Early warning method and device for customer loss of automobile 4S shop

Abstract

The invention relates to an early warning method and device for customer loss of an automobile 4S store. The method comprises the following steps: acquiring historical data of a customer, and generating a data set; constructing an initial independent variable and a dependent variable according to the historical data; performing logistic regression modeling on the initial independent variables, and screening out a plurality of final independent variables from the initial independent variables; re-establishing the logistic regression model by adopting the final independent variable to obtain an early warning model; and inputting customer data to be predicted into the early warning model for processing to obtain an early warning result. According to the scheme of the invention, deep analysis and mining are carried out according to a large amount of data which is accumulated in the store and is related to customer consumption behaviors, and the problem of single analysis dimension of a traditional means is overcome through a model method, so that the accuracy and timeliness of early warning are effectively improved.

Description

technical field [0001] This application relates to the technical field of data mining, in particular to a method and device for early warning of customer loss in automobile 4S stores. Background technique [0002] The fierce competition in the automobile market has shifted to the automobile aftermarket. In this context, competing for customers and maintaining customer loyalty has become the main topic of 4S stations. However, in the face of fierce competition, customer relationships have become increasingly fragile. Therefore, it is extremely important for car companies to predict whether customers are about to churn, and then more accurately grasp customer behavior, improve the overall customer retention rate, and even find out the key factors that affect customer churn. [0003] In related technologies, the current 4S store’s judgment on whether a customer is about to lose is mainly based on the time when the customer entered the store last time. This method has a single ...

AI Technical Summary

Problems solved by technology

[0003] In related technologies, the current 4S store’s judgment on whether a customer is about to lose is mainly based on the time when the customer entered the store last time. This method has a single analysis dimension and cannot effectively use the large amount of data accumulated by car companies and 4S stores, resulting in early warning of the loss of car owners. untimely, inaccurate

Images