As the global economic downturn continues to impact, many industries are facing unprecedented changes and challenges. However, during such turbulent times, there is an industry that has "quietly recovered". According to data released by the National Bureau of Statistics, total retail sales of consumer goods increased by 8.2% year-on-year in the first half of 2023, and final consumer spending contributed 77.2% to economic growth, a significant rebound from last year. Consumer demand has become the three major demands driving economic growth. The most important factor.
Under the above market background, on the one hand, consumer retail companies need to actively expand new businesses to open up the market and transform towards brand concentration, product diversification, and model diversification; on the other hand, the operation of existing member customer groups has become a spontaneous initiative for consumer retail companies. The second core focus of sexual operations is the key core that tests the "active hematopoiesis ability" of consumer retail enterprises.
Therefore, this article aims to discuss in detail the construction path and experience summary seen by Fanruan from the perspective of digital management of membership.
1 Under the new consumption situation, what changes are taking place in the membership customer base?
As mentioned earlier, the entire consumer industry is transforming towards brand concentration and product & model diversification. Its essence is the change in the characteristics of group consumption behavior, and the corresponding matching characteristics are that consumers are gradually beginning to have big-name concepts, pursue individual needs, and consume The behavior gradually declined.
Based on the analysis of the current status of the member customer base business of several consumer companies, the author summarized the following two core issues:
1. Lack of hierarchical positioning & differentiated marketing for members
Hierarchical positioning: Most of the hierarchical positioning of members is based on membership card levels, lacking key membership indicators such as repurchase rate and activity level. Insufficient depth in member stratification, customer segmentation characteristics and added value mining
Differentiated marketing: Current marketing mainly develops activities such as coupon issuance and full discounts for member groups, which lacks pertinence and cannot enhance the loyalty of different members.
2. Lack of full-link data on member operations and lack of awareness of data analysis
Lack of data: The sales of marketing activities are often not calculated until the evening or the next day. There is a lack of visual process data tracking for customer flow, unit price, sales, etc. during the activity.
Lack of awareness of analysis: Taking promotional products as an example, they rely on manual judgment. Sales staff usually consider product selection from the dimensions of unsaleable, high inventory, new products, etc., but lack the data to conduct a rational assessment of the category product structure before proceeding. Analytical awareness of promotional product selection.
2 In response to the above changes, Fanruan’s empirical logic & link thinking for member operation business
Based on the core issues summarized above, the author believes that membership operations are actually a long-term task in consumer companies. The starting point of the entire business link comes from the analysis of the "member assets" it owns, and then the comparison of people, goods and stores based on existing goods, and finally determines how to design operational marketing actions, and gradually iterates and optimizes the process.
The picture above is based on Fanruan’s experience in the membership business of some consumer companies.
In summary, consumer retail companies need to build member portraits in terms of membership operations and management, position core member groups and products based on consumer needs, give full play to user assets, and create an enterprise-level closed-loop membership operation system, that is, answer the question "Who are they?" "Three core questions: "What is needed" and "How does it work?"
Core question one: "Who are they?" - Open the existing membership group
Faced with the question of "who are they?", the core of consumer retail companies lies in the construction of "member portraits", using grouping strategies to drive consumer value, while comprehensively considering consumer needs, purchasing behavior, purchasing scenarios, consumption themes and other factors.
For the construction of member portraits, the RFM model is an unavoidable content and topic. Everyone is familiar with the common RFM model, but without going into details, I only give an iterative plan based on RFM for reference: Based on
this type of indicator design, a series of analysis and disassembly logic can be derived. Examples are as follows:
Worth it One thing to mention is that based on the above business logic, the author discovered a seemingly "simple but troublesome" scenario during research - labeling. That is, based on different logical attributes, all member groups are given corresponding labels.
In the past, most business personnel would use Excel for labeling and analysis. However, on the one hand, Excel labels can only be expanded based on complex If-type function formulas. Not only is it troublesome to write, but it is even more difficult to adjust. After the colleague has finished processing, the reading cost for other people is quite high. Over time, the colleague responsible for labeling becomes a "living dictionary", and the efficiency of information communication is extremely low.
