With increasing user acquisition costs, many enterprises have embarked on finding out how to move towards a refined way of operating, but they are facing a challenge: how exactly can this be done?
This challenge is closely related to content marketing, event marketing, channel expansion, and product design. However, the most fundamental thing needed to overcome the challenge is by achieving an in-depth understanding of users.
Do you know your product's user group?
Which of them are your target users?
Which of them are potential high-value users?
How many users are added every day?
What is the change trend of active users?
You will find the answers to all of your questions in the user analysis model of HUAWEI Analytics Kit.
What Is the User Analysis Model?
The user analysis model, commonly based on the user model, uses the user layering model to analyze the entire user base and find behavior and feature differences between users to formulate targeted marketing policies, as well as fully explore and improve user values. The following will describe the user model, user layering, and capabilities provided by the user analysis model of Analytics Kit.
COLOR="darkorange"]About the user model[/COLOR]
As a systematic method for researching users, the user model helps you understand users' real requirements to avoid making mistakes when coordinating product design and operations. In the early phase, there are two methods for building a user model: one is based on in-depth revisits with users, and the other is based on the opinions and market research data of authoritative experts in the industry. Since these methods may be too time-consuming or not sufficiently objective, they are not a useful way of keeping pace with the rapid iteration of Internet products and the surge in the user base.
That is why we are putting forward a refined operations method based on user behavior data and centering on the cycle of "development-release verification-iterative optimization". By continuously collecting and integrating user behavior data, you can manage users by layer to build the user model.
About user layering
User layering is to group users by dimension based on the layering model. You will need to set user attributes and group users in detail. Common layering models include the user lifecycle model, user value pyramid model, AARRR model, and RFM model.
About user analysis
The user analysis model provided by Analytics Kit is built based on user behavior data. It provides the following capabilities:
1. Basic user analysis: Analyzes new users and active users to understand the changes in key operation indicators such as the daily active user (DAU) and monthly active user (MAU).
2. User layering model: Use the user lifecycle model as an example. It analyzes the user lifecycle to present the user growth and conversion data in each phase of the lifecycle, helping you accurately locate the channels that effectively acquire new users, increase active users, and attract high-value users.
3. Audience analysis model: Gains insight into user segmentation by audiences preset in the system or from your own customizations.
The following uses the user analysis model as an example. The user layering model and audience analysis model will subsequently be introduced and shared.
What Are the Application Scenarios of User Analysis?
Personnel at an online education app launched an online and offline activity from February 6 to February 19 to attract new users. They also wanted to know the trends of new and active users during the activity.
On the user analysis page, they could view the trends of new and active users, collect statistics by day, week, or month, and view the number of new users, proportion of new users, number of active users, proportion of active users, number of churned users, churn rate, and total number of users in a period during the activity.
The analysis report by week showed that the number of new and active users increased during the activity compared with before the activity, indicating that the activity really contributed to new user attraction.
What operations personnel care about the most is the activity level. Under User analysis > Active users, they can view the DAU, weekly active user (WAU), and MAU.
In conclusion, the basic user analysis function of Analytics Kit provides trend changes and details about operation indicators such as new and active users. By using these trends, you can further understand user retention by the retention analysis model and gain a deeper insight into users via the user layering model and audience analysis model. All of these mode will be explained later.
Related
Marketing strategies have altered dramatically in recent years to meet ever-changing consumer demand in the digital era. There's unprecedented demand for real-time data analysis among enterprises that stand to benefit from more agile decision-making.
What is real-time analysis?
Real-time analysis is a feature in HUAWEI Analytics Kit that helps collect, process, and analyze app data with nearly zero latency. It is capable of conducting event analysis, user trend analysis, hot event composition analysis, as well as location-, model-, and app version-based top N analysis for the most recent 30 minutes. This provides crucial insights into real-time user behavior and empowers you to adjust product operation policies to adapt to real world conditions.
What benefits does real-time analysis offer?
