Research On Analysis And Optimization Method Of After-sales Business Process Node For Home Appliance

With the trend of homogeneity in the price,performance and other aspects of various home appliance brands has been increasingly obvious,it has become the key of establishing their own brands and winning the consumers to improve the quality of after-sales service and optimize the after-sales business process for home appliance enterprises.For one thing,since there is the particularity of the home appliance industry,customer demands are diverse and the after-sales business process is complex,it is not enough to meet the home appliance enterprises' need which aims at optimizing after-sales process only by qualitative analysis of the after-sales process.For another,the importances of the business process nodes should be distinguished in order to achieve the purpose of resource allocation optimization.To solve the two issues above,this paper mainly carries out three contents of research.Firstly,this paper analyzes the after-sales business process and summarizes six business process nodes: order processing,logistics distribution,installation,customer service,price insured and product quality.Besides,according to customer satisfaction models including ACSI,ECSI and CCSI,the paper designs a customer satisfaction framework composed of six variables: perceived quality,perceived value,customer satisfaction,customer complaints,customer loyalty and consumption level.Combined with the after-sales business process nodes and customer satisfaction framework,this paper presents a home appliance after-sales business process node index system which consists of 59 measurement indexes.Secondly,based on a large number of online review data of a well-known home appliance enterprise,this paper adopts the CRISP-DM standard process for data mining and uses association rule algorithm to filter the key business process nodes in the index system which have key influence on the target variables.Moreover,the random forest algorithm is adopted to distinguish the importances of the filtered key nodes.As a result,the after-sales key business process node model is established.Thirdly,this paper performs the quantitative analysis of the key business process node model by multiple linear regression and verifies the moderating effect of consumption level variable.In this paper,33 key indexes are obtained by data mining,and the influence of each key index on target variables is quantitatively analyzed.The reliability of the analysis results is confirmed by comparing the results by two methods of random forest and linear regression.Finally,this paper explains the analysis results by the relevant theories and puts forward the optimization method for home appliance business process node based on the analysis results.