But currently, there is actually a better solution in the industry, which is to use the "conditional label column" function in BI tools to quickly adjust member grouping scenarios and quickly classify customer groups based on different member dimensions, compatible with "and" and "or" ” analysis scenarios, and multiple classification logics can be repeatedly added to quickly build an indicator group for customer portraits.
For example, take the following figure as an example. For example, I hope to quickly classify my customers, divide a label based on historical and recent cooperation data, and classify customers who need to be operated or reactivated in the future. At this time, you can use the above functions to perform the following configurations to quickly classify customers with different labels:
Based on the above operations, we can quickly design customer group portraits that meet the needs of our own business based on the analysis needs. Here is the author’s Reference to analysis charts of consumer companies surveyed in the past:
Core question 2: “What do you need?” – Find the products they need
Based on the basic consumption information of members, we can initially segment the distribution and structural composition of members. The next step is to find products that match various customer groups. Then, what we have to do is to find out the products they really need from the member data, so the consumer behavior analysis for members will derive a complete multi-level analysis system based on the RFM model, which the author calls " "Multi-level member analysis link": the entire shopping process from "before consumption" to "during consumption" to "after consumption" to establish a data system of consumption behavior.
Here is one of the logics summarized by the author for reference. The entire analysis link is based on different business entity characteristics and will derive different value systems:
In the entire system link, the most important thing is obviously the "consumption" content. Members’ consumption categories, products, consumption periods, etc. are monitored, which can be used as the basis for precise marketing categories and brand positioning.
Here, we can complete the most important "leap" in the member analysis business: from "member analysis" to "matching people and goods."
The analysis of consumption preferences can help us build a target pool for a series of commodities. In the initial granularity of the analysis, this type of target pool often cannot reach the bottom SKU of the commodity subject. Generally, it can only list some middle categories, such as snacks. Simply snacks, dairy products, etc. The analysis that continues down to SKU also tests the skills of the marketing business. Here is a marketing disassembly logic of a retail enterprise that the author has served: This type of analysis needs to rely on the "member
management" dimension and "commodity management" The high-frequency linkage of dimensional data often leads to mutual causation in the process of exploratory analysis.
However, when the author went deep into corporate practice, he found that most companies still rely heavily on Excel tools for exploratory analysis. Even the consumer goods giant with an annual revenue of over 10 billion still uses the traditional Excel offline method to process and display data, often facing problems such as inaccurate data versions and millions of data piled in Excel. If you want to implement a monthly membership analysis report or product selection report, you will need to spend several nights or even a week processing the data.
It can be seen that Excel limits the data management & analysis capabilities of consumer companies. What BI can do is to help consumer companies go an extra mile on the basis of traditional data analysis and solve business data processing & linkage analysis based on data platforms.
Among them, FineBI, as a domestic BI, has done targeted research on data linkage analysis and exploration. Through Excel-like data editing and exploration logic, it can directly link the analysis operation configuration, and can quickly solve the high-frequency data matching side of consumer enterprises. Linked analysis needs:
All data tools are designed to improve efficiency, and the purpose of all analysis is to try to find breakthroughs in the business, thereby improving corporate efficiency. We have found out who our customers are and roughly positioned what they need. The final step is how to push them.
Core question three: "How does it work?" - looking for matching items and power points
The essence of "operation" is differentiated marketing actions based on member operation analysis. In the marketing link, we divide the full life cycle of members into "attracting new customers" → "promoting activity" → "retention" → "conversion" → "fission". Based on the above full life cycle of members, we can adapt and derive different products based on the full life cycle of members. Stage membership marketing activities and assign different marketing actions.
