The purpose of this paper is to establish customers’ markets and rules of dynamic customer relationship management (CRM) systems for online retailers.
This research proposes a procedure to discover customers’ markets and rules, which adopts the recency, frequency, monetary value (RFM) variables, transaction records, and socioeconomic data of the online shoppers to be the research variables. The research methods aim at the supervised apriori algorithm, C5.0 decision tree algorithm, and RFM model.
This research discovered eight RFM markets and six rules of online retailers.
The proposed framework and research results can help retailer managers to retain and expand high value markets via their dynamic CRM and POS systems.
This research uses data mining technologies to extract high value markets and rules for marketing plans. The research variables are easy to obtain via retailers’ systems. The found customer values, RFM markets, shopping association rules, and marketing decision rules can be discovered via the framework of this research.
