Former Tencent Data Director: The basic APP data analysis system that operators must master

Introduction: In Internet companies, any APP must plan its data system in advance before it is allowed to go online. Only with data can it operate more scientifically. Therefore, this article will introduce you to the basic data indicator system of APP.

The data indicator system of APP is mainly divided into five dimensions, including user scale and quality, participation analysis, channel analysis, function analysis and user attribute analysis. The user scale and quality dimensions mainly analyze user scale indicators, which are generally key indicators for product assessment; engagement analysis mainly analyzes user activity; channel analysis mainly analyzes channel promotion effects; function analysis mainly analyzes function activity, page access paths, and conversion rates ; and user attribute analysis mainly analyzes user characteristics. This article will elaborate on these five dimensions.

1. User scale and quality

The analysis of user scale and quality includes five common indicators: active users, new users, user composition, user retention rate , and total active days per user. 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.

1. Active User Indicators

Active users refer to users who launch an application (APP) within a certain statistical period. The number of active users is generally counted by device dimension, that is, the number of devices (such as mobile phones and tablets) that have been activated within a period of time. Active users are an indicator of the user scale of an application. Usually, if we only look at one metric to determine whether a product is successful, that metric must be the number of active users. Many Internet companies use the number of active users as the KPI assessment indicator for product managers. The number of active users can be divided into daily active users (DAU), weekly active users (WAU), and monthly active users (MAU) according to different statistical periods. Most apps that users are expected to open every day, such as news apps, social apps, music apps, etc., have KPI assessment indicators for their products.

is the number of daily active users (DAU). Why? What would happen if the assessment indicator for these apps was the number of monthly active users? Monthly active users only require users to launch the app once a month to be counted as monthly active users. Therefore, if an app that should be launched every day is evaluated using the number of monthly active users as a KPI, the person in charge of product operations may be "lazy". The product marketing staff only needs to find a way to get users to launch the app once a month, and perhaps this can be achieved by pushing two or three activities to users. Such an evaluation will make the product unattractive or even unhealthy. If daily active users are used as the KPI to evaluate the product, then the person in charge of product operations will definitely design features that users want to use every day or update content that users want to see every day to attract users to use it.

2. Add user indicators

New users refer to users who launch the app for the first time after installing the app. According to the statistical time span, new users are divided into daily, weekly and monthly. The indicator of new user volume is the most basic indicator to measure the effectiveness of marketing promotion channels; on the other hand, the proportion of new users to active users can also be used to measure product health. If the proportion of new users of a product is too high, it means that the activity of the product is achieved through promotion. This situation is worthy of attention, especially the user retention rate.

3. User composition indicators

User composition is an analysis of the composition of weekly active users or monthly active users, which helps to understand the health of active users through the structure of new and old users. Taking weekly active users as an example, weekly active users include the following categories of users, including returning users this week, users who have been active for n consecutive weeks, loyal users, and continuously active users. This week's returning users refer to users who launched the app last weekend and launched the app this week; users who have been active for n consecutive weeks refer to active users who have launched the app at least once a week for n consecutive weeks; loyal users refer to users who have been active for 5 consecutive weeks or more; continuously active users refer to users who have been active for 2 consecutive weeks or more; recently churned users refer to users who have not launched the app for n consecutive weeks (greater than or equal to 1 week, but less than or equal to 4 weeks).

4. User retention rate indicator

User retention rate refers to the proportion of new users in a statistical period who still launch the app after a certain period of time. User retention rate can focus on the next day, 7 days, 14 days and 30 days retention rate. The next-day retention rate refers to the proportion of new users in a certain statistical period (such as today) who launch the app again on the second day (such as tomorrow); the 7-day retention rate refers to the proportion of new users in a certain statistical period (such as today) who launch the app again on the 7th day; the 14-day and 30-day retention rates are similar. User retention rate is an important indicator to verify the user appeal of a product. Typically, we can use user retention rate to compare the user appeal of different apps in the same category. If for a certain application, in a relatively mature version, if the user retention rate changes significantly, it means that the user quality has changed significantly, which is likely caused by changes in the quality of the promotion channel.

5. Total active days per user indicator

The Total Active Days per User (TAD) metric is the average number of days each user is active in the app during the statistical period. If the statistical period is relatively long, such as more than one year, then the total active days of each user can basically reflect the number of days the user spent on the APP before churn. This is a very important indicator reflecting user quality, especially user activity. User attribute analysis is mainly conducted from the perspectives of the device terminals used by users, network and operator analysis, and user portraits.

