Research On The Application Of Clustering Analysis In Customer Segmentation

Customers are the groundwork for the existence and development of enterprises. Retaining customers, attracting customers and fully mining the payoff profits of customers are the key to increasing the core competition force for enterprises. CRM (Customer Relationship Management) is a process of acquiring customers, retaining customers and increasing profitable customers.As an important method in the data mining field, clustering analysis has become a popular research subject in this field. Clustering analysis has been applied in CRM in the following aspects: customer segmentation, new customer acquisition, customer retention, customer deletion, shopping basket analysis, requirement prediction and target management, cross sale and active marketing, etc. The thesis mainly researches on the application of clustering analysis in customer segmentation, where the adopted datasets is from the PKDD'99 Discovery Challenge. That is a group of financial datasets (bank). Currently, there are some applications of clustering algorithms in customer segmentation of bank for experiments. It is hard to clearly say that a customer belongs to or does not belong to a particular customer group while segmenting customers. On the contrary, a customer can belong to different groups. Fuzzy c-means (FCM) clustering algorithm can commendably reflect the characteristics of customers for the introduction of membership concept, so fuzzy c-means (FCM) clustering algorithm is adopted here.The method used for validating the FCM validity is based on the Xie-Beni validity, the thesis modifies the Xie-Beni validity and a step of eliminating empty clusters is added after the FCM loop to improve the efficiency of the algorithm. In the experiment phase, we use different parameters for customer segmentation and get different results, and then the validity of the algorithm is validated by analyzing these results. Finally, a prototype system of customer segmentation is implemented in java in the environment of JBuilder9.0 in the thesis, which can perform some basic functions, such as attribute selection, data preprocess, customer segmentation.