With real-time analysis, product operations personnel are able to determine the stability of user behaviors by monitoring changing trends for key indicators, and fully leveraging this vast range of information to perform multi-dimensional analysis. This in turn enables them to propose marketing policies that are tailored to different regions and device models.
When is real-time analysis most effective?
Scenario 1: A shopping mall plans to launch a sales promotion, and has partnered with a travel app with the goal of guiding more customers to the shopping mall during the promotional period.
Travel app operations personnel can determine the points in time when users are most active, based on user and event count trends over the previous 30 minutes, as well as longer term fluctuations. They can send push or in-app messages related to the promotion to users at specific time points. This can result in higher message click-through and conversion rates.
Summary: In this scenario, real-time analysis supports high-precision marketing and promotional activities, by providing accurate time-based data for operations personnel to send push or in-app messages.
Scenario 2: After a new version of a fresh food app is released in gray mode, operations personnel can determine how popular the new version is by observing how it is being used.
Operations personnel can monitor real-time trends for hot events, and draw a diagram to compare hot events from multiple dimensions, including by event, location, device model, and app version, to determine the popularity of the new app version.
They can also analyze the locations, device models, and app versions of hot events over the previous 30 minutes, to get a better grasp of user features, and adjust the gray release policies accordingly.
Summary: In this scenario, real-time analysis provides for optimal gray release policies, by enabling operations personnel access to determine the popularity of the new app version by region and by device model.
Visit the Huawei Developers website to learn more about HUAWEI Analytics Kit.
Good examples of scenarios where this would be really useful.
Lots of apps these days are finding it difficult to maintain user growth. The main reasons for this are that the demographic dividend for users has gradually subsided, user bases and growth rates are decreasing, and some apps even have negative user growth. Also, competition in app categories such as e-commerce, lifestyle, and gaming is on the rise, while the retention rate of new users is dropping. Low user acquisition and retention rates have been long-standing challenges for app operators.
So how do app operators resolve this pressing issue?
One obvious solution is to leverage data to search for growth opportunities in the entire user lifecycle, and to maintain growth through refined operations.
The first step of refined operations is to divide users into different phases of the user lifecycle. With HUAWEI Analytics Kit, the lifecycle of a user can be divided into the following phases: beginner, growing, mature, inactive, and lost.
For beginner users, growth plans should be made based on maximizing return on investment (ROI) and user activation to ensure that you can quickly acquire the users you want and then convert them into growing users.
For growing and mature users, the key areas of focus is to improve retention and conversion rates. It is very important to maximize the value of these users and make them more active and stable.
For inactive and lost users, prevent user churn, try to win back lost users, analyze the causes of churn, and optimize user activation promotion plans.
1．For Beginner Users: Reduce the User Acquisition Cost, and Promote User Activation and Growth
How can HUAWEI Analytics Kit help you reduce user acquisition costs in the beginner phase? It does so by providing you with various analysis capabilities such as event analysis and comparison analysis, which allow you to view event trends and the distribution of event-generating device models and operating systems. Then, we can use the filter to perform comparison tests of different types of events and select the optimal channel to place services.
How to promote activation and growth of beginner users? This can be done by guiding users to complete key operations based on their interests. For example, for video apps, guide users to watch videos for a certain period of time or purchase membership; for game apps, help users pass early levels; and for e-commerce apps, enable users to place the first order within a short period of time. Then, perform funnel analysis and attribution analysis to analyze the conversion rates of users based on their behavior at key nodes when using apps, so that you can optimize the process, improve the provisioning mode, and UI design of individual nodes.
The app delivered ads to acquire new users in various ways. However, the problem was that the contribution rate of each channel cannot be accurately calculated, and the user churn rate was high.
By using the attribution analysis model of HUAWEI Analytics Kit, operations personnel of the app defined the target conversion event as "new download and use", and the to-be-attributed event as "ad clicks on each channel". According to the generated report, channel A had the highest contribution rate, while channel B had the lowest. So they shifted their marketing budget from channel B to channel A. After three months of optimization, their user acquisition costs decreased by 26% and the new user retention rate increased by 15%.
