Some students asked: Leaders always ask us to tap user needs, what is the mining method? In particular, there is no data at hand, and there is only one user purchase record at most, and I feel that I can't dig out anything. The system will answer today. When it comes to user demand mining, there are many popular unanswered questions, which are also clarified today. First, the wrong approach to user demand mining Many people have heard this passage: A little brother came to the hardware store to buy nails. He bought nails because he wanted to hang a painting. He hung a painting because he was lonely. He was lonely because he really wanted a girlfriend, so his real need was a girlfriend.
You should introduce him to a girlfriend. The story is very good, but it is very wrong... From a business point of view, if a hardware store owner does not think about how to sell metal equipment, but studies matchmaking, the small store is not far from closing down. Judging from the data, if you want to find a girlfriend or not, it is estimated that mobile number list even your own aunts and aunts are too lazy to tell a stranger. (Besides, he is a steel seller). This is a common mistake: it is mistaken to think that users need to dig up gossip anecdotes that others don’t know before they can be considered deep, and they must meet very deep needs before they can be considered real needs. In fact, there are very few industries that understand users so deeply and can satisfy users indefinitely.
For example, in the financial industry, private services for very high-end customers may be able to do this (branch presidents personally drive the sons of major customers to school is not new). However, most enterprises have limited business scope and face a large number of users. Therefore, it is not possible to do too delicate and profound digging out of business reality. Whether it is business or data, it can't be done, and there is no need to do it. Therefore, the essence of user demand mining is to filter key differentiating dimensions from limited data to improve the probability of user response. What we need to do is not to figure out the needs of each user at each level. Rather, by distinguishing, improving the probability of user response and identifying core user groups. It is better to let users respond to our business than blindly with eyes closed. Every percentage point higher is the contribution of data analysts to the enterprise.