Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining

This is a post-peer-review, pre-copy edit version of an article published in Journal of Database Marketing and Customer Strategy Management. The definitive publisher-authenticated version Journal of Database Marketing & Customer Strategy Management, 2012, Volume 19, Number 3, Page 197 Daqing Chen, Sai Laing Sain, Kun Guo DOI:10.1057/dbm.2012.17 is available online at: https://link.springer.com/article/10.1057/dbm.2012.17 Many small online retailers and new entrants to the online retail sector are keen to practice data mining and consumer-centric marketing in their businesses yet technically lack the necessary knowledge and expertise to do so. In this article a case study of using data mining techniques in customer-centric business intelligence for an online retailer is presented. The main purpose of this analysis is to help the business better understand its customers and therefore conduct customer-centric marketing more effectively. On the basis of the Recency, Frequency, and Monetary model, customers of the business have been segmented into various meaningful groups using the k-means clustering algorithm and decision tree induction, and the main characteristics of the consumers in each segment have been clearly identified. Accordingly a set of recommendations is further provided to the business on consumer-centric marketing. SAS Enterprise Guide and SAS Enterprise Miner are used in the present study.