How does an APP build a user operation system?

I believe that my colleagues in the Internet industry also understand the current market situation, where the cost of acquiring customers is getting higher and higher, while the total number of market users remains roughly unchanged. It was a sea of ​​red everywhere.

Therefore, when "the cake is so small, but more and more people are sharing it", it is naturally crucial to maintain every user who has been fought hard for. This matter is mainly handled by user operations .

So, how to do user operations ? In fact, most colleagues engaged in operations have different methods. As for operations, it is nothing more than attracting new customers , promoting activation, improving retention , increasing orders, and increasing turnover, etc. For different operational purposes, each senior operator has different operating methods.

Below, I will introduce a user operation system method converted through the RFM model . It is also the author’s recent experience. Experienced operators please take a detour.

1. Define different levels of user types

First, we need to distinguish different types of users, as follows:

Determination of critical value

Here we can distinguish between two dimensions: horizontal and vertical. Here are some examples:

Data value range: 1.1-3.1 (two months of data)

(1) Horizontal: Differentiate according to user consumption frequency (maturity), such as:

1. Primary users: Consume 1-5 single users;
2. Growing users: Consume 6-10 single users;
3. Mature users: users with more than 10 orders.

(2) Vertical: Differentiate by user activity, such as:

1. Active period : users who have made purchases in the last 4 days;
2. Decline stage : users who have not consumed for 5-10 days;
3. Churn period : users who have not made purchases in 11-20 days;
4. Death period : users who have not made any purchases for more than 20 days.

For example: Primary users & active period: This means that the user has made less than 5 orders in the past two months. But the user has made purchases in the last three days.

Data requirement table reference:

Note: Here you can see the first three columns, and the last two columns are for data reference.

The above are just examples (mostly e-commerce type businesses). The consumption frequency and activity of users may be different in different industries and businesses. It depends on the specific business.

2. Develop a corresponding user system model

Through the above user definition: we can draw the framework of our entire user operation

Note: This model was inspired by a great article.

The above diagram is simple and easy to understand, here is an explanation:

Our goals can be broken down into:

1. Primary users are converted into growth users, and growth users are converted into mature users.
2. Wake up dormant and lost users and turn them into active users.

Through the above two methods, a closed loop is formed.

A few small details are key elements:

(1) Since primary users are not familiar with the product, there is a high possibility of churn and dormancy among them. Therefore, special analysis and operations need to be carried out specifically for this group of users.

(2) After the growth and maturity stages, special attention should be paid to lost and dormant users.

Especially for the churned users in the mature stage, most of these users have become our loyal fans after long-term maintenance and a large amount of resource expenditure. If the churn data fluctuates greatly, we must set up a project to conduct special research. The people in charge of this area must go further and communicate in depth with these users to find out the real reasons, rather than just guessing the reasons by looking at the data in the office. The results of the analysis must be repeatedly verified with real users.

(3) Of course, the most important thing about a detailed analysis of the user system is to prescribe the right medicine, reduce the budget and increase conversion.

Therefore, the operational strategies and methods may be different for different types of users.

3. Specific operation methods

First of all, we can use the above model to mine the corresponding user data.

Let me digress here: many students may have a headache about obtaining this batch of data. Especially for start-up companies, there is no corresponding data platform at all. So here are 3 suggestions:

1. Export the corresponding order data source table through the background, and process it by yourself through tools such as Excel (the specific Excel method will not be repeated)
2. Communicate with the development brother who is in charge of the database and export it through SQL statements. (It’s convenient, but it’s not good to disturb others frequently.)
3. If this system is established. Be sure to communicate this requirement with product management. Personally, I think this aspect is very critical. Data is the basis of operations. Having a strong data background will save a lot of workload and improve work efficiency.

The following exports the corresponding data:

Note: The data is for reference only.

Based on simple data analysis , you can try the following operational actions:

1. Primary users have the largest number of dormant and churn users. You can apply for a batch of combination coupons to wake up. For example: use 1 high-denomination, low-threshold coupon and 1 low-denomination, low-threshold coupon to promote activities, and provide reminders for coupon expiration. (The meaning of two coupons is to allow users to stay longer and spend more. Try to extend the time of using our products)
2. Users who are in the growth stage are relatively familiar with our products. You can set up consumption rebates or issue medium-value coupons, and push corresponding copy based on the user's purchasing habits.
3. For mature users, focus on those in their churn and dormant stages. Customer service staff can be arranged to conduct random interviews to understand the reasons for the loss. And apply for a batch of coupons or gifts (requires redemption with products) as a reward or compensation for apology, etc.
4. The purpose of growing and maturing users during the active period is to increase the average spending and frequency of users. Therefore, you can issue some low-value, higher-threshold coupons and push popular or hot-selling products.

The above are just some of the methods listed by the author. Specific actions need to be determined based on the business and specific data feedback. But the general form can be used as a reference.

Through cross-analysis of multiple dimensions including primary, growth, maturity, active, decline and dormant, we can always identify problems and formulate corresponding operational strategies.

4. Operation cycle and push method

Operation cycle

The user operation system requires long-term operation. The specific actions may be adjusted based on the data feedback during the process. But the methods and general direction should basically remain consistent.

Of course, depending on the different businesses, the operations will vary.

Taking e-commerce business as an example, in e-commerce business, the frequency of operations of different user types is also different.

like:

• For users in the death period : data will be collated once a month/half a month for issuing coupons and push notifications, etc.
• Users in the churn period : These users are on the verge of death and need to be analyzed every 3 days/7 days for operational activation.

The key point is: after you perform an operation on a group of users, you need to conduct corresponding tracking. For example, after a week, observe the corresponding data conversion and the proportion of this batch of data in each level in the new time dimension. It will also help you make corresponding reports and summaries.

For example: After conducting the above analysis on the data of the previous two months on March 1, the data retrieved in April can be used to evaluate the effectiveness of your operations and guide subsequent actions by looking at the situation of the March data in the latest data. At the same time, you can also analyze the effects by comparing daily activity, retention and other indicators by combining multiple maintenance.

For user push

This aspect may be another article to discuss in detail. Let’s put aside factors such as time and frequency. Here are some simple strategic suggestions:

1. Push discounts to primary users and deceased users to guide consumption.
2. For growing and mature users, we push corresponding hot products, featured products, product activities, etc. based on user data feedback.
3. The shorter the user guidance link and jump method after push, the better.

V. Conclusion

Finally, let’s briefly sort out the methods described in this article:

1. Finely stratify our product users;
2. Develop corresponding user models (application of RFM model);
3. Collect corresponding user data according to the determined model;
4. Formulate corresponding operation strategies through data analysis;
5. Operation cycle, push method and evaluation criteria for data effects.

When we do user operations, we must determine the goals. Whether it is promoting activation, improving retention, increasing orders, increasing average customer spending and increasing turnover, there are differences in the specific operational actions. Be sure to remember to prescribe the right medicine for your condition, set 1-2 goals, and proceed step by step. The subsequent review and analysis must also be conducted carefully, with statistics done and timely adjustments made.

Of course, the RFM model also factors in a user’s spending amount. You can also take this metric into account when you need to stratify your users in more detail. Then weighting the data may yield different results.