Customer loss prediction method and device and storage medium

Abstract

The invention discloses a customer loss prediction method and device and computer equipment. In a process of obtaining a customer loss prediction model, the clients are classified according to the virtual resource balance of the preset service to obtain a plurality of client bases which are more refined and reflect personal preferences of clients in the preset service; then, aiming at each customer group, original feature data of multiple dimensions of a corresponding client in a preset historical time periodand derived characteristic data of multiple dimensions are processed to obtain a modeltraining sample of the customer group; the types of model training samples are greatly enriched; thus, the model training samples are trained by using a machine learning algorithm, the prediction accuracy of the customer loss model obtained by training is greatly improved, and then the service personnel can accurately and timely know the possible lost customer list in advance according to the prediction accuracy, save the customers by adopting appropriate policies, and reduce the customer loss amount.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular to a customer churn prediction method, device, system and storage medium. Background technique [0002] In today's fierce market competition environment, companies often need to spend a lot of energy to develop new customers. According to statistics, it takes nearly 6 times more time to develop a new customer than to maintain an old customer. At the same time, the company The success rate of recommending products or services to existing customers is about 50%, while the success rate of recommending products or services to new customers is only 15%. It can be seen that it is very important for enterprises to maintain good existing customer relationships and avoid customer loss. [0003] In this regard, the existing technology proposes to predict the customer churn situation, so that the enterprise can formulate corresponding strategies in advance to retain the ...

AI Technical Summary

Problems solved by technology

[0003] In this regard, the existing technology proposes to predict the customer loss situation, so that the enterprise can formulate corresponding strategies in advance and retain the old customers who may lose, but , the prediction results of the currently trained customer churn prediction model (that is, the probability of customer churn) are relatively low in accuracy, and cannot reliably help business personnel to retain real lost customers

Images