Research On The Automotive After-sale Service Recommendation Based On Differences Between Customer Behaviors

Currently, with the rapid development of China’s auto industry, auto after-sales service is expanding rapidly. Although the increase in domestic auto service has great potential, the overall market started too late. Automotive service providers vary greatly and their channel network is disorganized, their service needs effective improvement measures, the quality of their employees are relatively low, professionals are even more scarced, customer complaints is frequent, customer satisfaction rate is low, and advanced management ideas and techniques are scarced. After-sales services basically rely on auto manufacturers’instructions and requirements and they are shot of initiative analysis on the market, so it is difficult for them to adapt to the complex market environment, their service lackes variety, personalized services is lacked, and service differentiation can’t be realized.This paper aims to build a customer behavior database, obtain every customer’s consuming and using preferences with various analytical and statistical tools, analyze the impact of customer differentiation behavior in automobile performance based on expert knowledge and service guide provided by manufacturers, and predict the most likely item and time for every customer’s next service based with prediction model, standard service guide and knowledge database, to provide technical support for service providers to take differentiated initiative service.In allusion to the limitations of previous researches, this paper put forward business strategy which is suitable for domestic auto after-sales enterprises with analyzing domestic auto after-sales services presentation and combining auto after-sales services industry’s characteristics. Meanwhile, it studies some theories on service mining and builds up auto after-sales mining frame based on customers behaviors differentiation. Later it constructs customers behaviors index system, through researching on customer behaviors ontology and after-sales services ontology, realizes service matching based on CBR. And constructs business intelligent decision support system based on integrated CBR. The main works are as follows:This paper defines auto after-sales services, analyses auto after-sales services industry’s features, compares common auto after-sales services business mode, analyses domestic auto after-sales services industry status, point out existed main problem, and puts forward domestic auto after-sales services enterprises’ business strategy based on auto after-sales services industry’s development prospects,.By analyzing human behavior pattern, this paper presented a factors system that influence the customers’behavior, and on the base of it, the physiological factors, psychological factors, natural environmental factors which would affect customer behavior and constitute of social environmental factors and its relationship with the automotive after-sales are analyzed. And then, this paper built the customer behavior index system with structural equation modeling to analyze the impact mechanism between many factors especially behavioral factors and inter-car service, and conducted model validation wiht sample data.By introducing Ontology and CBR into service mining research, this paper builds physiological factors ontology(PFO), psychological factors ontology(POFO), natural environment factors ontology(NFO), social environment factors ontology(SFO), auto domain ontology(ADO), auto after-sales services ontology(AASO) with OWL and protege, and combines relationship between above ontology, constructed customers behaviors service ontology model(CBSO), and then, based on this model, put forward auto after-sales services program matching method based on similarity case reasoning.This paper discussed automotive after-sales knowledge reasoning method in the case of conflict situations. First, extract characteristics of the case base properties and thus to form a basic reasoning evidence with rough set theory to reduce the case library, then determine the basic probability assignment of each evidence with decision support strength method and expansion decision support method, and then realize the knowledge reasoning within conflict in the case with D_S evidence theory for the synthesis of evidence for each case and thus. Finally, this paper performed a case study with the method described above on a car brake pads case library from a Hubei automobile sale and service company and demonstrated the effectiveness of the method.This paper analyzed the difference between the concepts of the automotive after-sales car service recommendation system, automotive after-sales car service recommendation, automotive service management systems, and automotive after-sales maintenance management system, and discussed basic functions of automotive service recommended system and proposed automotive service recommendation system supporting technology. This paper integrated rules reasoning and case-based reasoning, and put forward the overall framework of the integrated system and the specific implementation steps. Finally this paper proposed automotive after-sales recommender system framework based on integrated CBR, presented and analyzed its composition in detail.Finally, the paper summarized the research work, and proposed issues and directions to be further researched.