User activity, retention, and churn, a very comprehensive illustration!

User retention, user activity, and user churn are the three major issues that user operations must face. In this article, the author explains these three issues and provides a super-complete diagram. Let's take a look.

Many students complain that the three major issues of user retention, user activity, and user churn are difficult to deal with.

First of all, these three questions seem to be the same, but not exactly the same, so it’s not quite clear what they are.

Secondly, these three issues are often interrelated and influence each other, and I feel dizzy while talking about them.

Thirdly, it is difficult to give analytical suggestions on these three questions. Once the numbers are calculated, we often don’t know what to say.

Every time we do analysis, we either say: "We need to aim high! We need to aim low!" or we write down with trepidation: We need to communicate with the business/ask the users for the specific situation, but we can't give any constructive suggestions. Today, let's get to the bottom of it and figure out what these three are.

1. What do the three mean?

User retention, user activity, and user churn are actually three different statistical methods of user activity behavior.

1. User activity

First, we define “active” behavior, such as logging in once, visiting for more than 10 minutes, making a purchase once, etc. After that, each time a user performs an active behavior, it is recorded as: active once (as shown in the figure below).

2. User Retention

If a user continues to be active after a period of time starting from a specified time, it is recorded as 1 retention. The most common is new user retention (as shown below).

3. User churn

Artificially define a time point as a churn node, such as when a user has not logged in for 12 months. Users who reach the node are considered churned users. Note that unlike user activity and user retention, user churn is not an objective fact, but a subjective determination. In theory, as long as the company does not actively cancel the account, you can assume that users will never churn. Of course, we know that this is self-deception, so a specific churn standard is generally given.

2. The connection and difference between the three

Students with quick reactions have already noticed that there is an obvious connection between these three.

The relationship between user activity and user retention is as follows:

The relationship between user retention and user churn is shown in the following figure:

So, if the activity criterion is logging in, and the churn criterion is not logging in for three months:

• Xiao Ming registered on February 1 and was counted as an active user of that month;
• Xiao Ming did not log in in March, April, and May, and was counted as an inactive user and a non-retained user;
• Xiao Ming still hasn't logged in in June and is counted as an inactive user, a non-retained user, and a lost user.

Since all three indicators point to active behavior, why do we need to distinguish them into three? Because these three indicators actually represent three directions in which the business can act:

1. User activity is an indicator that can be counted in real time

Therefore, in the short term, actions will be reflected in it immediately. Positive actions, such as big sales, can see immediate feedback; negative actions, such as server downtime, can see the impact immediately.

2. User retention requires a longer period of observation

Therefore, it can reflect more systemic and structural problems, such as poor product experience, insufficient competitiveness, inadequate operations, etc.

3. User loss is the bottom line for saving users

The longer a user doesn’t come back, the higher the cost of recalling them. Users may even have forgotten that there is such a product. Setting churn indicators can better remind you: how many users have reached the bottom line, pay attention!

When doing business, it is important to combine the long and short sticks. The three indicators of user activity, user retention, and user churn point to the short-term, medium-term, and long-term measures of the business. Therefore, they need to be counted and analyzed separately.

In fact, when the user group activity decreases, these three indicators often change together, making analysis difficult. However, it is not just the number-crunching problem that makes analysis difficult. The following are the real troubles.

Why is analysis difficult?

The root of the difficulty is: if the user is logged in, we know exactly what he did after logging in, but if the user is not logged in, we don't know why he is not logged in. We can only guess the reason for not logging in. This is the core of the difficulty of these three problems. If the leader asks us: "Which pages did the logged-in users visit today?" I guess everyone can answer fluently.

• But if the leader asks:
• What is the reason for inactivity?
• What is the reason for the low retention rate?
• What is the reason for the increase in churn rate?

I guess many people would be upset. If you add a sentence at the end: "Give me the specific reason! Don't just talk about numbers!" I guess many people would collapse. All they could think about was: "I'll ask the product manager," "I'll talk to a few users," and their hands would probably have already started to touch the phone...

However, this is only the first trouble. The second trouble is more difficult. Except for super apps like WeChat, there are no apps that users must open every day.

• Some applications have a very obvious window period, such as renting a house and traveling;
• Some applications are just naturally inactive, such as Internet financial products;
• Some applications are loved to death, while those that are not loved will be thrown away immediately, such as games, short videos, and social products.

So a concept is derived: the natural life cycle.

• The natural state of users is to log in during winter and summer vacations;
• The natural state of users is to churn after using for XX days;
• The natural state of users is to log in 1 or 2 times every quarter.

The difficulty lies in the word "natural". You will find that this "natural life cycle" is like air. Everyone knows that it exists, but it is invisible and intangible. Who stipulates that there are only so many natural cycles? What reason do you have to be sure that this user is a natural loss, rather than a poor product? It seems that this value exists, but when it comes to a specific user, it seems that it does not exist.

So, just like the natural growth rate, the so-called natural life cycle can easily become the main battlefield for operators to pass the buck. The high activity rate and retention rate are my doing. The low activity rate and retention rate are natural reasons. If I don’t do anything, they will be even lower.

This kind of argument will directly lead to the boss' dissatisfaction and also lead to higher requirements for data analysis - everyone hopes that the data analyst can clearly say: Zhang Zihan left for natural reasons. He was very satisfied with our product and just stopped using it naturally. There is no other reason. Hmm, do you really want to check Zhang Zihan's data line to read his brain waves directly...

However, this is only the second trouble. The third trouble is even more difficult to deal with. Everyone instinctively thinks that as long as they figure out the reasons for user inactivity, low retention, and churn, they can pull users back. This is a very futile and stupid idea, just like when breaking up, the boy grabs the girl's hand and asks bitterly, "Why are you leaving me, why, why, why is this, give me a reason"

Anyone who has watched two episodes of a romantic drama knows that this is meaningless. But when it happens to you, you can't help but ask "Why, why, why!" The only difference is that this time it's the boss who asks. You can't just go to a late-night snack stand, buy a few beers and a bunch of skewers, and get him drunk like in a romantic drama.

The above three problems make it easy to calculate the three values ​​of user activity rate, user retention rate, and user churn rate, but it is very difficult to analyze the reasons behind them and find targeted countermeasures. Especially when the user activity rate decreases, the user retention rate decreases, and the user churn rate increases, the performance pressure will cause the motivation of various departments to shirk responsibility and make unreasonable demands.

If students who work with data want to get out of this situation safely, they must keep in mind the principle that "truth is the first to die on the battlefield", carefully analyze the pros and cons of specific problems, and find solutions.

Author: Down-to-earth Teacher Chen, Source: WeChat public account “Down-to-earth Teacher Chen”.