Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining
Chen, D (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing and Customer Strategy Management. 19 (3), pp. 197-208.
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.
|Keywords||1505 Marketing; 1503 Business And Management|
|Journal||Journal of Database Marketing and Customer Strategy Management|
|Journal citation||19 (3), pp. 197-208|
|Publisher||London South Bank University|
|Digital Object Identifier (DOI)||doi:10.1057/dbm.2012.17|
|27 Aug 2012|
|Publication process dates|
|Deposited||04 Dec 2017|
|Accepted author manuscript|
dbm201217a Data mining for the online retail industry - A case study of RFM model-based customer segmentation using data mining.pdf
CC BY 4.0
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