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In the process of user operation, user churn is an inevitable phenomenon, but how to effectively analyze and deal with this problem is a key skill that every operator needs to master. This article deeply explores the common misunderstandings, basic ideas and specific analysis methods of user churn analysis, aiming to help readers systematically understand and master the essence of user churn analysis.

How to analyze user churn? The data of user churn rate can be calculated, but what can be done after the calculation? It seems that the reason for churn cannot be seen from the data alone. We only know that the user has not come for X months, but we don’t know what to do with this. Let’s discuss it systematically today.

1. Common Mistakes in User Churn Analysis

Mistake 1: Trying to retain every user

This is the most common mistake in operations, and many newcomers will fall into this trap. They will issue coupons when they don't shop, and spin the wheel when they don't log in. As a result, they waste funds and raise a bunch of unprofitable customers.

In fact, user churn is inevitable, and there is no 100% retention in the world. Every business must focus on its core users.

When talking about user churn, what we really need to do is to put the churn rate in a cage and control it at an acceptable level.

Mistake #2: Trying to understand every churn reason

This is the most common mistake in analysis, and many newcomers will fall into this trap. Users don't like it? We didn't do it well? The competitors are too strong? Users have no money? In short, they want to give everyone a reason. But there is no data at all, so they just stare at each other.

In fact, we don’t need to and we don’t have the ability to list all the reasons.

Same as the previous point, we just need to control the controllable factors and reduce obvious errors.

Mistake 3: Focusing on churn and not activity.

This is another common mistake. Analyzing churn after it has actually increased. As a result, the data is already done and the users have left. It is useless to analyze. Churn is a relatively lagging indicator.

Before the user is "lost" in the data, he may have already left and has not been active in the past few months. Therefore, the churn rate should be combined with the activity rate.

Pay attention to events that affect user activity as early as possible, and closely track the activity rate of core users to avoid doing useless work afterwards.

2. Basic Ideas for User Churn Analysis

The goal of user churn analysis is to put the churn rate in a cage, so in terms of data, we first focus on the churn rate trend, especially on three types of problems (as shown in the figure below).

1. Event-based issues: Short-term fluctuations in churn caused by one or more events.
2. Systemic issues: The company's overall turnover rate is higher than its peers/experience level and remains high.
3. Persistent problem: The churn rate has been increasing since a certain time, with no signs of improvement.

Churn rate is a concept relative to active rate. Although we usually define churn as "users who have not logged in or purchased for X months", when users are already inactive, real churn may have already occurred.

In order to better identify churn problems, we often use the natural cycle and life cycle methods in combination with the activity rate. The natural cycle often points to event-based problems (because events occur on natural dates), while the life cycle often points to system-based problems (poor business performance, short user life cycle or breakpoints).

3. Event-type problem analysis method

Negative events can cause user churn. For example, out-of-stock, price increase, system bugs, user complaints, big sales by competitors (which we haven’t done yet), etc. This type of event is the easiest to identify. In terms of data, the activity rate of the user group affected by the event will drop immediately after the event, and the churn rate will start to increase N months later.

When analyzing, you need

1. Collect and pay close attention to relevant events.

2. Classify events properly (internal/external, system/price/product…).

3. Identify the affected user groups (label them for future observation).

4. Pay attention to the active changes of affected users.

5. Observe the impact of the event on overall churn.

This way, we can focus on the facts and see the results more easily. It is also easier to find the right solution when designing retention methods. Finding the real reason that makes users unhappy is more likely to retain users than simply giving them coupons.

Note: Positive events can also increase the churn rate, especially user acquisition, activation, retention, awakening, etc. Simply stimulating non-consumption soft indicators is most likely to cause false prosperity.

Objectively speaking, as long as there are promotional activities, they will attract arbitrage customers, and this type of users has a high inherent churn rate.

Subjectively speaking, in order to create good-looking data, operators will also reduce restrictions and leave room for arbitrage. These two effects often reduce the effectiveness of positive activities. For example, for new user registration, the user life cycle churn rate generated by new user activities is likely to be significantly higher than that of normal new users (as shown in the figure below). In the next N months, the churn rate of this group of users is bound to be high.

Therefore, when doing an event, you have to consider the relevant consequences in advance. Positive events are different from negative ones. We still have to do what we should do. We just need to evaluate it comprehensively. Although the final result may be something that the planners and operators do not want to face, what is actually examined here is everyone's moral integrity.

4. Systematic Problem Analysis Method

If a systemic problem occurs, it only means one thing: our business is worse than our competitors. At this time, diagnosing business problems and improving business performance are the core. The diagnosis method can refer to the user life cycle theory.

Stop copying books, this is how to do user life cycle analysis

The reasons for user loss in the entry, growth and maturity stages are different, and the focus of the analysis is also different. In order to save space, here is a brief summary as shown in the following figure.

When dealing with systemic problems, different focuses are considered at different stages.

Entry Period

Generally, there will be no difference in improvement during the entry stage. During the entry stage, users have not actually experienced the core selling points we provide, so we need to improve the process without difference to let users experience the core selling points as much as possible.

In the Internet industry, people often focus on the black minute (the minute from downloading to registering) and the process of novice tutorials. In traditional industries, people often emphasize welcoming words, allowing users to experience and try out the product as soon as possible.

Growth

After entering the growth stage, you need to treat them in different ways. After entering the growth stage, marginal users and wool users will be eliminated, and the user value will also begin to differentiate. Non-core users should be lost. Blindly retaining them is just a waste of money, and frequent discounts will also cause the brand to depreciate.

At this time, we should pay special attention to the loss of core users, the decline in the activity rate of core users, the shortening of the life cycle, and the decline in the proportion of core users among new users. These are all big problems that need to be carefully sorted out and solved. It is possible that we have already started to take action before the churn rate really increases.