2.For Growing and Mature Users: Promote User Activation and Retention, and Increase the User Conversion Rate
In addition to user activation and retention, we must also focus on the user conversion rate. How to retain and convert users is a common challenge for most apps. For retention, perform path analysis to detect the actual behavioral paths of users when they are using apps, then check whether the paths are different from the designed ones. If not, we can guide users to the designed paths by operational means. In addition, funnel analysis can also be performed which will give you an intuitive picture of the conversion rate and churn rate of each phase, which facilitates app optimization. For conversion, use both the filter and behavior analysis model to analyze users by segment, and to know the behavior characteristics of different users. After that, use audience analysis model to segment users for precise services based on the analysis result, thereby increasing the user conversion rate.
The operations personnel of the app found that the user retention rate and purchase conversion rate in the last two months had decreased. How did they quickly solve the problem? Operations personnel first segmented users based on user attributes (such as the gender, age, region, and phone brand) and user behavior (such as browsing products, adding to cart, and purchasing). Then they figured out different behavior characteristics through path analysis and funnel analysis models according to the segmented users. It was then discovered that the churn rate from submitting an order to make a payment was the highest. Moreover, inactive users commonly made fewer than three purchases, and functional areas on the homepage were not clearly divided. Based on these findings, the app's operations personnel formulated an optimization solution to improve the purchase conversion rate.
3.For Inactive and Lost Users: Prevent User Churn, Wake Up Inactive Users, and Summarize Experience
Inactive and lost users operations personnel want to see.
For inactive users, the key is to prevent user churn and precisely choose which inactive users to wake up. We can formulate operations policies in advance to avoid user churn based on the predicted user groups that had churn risks or winback potential in each phase based on the user lifecycle analysis model of HUAWEI Analytics Kit. In addition, behavior analysis can help to detect users whether they have the value and possibility to be woken up, as well as to send them messages to try to wake them up. For lost users, it is more difficult to win them back than to acquire new users. Therefore, it is recommended that we focus more on summarizing experience and optimization to avoid churn of active users. For example, figure out the characteristics of lost users, enhance the detection capability before the churn, use data from lost users to help optimize the promotion of current users, and increase the activation and loyalty of active users to avoid user churn by improving operations policies based on funnel analysis, behavior analysis, and comparison analysis.
Let's take a look at how the operations personnel of this game app woke up inactive users. The operations personnel first selected the inactive users who were worthy of and likely to be waken up by performing behavior and audience analysis. It was determined that such users were users who had made at least three in-app payments and passed more than five in-game levels. The operations personnel then designed specific plans for waking up inactive users. The user lifecycle analysis model provided the predicted user groups that had churn risks. Therefore, they avoided user churn by improving user experience and giving users benefits through in-app messaging or push messages. Also, a detailed analysis of the behavioral attributes of churned users was carried out. It turned out that such users had less than 5 friends in-game, and most have complained about the game freezing. The operations personnel then tested and determined the causes of user churn, and optimized their app and operation policies accordingly. Such in-app improvements included providing a multi-channel account sign-in feature, a one-touch friend adding feature, and optimizations to the interaction logic. After optimization, the app successfully decreased the inactive user rate by 12%, and the user churn rate by nearly 8%.(*Source: Developer feedback)
Lastly, let's recap on how to increase the number of users of the entire user lifecycle through the use of the HUAWEI Analytics Kit.
Once you integrate the HMS Core Analytics SDK (Android, iOS, and JavaScript), you can upload user attributes and behavior data, so that the actual behavior of users at a specific time can be displayed, giving you the basis of data analysis. HUAWEI Analytics Kit supports the automatic collection of 11 user attributes and 27 events, as well as customized user attributes and 500 customized events, making your optimizations easier and providing more data for refined operations.
Moreover, it provides abundant analysis models based on the atomic data, such as events, behavior, funnels, audience, lifecycle, and attribution, enabling you to learn about user growth, user behavior, and product functions. Backed by these models, the filter can be used to perform segmentation analysis of app types, user attributes, and audiences. More importantly, it supports various types of apps, including iOS, Android and Web apps to meet your cross-platform analysis needs. You can complete the integration and release your apps in half a day. HUAWEI Analytics Kit has become one of the most popular services globally due to its quick development speed and powerful analysis capabilities. Integrate HUAWEI Analytics Kit today, and explore its myriad of enriching features.
