefore, to enhance the user retention rate, the operations team decided to push a message that encouraged users to add the course they have joined to favorites.
Moreover, for e-commerce apps, messages about discounts and stock shortage can also be automatically pushed to users after they add a product to the shopping cart but have not paid, in order to improve the payment rate.
It takes you only 5 minutes to integrate HUAWEI Analytics Kit, which helps you achieve scenario-specific precise targeting and improve the conversion rate of active users.
Integration guide:
Android
iOS
Web
Sample code:
Android
iOS
Web
If you encounter any problems during the integration, you can submit a ticket online.
We look forward to your participation!
An app's ability to retain users has a major impact on its profitability. Retaining users has always been a major challenge for developers, given the presence of competitor apps, and the willingness of today's users to shop around to find the best possible experience.
Therefore, developers have needed to invest more in marketing in order to acquire users. Furthermore, paid traffic has proved even harder to retain, as such users do not tend to install apps solely based on their original needs. Fortunately, there are effective ways to prevent user loss and maximize the value of paid traffic.
This article will highlight a few case studies which illustrate how the Prediction service can help you predict user loss and retain users.
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The figure above shows how a game app was able to improve its user retention, by following just a few steps in the Prediction service. The app's original retention rate was low, and thus its developers enabled the Churn prediction model in Prediction to identify users who demonstrated a high churn probability. They then utilized Prediction to gain actionable insights into the characteristics and behavior of these users, saved them as an audience, and then pushed tailored messages to engage with them on a more targeted basis. This process can be broken into several different stages, and we'll address the key stages for the Prediction service.
1. Precise user targeting
If a user is about to churn, they will likely become less active. The user's activity can be measured based on behavioral data, such as the daily launch rate, launch frequency, and use duration.
Let's take ARPG games as an example. Typical active players of such games tend not to launch the game many times a day, but will stay in the game for relatively lengthy periods of time after each launch. For games in this genre, we regard players who launch the game 2-3 times and play about 5 rounds a day as active players. If an active player has not launched the game very often over the course of a week, or even failed to sign in for two consecutive days, the Prediction service, based on the churn prediction model trained via machine learning, will determine that the player has a high churn probability in the next week.
The Prediction service uses the active user data over the most recent two weeks to train the churn prediction model, which is used to predict the probability that active users of the app in the past week will be lost during the next week. It's important to note that users who are inactive the next week, or uninstall the app are considered churned users.
The Prediction service does not require any high-level algorithm expertise to implement user targeting. Instead, prediction tasks are automatically executed in the background on a daily basis.
2. In-depth insights into audiences
For a targeted audience, the attributes and behavioral characteristics are analyzed, in order to implement a more actionable operations plan for better user retention.
*The data provided is for your reference only.
*The data provided is for your reference only.
This figure is an example of the background data in the Prediction service. It shows that users with high churn probabilities still have game sessions from the past seven days, though these are infrequent. In addition, Prediction enables you to query such users by device model, making it easier to reach them via periodic message pushing, in HUAWEI Push Kit, which users their most recent usage times.
3. Reaching users more effectively
For a targeted audience whose characteristics have been learned, you'll need to implement the right measures to retain its users. HUAWEI Push Kit, mentioned above, is the most handy method for doing so.
An audience generated by the Prediction service can be directly applied by HUAWEI Push Kit to target users. In the case of retention, push messages are sent specifically to users who demonstrate high churn probabilities. Common examples of this include the pushing of time-limited game gift packages, new game characters skills, and even simple greetings.
Prediction tasks incorporate data that is updated on a daily basis. As a result, push messages can be sent periodically to continuously engage recent users with high churn probabilities.
Acquiring traffic is more challenging than ever, but thanks to intelligent data technologies, you can now conduct refined operations that help you extract the most value from existing users. The Prediction service and Push Kit are indispensable for helping enterprises effectively improve ARPU, while also promoting digitization and intelligence across the board.
To learn more about Prediction, please visit our official website.
For more details, you can go to:
l Our official website
l Our Development Documentation page, to find the documents you need
l Reddit to join our developer discussion
l GitHub to download demos and sample codes
l Stack Overflow to solve any integration problems
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AARRR — short for acquisition, activation, retention, referral, and revenue — is a key operations model, where acquisition, as the very start, greatly affects how users will be converted. You may have tried different methods to improve the acquisition effect, user engagement, and user retention, but to no avail. So, what else can you do?
With the payment analysis report in Analytics Kit 6.0.0, you can analyze the behavior of your users by referring to data such as their payment frequency and preference. By combining this function with other analytical models in the kit, you'll have an array of data to work and plan from for higher revenue.
Enticing Users to Pay QuicklyThe first payment made by a user is the most significant as it implies they are satisfied with the app — but it is a process that can take some time.
This process inevitably varies app by app, so we can only touch on how to guide quick user payments in general.