Systemic problems may not be solved in one step, but rather through a continuous iterative process. It is possible that we can diagnose the problem, but the solution is not easy to use and does not improve the data. Therefore, if a systemic problem is found, you need to:

1. Choose a good reference benchmark and identify the gap

2. Design solutions and put them to the test

3. Record test results and observe data changes

4. Accumulate experience and retain effective methods

Finally, we see that our user retention curve is getting closer and closer to that of our competitors, and the churn rate continues to decline. At this point, we can say that the systemic churn problem has been solved. This may require many experiments and attempts, so we need to observe and record well and fight a protracted war.

5. Analytical methods for persistent problems

Sustained problems are often the most difficult to solve because in reality, data such as churn rate, activity rate, and retention rate often fluctuate irregularly in small amounts rather than growing continuously in large amounts.

This is a real useless problem: I leave it alone, but the boss always asks about it. I want to take care of it, but I have no idea. There are even cases where the churn rate increases for a few days, but it drops back before the analysis report is written. It's really embarrassing.

The order of processing is event type > system type > continuous type. This is because a single major event is the easiest to identify and can be easily seen through data. At the same time, a series of events are often the root cause of systemic and continuous problems. Identifying specific events is also helpful in dealing with other problems. Systemic problems are relatively easy to handle if the business side is experienced and can find a suitable benchmark.

The most difficult problem is the persistent problem. Often the change in churn rate does not last to be particularly serious, but fluctuates repeatedly in a small range (as shown in the figure below). In the absence of experience and data accumulation, it is difficult to fully identify these small fluctuations, so they are solved at the end.

If you really can't solve it, set up observation indicators and track them first. When you reach a certain level, you may be able to find clues.

VI. Differences in handling churn in different business types

Because the churn problem is highly related to the business, the analysis direction of churn in different businesses is also different. From a broad category perspective, there are two most important distinguishing dimensions.

Expensive low-frequency products VS cheap fast-moving consumer goods

The more expensive the product (cars, houses, large furniture, weddings, etc.), the longer the user decision process is, the more inclined they are to make judgments in advance, and there is no such thing as repurchase. For this type of business, users have a clear window period for decision-making, and the closer they are to the deadline, the more likely they are to make a final judgment.

Therefore, user churn for this type of business is a countdown hourglass. When you first come into contact with a user, you must understand the user's status: what the user's needs are, which competing products have been compared, and whether bargaining has begun.

This way we can roughly judge how much time we have left, so as to better seize the opportunity to close a deal and quickly follow up, instead of foolishly introducing and following up step by step without distinguishing the needs, and then the opportunity will be cold.

Fast-moving consumer goods, or consumer products with high purchase frequency (such as clothes, shoes, and mobile phones), have low user loyalty by nature, and their attitudes can be easily changed by popular trends and promotions. You can completely adopt a strategy of retaining customers without any gaps. Anyway, if users don’t buy this time, they will come back to buy after a while.

Therefore, when dealing with such products, Internet companies often distinguish between two types of loss retention: platform loss and product loss.

As long as the user stays on the platform, we will continue to wake them up. Traditional companies often use seasonal changes, new product launches, periodic celebrations, holiday events and other means to activate users at multiple times. In short, as long as the user value is large enough, we will not abandon or give up.

Traditional industries VS Internet industries

The amount of data accumulated by the two in the user life cycle is different. The Internet industry has more data, which can often record the entire process of users from clicking on the promotion link - landing page - registration - browsing - ordering.

Therefore, funnel analysis is often used to see which steps the lost users are stuck at, and to identify the problem points for improvement. Especially in the new user registration stage, there is often indiscriminate optimization.

Traditional industries often only have consumption data, so they can only measure users by consumption frequency and consumption interval. Generally, after users consume n times, those who don’t like the product will leave, and those who like the product will continue to buy. This is the so-called magic number.

By comparing the size of the magic number, you can know the gap between yourself and your competitors. As for the user's behavior level such as visiting the store, welcoming customers, experiencing, serving, and evaluating, there is no data at all, which needs to be supplemented by market research and other means.

The main point here is to remind you that there are huge differences between businesses. Although the definition of churn can be defined as no login/no purchase in XX months, the actual churn scenario may have already occurred, and the key actions to stop churn may not have data records. It is more effective to think about solutions based on specific businesses than mechanical code numbers.

VII. Summary

Many students think that the user churn problem is difficult to deal with. On the surface, it is because there is little data on user churn and we don’t know what the user thinks.

But in essence, the reasons that lead to user churn are related to many factors such as user life cycle, user segmentation, user decision-making process, user growth path, new user conversion process, user experience, user MOT, and the influence of competing products.

Any of these topics can be written in its own article. If you understand all of these, you will basically understand the entire user operation process. In essence, user churn analysis is difficult because few people who do analysis understand the business of user operation.

Pull out a classmate who does analysis and ask:

● How long should the life cycle be?

● What is the industry retention rate?

● What group of users are the core users?

● What is the core selling point of experience?

● How different are the competitors?

● What happened in operations recently?

● What unexpected bugs occurred?

● What impact do the latest changes have?

● ……

The answer is: I don't know much. Or even: I don't know at all. You ask him what he knows? He only knows how to calculate the churn rate data, and then make a lot of cross-tabulations based on user age, gender, registration channel, purchase frequency, etc. Then he stares blankly at the 1%, 2%, and 3% differences in the data sets: What does it mean?

The above is a joke. In short, analysis is not just about running data and pulling tables, but also about getting deep into the problem and finding the real root cause of the business problem.