Customer Clustering and Marketing Optimization in Hospitality: A Hybrid Data Mining and Decision-Making Approach from an Emerging Economy
Year: 2025
Type: article
Abstract: This study introduces a novel Recency, Monetary, and Duration (RMD) model for customer classification in the hospitality industry. Using a hybrid approach that integrates data mining with multi-criteria decision-making techniques, the study aims to identify valuable customer segments and optimize marketing strategies.The research applies the K-means clustering algorithm to classify customers from a hotel in Iran based on RMD attributes. Cluster validation is performed using three internal indices, and hidden patterns are extracted through association rule mining. Customer segments are prioritized using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and Customer Lifetime Value (CLV) analysis. The outcomes revealed six distinct customer clusters identified as New customers; Loyal customers; Collective Buying Customers; Potential customers; Business customers and Lost customers. This study helps hotels to know different types of customers with spending patterns which enable hotels to tailor services and improve customer retention and provides managers appropriate tools to allocate resources efficiently. This study extends the traditional Recency, Frequency, and Monetary (RFM) model by incorporating Duration, an overlooked dimension of customer engagement. It is the first attempt to integrate data mining and multi-criteria decision-making for customer segmentation in Iran’s hospitality industry.
Source: Tourism and Hospitality
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,448
Funder: University of Tehran
OpenAlex ID: W4410243014