How to prevent user churn starting from the user life cycle?

I've been thinking about some things about user classification recently. Because when the platform operates to a certain stage, it will accumulate a large amount of user data. This user data is the golden asset of the operators. How to manage and operate these users is one of the key tasks of the operators.

Users can be classified differently from different perspectives: for example, from the perspective of user activity, they can be divided into zombie users, low-frequency users, active users and deep users. If we look at the value of users to the platform, the classification may become seed users , ordinary users and core users. Different categories of users should have different operating strategies. Analyzing and managing users from the perspective of the user life cycle is one of the common methods of data-driven operations.

What is User Lifecycle?

The so-called user life cycle is a derivative concept based on the "customer life cycle" in traditional marketing .

On Baidu Encyclopedia , the customer life cycle is explained as follows:

The customer life cycle refers to the period from when a customer begins to understand a company or the company wants to develop a customer, until the business relationship between the customer and the company is completely terminated and all related matters are completely handled.

In the current field of Internet products, it should refer to the whole process from the user's interest in the product to the user no longer paying attention to the product. Unlike the customer life cycle, the life cycle of an Internet product may be very short, or even end before it begins. This is because without operations, it may go directly to loss at any stage of the process (rather than having to mature before dying). Therefore, the job of operations is to increase the life growth rate ( conversion rate ) at each stage to assist a user in moving from the interest stage to the loyalty stage.

Common user lifecycle stages and goals

The traditional "customer life cycle" basically includes five stages, and the goals of different stages are shown in the figure:

From birth to death, it is like the growth process of a person's life.

When the “customer life cycle” is extended to the field of Internet products, it becomes the “user life cycle”. However, unlike the pyramid structure of the customer life cycle, the user life cycle is more like a funnel. Without operations at each stage, users may directly leave and end their lives.

The "A AR RR Conversion Funnel Model" mentioned in Growth Hacker and user behavior research are closely related to the user life cycle

Because the length of the user's life cycle is closely related to operations, no matter what product you have, you must research and distinguish the user's life cycle stage based on your product features and business characteristics, and adopt corresponding countermeasures. As long as the product is not discontinued, the operation will not stop.

How to determine which life cycle a user is in

Previous tweets have discussed some of my own thoughts on how to improve operational results at different stages of the life cycle: 1. "Official Account Operation: Three Routines for Attracting New Users" 2. "Official Account Operation: Four Driving Forces for Improving User Retention" I will not discuss this in today's article. What I want to share today is that we know that users have a life cycle, so in the face of various user data, how do we determine which user is in which life cycle?

I must admit that big data is really my weak point, but the definition of user life cycle is closely related to big data, so I can only combine my learning and thinking to make some simple sorting, I hope I will not mislead you:

1. Potential users (users who have not paid attention to the platform but may pay attention to it)

Labeling: After refining the core value of your product and sorting out existing users, you can profile potential users, describe usage scenarios, and then label potential users.

Find channels : Through what channels do competitors’ users come? What channels do our users usually pay attention to and focus on? (This data can be obtained through many big data tools such as iResearch)

Mining data: Currently, the main ways to obtain data from potential users include data purchase, data leasing and data cooperation. After getting the labels and channels, you can either do publicity and promotion or mine data. Only in this way can potential users have their vitality.

2. New users (users who have a first experience with the platform)

Time dimension: Depending on different needs, the time limit for new users may vary, and different platforms will also have different lengths of operational analysis for new users. For example, when our company is doing operations, we usually divide it into weeks or months. I think that platforms such as credit cards may use quarters and half years to determine new users in order to more effectively analyze conversion rates.

Interaction dimension: In addition to time, different platforms have different emphasis on user data. The definition of new users in the operational sense may also be affected by interaction. Whether they are new visiting users, new following users, new registered users, new investment users or new purchasing users, the initial experience of users on different platforms may be different.

3. Retained users (users who remain on the platform for a period of time)

It is said that users who remain on the platform "for a period of time" are retained users. The definition of this period of time will also vary from platform to platform depending on different businesses. Many analysis tools generally provide different user retention data based on days, weeks, and months. Similar to the above, user retention is also affected by interaction, whether it is attention, use or purchase, and different platforms have different requirements. However, in addition to determining user retention based on time and interaction, it is recommended that operations must also bind retained users with attributes such as channels and behaviors for comparative analysis.

4. Active users (users who meet specified business standards)

The definition of active users on a content platform may be users who produce X pieces of content, or users who make X comments. The definition of active users on an APP platform may be users who open the APP once every X days, or users who perform X operations on the APP. For an O2O platform like our company, the active user indicator becomes X purchases per month and X payments per month. Therefore, the determination of active users on different platforms is inseparable from their business objectives, development directions, and big data requirements.

5. Loyal users (users who are willing to contribute to the development of the platform)

Active users may eventually become product advisors, managers, continuous content contributors, and active disseminators and sharers. I think when the activity index reaches a peak, they can be judged as loyal users. Incentives for loyal users are absolutely a top priority for platform development.

Operational resurrection: How to awaken and recall lost users

If the purpose of determining the previous user life cycle is to prevent user churn, then when users are really lost at a certain stage, the operation must perform a resurrection operation and try to wake them up and recall them again, rather than letting them go.

Research is important: For lost users, research is necessary to determine why they lost. Is it due to product issues, operational issues, customer service issues, or something else? Blindly discussing recalls without conducting research is actually a false recall that only treats the symptoms and not the root cause.

Improvement is very important: After the investigation, if it is because of dissatisfaction with the product content, then improve the content; if it is because of unfriendly interface, optimize the interface; if it is because of customer service attitude, train customer service. The user is your mother. Only if you listen to your mother can you grow fast.

Notification is very important: I remember reading an email before, which was sent by a certain APP in the name of the CEO. The content was very sincere, telling me what kind of improvements they have made to their platform and what kind of footprints I have left on this platform. After reading it, I re-downloaded the APP. Be less tricky, more sincere, and more true, and then tell all this to the users who have already lost. How can you talk about recalling them without notifying them?

Benefits are also important: After taking improvement measures, you may want to do two tests when making decisions on returning lost users. One is to use purely improved emotional notifications, and the other is to add benefit touchpoints for return. I believe the return rate of the latter should be higher, but this requires effect tracking to draw accurate conclusions.

Does your product have user lifecycle analysis and management? Why not come and have a chat!

Mobile application product promotion service: APP promotion service Qinggua Media advertising

The author of this article is @跑跑大橘子 and it is compiled and published by (青瓜传媒). Please indicate the author information and source when reprinting!