This example uses three working examples to illustrate how to accurately market customers through data analysis.
Tools/raw materials
Big data marketing
Analysis of Three Big Data Marketing Cases
Case 1: The author works in a bank, and through the massive analysis of the depositor's ID card information, I found an interesting phenomenon, that is, most customers who buy wealth management products are women aged 40-50.
Experienced wealth management managers can accurately analyze the qualified customers in the sub-branch according to these information and ID card information, and quickly conduct telemarketing on the newly launched wealth management products, so that they can sell without going out and complete the sales task quickly.
There are also some innovative wealth management managers who organize customer invitation activities at outlets in the salon during Valentine's Day through ID card information, conduct telemarketing for male customers aged 18-30 and 30-45, and conduct wealth management product marketing by sending flowers, cosmetics and high gifts to their loved ones, which better aroused the interest of male customers and effectively boosted performance growth.
These data analysis methods can realize personalized marketing and positioning, strengthen customer cognition, find value for customers, and thus drive sales.
Case 2: In the process of cooperation with the power supply department, the power supply department provided information that the peak time of surfing the Internet in the whole city is mainly after noon 12 and before 12 in the evening. The power supply department believes that the reason for this "strange phenomenon" is that people generally have the habit of surfing the Internet before going to bed.
At that time, many people didn't pay attention to the news, as if it had nothing to do with the bank. And a young college student in our marketing department cooperated with merchants through mobile banking, and launched a promotional spike activity in the afternoon 12, which not only promoted the business volume of mobile banking, but also doubled the sales volume of merchants, achieving a win-win situation.
Case 3: In the data of enterprise's salary payment, we found a phenomenon, that is, the accounts paid by employees in general enterprises will deposit a certain amount of balance every month, and the amount is not large, ranging from 1 000 yuan to several thousand yuan, and some will remain unchanged for a long time. The current interest rate is very low, but the amount of these customers' accounts can't reach the minimum payment of wealth management products, and these customers' wages are used up. This is the so-called "moonlight"
How to organize customers directly by analyzing these data information, tailor-made financial services for these people with the same needs, and enjoy the customized service of "one household (group) and one policy", we conduct special research.
Finally, the products such as lump-sum deposit and withdrawal, fixed investment and timely receipt of wealth management products were packaged and promoted, and credit cards were used to promote them simultaneously. Several on-site special salons attracted the attention and interest of many employees, which really provided a real financial channel for these people with low income.
These three short stories are the results of mining historical data and reflect the laws at the data level. By extracting and integrating valuable data from a large number of data systems, they realize the transformation from data to knowledge, from information to knowledge and from knowledge to profit.
To put it simply: five suitable products are provided to the right people in the right way at the right time and place.
five
Specifically, when we analyzed the data, we found the same law, and of course there were some personalized data. Therefore, specific application scenarios need to be accurately planned and designed according to the specific conditions of enterprises and businesses.
To sum up, we need the following three steps:
The first step: data collection, understanding users, by collecting all the data of users, mainly including static information data and dynamic information data, static data is the relatively stable information of users, such as gender, region, occupation, consumption level and so on. And dynamic data is the user's ever-changing behavior information, such as consumption habits, purchase behavior, etc.
Step 2: Analyze these data, give a portrait to the customer, which represents the customer's interest, preference and demand for marketing content, and analyze and calculate the customer's interest, demand and purchase probability.
The third and final step is to combine these pictures into a relatively complete picture, so that we can have a general understanding of our customers.