Identifying loyal customers and predicting customers purchase behavior using k-means and SOM algorithms
Author(s): Amir Ehsani and Abdolreza Hatamlou
Abstract: Despite the importance of data mining techniques to customer relationship management (CRM) and measuring customers loyalty and profitability, there is a lack of resources and articles related to this topic. Data mining is a useful tool to help companies for mining patterns and discovering hidden information in customers' data. In this study we cluster customers using k-means and SOM clustering algorithms with respect to apply RFM analysis based on behavioral characteristics such as recency, frequency and monetary variables and identify loyal customers and determine degree of loyalty. Then we apply C5.0 model on the resulting clusters to predict future customer behavior. In the end, evaluate accuracy of classification and compare the results. Proposed model implemented on M&S clothing store's dataset. Results of this study provide a background for identifying valuable and key customers and analysis their characteristics and loyalty.
Amir Ehsani, Abdolreza Hatamlou. Identifying loyal customers and predicting customers purchase behavior using k-means and SOM algorithms. Int J Comput Artif Intell 2020;1(1):21-27. DOI: 10.33545/27076571.2020.v1.i1a.5