Big Data Applications for 4 Customer Lifecycles

In 2013, at EME Summit and the 5th Integrated Marketing Summit, Xu Lei, a senior vice president of Jingdong Mall, pointed out that the most important thing for an enterprise is to drive sales and profits through customers. In order to obtain and maintain high-quality customers, Improve customer's shopping frequency and basket, improve customer life value, drive higher sales, and provide brand loyalty to provide effective operation. To carry out word of mouth to form a closed loop in five aspects.

At the meeting, Xu Lei said that comparing the shopping trajectory differences between new and old customers can see that the purchase amount of new customers (less than 13 months of age) changes about 400 yuan, and the change range is relatively obvious, and the loss rate is relatively high. On the contrary, the purchase amount of old customers (aged from 12 to 24 months) is about 600 yuan, the change is relatively stable, the loss rate is low, and the steady trend also gradually highlights. It can be seen from the above, the customer life cycle of 14-24 months is the client stage of development to stabilize the client node.

At the same time, Jingdong also analyzed the difference of new customer login behavior, the proportion of old customer login is far lower than the proportion of new customers in the total number. Compared with the number of customers purchased in the past 30 days, only 110% of the new customers do not buy, and 6% of the old customers. Some users do not log in to buy and lost.

Jingdong in the customer life cycle of big data, there are 4 applications, namely, offline customer life cycle mainly through the behavior of customers after shopping to determine the actual value of customers; online customer life cycle through big data to organize pre-shopping behavior to determine customer forecasts Value; customer actual value and customer forecast value will form the value of customer life, and the value of life will determine the level of shopping conversion rate; through big data technology to find customers in different stages of life cycle nodes, and for different life stages Customers conduct online shopping sales stimulus, thereby enhancing the value of the full life of customers.

Xu Lei pointed out that Jingdong also recommend personalized application of big data for customers, by grouping customers before shopping behavior, combined with personalized recommendations and thus affect each customer's pre-shopping behavior.

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