Why do data analysis? It can effectively avoid clapping your head to do things, and blocking the subjective imaginary black hole with the results of objective data analysis; it can provide convincing support for decision-making. Through data analysis, you can also see the effects and problems after decision-making and for the next Decisions provide direction. The following are the key analysis objects of APP data analysis:
I. Industry data helps analyze the pros and cons of APP
Industry data is very important for understanding your own APP. With the comparison of industry data, you can know the level of your APP in the entire industry, analyze the advantages and disadvantages of your own APP in the industry, and find out the problems in it. And make targeted adjustments in future APP operations and promotion. Understanding the industry data is no less important than understanding your own APP. Because a product is everywhere, there is a difference in comparison. Therefore, it is absolutely impossible to immerse yourself in a small world. A mature APP operator knows how to Let your app kill the birth day.
2. Understand that user indicators are the foundation and focus
The newly added user refers to a user who starts an APP for the first time after the application such as the APP is downloaded and installed. This indicator is mainly used to measure the effectiveness of marketing promotion channels, and is the most basic data indicator of APP. APP brush retention
① According to the time dimension, new users are divided into daily new users, weekly new users, and monthly new users.
② According to the source of the channel, the new users can also be divided into channel new users, operator new users, and regional new users.
PS: If the proportion of newly added users to active users is too high, it means that most of the APP's activity depends on promotion and promotion of new ones, otherwise, the APP promotes activation.
Active users refer to those who open the APP within a certain statistical period and bring some value to the APP. It is generally used to measure the current status of the APP's operation-the scale of users in the true sense. Many products talk about the size and quality of users, not the total number of users, but the number of active users.
Active users are divided into daily active users (DAU), weekly active users (WAU), and monthly active users (MAU) according to different statistical periods. Different types of APPs have different KPI indicators. Social apps such as WeChat, news apps such as NetEase, and music apps such as Kugou are apps that developers want users to start every day, so these apps focus on the number of daily active users.
Among the active user indicators, there is a very important indicator: the total number of active days of a single user refers to the average number of active days of each user in the APP within a certain statistical period, which reflects the amount of time TA spent on the app before the user was lost. Days.
3. Churn users
Lost users is a concept relative to active users, which refers to users who have started the APP and registered it after downloading the APP, but gradually lost interest in the APP, and then completely detached from this product. If active users are used to measure the current operating status of the app, lost users are used to analyze whether the app is at risk of being eliminated and whether your app is capable of retaining new users.
4. User composition
The user composition refers to the composition of active users in a certain period, and generally analyzes the composition of weekly active users or monthly active users. In terms of monthly active users, its user composition includes:
① Loyal users: Also called super active users, use APPs continuously for a long time, such as 4 weeks of continuous active, or 15 days within 1 month.
② Recently lost users: Users who have not opened the APP within one month. app branding
③ Returning users this month: Users who did not open the app last month, but reopened the app this month.
④ Continuously active users: Users who have been active for two weeks or more. Here we pay attention to distinguishing between the amount of loyalty users and continuous active users.
User composition analysis helps to understand the health of APP active users through the structure of new and old users among active users.
5. User retention rate
User retention rate refers to the proportion of users who still open this APP after a certain period of time among the number of new users in a certain statistical period, including the next day retention and 7th retention The proportion of APP is retained on the 14th and 30th, and so on), retained on the 14th, and retained on the 30th. This indicator verifies whether your app is attractive to users.
3. Understand APP active indicators to check product quality
1. Number of startups: The number of times a user opened the APP during a certain statistical period. Generally there are daily start times, week start times, monthly start times, and per capita start times in the corresponding cycle.
2. Duration of use: The duration of use refers to the total duration of all users from opening the app to closing the app during the statistical period. From the use time can also extend the per capita use time, single use time. This indicator evaluates whether your APP users are sticky or not, which also reflects the quality of the APP's products. The length of use is generally analyzed in conjunction with the number of startups.
3. Interval of use: Refers to the time interval when the same user opens the APP twice.
4. Distribution of Visited Pages: Refers to the number of pages visited by the user at one time.
Four, transformation analysis indicators
Operators are most concerned about the conversion rate. The conversion rate refers to the ratio of the number of people who complete a conversion action (such as shopping) to the number of people on the current page within a statistical cycle.
The conversion rate reflects the profitability of the APP. It pays attention to and researches the conversion rate. It can analyze the shortcomings of the APP in terms of which aspects and which activities have better results. It can quickly improve the user experience, save advertising costs, and improve the conversion process. effectiveness.
Conversion analysis indicators involve:
① The number of people on the current page (or PV), ② The number of people on the next page (or PV), ③ the number of people who completed the conversion, and ④ the total number of promotions.
For example, if a user opens a shopping app, browses the product, puts the product in the shopping cart, and finally pays, every link has a conversion and analysis. Many times we will use the funnel chart to see the conversion of each link, and the real-time funnel chart can monitor the daily conversion rate. Once problems are found, the operating staff can immediately adjust to avoid greater fluctuations.
Five, user portrait analysis
With user data, it is easier to do user portrait analysis. User portraits are the feature analysis, interest analysis, and user behavior analysis of population attributes. Tagging users when making user portraits is the core part.
The data indicators involved in user portraits are:
1. Characteristics of population attributes: name, gender, age, height, weight, occupation, region, education level, marriage, constellation, blood type, etc.
2. User interests include user personal interests and user business interests. The personal interests of users refer to personal hobbies, such as pets, watching movies, listening to popular music, etc. The user's business interests refer to the analysis of consumer interests such as shopping, real estate, automobiles, and finance.
3. User behavior analysis: including behaviors within the app and social network behavior. In-app behavior refers to behaviors such as searching, browsing, commenting, liking, collecting, scoring, adding to a shopping cart, purchasing, and receiving coupons during the use of an APP.
Social behavior refers to behaviors such as sharing and forwarding to social networking platforms that occur during the use of the APP. For example, the mobile Taobao APP has the behavior of sharing to Taoyou (internal), sharing to WeChat, contacts, Weibo, QQ, and SMS.
This requires a high degree of attention. The behavior of users in the APP can determine the value that the APP can bring. User behavior analysis can be combined with the funnel graph conversion model to improve the overall conversion level.
User portraits can help APPs gradually achieve precise marketing and directly perform point-to-point interactions between APPs and designated users. There are not many products on the market that help app developers build user portraits because extracting and analyzing data may depend on different tools. app promotion method
Analysis of APP promotion channels
APP operators have to promote in different channels every day. At this time, we must monitor which channels are effective, the unit price is cheap, which channel has a particularly high user conversion rate, and which channel has the fastest user churn. This all requires Monitoring and analysis of channel data. Spend time and money on drainage, and make sure that your resources are being used to a value. Under certain conditions, different scenarios can be given to users from different channels.