Customer Segmentation Architecture Based on Clustering Techniques

Knowledge on consumer habits is essential for companies to keep customers satisfied and to provide them personalised services. We present a data mining architecture based on clustering techniques to help experts to segment customer based on their purchase behaviours. In this architecture, diverse segmentation models are automatically generated and evaluated with multiple quality measures. Some of these models were selected for given quality scores. Finally, the segments are compared. This paper presents experimental results on a real-world data set of 10000 customers over 60 weeks for 6 products. These experiments show that the models identified are useful and that the exploration of these models to discover interesting trends is facilitated by the use of our architecture.