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A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing

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  • Fatma M. Talaat

    (Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
    Faculty of Computer Science & Engineering, New Mansoura University, Gamasa 35712, Egypt)

  • Abdussalam Aljadani

    (Department of Management, College of Business Administration in Yanbu, Taibah University, Al-Madinah Al-Munawarah 41411, Saudi Arabia)

  • Bshair Alharthi

    (Department of Marketing, College of Business, University of Jeddah, Jeddah 22425, Saudi Arabia)

  • Mohammed A. Farsi

    (College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia)

  • Mahmoud Badawy

    (Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah 41461, Saudi Arabia
    Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

  • Mostafa Elhosseini

    (College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia
    Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

Abstract

In the evolving landscape of targeted marketing, integrating deep learning (DL) and explainable AI (XAI) offers a promising avenue for enhanced customer segmentation. This paper introduces a groundbreaking approach, DeepLimeSeg, which synergizes DL methodologies with Lime-based Explainability to segment customers effectively. The approach employs a comprehensive mathematical model to harness demographic data, behavioral patterns, and purchase histories, categorizing customers into distinct clusters aligned with their preferences and needs. A pivotal component of this research is the mathematical underpinning of the DeepLimeSeg approach. The Lime-based Explainability module ensures that the segmentation results are accurate and interpretable. The mathematical rigor facilitates businesses tailoring their marketing strategies with precision, optimizing sales outcomes. To validate the efficacy of DeepLimeSeg, we employed two real-world datasets: Mall-Customer Segmentation Data and an E-Commerce dataset. A comparative analysis between DeepLimeSeg and the traditional Recency, Frequency, and Monetary (RFM) analysis is presented. The RFM analysis, grounded in its mathematical modeling, segments customers based on purchase recency, frequency, and monetary value. Our preprocessing involved computing RFM scores for each customer, followed by K-means clustering to delineate customer segments. Empirical results underscored the superiority of DeepLimeSeg over other models in terms of MSE, MAE, and R 2 metrics. Specifically, the model registered an MSE of 0.9412, indicative of its robust predictive accuracy concerning the spending score. The MAE value stood at 0.9874, signifying minimal deviation from actual values. This paper accentuates the importance of mathematical modeling in enhancing customer segmentation. The DeepLimeSeg approach, with its mathematical foundation and explainable AI integration, paves the way for businesses to make informed, data-driven marketing decisions.

Suggested Citation

  • Fatma M. Talaat & Abdussalam Aljadani & Bshair Alharthi & Mohammed A. Farsi & Mahmoud Badawy & Mostafa Elhosseini, 2023. "A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing," Mathematics, MDPI, vol. 11(18), pages 1-26, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3930-:d:1240862
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    References listed on IDEAS

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    1. Catalina Lozano-Murcia & Francisco P. Romero & Jesus Serrano-Guerrero & Jose A. Olivas, 2023. "A Comparison between Explainable Machine Learning Methods for Classification and Regression Problems in the Actuarial Context," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
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    5. Aitor Goti & Leire Querejeta-Lomas & Aitor Almeida & José Gaviria de la Puerta & Diego López-de-Ipiña, 2023. "Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review," Mathematics, MDPI, vol. 11(13), pages 1-32, June.
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