汽車客戶售后服務(wù)項目個性化推薦研究
[Abstract]:With China entering the automobile society, more and more attention has been paid to the development of automobile service industry. In recent years, more and more families have owned automobile, and the consumption structure of automobile in our country has changed greatly. Automobile enterprises gradually realize the importance of automobile after-sales service to strengthen customer relationship and improve customer satisfaction. However, due to the late start of automobile industry in China, the level of after-sales service is still in the primary stage of development, and there is a huge gap between China and developed countries in every link. The development of automobile after-sales service market is slow, which to some extent affects the development of automobile industry in our country. In the automobile market, service is a very important part of the whole marketing market. Automobile after-sales service market must reach the goal of serving customer demand, only then can enhance its own level. Different from other commodities, the consumption of various services produced by the automobile after purchase is continuous and diversified, and the consumption of these services accounts for a significant proportion of the total consumption of the automobile. Automobile after-sales service projects involve a wide range of initiative to provide personalized service items for customers to recommend can help improve customer satisfaction increase customer loyalty enhance the competitiveness of enterprises. According to customer's new and old degree, this article carries on the automobile after-sale service project recommendation separately. On the basis of data mining and service recommendation, combined with graph theory knowledge, community network and bipartite graph model are used to subdivide customers and recommend service items. To study the personalized service recommendation method between similar customers. The main research contents of this paper are as follows: firstly, this paper analyzes the automobile after-sales service and community network by consulting relevant domestic and foreign literature. The theoretical knowledge and research status of bipartite graph matching and service recommendation. Secondly, this paper uses association mining algorithm to subdivide the old customers, divide the customer groups with similar consumption behavior, and construct a bipartite graph model to recommend the service items. For new customers, the problem that service mining can not be done through a large amount of history is studied. The demographic characteristics are introduced, and the calculation formula of customer similarity is constructed to find out the old customers with high similarity to customers. And combined with the trend of service selection to the new customer personalized service recommendation. Finally, based on the customer data of automobile 4S store, this paper analyzes and verifies the recommended method of automobile after-sales service project through supplement and simulation. Based on the different research of new and old customers, the customer group is divided into different individuals, and according to its differentiation, auto repair and maintenance services, auto beauty services, auto insurance services and other additional services are provided selectively. The service items are subdivided to enable customers to enjoy appropriate services and meticulous care, to improve customer relations and service quality.
【學(xué)位授予單位】:武漢理工大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2013
【分類號】:F274;F426.471
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