Elucidating strategic patterns from target customers using multi-stage RFM analysis

ABSTRACT

Predicting profitable customers is a strategic knowledge portfolio of retailer managers because some customers are better profitable than others in a business. The present work is an effort to demonstrate a better model of predicting profitable customers. We apply the k-means algorithm to identify customer patterns based on Recency, Frequency, and Monetary (RFM) attributes computed from a real-life dataset of UK-based and registered non-store online retail. Six data mining models have been applied to each identified pattern and overall data to predict whether each customer would purchase in the next six months or not. A comparative analysis of identified pattern characteristics and predictable performances and Type I and Type II errors have been performed to identify the target customer group in terms of better predictability and profitability. The identified patterns help to generate novel marketing strategies. Thus, the retailers may successfully target the most consistently profitable customer groups to apply diverse knowledge on marketing strategies for the specific pattern.

KEYWORDS:

• RFM analysis
• strategic knowledge
• data mining
• clustering
• target customer selection

Disclosure statement

No potential conflict of interest was reported by the author(s).