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

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 seg...

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Vydané v:Mathematics (Basel) Ročník 11; číslo 18; s. 3930
Hlavní autori: Talaat, Fatma M., Aljadani, Abdussalam, Alharthi, Bshair, Farsi, Mohammed A., Badawy, Mahmoud, Elhosseini, Mostafa
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2023
Predmet:
Algorithms
Behavior
Cluster analysis
Clustering
Customer satisfaction
customer segmentation
Customers
Datasets
Decision making
Decision trees
Deep learning
Demographics
Empirical analysis
Explainable artificial intelligence
explainable artificial intelligence (XAI)
Machine learning
Market segmentation
Market strategy
Marketing
Mathematical analysis
mathematical model
Mathematical models
Mathematics
Methods
recency, frequency, and monetary (RFM)
Robustness (mathematics)
Segmentation
Segments
Vector quantization
Saudi Arabia
ISSN:2227-7390, 2227-7390
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Shrnutí: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 R2 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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math11183930