Please save these tips for achieving mobile app user growth!

When traffic and users in various industries are monopolized by giants, and when the Matthew effect becomes increasingly stronger, how can emerging apps break through the dilemma of difficulty in user growth?

When users tend to be calm and rational, and product functions are similar, how can emerging mobile apps achieve user growth ? How can products be optimized to be closer to users?

When analyzing a product, you should focus on three things: users, needs, and data.

Most people focus on users and needs but ignore data. On the one hand, people subjectively do not attach importance to it and do not pay attention to it. On the other hand, if you feel that it is not part of your job, just let the operations partner do it.

In fact, data is important to product managers . After the mobile APP is launched, paying attention to changes in data can help you understand users' attitudes, which is much more objective than looking at user feedback.

1. Data indicator system that mobile App products focus on

The data indicator system of a product (especially APP) can generally be divided into: user scale and quality, channel analysis, participation analysis, function analysis and user attribute analysis.

1. Analysis of user scale and quality includes the total number of users, number of new users, retained users, and conversion rate. User scale and quality are the most important dimensions of APP analysis, and their indicators are also the most compared to other dimensions. Product managers should focus on the indicators of this dimension.

2. Channel analysis mainly analyzes the changes and trends in the relevant channel quality of each channel in order to scientifically evaluate channel quality and optimize channel promotion strategies. Channel analysis should be given special attention, because cheating in the mobile application market is an open secret in the industry.

Channel analysis can compare the effects of different channels from multiple dimensions of data. For example, users from different sources can be compared from the perspectives of new users, active users, next-day retention rate, single-time usage duration, etc. This way, you can find the channel that best suits you based on the data and achieve the best promotion effect.

3. Engagement analysis mainly analyzes the user's activity. The analysis dimensions mainly include launch times analysis, usage time analysis, page visits analysis and usage time interval analysis.

4. Functional analysis mainly includes:

4.1. Functional activity indicators : active users of a certain function, usage; functional verification; data analysis of product functions to ensure the rationality of function selection.

4.2. Page access path : The page access and jump status of each step in the entire process from user opening to leaving the application. The page access path is a full statistics. Through path analysis, we can derive the diversity of user types and the diversity of users’ purposes for using products, and restore user purposes; through path analysis, we can perform user segmentation; and through user segmentation, we can return to product iteration.

4.3. The funnel model is used to analyze the conversion rate of key paths in a product to determine whether the design of the product process is reasonable and to analyze user experience issues. The analysis of user conversion rate focuses on analyzing the reasons for loss at each level of the funnel. Focus on the conversion rate of each step in the app and its impact on revenue levels by setting up custom events and funnels. By analyzing event and funnel data, we can optimize the steps with low conversion rates in a targeted manner and effectively improve the overall conversion level.

5. User attribute analysis Whether in the early stages of our product launch or in the adjustment of our strategy, analyzing user portraits is of great significance. For example, before designing a product, we need to build a user portrait to guide design, development, and operation; the product iteration process requires collecting user data to facilitate user behavior analysis and link it to the business model, etc.

User attributes generally include gender, age, occupation, location, mobile phone model, and network usage. If you are interested in other attributes of the user, you can go to the background of your own WeChat public account or other backgrounds such as Toutiao, UC, etc. to see what dimensions the user attributes include.

2. Key areas of focus for mobile app product optimization

There are generally three key areas of focus for mobile app product optimization: how to increase activity, how to improve retention, and how to broaden the conversion funnel.

Taking information APP as an example, 99click Business Assistant briefly analyzes how to use big data for user operations and product optimization from the perspective of user trajectory:

1. How to improve activity

For information apps, improving activity can be considered from two aspects: one is the number of launches per day, and the other is the usage time. Therefore, operators must constantly plan things that suit the user's tone and meet the user's reading needs.

For example, Qutoutiao, after receiving 200 million yuan in financing from Tencent, switched its focus to content creation, hoping to gain long-term attention and support from users.

2. User retention and retention rate

Retained users and retention rate are important criteria for evaluating the user quality of an application. The higher the user retention rate, the more attractive the application is to users.

Commonly used retention indicators include: next-day retention rate, 3-day retention rate, 7-day retention rate, 15-day retention rate, etc.

When checking the retention rate, developers can pay attention to the changing trend of the retention rate over a period of time. They can also evaluate the quality of versions and channels or locate the reasons for the decline in certain indicator values ​​of the application by comparing the user retention rates of different application versions and different distribution channels.

3. User behavior analysis: funnel model

The funnel model is a quantitative conversion model that triggers processes in a certain order. The analysis and application of the funnel model is a function that every product developer and operator needs to have, because it helps us discover problems and solve them.

The era of traffic-centric, barbaric operations has ended. The next era will be an era of refined operations based on scientific data and centered around users.

Source: 99click