Research On Online Customer Churn Prediction And Management Countermeasures Of C Institutions In The Detection Industry

In the era of "Internet + detection",testing organizations represented by C agencies have been collaborating with major e-commerce platforms.They have become their designated official testing institutions.This cooperation mode is also favored by different types of testing institutions.With the continuous participation of the third-party testing institutions,more and more testing institutions participate in the market competition.In order to seize the market share as soon as possible,many agencies seize customers at low price,which has led to a large number of customer being lost.Therefore,by analyzing the current customer churn situation of state-owned inspection C institutions,a customer churn prediction model was built by using XGBoost algorithm.At the same time,based on the analysis and verification of the churn situation of C institutions,and combined with the actual transaction order situation,a management strategies for the customers being lost was designed.Firstly,this paper analyzes the historical data before customer churn,and extracts a series of features that can predict customer churn,by taking the historical data of online detection business in a certain period of time of C institutions as the research object and the support of customer relationship management and customer churn theory.Then,a prediction model is built based on XGBoost algorithm to predict the potential lost customers.In addition,according to the important characteristic parameters fed back by XGBoost model,this paper infers the main factors that lead to customer churn,and analyzes these factors effectively,which could point out the direction for the corresponding one-to-one management strategy in the following paper.Secondly,for testing institutions,testing resources are often scarce and limited,and it is impossible to adopt the same incentive strategy for all potential lost customers.In order to balance the relationship between the lack of quality inspection resources and the retention of incentives,and to maximize the use of limited detection resources,this paper uses RFM theory model to segment customer according to customer value and type.At the same time,in order to further grasp and understand the customer concentration distribution trend,this paper uses K-Means clustering algorithm to cluster the customers,determines the customer segmentation categories according to the clustering results,and then takes different targeted and operational management strategies for different types of customers.Finally,according to the retention of customer lost and improvement of customer relationship management services,corresponding management strategies were designed.Among them,combining with the factors of customer churn,this paper adopts different retention strategies for churn customers of different types.At the same time,in order to further improve customer satisfaction and loyalty,the paper also puts forward the improvement strategy for the customer churn caused by internal and external factors.This paper not only solves the practical problems faced by the organization,but also provides a powerful reference value for how to predict customer churn,retrieve customer churn and improve customer satisfaction.