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Customer Clustering and Marketing Optimization in Hospitality: A Hybrid Data Mining and Decision-Making Approach from an Emerging Economy

Author

Listed:
  • Maryam Deldadehasl

    (School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University, Carbondale, IL 62901, USA)

  • Houra Hajian Karahroodi

    (School of Management and Marketing, Southern Illinois University, Carbondale, IL 62901, USA)

  • Pouya Haddadian Nekah

    (Barney Barnett School of Business and Free Enterprise, Florida Southern College, Lakeland, FL 33801, USA)

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, this study aims to identify valuable customer segments and optimize marketing strategies. This 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 be aware of different types of customers with particular spending patterns, enabling hotels to tailor services and improve customer retention. It also provides managers with 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.

Suggested Citation

  • Maryam Deldadehasl & Houra Hajian Karahroodi & Pouya Haddadian Nekah, 2025. "Customer Clustering and Marketing Optimization in Hospitality: A Hybrid Data Mining and Decision-Making Approach from an Emerging Economy," Tourism and Hospitality, MDPI, vol. 6(2), pages 1-19, May.
    • Handle: RePEc:gam:jtourh:v:6:y:2025:i:2:p:80-:d:1651954
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    References listed on IDEAS

    as
    1. Kayalvily Tabianan & Shubashini Velu & Vinayakumar Ravi, 2022. "K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data," Sustainability, MDPI, vol. 14(12), pages 1-15, June.
    2. Hannan Amoozad Mahdiraji & Edmundas Kazimieras Zavadskas & Aliakbar Kazeminia & AliAsghar Abbasi Kamardi, 2019. "Marketing strategies evaluation based on big data analysis: a CLUSTERING-MCDM approach," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pages 2882-2898, January.
    3. McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
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