User stratification is the result of business operations, and user stratification is applied to business operations. Therefore, the seemingly simple layering can be easily explained and the problem can be easily located as long as it is combined with the business. A classmate asked:
Are there any standard layering specifications? Before answering this question, let's look at a simple question:
(Total revenue = number of paying users * average payment per paying user) General role of user segmentationIn essence, user stratification is a special form of user segmentation: users are segmented according to their value, with those in the upper layer being high-value users and those in the lower layer being low-value users. The biggest benefit of user stratification is to eliminate averages . For example, for the question above, most students would blurt out:
Looking at the averages, we can come to this conclusion. But in reality, if the average value is reduced by 25 yuan, does it mean that the business has to find a way to increase it by 25 yuan? Of course not, because we don’t know the composition of these more than 10,000 users. If we tell you that these 10,000 people are composed of the following two forms, would you still think that raising the per capita price by 25 yuan would be enough? This is a direct demonstration of the role of user stratification. We will find that the trend observed through the average is correct, but the derived execution plan is often wrong. When it comes to execution, user segmentation is needed to more easily identify the real problems and develop feasible plans. The special role of user segmentationUser stratification also has a special function, that is, the products/services/experiences that an enterprise provides to high, medium and low-end users are limited, and are often fixed high, medium and low-end packages, high-end/standard/low-end products, and advanced/intermediate/primary VIP services. When we observe users separately in high, medium and low-end categories, it is easy to see intuitively whether there is a problem with the products/services/experience we provide and which category of customers we are losing. This type of analysis is highly directional and can quickly locate problems and help operations find breakthroughs. For example, the AB form in the above example has been simplified for the sake of convenience, but it represents two very classic business forms, the big R type business that relies on wealthy users and the big DAU type business that relies on a large number of ordinary users (big R and big DAU are terms in the game industry, and these two forms are most clearly distinguished in the game industry, so they are directly applied here). Their common user stratification forms are as follows: After understanding this, looking back at the AB patterns, we can locate the problem more accurately. After this level of interpretation, isn’t it more insightful than just looking at the average and then saying “the average order value is low, it needs to be increased”? This is the further role of user segmentation: by combining the segmentation of business behaviors, business problems can be quickly located . Common mistakes with user segmentationAfter looking at the examples, some students may say: "It looks like the stratification is very simple. User value, paid ≥ active ≥ registered, why don't I just stack a pyramid (as shown below)? I see that this is how it is stacked online." A: This is a common mistake in user segmentation and it misses the point . Remember, the purpose of user segmentation is to quickly identify problems. If you simply stack registration, activity, and payment into a pyramid like the picture above, the graph will look pretty impressive. But in essence, isn’t it just repeating the three indicators of user volume, activity rate and conversion rate in the form of a pyramid chart? The meaning of the graph and the report are the same, so making an extra weird graph is like taking off your pants to fart, which is also the reason why it was criticized at the beginning. Some students may say: "I see examples, that is, one dimension is cut into several segments, so I will look at the dimensions of payment and activity, and make a picture similar to the following: The imagined layered effect, isn't it good?" Answer: This is another common mistake in user segmentation, where dimensions are crossed . It is common to see overlaps between user payment and activity indicators. When the classification dimensions intersect with each other and one layer of users contains another layer, it becomes difficult to interpret. At this time, it is better to directly make a matrix classification to see it more clearly. In summary, the reason why user segmentation is often superficial is largely because the students who do the analysis lack the awareness of detailed thinking, and excessively pursue drawing a layered diagram to appear to be thinking comprehensively; they ignore the role of this diagram in the business and ignore the different needs of the business itself at different stages. The basic idea of user stratificationIt is actually very simple to do user segmentation. As shown in the figure below, it only requires two things: "classification dimension + classification standard". We have said that the biggest user of user segmentation is to quickly locate problems and suggest business breakthroughs. To achieve these two goals, you need to do this (as shown in the figure below):
The key issues and business actions for business development are not completely whimsical or "following the instructions of the leadership", but are highly related to the life cycle of product/business development. Every time when we talk about “to understand the current key business issues”, some students say to just go and ask. Direct communication is a good thing, but if you don’t understand anything, not only will you be too lazy to share the business, but you may also not understand a few words you occasionally say; you may even be confused in operation and do whatever the leader says without using your brain. Therefore, students who do analysis still need to have some understanding of it. Select classification dimensions based on development stageUsually, the launch of a product/business goes through five stages (as shown in the figure below). The core indicators and key issues to focus on are different in each stage. Traditional companies will choose to sell off their products at the end of their product life cycle and wait for the next generation of products to be launched, while Internet companies are more likely to make multiple iterations. At different stages, the business concerns will be different, as shown in the following figure: With these foundations, we can make preliminary judgments on the current situation and communicate with business more smoothly. By determining the key issues of current concern, you can lock in the classification dimensions and then look at the classification standards below. Set classification standards based on business actionsThe products/services/experiences that an enterprise can provide to users are limited and are subject to third-party restrictions.
