Click "Read Original" for details -01- What exactly is digitalization useful for? This question may be on everyone's mind. It's not easy to answer. The digitalization of e-commerce platforms is done by the platform, and the data merchants receive is processed data. So even if you've done e-commerce, you may not truly understand digitalization. Some people describe the role of digitalization abstractly, like calling it a big trend. This is a scare tactic; it might work, but it doesn't clarify. In this article, I want to be specific and practical. The more specific, the more insightful. -02- Let's start with B2B data. In deep distribution, what terminal data do we know? Roughly the following: 1. Purchase volume (or stocking volume); 2. Inventory checked during each visit; 3. Display space and quantity at the terminal. Based on the above data, we can roughly estimate: 1. Normal sales speed; 2. Next restock time and quantity; 3. Whether sales are normal. Among the above data, purchase volume is certain, but inventory is only known during visits. If the visit cycle is long, sales between visits are unknown. If a terminal's sales have issues, it's only discovered during a visit. Under the current stocking system, what is the typical visit cycle? In the early days of deep distribution, visits were usually once a week. Now, with the stocking system, my research results are disappointing: good ones are twice a month, average ones once a month. This situation is called a gray box. The terminal is gray; you know a little, but not everything. What can digitalization achieve? Full transparency at the terminal. Not only do you know purchase volume, but also daily sales. How do you know daily sales at the terminal? With digital tools, it's simple, such as one-code-one-item or cloud stores; these can do it now. Of course, many people still don't know that one-code-one-item can do these things, thinking it's just for scanning codes to get red packets. Daily sales at the terminal is the most important data because it allows you to judge each terminal's inventory, flow rate, and whether sales are normal. With this data, managers can take targeted measures. Besides your own products, you can also obtain data on competitor or related product sales. With data on your product, competitors, and related products, you can determine what is "normal" or "abnormal" at the terminal and take action. Sales that are "too good" or "too bad" are both "abnormal." If abnormal, you need to understand the situation, or visit on-site to solve the problem. More important than sales data is using data for terminal management, intervening until it returns to normal. Therefore, data-driven B2B sales management can achieve: Data transparency → Status judgment (abnormal) → Human intervention → Conversion. -03- Next, let's talk about B2C data. Traditional marketing has no B2C data. Digitalization cannot obtain all B2C data, but it can always get some C-end data. Where does C-end data come from? Cloud stores have users online, so they definitely get C-end data. At the same time, one-code-one-item can also get some C-end data. Once you have C-end data, you must use it well. What does "use it well" mean? In one word: "conversion." For a new user, we hope to gradually convert them. This conversion process can be divided into: New user → Regular user → Heavy user → Cross-product user → User fission. In the past, these processes happened naturally, and manufacturers didn't know about them or couldn't intervene. We used to talk about customer loyalty. Now there are loyalty indicators: repurchase and referral. Customer loyalty was unknown in the past, but now it can be known. Repurchase, referral, and fission are visible in data. This process is called the AARRR model in online sales. AARRR stands for Acquisition, Activation, Retention, Revenue, and Referral. Traditional enterprises don't feel the AARRR model because they can't use it; they don't know which stage a user is in. With digitalization, you know which stage a user is in the AARRR model. Once you know, you can take measures to convert them to the next stage. The conversion measures differ at each stage. For acquisition, you need to solve the first purchase problem. After the first purchase, you need to solve the repurchase problem. After repurchase, you need to solve the bulk purchase problem. After bulk purchase, you need to solve the cross-purchase problem. After cross-purchase, you need to solve the referral and fission problem. For each of these processes, the enterprise must have targeted marketing methods and policies. Customer loyalty, in digitalization, becomes stage-based work for each user. Stage-based work can even be automated, programmed, and completed without human intervention. Why can measures be taken in stages? Because in the face of digitalization, users are transparent, and you can judge which stage they are in and what measures to take. Therefore, data-driven B2C sales management follows the same logic: Data transparency → Status judgment (abnormal) → Human intervention → Conversion. In one sentence, the above process is: User lifetime value management. -04- Finally, let's talk about BC integrated data. BC integrated data means that B-end and C-end data are linked. You know which B-end a C-end user purchased from, and you can also guide C-end users to purchase from specific B-ends. If a manufacturer wants to activate a specific B-end, they can create "incremental sales" for that B-end through traffic diversion. "Incremental sales" is an unexpected gain for the B-end, and through it, the B-end may be activated. Because the B-end is activated, its existing sales may be directed to the manufacturer. At the same time, you can also activate C-end through B-end. For example, you can set special C-end policies for certain B-ends through the system backend, thus activating C-end. Of course, once C-end is activated, B-end is also activated. The mutual activation of BC integration is also due to data transparency. Because both B-end and C-end are transparent, targeted intervention is possible. -05- In traditional deep distribution, B-end is a gray box, and C-end is a black box. Marketing measures under gray box and black box conditions can only be like flooding, with huge losses and waste. Marketing digitalization brings transparency through data. Therefore, transparency is an important feature of digitalization. Because of transparency, manufacturers have a "God's eye view" and can see through users. For example, from a teacher's perspective, not all students are the same; they are divided into kindergarten, primary, junior high, senior high, and university. This process is called user classification, or user status judgment: normal or abnormal. With user classification or status judgment, teachers can "teach according to aptitude," and marketing can be "personalized for each person," taking targeted measures. Managing the evolution process of a new user in such detail is only possible in a digitalized environment. Of course, its competitive advantage is incomparable to marketing under black box conditions. Targeted conversion measures may be automatically distributed by the system. For example, for C-end, based on user profiles, the system automatically classifies and judges, then takes measures according to established policies. For B-end, personnel visits may be needed. BC integrated measures can also be programmed and automatically distributed.