Official website of Huawei Developers
Development Guide
HMS Core official community on Reddit
How is the sales conversion rate? Which categories of products are most popular? How can we boost the gross merchandise volume (GMV)? These are just a few of the tough questions that operations personnel are facing these days. As e-commerce has flourished, it is increasingly important to collect a wide range of user-related data, from basic user behavior analysis, such as the numbers of new users and active users, to payment information, including product sales amount and categories. That's why accessing a comprehensive analysis report on the e-commerce sector can be so valuable.
And now, Analytics Kit 6.2.0 is ready to help. It offers e-commerce analysis reports, which display key indicators for e-commerce apps, from dimensions like data overview, payment analysis, user analysis, product sales analysis, and product category analysis, giving operations personnel high-level insight on precision marketing and product strategies. In addition, the intelligent data access function provides event tracking templates and sample code, which spur greater efficiency across the board.
1. Overview of Core Indicators
Data overview can display your app's real-time usage and payment information, such as the number of online users, number of paying users, and payment amount. You can add filter criteria to filter data by platform, app, user attribute, or audience. Such a broad range of data gives you an accurate glimpse at the basic running status of your app.
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2. Payment Analysis Indicators, Revealing Business Growth Trends
For the e-commerce industry, payment is a direct indicator for measuring product operations status. With Payment analysis, you can view the payment amount, number of users who have made a payment, average payment amount per user, and other indicators. You can also filter user groups based on the configured filter criteria and time period. For example, to view the payment data of active users in your e-commerce app, click Add filter, and then Audience, before selecting Active users.
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3. User Analysis in 10 Dimensions, Providing Key Insight on User Behavior
User analysis shows user growth and behavior through broad-ranging indicators, including the numbers of new and active users, sign-in time segments of active users, number of daily won-back users, average usage duration per user, average usage duration per sign-in, and retention of new and active users. You can compare the appeal of different sharing channels and promotional assets, based on indicators like sharing channels and operations slot clicks.
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4. Product Sales and Category Analysis, Helping You Pursue Growth-oriented Strategies
It is important to track sales volumes and the allocation of sales by product category, in order to implement effective marketing schemes.
The Product sales analysis tab page presents a comprehensive overview of sales data, including the GMV, numbers of orders, and product details. The GMV trend card, for instance, clearly shows the recent revenue status. But success is dependent on far more than just overall revenue. In e-commerce, a number of conversion rates, such as the payment conversion rate and the order conversion rate, are critical to success. An increase in the payment conversion rate means that users find your products or marketing activities appealing. To better analyze the conversion rate, you can create a conversion funnel to perform drill-down analysis using the funnel analysis function provided by Analytics Kit.
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Product category analysis gives you a breakdown for the allocation of each product category in terms of total sales revenue, with indicators like the number of purchasers and the sales volume. Furthermore, indicators like the percentages of categories with canceled orders, returns, and favorites allow you to see which products are popular, so that you can invest resources in an optimal manner. On the contrary, for products with a large number of canceled orders and returns, it may indicate that they are not popular with users.
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As if that were not enough, you can also perform comprehensive and refined analysis on users via the audience analysis, user lifecycle analysis, and funnel analysis functions provided by Analytics Kit.
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.
As exercise and health apps have become mainstays, home-based workouts have also become the norm.
However, the sheer number of workout apps in the market has led to fierce competition, which places high requirements for user retention and conversion. To help you fine-tune your operations based on user behavior, Analytics Kit 6.3.0 comes with newly-released exercise and health industry reports that provide in-depth insight on user behavior, which can prove invaluable for optimizing marketing strategies, via data overview, payment analysis, behavior analysis, and community and after-sales information.
