E-commerce apps facilitate transactions between sellers and buyers and user traffic is a direct indicator of the transaction amount. Therefore, e-commerce apps attach greater importance to their operations than other types of apps in order to attract more users and promote conversion.
Among the different user acquisition channels, the message push plays a significant role, but is one that is always being ignored.
The message push has the advantages of high delivery rate, and the ability to display simple and concise content for users instantly, which makes it easier for users to understand and accept the content. This can improve the e-commerce operations conversion rate and achieve business growth. However, many e-commerce operators will experience the following issue: frequent messaging causes poor tap-through and conversion rates, or even app uninstallation.
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In this article, we summarize the problems that e-commerce operators may encounter during messaging and provide a collection of marketing skills for messaging that exploits the features of HUAWEI Push Kit that help resolve the problems. We hope you find it useful for increasing your conversion rate.
Problem : Why is the tap-through rate lower than expected for an attractive and competitively-priced product?
Answer: It's because selecting the right audience is key. Only push a product to an audience that really needs it!
Skill 1: Use target audience segmentation to push product messages to the right audience
E-commerce apps provide a large number of products, and each type of product is intended for specific audiences. For example, it is obviously inappropriate to push messages about baby products to college students, and your conversion rates will suffer correspondingly if you do so. In the aforementioned example, you can use the audience segmentation function to filter out users within a specific age range who have browsed baby products before, and then push messages about the release of new baby products to such users. This will help increase your tap-through and user conversion rates.
Skill 2: Precisely push messages to users based on subscribed topics
Pushing products or activities according to user preferences is a way to obtain twice the result with half the effort. With the topic subscription function, you can send messages relating to topics that users have subscribed to, in order to improve the tap-through and conversion rates. For example, sending push notifications to users who have subscribed to the beauty topic about a sale on makeup products, or sending product release messages to users who have subscribed to the pre-sales reminder topic.
Push Kit provides audience segmentation and topic subscription functions for you to classify users by attribute, behavior, and preference, allowing you to precisely push messages with just the right content.
Problem : What can I do if the content of my push messages is not having the intended effect?
Answer: Let data help you choose the best push message content.
Skill 3: Use the A/B testing feature to select the best push message content
Saying the right things in your push messages can make a massive difference on your tap-through and conversion rates. On the other hand, saying the wrong things can prove disastrous for your users. By using the A/B testing function, you can compare the tap-through data for different push messages, and then select the ones with the highest tap-through and conversion rates to push on a large scale.
Skill 4: Add emojis to enrich text
You can add emojis to push messages to make them more eye-catching and engaging.
Thanks to the A/B testing function, you can create multiple test messages with different emojis for different user groups in order to compare their performance.
Problem : Pushing dozens of messages does not increase the tap-through rate, or even causes users to uninstall my app. Why does this occur?
Answer: The content of your messages and their target audience may be correct, but if you push too many messages to users at the wrong time, it may have the opposite of the desired effect.
Skill 5: Configure a messaging schedule and frequency
When and how many times to push messages is an important decision for e-commerce operators. If you send too many push messages to a user at the wrong time, the user will regard the messages as spam, which at best annoys the user and at worst makes the user uninstall your app. Data shows that people use their mobile phones the most during the following times of the day:
09:00 – 10:00: Breakfast and commute to work
12:00 – 14:00: Lunch and noon break
18:00 – 20:00: Commute home and dinner
22:00 – midnight: Relaxing before going to bed
During these times of the day, users are more likely to see and tap your messages. According to available data, mainstream e-commerce operators usually push messages at 10:00 a.m. and 8:00 p.m. You can further pinpoint your push messaging times based on the characteristics of your users, and avoid times when competitor apps push messages to users.
Push Kit's scheduled messaging function allows you to set push tasks in advance, which helps optimize your messaging timing for maximum user engagement.
Skill 6: Intelligently reach users at appropriate locations by using the geofence messaging function
When using geofence messaging, select a location and create a geofence with a radius of R meters around that location. When a target user enters, leaves, or stays in the geofence for a predetermined amount of time, a message is sent to the user. With user authorization,you can also use the geofence messaging function to send messages to users when they are near a certain location.
For O2O e-commerce apps, you can use the geofence messaging function to select one or more offline stores. When a user goes near a store, the geofence messaging function will automatically push messages such as product discounts and new releases to the user, encouraging the user to make a purchase. This function also helps increase business for stores that are not in prime locations and facilitates the purchase of new products in newly-opened stores.
Skill 7: Push what users care about to improve user loyalty
In addition to the messages about products and activities, you can send logistics update information to users because most of them are eager for receiving products, improving user activeness and loyalty.
As an efficient push channel, Push Kit can send messages in real time even if the app is not started or is not running in the background, so that users can receive messages in a timely manner.
Besides the above, Push Kit also enables you to view a vast amount of push messaging-related statistical data to allow you to trace and analyze the effectiveness of your push messages. All of this will make your e-commerce operations much easier to carry out.
Integrating Push Kit is really straightforward. Huawei provides a tool for you to integrate Push Kit into your app within half a day, and provides sample code for you to reuse in your app server. If you run into any problems, we have dedicated technical support who can help you with the integration process.
