Customer Data Analysis And Management Of The Tire Testing Sector Of Company C

With the rapid development of the automobile industry,the demand for testing of automobiles and parts in my country is becoming more and more vigorous.Especially in the past two years,the state has relaxed the qualifications of compulsory inspection items,and more and more third-party testing institutions have emerged,and the market competition has become more and more fierce.However,the number of customers and business volume are often limited.Only by analyzing customer data,mining customer value,and formulating reasonable management plans can business development be guaranteed.Company C is a state-owned enterprise whose main business is automobile and parts testing and operates on a project-based system.The tire testing section below it has always been the main business of national mandatory testing business,but with the liberalization of national policies,it has to participate in more intense market competition,and currently mainly entrusted research and development testing business as the main business.This paper takes the corporate customers of the tire testing sector of Company C as the research object,and consults a large number of domestic and foreign literature on customer classification and management.Combined with theories of customer segmentation,customer value and key customer management,and based on the status quo of the evaluation of the dual indicators of output value and payment collection in the tire testing sector,first of all,it systematically summarizes the problems of unclear identification of key customers with output value and unsatisfactory payment effect.Secondly,by using the traditional RFM model and K-means clustering analysis method to complete the customer classification and rating of the output value dimension,the problem that the traditional classification method is not precise is solved.And a new PCA model is proposed,which combines the analysis method of K-means clustering,comprehensively considers the payment rate,payment cycle and payment amount and other factors,and completes the classification and rating of customers in the payment collection dimension.Then,the high-rated classification results of the two models are compared and analyzed,and it can be seen that some customers are identified by the two models.Finally,based on the concept of data analysis serving countermeasures and suggestions,this paper calculates the maximum,minimum and average values of the classification results of the RFM model in the output value dimension and the PCA model classification results in the payment collection dimension,and analyzes the unique characteristics of each rated customer.According to the customer characteristics in the dimension of output value,targeted countermeasures and suggestions are put forward,such as strengthening the cultivation of service awareness,differentiated docking management of customers with different ratings,and differentiated visit and return visit management of customers with different ratings.And according to the customer characteristics in the payment collection dimension,it puts forward personalized countermeasures and suggestions such as strengthening the payment collection assessment and publicity,clarifying the connection of payment collection of different rated companies,and clarifying payment collection cycle and constraint methods of different evaluation companies.In this paper,the analysis of enterprise customers of third-party testing institutions enriches the scope of application of the RFM model.And based on its principle,the PCA model is proposed to complete the customer analysis and management of the payment dimension,which has theoretical guidance and practical significance.