Affiliation:
1. Department of Management Information Systems, Central Taiwan University of Science and Technology, Taichung, Taiwan & Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
2. National Chung Cheng University, Taiwan
Abstract
Formulating different marketing strategies to apply to various market segments is a noteworthy undertaking for marketing managers. Accordingly, marketing managers should identify sales patterns among different market segments. The study initially applies the concept of recency–frequency–monetary (RFM) scores to segment transaction datasets into several sub-datasets (market segments) and discovers RFM itemsets from these market segments. In addition, three sales features (unique, common, and particular sales patterns) are defined to identify various sales patterns in this study. In particular, a new criterion (contrast support) is also proposed to discover notable sales patterns among different market segments. This study develops an algorithm, called sales pattern mining (SPMING), for discovering RFM itemsets from several RFM-based market segments and then identifying unique, common, and particular sales patterns. The experimental results from two real datasets show that the SPMING algorithm can discover specific sales patterns in various market segments.
Subject
Hardware and Architecture,Software
Reference39 articles.
1. A new framework for itemset generation
2. Aggelis, V., & Christodoulakis, D. (2005, July). Customer clustering using RFM analysis. In Proceedings of the 9th WSEAS International Conference on Computers (p. 2). World Scientific and Engineering Academy and Society (WSEAS).
3. Fast algorithms for mining association rules.;R.Agrawal;Proc. 20th int. conf. very large data bases, VLDB,1994
4. A New Approach of Eliminating Redundant Association Rules
5. Ashrafi, M. Z., Taniar, D., & Smith, K. A. (2005). Redundant Association Rules Reduction Techniques. In Proceedings of theAustralian Conference on Artificial Intelligence (pp. 254-263). Academic Press.
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