Six pictures to explain user group segmentation

We have said that user stratification is a special form of user segmentation: segmentation by value. So how do we do general user segmentation? Why do many students criticize the segmentation as "useless"? Today I will give you a systematic answer.

1. Intuitive experience of user segmentation

It is easy to segment users, but difficult to segment users effectively. By effective, we mean that it can help operations, products, marketing, and sales.

For example, we have differentiated between high, medium and low-level customers. We know that high-level customers are rich, but how should we serve them? What time, what scene, and what activities? Still unclear. Therefore, it is not enough to rely on one dimension for stratification. We need more classification dimensions to make more detailed divisions.

Let’s look at a small example:

Let's see how this example can be analyzed:

Let’s first look at spending habits. Judging from the income contribution to the company, the three categories of ABC are at the same level.

To decide which one to use, you can refer to the proportion of the three categories of ABC in the entire user structure and choose a main tactic. The effects are as follows:

Please note that just because something exists doesn’t mean it is reasonable. It is also possible that the leader said: Although we currently have Group A accounting for 60%, we hope that Group C will account for 60% in the future and we want to change the status quo. In this way, when choosing tactics, you have to consider the characteristics of the C group more and find activities, products, and discounts that better meet the needs of C users. In short, a more detailed understanding of user characteristics can help with operations.

This is the intuitive role of user segmentation: through segmentation, more detailed data guidance is provided for operations. Of course, for the sake of teaching convenience, the data in this example is extreme. In actual operation, as long as you can find a classification dimension with high enough discrimination, you will achieve a similar effect. The core question is: how to find it. This is the key to user segmentation.

2. User segmentation steps

Step 1: Define what is “effective”

This step is very, very important. There are endless ways to segment users. If you don’t define clearly in advance what is an “effective” segmentation, you will be stuck in a sea of ​​details trying to find a needle in a haystack.

Many novices tend to overlook this point. When it comes to user segmentation, they rush to cram a bunch of user feature variables into the clustering model. After the clustering is completed, they are at a loss and ask everywhere: "Is there a scientific and authoritative standard for user classification, and anyone who challenges it will be dragged out and severely punished?"

In the end, the operation department criticized them as: What the hell are you doing! This is because they have become detached from the actual business and only focus on addition, subtraction, multiplication and division.

Effective classification standards, of course, are based on operational needs. We can break down the corresponding data indicators from the operational goals, KPIs, and tasks. For example, the task of operations is to increase revenue. We follow the steps below to transform the business problem into an analytical problem.

With classification criteria in place, you can check whether the segmentation is effective. For example, the goal is to find user groups with high cumulative consumption. Finally, it depends on whether the consumption differences among the segmented groups we have found are high enough and whether we have really targeted the high-spending groups. The specific effect is shown in the figure below:

▌Step 2: Find the classification dimension from the perspective of operational means

After finding the classification criteria, we can see from which dimensions to segment users so that the differences between user groups can be more obvious. This is another big pitfall, because it seems that there are so many optional dimensions.

Many students are confused and wonder how they should choose. Or after finally selecting them, the operation staff asked: Why are they divided like this? He replied: This is a huge difference! Then he was criticized for not understanding the business and doing things recklessly. So depressed...

In fact, there are certain standards for filtering classification dimensions, and there is no need to run around everywhere:

Based on the above three standards, you can avoid blindly conducting experiments to find a needle in a haystack, and also avoid being criticized by operations as "what's the use of this".

Some students may think that this process is very similar to finding features when building a risk control model. It's really similar, but there are differences. The business actions corresponding to the risk control model only include "pass/reject", so there is no need to consider so much.

When doing user segmentation for operations, you need to consider: activity theme, time, product, selling point, communication channels... a lot of things, so you must consider which dimensions are useful for operations.

Step 3: Try segmentation and observe the results

With the classification dimension, we can try to divide the classification criteria:

Let’s start with the results: In principle, the final number of categories should not be too many, and each group should have operational significance.

To operate an event, one needs to design posters, prepare goods, develop systems, and prepare to deploy resources. Therefore, if the group size is too small, it is not suitable to run an event alone. Therefore, when doing user segmentation, it is customary to limit the groups to a maximum of 8 categories (each group has a share greater than 10%). As for the specific size, it can be designed based on project goals and operational conditions.

Under this general principle, it means that there cannot be too many classification dimensions and divisions of each dimension. Try to select key dimensions and key division points.

If there are too many dimensions, you can consider using a dimensionality reduction algorithm for compression. When segmenting in each dimension, pay attention to the following issues: If you segment in a single dimension and find that some segments are special, you cannot merge them at will (as shown in the figure below)

In short, the classification process requires repeated attempts in many steps until the ideal result is finally output.

3. Special Note: The Difference Between User Segmentation and Recommendation Algorithms

Many articles online confuse user segmentation with personalized recommendations for each individual. Although verbally, many people would say: We do user segmentation in order to understand user needs and achieve the effect of providing different services for different people, but in business these have two different meanings.

Targeting a specific group, operations can take many leading and innovative actions. For example, if we want to expand the high-end user group, we can launch a new product line, a new reward policy, and a new service to attract high-end users. As long as I understand their preferences and behavioral habits, I can do it very accurately.

However, the premise of a new design is that the user has a certain size and it is worth it for me to do so. Therefore, when doing segmentation, you cannot consider too many dimensions, and cut it too finely, which makes the promotion extremely complicated. I want to announce it to the world and let everyone know that we are doing this, so that we can create a herd effect and achieve greater results.

The recommendation system is not subject to this restriction. The recommendation system completely closes the information channel. Everyone sees things differently, so as long as the user response rate can be increased a little bit, it will be fine. Therefore, we recommend existing products, and try to match users and products as much as possible.

Recommendation systems cannot generate new ideas and new effects, nor can they design new products. So there is no need to worry about whether the split is detailed or not, as long as the business goals can be achieved.

4. Summary: The real difficulty of user segmentation

After watching the whole process, you will find that user segmentation is something that is simple in principle but complex in operation . The operation is complicated. It is not a modeling process at all. Instead, it is about grasping the target, screening the dimensions, and grasping the size of the segments. All of these must take into account business needs.

Although data and statistics provide us with many tools (classification tools, dimensionality reduction tools), we still need to consider specific business scenarios to truly use them. We have never lacked students who can recite textbooks, what we lack are analysts who can consider practical scenarios.

Their answer, of course, was: I don’t know anything.

Then he asked stubbornly: Why do you care about this! ! ! I just want to know, is there no authoritative standard for the number of categories for Kmean clustering in the e-commerce industry? ! ! Is it 5 or 8? ! !

Author: Down-to-earth Academy

Source: Down-to-earth School (gh_ff21afe83da7)