Under these three restrictions, operators often choose a blockbuster strategy, using a blockbuster product/competitive service/high-quality customer experience to attract users and achieve their goals. When users are in the novice stage, there is an entry-level product; during the growth stage, special preferential rewards will be set up at a certain point. These nodes become natural classification standards. In this regard, traditional companies are doing better than Internet companies. Traditional companies rely on gross profits from selling products to survive, so they have clear definitions of how much feedback to give to customers. Generally, a fixed proportion of the gross profit is allocated as feedback, and then the proportion of competitors is referred to to choose the main gear to form a competitive advantage. The corresponding classification standards can also be directly applied to business standards (as shown in the figure below). Under the guidance of this stratification standard, it is easy to find the corresponding problems according to the changes in stratified data. Just like the effect in the example at the beginning, when we see that there are fewer users of a certain level, we immediately realize that we are looking for the wrong people and we need to review the competitiveness of our product. This will provide clues for further in-depth analysis. On the contrary, among Internet companies, except for a few leading ones, a large number of companies are still at the stage of working hard and fast and burning money for subsidies. There is a lack of clear product line planning and competitive strategy. As long as there is money in operation, they will issue coupons to the utmost, expand the scale and go public to raise money. Therefore, Internet companies often develop a mindless operation of "few registrations - issue coupons, low activity - issue coupons, low retention - issue coupons". If you are a data analyst, you will find that your company's operations are really brainless. You just look at which AARRR indicator drops and then organize some short-term activities. There is no overall planning and no strategy at all. Then you can try to refer to the situation of competing products and do a competitive product analysis to clearly distinguish the differences between your product and competing products among users with different consumption/activity levels, and help them see: we actually have an advantage in XX level and a disadvantage in XX level. Therefore, we can develop user segmentation strategies and further optimize the system. After all, we are engaged in operations, not activities. summaryUser segmentation may seem simple, but if we explore it in depth, we will find that there are many business details involved. Many students are troubled when doing this, and they are all troubled by the question "My leader asked me to divide the users into high-end ones. Is 8,000 considered high-end, 10,000 considered high-end, or 12,000 considered high-end?" Yes, he was obsessed with this line and was struggling to death. You ask back:
He knew nothing, but he was still hoping that there would be a machine learning algorithm that would do some calculations and tell him: "Artificial intelligence AlphaGo tells you that 10,000 is the perfect standard. Anyone who disagrees will be bitten to death by AlphaGo." This is going in the opposite direction. User stratification is the result of business operations, and user stratification is applied to business operations. Therefore, the seemingly simple layering can be easily explained and the problem can be easily located as long as it is combined with the business. Let’s share this with everyone. Author: Down-to-earth Teacher Chen Source: Public account "Down-to-earth Academy" |
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