Activity rate = active users/total users? So what is an active user?

What is an active user? Some analysis tools define users who open the app within a period of time as active users. How do you feel about retained users? The activity rate reflects the health of the product, and the number of active users reflects the product’s market share. But if we don’t even know what an active user is, how can we do the analysis?

1. What is the number of active users?

First, we need to redefine the number of active users. Many analysis tools count a user opening an app or any other behavior within a period of time as one active activity. So what is the use of such data? What is the difference between activity and retention?

For example, if a user opens push messages every day, or comes to collect points once a day, is such a user valuable to the product? Can we say that such users are active users?

Redefine active users: active users = high-quality users = users who truly reflect the value of the product

For example:

Active users of information products are those who read for more than 10 minutes a day;

Active users of e-commerce products are those who browse 10 products per week;

Active users of online education products are those who complete 80% of their weekly study plans.

Of course, specific numbers and indicators need to be designed according to specific products, and active user indicators can be designed in multiple dimensions. For example, there are several types of active users in information products:

Users who read for more than 10 minutes a day;

Users who read more than 5 articles per day;

Users who comment more than 3 times a day;

Users who forward more than 2 times a day.

Design active user indicators based on different key behaviors of users to meet the subsequent needs of active user operations . In addition to designing from different usage dimensions, you can also design from different nodes, such as:

Users who participate in the event and receive coupons are recorded as active users of the event;

Users who log in for more than 2 days during holidays and whose daily visit duration is 30% higher than usual are counted as holiday active users.

Most data analysis tools have fixed statistics on active users, but the statistics on active users need to be adjusted at different nodes and stages, just like the custom active user statistics function launched by Zhuge.io.

2. Why do we define active users in this way?

The significance of analyzing active users is to provide feedback on the health of product functions. The number of active users, to a certain extent, reflects the effectiveness of product functions and the goodness of product experience. When evaluating a product, one needs to consider the extent to which users use the core functions of the product. By defining whether users are active through their key behaviors, one can more intuitively see users' feedback on the product.

What is the significance of daily active users, weekly active users, and monthly active users? How to count?

First of all, we need to think about a question. Weekly active users are active users within 7 days, and monthly active users are active users within 30 days. So is it necessary to check for duplication when counting weekly and monthly active users?

No duplicate checking: Users who are active for several consecutive days are counted cumulatively. The number of weekly and monthly active users is higher, but it cannot reflect the health of the product.

Duplicate check: When counting weekly and monthly active users, only one count is kept for users who have been active for multiple consecutive days. This can show the net value of active users within a week or month, and reflect the absolute number of active users within a period of time, but it seems meaningless.

So how can statistics be considered meaningful?

Active users reflect the degree of user recognition of product features, and continuous activity represents the product's continued satisfaction of user needs. The number of active users within a week or month does not have much meaning, but what is meaningful is the number of users who are active for a week or 30 consecutive days.

This involves analyzing user stratification through active user indicators. The number of active users in different time periods such as 2 consecutive days, 3 days, 5 days, 7 days, and 15 days is counted, and these users are screened out. They are divided into multiple levels such as general active, moderately active, heavily active, and absolutely active according to the number of consecutive active days, and targeted operations are carried out for users of different levels.

For example, why would a user who had been active for 7 consecutive days suddenly become inactive? You can analyze user behavior , observe changes in 7-day active data, analyze the causes and make targeted improvements.

In addition to stratifying active users by consecutive active days, you can also stratify active users by product usage behavior. For example, if you use the product for 10 minutes a day, you can count it as an active user. Then, users who use the product for 30 minutes, 60 minutes, and 120 minutes need to have different marks and increase their usage time in a targeted manner.

Another significance of active user analysis is to analyze the behavioral characteristics of existing active users, compare them with the behavioral characteristics of inactive users, find out the characteristics of user activity and the reasons for inactivity, and improve the activity of inactive users.

Source: Zhuge Jun