Click>>Find out more on our official website
We hope you enjoy using Push Kit!
Can we send push kit in Hindi?
very interesting
Related
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.
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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
An app's ability to retain users has a major impact on its profitability. Retaining users has always been a major challenge for developers, given the presence of competitor apps, and the willingness of today's users to shop around to find the best possible experience.
Therefore, developers have needed to invest more in marketing in order to acquire users. Furthermore, paid traffic has proved even harder to retain, as such users do not tend to install apps solely based on their original needs. Fortunately, there are effective ways to prevent user loss and maximize the value of paid traffic.
This article will highlight a few case studies which illustrate how the Prediction service can help you predict user loss and retain users.
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The figure above shows how a game app was able to improve its user retention, by following just a few steps in the Prediction service. The app's original retention rate was low, and thus its developers enabled the Churn prediction model in Prediction to identify users who demonstrated a high churn probability. They then utilized Prediction to gain actionable insights into the characteristics and behavior of these users, saved them as an audience, and then pushed tailored messages to engage with them on a more targeted basis. This process can be broken into several different stages, and we'll address the key stages for the Prediction service.
1. Precise user targeting
If a user is about to churn, they will likely become less active. The user's activity can be measured based on behavioral data, such as the daily launch rate, launch frequency, and use duration.
Let's take ARPG games as an example. Typical active players of such games tend not to launch the game many times a day, but will stay in the game for relatively lengthy periods of time after each launch. For games in this genre, we regard players who launch the game 2-3 times and play about 5 rounds a day as active players. If an active player has not launched the game very often over the course of a week, or even failed to sign in for two consecutive days, the Prediction service, based on the churn prediction model trained via machine learning, will determine that the player has a high churn probability in the next week.
The Prediction service uses the active user data over the most recent two weeks to train the churn prediction model, which is used to predict the probability that active users of the app in the past week will be lost during the next week. It's important to note that users who are inactive the next week, or uninstall the app are considered churned users.
The Prediction service does not require any high-level algorithm expertise to implement user targeting. Instead, prediction tasks are automatically executed in the background on a daily basis.
2. In-depth insights into audiences
For a targeted audience, the attributes and behavioral characteristics are analyzed, in order to implement a more actionable operations plan for better user retention.
*The data provided is for your reference only.
*The data provided is for your reference only.
This figure is an example of the background data in the Prediction service. It shows that users with high churn probabilities still have game sessions from the past seven days, though these are infrequent. In addition, Prediction enables you to query such users by device model, making it easier to reach them via periodic message pushing, in HUAWEI Push Kit, which users their most recent usage times.
3. Reaching users more effectively
For a targeted audience whose characteristics have been learned, you'll need to implement the right measures to retain its users. HUAWEI Push Kit, mentioned above, is the most handy method for doing so.
An audience generated by the Prediction service can be directly applied by HUAWEI Push Kit to target users. In the case of retention, push messages are sent specifically to users who demonstrate high churn probabilities. Common examples of this include the pushing of time-limited game gift packages, new game characters skills, and even simple greetings.
Prediction tasks incorporate data that is updated on a daily basis. As a result, push messages can be sent periodically to continuously engage recent users with high churn probabilities.
Acquiring traffic is more challenging than ever, but thanks to intelligent data technologies, you can now conduct refined operations that help you extract the most value from existing users. The Prediction service and Push Kit are indispensable for helping enterprises effectively improve ARPU, while also promoting digitization and intelligence across the board.
To learn more about Prediction, please visit our official website.
For more details, you can go to:
l Our official website
l Our Development Documentation page, to find the documents you need
l Reddit to join our developer discussion
l GitHub to download demos and sample codes
l Stack Overflow to solve any integration problems
Original Source
When your app user base reaches a certain threshold, you may find it harder to acquire new users continually. In this case, rather than struggling to attract new users, it's better to try to activate your inactive users. Let's take a look at how HUAWEI Analytics Kit can help you activate and win back inactive/lost users.
1. Defining Inactive and Lost UsersTo win back inactive and lost users, the first thing you'll need to do is reasonably define them. To do this, you'll need to take your business characteristics into consideration, and quantify key user behaviors.
For example:
If you have a game app, you can define these users by the number of consecutive days that they do not sign in to your app.
If you have an e-commerce app, you can define these users by the number of consecutive days that they do not place an order in your app.
If you have a video app, you can define these users by the number consecutive days that they do not watch a video in your app.
Analytical models in Analytics Kit are the tools you need to define inactive and lost users accurately and quickly.
Identifying turning points that lead to user churn
Let's use the demo app for Analytics Kit as an example. Its revisit user report indicates that the first turning point appears in day 31–90. This means that there is a small possibility that users who have been inactive for 30 days become active again. The second turning point appears in day 91–180. According to this information, it would be wise to define users whose consecutive days with no app use are greater than or equal to 30 days but equal to or smaller than 90 days as the inactive users, and define users whose consecutive days with no app use are greater than 90 days as the lost users.