2. Participation Analysis

Common engagement analysis includes launch count analysis, usage duration analysis, page visits analysis, and usage time interval analysis. Engagement analysis mainly analyzes user activity.

1. Start-up times indicator

The number of launches refers to the number of times a user launches an application within a certain statistical period. When conducting data analysis , on the one hand, we need to pay attention to the total trend of the number of startup times, and on the other hand, we need to pay attention to the average number of startups per person, that is, the ratio of the number of startups to the number of active users in the same statistical period. For example, the average daily number of startups per person is the ratio of the daily number of startups to the number of daily active users, which reflects the average number of startups per user per day. Usually, the average number of startups per person and the average usage time per person can be analyzed together.

2. Duration of use

The total usage time refers to the total duration from the start of the APP to the end of its use within a certain statistical period. The usage time can also be analyzed from the perspectives of average usage time per person and single usage time. The average usage time per person is the ratio of the total usage time and the number of active users in the same statistical period; the single usage time is the ratio of the total usage time and the number of launches in the same statistical period. Indicators related to usage time are also important indicators for measuring product activity and product quality. The reason is simple: users’ daily time is limited and precious. If users are willing to invest more time in your product, it proves that your application is important to users. The number of launches and usage time can be analyzed together. If the number of user launches is high and the usage time is high, the APP is an application with very high user quality and good user stickiness, such as the popular social applications.

3. Visit the page

The number of pages visited refers to the number of pages that a user visits at one time. We usually need to analyze the distribution of the number of visited pages, that is, to count the distribution of the number of active users of the number of visited pages of the application within a certain period (such as 1 day, 7 days or 30 days), such as the number of active users visiting 1-2 pages, the number of active users visiting 3-5 pages, the number of active users visiting 6-9 pages, the number of active users visiting 10-29 pages, the number of active users visiting 30-50 pages, and the number of active users visiting more than 50 pages. At the same time, we can discover user experience problems by looking at the differences in the distribution of visited pages in different statistical periods (but with the same statistical span, such as 7 days).

4. Use time intervals

The usage time interval refers to the time interval between two consecutive starts by the same user. We usually need to analyze the distribution of usage time intervals. Generally, we count the number of active users of the application within a month, such as the distribution of active users within 1-day, 1 day, 2 days...7 days, 8-14 days, and 15-30 days. At the same time, we can discover user experience problems by analyzing the differences in the distribution of usage time intervals in different statistical periods (but with the same statistical span, such as 30 days).

3. Channel Analysis

Channel analysis mainly analyzes the changes and trends of each channel in terms of relevant channel quality, so as to scientifically evaluate channel quality and optimize channel promotion strategies . Channel analysis requires the focus of those responsible for channel promotion, especially when channel cheating is prevalent in the current mobile application market . The analysis of channel promotion should especially focus on the analysis of channel cheating.

Channel analysis includes indicators such as new users, active users, number of launches, single usage duration and retention rate. These indicators have been explained above and will not be repeated here. The above mentioned are only preliminary dimensions of channel quality assessment. If further research on the channel is needed, especially on the anti-fraud level of the channel, more indicators are needed, including: indicators to determine whether user usage behavior is normal, such as the ratio of key operation activity to total activity, whether the time when users activate the APP is normal; to determine whether the user's device is real, such as analysis of the concentration of models, operating systems, etc.

In short, if we want to conduct an in-depth study on channel cheating, the core idea of ​​the algorithm is to study whether the users brought by the promotion channel are real "people" who are using it, and design relevant evaluation indicators and algorithms from this direction. For example, if most of the users brought by a certain channel use the APP at 2 a.m., we believe that the users brought by this channel are likely not normal people, or even machines are cheating.

4. Functional Analysis

Functional analysis mainly analyzes functional activity, page access paths, and conversion rates. These indicators require the special attention of product managers in charge of functional operations.

1. Functional activity indicators

The function activity indicators mainly focus on the number of active users of a certain function, the number of new users of a certain function, the user composition of a certain function, and the user retention of a certain function. The definitions of these indicators are similar to those of the “User Scale and Quality” indicators in the first part of this article. However, this section focuses on a certain functional module rather than the entire APP.

