Research On Customer Segmentation And Loss Prediction Of L Company

In recent years,due to the development of the Internet,buyers can choose from manufacturers around the world and choose sellers who can better meet their own needs.Buyers are no longer limited to the products of domestic manufacturers.This leads to more intense competition in the market.In such an environment,L company,as the main force in the field of wind power generation,has a large number of customers.For the company,it may lose customers due to imperfect infrastructure,imperfect service measures and other reasons.However,as intangible assets of the company,continuous loss of customers will bring great losses to the company.Therefore,this paper forecasts the customer loss of L company,so as to find out the characteristics of customer loss for the company,and then provide a basis for the company to develop strategies to recover customers lost.In view of the above situation,this paper analyzes the product sales information and product characteristics of L company.Then,it improves the traditional RFM customer segmentation model and designs OFP customer segmentation model suitable for L company.In this paper,it carries out customer segmentation of L company and predicts customer loss.The specific work is as follows:Firstly,based on the analysis of product sales information and product characteristics of L company,the traditional RFM customer segmentation model is improved into three new indicators: O(order time ratio),F(consumption frequency)and P(profit).Secondly,O,F and P are used to construct OFP customer segmentation model.And then,the weights of O,F and P in the model and comprehensive score of customers(G)are calculated.Thirdly,k-means clustering uses to segment customers.Finally,according to the result of comparing the customer segmentation of OFP model and RFM model,the improved customer segmentation model makes the customer classification of L company more accurate.After that,this paper will make customer churn prediction and determine the target variable and prediction variable of customer churn prediction.First of all,in order to improve the accuracy of the prediction model of loss,this paper firstly uses factor analysis for information enrichment.And this paper uses logistic regression to filtrate the predictive variables in order to remove the predictive variables that have no significant impact on the target variables.Then this paper constructs the tree model of customer churn prediction by using decision tree and finds out the characteristics of customer churn.Finally,in order to verify the validity and accuracy of the model,this paper divides the customer data into training sets and verification sets--training sets are used to extract the rules for predicting customer churn;Verification sets are used to prevent the over-fitting of the prediction model of customer churn and to verify the prediction accuracy of the model.After customer loss prediction,the customer loss situation is combined with the customer classification of L company to give strategies for preventing customer loss.