Identifying common events that lead to the first payment
Sign in to AppGallery Connect. Find your project and app, and go to HUAWEI Analytics > Audience analysis. Create an audience of users who made the first payment. Then, check the report for this audience to identify the functions they frequently use. Let's say for an education app, most users tend to search for or share a course before making their first payment.
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* For reference only
Go to Payment analysis. Under Add filter, select the audience just created. Then, the report will present data about this audience, allowing us to optimize our operations strategies.
Leading non-paying users to frequently used or core functions
As mentioned above, the course searching and sharing functions most likely lead users to make their first payments. We can therefore guide users to use these functions more often. Or, we can send non-paying users push notifications that introduce the functions in detail, to guide such users to use them.
Increasing the ARPU & Payment RateIncreasing the average revenue per user (ARPU) and payment rate is important for boosting total user payment. To this end, we need to implement different operations strategies for different audiences, which can be created using the RFM model. The reason is simple: user payments vary by their payment abilities and preferences.
Determining users' paying habits
Go to Payment analysis. The report here shows changes in the paying users and the amount they pay. Using the filter and comparison analysis functions, we can easily locate the paying habits of different audiences.
* For reference only
If we find that most high-paying users are active users in Beijing, we can specifically target them with campaigns to make recurring payments.
Making audience-specific strategies
We can first segment users into different audiences by using the RFM model.
R: Recency, indicating the last consumption users made before the data collection date. It can be used to measure the user consumption period.
F: Frequency, indicating the consumption times of users in a given period
M: Money, indicating the consumption amount of users in a given period
* For reference only
After creating audiences, we can send them coupons or different push notifications with content that interests them, such as membership-related campaigns and promotions including price-break discounts.
In short, targeted operations based on analysis of how different audiences make payments in the app can help improve payment-related indicators and ROI.
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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The real estate industry is a prime example of a service sector that has rapidly digitalized in recent years, in order to meet the needs of a robust market and soaring customer base.
This phenomenon has not gone unnoticed by Analytics Kit, which offers special reports and event tracking templates for house rental and purchase apps in its 6.2.0 version. These reports and templates focus on attributes and behavior of users when they're viewing housing listings online. This makes the kit ideal for housing service platforms that hope to pique user interest in housing listings, and streamline the rental/purchasing process.
1. Data Overview: A Glimpse at the Whole Picture
The Data overview report shows you the overall status of your app, including the number of new users yesterday, number of active users yesterday, and the average usage duration yesterday.
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* Report example
The report also offers a filter function, allowing for further analysis on user changes. Let's take city-based data as an example. Real estate purchasing policies in different cities directly affect users' needs for buying houses. You can select cities in the filter to compare differences in user growth trends, making it easy to assess the effects of policy and economic conditions on behavior.
2. User Analysis: In-depth Data Display
The User analysis report details every aspect of how users interact with your app, with data like distribution of active users, sign-in time segments of active users, average usage duration per user, and how users are retained. This in turn, can help your operations team allocate resources in a more efficient way.
* Report example
* Report example
3. House Data, for Seamless Resource Allocation
One of the major drivers of user growth in the online housing market is the access to premium data. In order to close house rental or purchase deals, housing service platforms need to ensure that their users can find houses of interest quickly and conveniently.
The House data report offers key data on the number of views on the new house pages, number of views on the pre-owned house pages, number of views on the rental house pages, preferences for house layouts, and preferences for housing prices. Armed with such information, housing platforms can send out reasonable messages that appeal to their users. The report page also makes it easy to compare how users in different regions approach housing, which can help with crafting targeted sales strategies. Offline real estate agents can also benefit from this data, as they can better understand their customers' housing preferences, thereby streamlining the process for closing a deal.
* Report example
* Report example
Out-of-the-Box Templates
To help developers configure event tracking more efficiently, Analytics Kit also provides tracking templates for each of the three reports mentioned above. These templates are all ready-to-use: To check these reports, simply configure event tracking by using the events and parameters available in the templates.
Tracking configuration can be done either via coding or adding visual events. By easing integration and event tracking configuration, verification, and management, Analytics Kit boosts the efficiency and accuracy of event tracking.
* Template example
As house rental and purchases are not common user transactions, closing deals online is dependent on a continually active user base. In order to better engage with users, you can depend on the analytical models in Analytics Kit, which cover every facet of the experience.
For example, we can combine knowledge about user housing views with the audience analysis model to engage with specific audiences in different ways. For users who have a rigid demand for housing and are interested in two-bedroom apartments, you can send them messages about cost-effective housing in convenient locations. Or alternatively, for users who have stringent requirements for a comfortable living experience, you can notify them about smart and well-furnished living spaces.
That's all for the introduction to the house rental and purchase reports that are available in Analytics Kit 6.2.0. 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.