2. Page access path analysis

The APP page access path statistics the page access and jump status of users at each step from opening the application to leaving the application. The purpose of page access path analysis is to help APP users complete tasks at different stages of using the APP while achieving the APP's business goals and to improve the efficiency of task completion. The following three aspects need to be considered when analyzing the access path of an APP page:

(a) The diversity of APP user identities . Users may be your members or potential members, your colleagues or competitors, etc.:

(b) The diversity of APP user purposes . Different users have different purposes for using APP. (c) The diversity of APP user access paths. Even if their identities and usage purposes are similar, their access paths are likely to be different. Therefore, when we analyze the APP page access path, we need to segment the APP users and then perform the APP page access path analysis. The most commonly used segmentation method is to classify users according to the purpose of use of the APP. For example, users of automobile APPs can be divided into attention-type, intention-type, and purchasing-type users, and path analysis is performed on each type of user based on different access tasks. For example, for intention-type users, what paths do they take to compare different car models and what problems do they encounter? Another method is to use an algorithm to perform cluster analysis based on all user access paths, classify users based on the similarity of the access paths, and then analyze each type of user.

3. Funnel Model

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. Conversion rate refers to the ratio of the number of people who enter the next page (or page views) to the number of people who enter the current page (or page views). When users enter the product and complete a key task (such as shopping), there will be loss in the conversion between different steps. For example, when a user enters an e-commerce website, browses the products, puts the products into the shopping cart, and finally pays, there will be a lot of user churn at each step. By analyzing the conversion rate, we can quickly identify whether there is a problem in the different paths that users take when using the product. Of course, product managers do not need to look at conversion rate reports every day. We can continuously monitor the daily conversion rate. Once the conversion rate fluctuates significantly, we will send an alert email to the corresponding product manager to detect product problems in a timely manner.

4. User Attribute Analysis

User attribute analysis mainly analyzes from the perspective of the device terminals used by users, network and operator analysis, and user portraits

1. Equipment terminal analysis

The analysis dimensions of device terminals include model analysis, resolution analysis, and operating system analysis. During the analysis, the main focus is on analyzing active users, number of new users, and number of startups for these objects. That is, analyze the number of active users, new users, and startup times of different models, analyze the number of active users, new users, and startup times of devices with different resolutions, and analyze the number of active users, new users, and startup times of devices with different operating systems.

2. Network and operator analysis

Network and Operators mainly analyzes the user's Internet access method and the telecom operators used, and mainly analyzes these objects in terms of active users, number of new users, and number of startups. That is, analyze the number of active users, new users and startup times of Internet access methods (including wifi, 2G, 3G, 4G), and analyze the number of active users, new users and startup times of different operators (China Mobile, China Telecom, China Unicom, etc.).

3. Regional analysis

The main analysis is on different regions, including the number of active users, number of new users and number of launches in different provinces, cities and countries.

4. User portrait analysis

User portrait analysis includes demographic characteristics analysis, user personal interest analysis, and user business interest analysis. Demographic characteristics include gender, age, education, income, expenditure, occupation, industry, etc.; user personal interests refer to the analysis of personal life interests and hobbies, such as listening to music, watching movies, fitness, raising pets, etc.; user business interests refer to the interest analysis in consumer areas such as real estate, automobiles, and finance. The data of user portraits needs to be collected in accordance with relevant portrait data in order to support more detailed portrait analysis.

This article mainly introduces the basic data analysis system of APP. There are more indicator systems that need to be specially designed according to the characteristics of APP. For example, search APP needs to pay attention to indicators related to its characteristics, such as the number of search keywords, the average number of search keywords per person, etc. In addition, another thing that is worth noting is that many product managers or operations personnel believe that many of the indicators mentioned in this article can be naturally seen after the product is launched. This is a very common misunderstanding. Because, for most of the indicators mentioned in this article, if you don’t do data management and reporting, and do relevant data development statistics, you will have to see the relevant data reports. Therefore, before the product goes online, the product manager must plan the data system of the product he is responsible for, drive development to collect and report relevant data, and dynamically optimize and enrich the data system during the operation process.

Mobile application product promotion service: APP promotion service Qinggua Media advertising

The author of this article @傅志华 is compiled and published by (APP Top Promotion). Please indicate the author information and source when reprinting!