Analytics Kit also offers the user lifecycle model, which allows you to customize the statistical scale for each phase of your app's users.
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* This figure shows the user lifecycle analysis report for Analytics Kit with virtual data.
2. Creating Lost User ProfilesA comprehensive understanding of your users makes it much easier for you to build the best possible app. Implementing an effective winback strategy requires that you create detailed lost user profiles that are based on a complete understanding of past behavior characteristics of inactive or lost users.
To create these profiles, you can refer to:
The number and trends for inactive/lost users
The user lifecycle report offers an intuitive glimpse at the number and trend of users in the inactive and lost phases.
* This figure shows the user lifecycle analysis report in Analytics Kit with virtual data.
Phases when users become inactive/lost
The user lifecycle report displays the ratios of inactive users converted from beginner users, growing users, and mature users to all inactive users.
From the figure below, we can roughly divide inactive users into two categories: those converted from the users in the beginner and growing phases, and those converted from the users in the mature phase. The ratio of the first category is much higher than that of the second — this indicates that the more dependent a user is on your app, the less likely it is that he/she becomes inactive or lost.
* The figure shows the user lifecycle analysis report in Analytics Kit with virtual data.
Users in the first category became inactive before they have fully experienced your app. This might have been due to a cumbersome user experience, undesirable product design, or failure to deliver the "Aha!" moment that hooks users to your product. Users in the second category became inactive or lost after they fully experienced your app. This is likely because the product failed to bring them the experience better than their expectation.
Whether most inactive/lost users have similarities
On the user lifecycle report page, you can save inactive/lost users as an audience with just one click on the number. You can then go to the audience analysis report to check whether most inactive/lost users have similarities in aspects such as the model, location, event, system version, or download channel.
* This figure shows the audience analysis report in Analytics Kit with virtual data.
* This figure shows the audience analysis report in Analytics Kit with virtual data.
Behavioral characteristics of inactive/lost users
Behavior analysis provides a filter function, in which you can select inactive/lost users. The session path analysis report will then tell you the behavior path of inactive/lost users before they became inactive/lost, and the session step where this occurred.
* This figure shows the session path analysis report in Analytics Kit with virtual data.
You can save nodes related to user churn as the funnel, and then pinpoint causes of churn by checking the funnel analysis report.
* This figure shows the session path analysis report in Analytics Kit with virtual data.
Analysis of the value inactive/lost users contributed in the past
It's a good idea to analyze the value inactive/lost users have contributed to your app in the past. By doing so, you can separate them into different groups and formulate winback strategies that have a greater chance of success.
The event analysis function enables you to identify previously paying users among inactive/lost users, and learn more information about them like total top-up amounts, gross merchandise volume (GMV), and top-up frequency. Thanks to this information, you can divide inactive/lost users into different groups according to the priority and difficulty of winning them back.
3. Specifying Winback Strategies
Determining which group should be won back first
In the previous step, we created inactive/lost user profiles, and separated them into different groups according to the difficulty of winning them back, through such indicators as the previous behavior and value of lost users, whether they have uninstalled the app, and whether they can be reached now. In principle, the users to win back first are those who have become inactive, but have not yet uninstalled the app.
Determining the focus of your winback strategy
Different winback strategies have different focuses, including:
Benefits: For example, you can send coupons to inactive/lost users, or remind them of existing coupons or virtual currencies that will soon expire.
User interests: Let's use a video app as an example. Some of the inactive users are interested in animation. To win them back, you can send them notifications about upcoming new animation series, or about activities related to animations they are interested in.
Emotions: Let's say you have a life simulation game app which features virtual pets. To entice users to open your app to take care of their pets, you can send them notifications about their pets' health or emotional status.
Selecting a winback channel
Ads: In addition to attracting new users, ads can also play a role in activating or winning back users. When you use ads for this purpose, you're likely to get better than expected results.
Push notifications: If you choose this channel, make sure that the time and frequency for pushing notifications are reasonable. Also remember to check the percentages of users who uninstall your app and disable the push notification after they receive the notification.
SMS messages: You should use this channel to reach only target users, since costs associated with SMS messages are a little higher. To achieve better winback results, you can tailor the content of messages sent out to different user groups.
Emails: This is one of the most common channels for reaching users. You'll need to consider how to best impress your inactive/lost users in a short email, in order to win them back.
Those are the three steps for targeting and wining back lost users with Analytics Kit. Though Analytics Kit makes it easier than ever to win back users, it's still better to create mechanisms that warn you about which users may become inactive, and take proactive measures to retain users who have been won back. This will ensure you to keep improving engagement and loyalty of your users.
About Analytics Kit:
Analytics Kit is a one-stop user behavior analysis platform for products such as mobile apps, web apps, and quick apps. It offers scenario-specific data management, analysis, and usage, helping enterprises achieve effective user acquisition, product optimization, precise operations, and business growth.
For more details, you can go to:
Our official website
Demo of Analytics Kit
Android SDK integration documentation
iOS SDK integration documentation
Web SDK integration documentation
Quick app SDK integration documentation
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%.
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* 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
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!!