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

A technology of customer churn and 4S stores, applied in complex mathematical operations, marketing, market data collection, etc., can solve the problems of single analysis dimension, untimely and inaccurate car owner warnings, etc., to overcome the single analysis dimension, increase accuracy and Timeliness, the effect of increasing accuracy

Pending Publication Date: 2022-01-21
RAINBOW WIRELESS BEIJING NEW TECH CO LTD
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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

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

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[0050] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this application. Rather, they are merely examples of methods and apparatus consistent with aspects of the present application as recited in the appended claims.

[0051] figure 1 It is a flow chart of an early warning method for customer loss of an automobile 4S store according to an exemplary embodiment. The method may include the steps of:

[0052] Step S1: Obtain the customer's historical data and generate a data set;

[0053] Step S2: constructing initial independent variables and dependent variables according to the historical data;

[0054...

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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 ...

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Application Information

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IPC IPC(8): G06Q30/02G06F17/18
CPCG06Q30/0201G06F17/18
Inventor 黄亮
Owner RAINBOW WIRELESS BEIJING NEW TECH CO LTD
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