In the previous article, we completed the research and thinking on member portraits. Based on the pre-positioned member selection and product selection, a new analysis indicator will be set, which the author calls "pull indicator", that is, each type of activity has a pulling purpose, which is often related to the growth of consumer retail enterprises. D. Pull indicators often correspond to a series of "supporting indicators", and such indicators must be related to a series of specific marketing actions, not necessarily one-to-one correspondence. It may be data of multiple types of indicators corresponding to multiple types of marketing actions, similar to An N:N relationship can be summarized as shown in the following figure:
This type of N:N relationship is often complicated in business analysis. We need to simultaneously solve the correlation problems of multiple "pull indicators" and "support indicators" , while solving the problem of dynamically adjusting marketing actions based on marketing data. Not only is it difficult to aggregate data, but it is also difficult to analyze and drill down.
For example, the data coordination process of most consumer companies is often as follows: IT colleagues prepare a large wide table of Excel data for business activities, and then the business analyzes it. However, the business is based on an actual wide table that IT has prepared, resulting in Business can only be analyzed based on a certain fixed activity dimension. When cross-comparison of multiple activities or comparative analysis of multiple supporting indicators is required, such analysis models and tools often become stretched.
Therefore, a tool that can optimize the IT business data coordination model and quickly configure multiple types of data such as N:N has become a "breakthrough" to solve the above problems.
And BI is this "breakthrough". On the BI platform, IT can fully integrate data, unify the caliber, and shorten the time for data logic exchange between IT and business. At the same time, businesses can independently complete the analysis process they want, no longer relying solely on IT development schedules, and quickly obtain the data they want through BI. If you want to try to specialize in a certain business or explore related issues, you can also use BI based on Multi-dimensional cross-analysis of platform data.
Taking the most commonly used "voucher issuance activity" in our consumer retail industry as an example, we often need to monitor the usage of consumer coupons through data analysis reports, analyze the coupon usage rate and trend changes of various members, and promptly discover abnormal coupon usage through Use coupon trend changes to adjust marketing strategies in real time to improve marketing effectiveness. According to the author’s previous research, during this process, on the data side, we actually have the following typical business scenarios:
First of all, consumer companies need to check the consumption status of different consumption coupons during an activity to see how many of these coupons have been issued and used, and to see if the activity is actually being carried out;
Secondly, we need to see if these coupons are really sent to the people who need marketing. This requires us to check whether the people who received the coupons match our expected member group, and we need to check the corresponding consumption of the coupons. Member details list of coupons;
Finally, we will study whether the consumption voucher activity really boosts our income, and if so, how much the increase in income is related to the consumption voucher.
These processes often occur in the process of membership marketing. Many business teachers I have communicated with in the past said that in the past, a lot of coupon code issuance data were often collected from the front line, and then correlated with the member information offline for comparison. , and then export a business data table and make it into a PPT, and then organize and report the activity content. The whole process is time-consuming and labor-intensive.
But if you sort through it carefully, you will find that these things, in summary, are the core scenarios that BI products can assist in business analysis:
Quickly see the data results after business execution: This analysis content may be produced by the business itself, or may be developed with the help of the IT team, but overall it is necessary to quickly see the actual situation through visualization;
Quickly find the data you want: through the enterprise-level platform, you can easily obtain standardized and timely updated business operation data;
Quickly research existing problems in business advancement: find answers through data, and then assist in business advancement again.
At this point, a complete membership operation is coming to an end. The end of the process is a summary and review. Through the "pull indicators" and "support indicators" designed in the activity, we can feedback member portraits through the activity value again and again, view changes in lost members and active members, and optimize the membership structure. Gradually and iteratively complete the closed-loop membership operation analysis.
3 Summary: The value that membership operations bring to enterprises
Looking back at the empirical logic and links of the entire membership operation, we can find that membership operation analysis is an internal business change driven by consumer value. It is a business action embedded in the capillaries of the enterprise through "matching of people and goods" and "marketing pull". , ultimately reacting on the entire management system of consumer companies, becoming an endogenous engine that drives consumer companies to grow again.
As the engine roars again and again, what is rolling forward is the company's ability to provide better and more precise services to consumers, and what is left for the company is more authentic and pure member assets.
I hope this engine can support consumer retail companies to go further.
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