What data indicators should APP operation focus on?

There are many types of APPs on the market, and each APP has its own unique indicators. This article starts from the universal indicators of APP to explain which data indicators we should pay attention to.

APP data indicators can be divided into five dimensions, including user quantity and quality, user behavior, channels, user portraits and revenue indicators.

1. User quantity and quality

The number and quality of users are the most important dimensions in data indicators, and this dimension also includes a large number of indicators.

1. Number of active users

Active users refer to users who have launched a product within a certain period. The number of active users indicates the scale of the product's users. This indicator is also the core indicator of most products. In TO C products, the number of users largely determines the commercial value of the product.

Due to different statistical periods, the number of active users can be divided into daily active users, weekly active users, and monthly active users. Different types of products also focus on different active user indicators. High-frequency and rigid-demand apps such as WeChat and Toutiao pay more attention to the number of daily active users. Low-frequency demand apps such as Beike and Ctrip will focus on the number of active users over a longer period of time.

2. Number of new users

New users refer to users who launch the APP for the first time after installing it. This indicator is mainly used to measure the effectiveness of product operation and promotion, and can also be used to determine the product's life cycle.

The number of new users can be divided into daily new users, weekly new users, and monthly new users due to different statistical periods.

In community products, if the proportion of new users to active users is too high, it needs to attract our attention. These new users are obtained through product marketing and promotion. The entry of a large number of new users in a short period of time may destroy the community atmosphere and create a false prosperity. The product may seem to be very active, but in fact the retention rate may be very low. The product is not healthy and we need to pay attention to this situation.

3. User Retention Rate

User retention rate refers to the proportion of users who launch the APP for the first time in a certain period and then continue to launch the APP after a period of time, to the number of new users; it is an important indicator to measure the user stickiness of the product and whether the product function iteration is successful; it can also measure the quality of the channel.

User retention rate can be divided into next-day retention rate, 7-day retention rate, 14-day retention rate, and 30-day retention rate; the next-day retention rate refers to the proportion of new users who still open the APP on the second day within a certain period, and so on.

4. Total active days per user

The total active days of each user refers to the average number of days each user is active in the APP within a certain period. It is an important indicator to measure the user stickiness, user quality and user activity of the product.

5. DAU/MAU

DAU refers to the number of daily active users of a product, and MAU refers to the number of monthly active users of a product. The value obtained by dividing the two is the average number of days a user visits per month. It can be used to determine user stickiness. Of course, we also need to make a comprehensive judgment based on the type of product. For example, the number of active users of Ctrip and Beike is relatively low, and a multi-dimensional analysis is required for comprehensive consideration.

6. User Structure

User structure mainly refers to the number of different types of users, which is used to measure the health of the product. The indicators included include the number of continuously active users, the number of loyal users, the number of churned users, the number of returning users, etc.

2. User Behavior

User behavior metrics refer to a series of metrics related to user operations in the product.

1. Product function indicators

Product function indicators mainly refer to a series of indicators similar to those mentioned above, such as the number of active users of a certain function of the product, the number of new users, user retention rate, user structure, etc.; however, product function indicators are not aimed at the entire product, but at a specific function.

2. Conversion rate

The conversion rate refers to the ratio of the visits to the next page to the visits to the current page. It is used to measure the user experience of the product path, and then guide the optimization of product pages, processes and functional iterations.

The most typical example is the shopping process of e-commerce. There will be a large number of user losses in each link. It is necessary to monitor daily which link’s conversion rate deviates from the normal value, analyze the reasons for the data abnormality and optimize it.

3. Number of starts

The number of launches refers to the number of times a user launches an application within a certain period. Generally, there are two major indicators to focus on when it comes to the number of launches: one is the total number of user launches, and the other is the average number of launches per person, which can reflect the user stickiness and user activity of the product.

4. Duration of use

Usage time refers to the time a user spends using an APP from the time it opens the APP to the time it closes the APP within a certain period. Usage time needs to focus on three indicators: total usage time, average usage time per person, and single usage time. It is mainly used to measure user stickiness and user activity, and is generally analyzed together with the number of launches.

5. Use time intervals

The usage time interval refers to the time interval between two consecutive app launches by a user. By comparing the user usage time intervals in different periods, we can monitor whether there are any problems with the user experience.

6. Page visit analysis

Page visits include the number of page visits, dwell time, bounce rate, number of pages visited by users, and user page visit paths.

The number of page visits refers to the number of visits to a certain page within a certain period, the dwell time refers to the length of time a user stays on a certain page during a page visit, and the bounce rate refers to the ratio of the number of visits generated by only visiting the page and then leaving to the total number of visits - these indicators can reflect the attractiveness of a specific page to users, and possible product process and user experience problems.

The number of visited pages refers to the number of pages visited by a user after a startup. We usually analyze the distribution of the number of users who visit pages, such as how many users visit pages 1 to 2, how many users visit pages 3 to 4, etc. We can also discover user experience problems by comparing the page visit distribution in different periods.

The user page access path is a statistics of the page access and jump situation of each step from the user opening the APP to the user closing the APP. Different types of users will have different page access paths. We need to distinguish users and then analyze their page access paths to make targeted optimizations.

3. Income Indicators

Revenue indicators mainly refer to revenue-related indicators involved in the commercialization process of the product, such as the number of paying users, ARPU, payment rate, GMV (e-commerce), renewal rate, LTV, etc.

1. ARPU

ARPU (Average revenue per user) refers to the average revenue generated by users in a certain period, and the users here refer to all users.

We also often use ARPPU, which stands for Average Revenue Per Paying User, the average revenue contributed by each paying user. We can use it to estimate the future revenue that the product can achieve and judge the quality of users.

2. Payment rate

The payment rate refers to the ratio of the number of paying users to the number of active users, which can be used to judge the payment conversion effect of the product.

3. GMV

GMV (Gross Merchandise Volume) refers to the transaction amount of a product, which includes the paid amount and the unpaid amount. It is a common indicator of e-commerce products and can reflect the size of an e-commerce platform.

4. Renewal rate

The renewal rate generally refers to the membership renewal rate, that is, the ratio of the number of members who renew after expiration to the number of members who expire, which can be used to measure the stickiness of paying users.

5. LTV

LTV, Life time value, refers to the user's lifetime value in the APP, that is, the revenue provided by the user for the product from the first time the user opens the APP to the last time the user closes the APP; it can be used to measure the quality of users in different product life cycles. When LTV is greater than the average customer acquisition cost and subsequent operating costs, the product obtains net profit.

4. Channel indicators

Channel indicators are actually similar to the indicators mentioned above, except that they differentiate users based on channels, and then look at indicators such as the number of active users, number of new users, and retention rate from different channels - it can be used to evaluate the quality of different channels and optimize channel delivery strategies.

5. User portrait

The composition of user portraits can be divided into three categories: basic attributes, user behavior attributes, and preference attributes.

Basic user attributes include demographic characteristics such as gender, region, age, occupation, education, income, and device attributes such as device brand, model, operating system, operator, and networking method.

User behavior attributes include usage duration, number of launches, active days, consumption frequency, page views, and other attributes.

In content products, preference attributes mainly refer to users' preferences for content, such as technology, games, life, politics, etc. Users' preferences can be reflected through data such as the number of clicks, favorites, likes, comments, and searches on different types of content.

In e-commerce products, it mainly refers to the user's preference for goods, such as skin care products, electronic products, snacks, clothing, etc. The user's preference can be reflected through data such as the number of views, purchases, comments, and additions to shopping carts of these goods.

Author: Gong Yu chats about products

Source: Gongyu Chat Products