1. Overview of Core Indicators
Data overview displays basic user data such as the numbers of total users, new users, active users, and paying users. Clicking Add filter, you can view these data by platform, app name, user attribute, and audience.
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2. Payment Analysis, Helping You Monitor Member Payments and Course Sales
Membership operations and payment conversion are also key components of operations. On the Payment analysis tab page, you'll be able to get a clear sense of member payments and course sales via indicators such as the number of new paying members, revenue from paying members, percentage of users who pay for membership, course revenue, and top 10 courses.
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3. Behavior Analysis, Allowing You to Quantitatively and Scientifically Analyze Data Indicators
How do we evaluate the contribution rate of each ad slot or determine which products in the app are most popular? How about the effects of each promotion activity? The Behavior analysis tab page answers these questions as it presents the number of ad slot clicks, completion rate of activity goals, number of activity sharings, as well as data related to course analysis and activity conversion analysis. For example, you can view the contribution rate of each ad slot to determine the amount of traffic they attract, and then optimize the allocation of resources accordingly.
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4. Community and After-Sales Analysis, Boosting Your User Stickiness via Feedback
Community and after-sales analysis displays user performance based on the following data: the numbers of active users in the community and in each section, sections that new posts belong to, post sharing channels, and the average number of times each user contacts customer service. This is enormously helpful, since the community and after-sales modules are frequently used by users, and community contents and posts also effectively reflect user retention. For example, through the number of active users in each section, you can determine which sections are most active, and then ramp up promotions there, or recommend these sections on the home page to boost user stickiness.
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Last but not least, based on the event tracking templates for the exercise and health industry, you can improve the quality of data collection and the efficiency of event tracking to implement the industry's digital and intelligent transformation.
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.
In an era of skyrocketing demand for online financial products, securities companies have to transform the way they attract users and do business. To help address long-standing challenges, like homogeneous services and lack of differentiated operations scenarios, Analytics Kit 6.3.0 provides securities industry reports and corresponding event tracking templates. You can use these tools to target users, based on the news items of interest and preferences, to streamline the financial decision-making process and craft personalized services.
1. Clear Overview of User Information
Data overview displays data about the overall user growth, such as the number of new users and total number of users, as well as user details like the numbers of users who have applied for opening a securities account, bound a bank card, or deposited money.
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You can also add filters to analyze the growth of each indicator. For example, you can compare new users from different channels for drill-down analysis, so as to select proper channels for data-driven marketing.
2. Trading Dashboard for a Glimpse at User Preferences
The Trading dashboard presents the overall sales information via the number of users who traded stocks, shares of stock bought and sold, sales volume of each financial product, and other indicators, providing you with a clear sense of user behavior and preferences. You can then use this information to craft an optimal product layout that can address user demand.
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3. News Dashboard for Key Insights into Investment Demand
Since users tend to purchase financial products by taking the overall economy and relevant news into consideration, you can use the News dashboard to see which news items are of must interest to users via indicators related to news viewing and sharing, thus gaining a fuller understanding of investment demand.
Likewise, you can also push targeted news that is in line with user preferences, summarizing the status of the market and streamlining the investment decision-making process for users.
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4. Out-of-the-Box Event Tracking Templates
To further bolster your event tracking efficiency, Analytics Kit also provides out-of-the-box event tracking templates for the securities industry, covering modules of data overview, trading, and news. After configuring events and parameters to be tracked based on the templates, you can view securities industry-related data to analyze user preferences and demand, and craft more personalized wealth management scenarios.
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Analytics Kit also provides a range of other analytical models. For example, there is performance analysis for key conversion nodes, which helps optimize the key process from new user registration to account opening. To do so, you will need to perform the following steps:
First, select the desired events, such as Register and Submit account opening application, on the Funnel analysis page, to build a funnel model of registration conversion. Then, filter data by app version and OS version on the Industry analysis page to analyze nodes with a high churn rate, so as to check whether the cause of churn is associated with the system compatibility. Finally, optimize the app in a targeted way to improve the registration and card binding rates.
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, Quick App, HarmonyOS, and WeChat Mini-Program.