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• W2376571845 abstract "With the rapid development of China's automobile industry, automobile enterprises are facing increasingly fierce competition. At the same time, with the dramatic growth in car ownership, automobile after-sales service market has become more and more beneficial. As a result, the competition among automobile enterprises has already changed from the car's sales to the after-sales service. As an important sector of automotive companies to maintain the customer relationship, the after-sales maintenance services department of 4S shop should accelerate the construction of the customer relationship management(CRM) system based on the information technology and take the customer as the center. For instance, a 4S shop needs to handle the vast customer group which consists of almost all the owners of its brand cars, and segment these customers. Data mining technologies have been applied more and more widely in business environment in recent years. After-sales maintenance services departments of 4S shop also hope to use the advanced data mining technologies to support enterprise customer segmentation and customer relationship management policy making. Many data mining technologies including K-means and Self-Organizing Map(SOM) methods can supply enterprises with better methods of segmenting their customers and developing marketing strategies tailored to specific segments and individuals. Experience, however, shows that business is ceaselessly changing and customers continue to evolve over time. Customer segments and related knowledge discovered from multiple data sources change over time as the customer base changes. However, much research has assumed that customer segments and their members are stable. To solve these problems, based on the characteristic of the automotive maintenance industry, this study discusses customer segmentation methods and customer cluster change mining methods based on the CRISP-DM model. After fully understanding the business needs and data attributes, a customer segmentation indicator model-YKFM is built based on the characteristic of automotive maintenance transaction data in the data preparation process. In the modeling process, the self-organizing maps neural network is firstly applied to the customer clustering, and the customer segmentation result is generated by processing the initial clustering result. Secondly, based on the segmentation results the customer change over time is analyzed from both customer cluster and customer individual perspectives. From the customer cluster perspective, the change of amount and attributes of cluster members are defined and detected. From the individual perspective, this study builds a transaction path for each customer and finds the dominant transition paths that the majority of customers follow during their life circle by association analysis. In the evaluation and deployment processes, this study incorporates the proposed methods into the application of customer segmentation of a 4S shop using the transaction data in resent five years. The result of the customer segmentation and the pattern of customer shifts are presented. The real-world data experiment indicates the feasibility and effectiveness of the proposed methods." @default.
• W2376571845 created "2016-06-24" @default.
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• W2376571845 date "2015-01-01" @default.
• W2376571845 modified "2023-09-26" @default.
• W2376571845 title "Customer Segmentation and Change Mining Based on Customer Behavior for 4S Shop" @default.
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