article · Mathematics
A new customer segmentation framework named DeepLimeSeg combines deep learning techniques with LIME-based explainable artificial intelligence. The approach uses a mathematical model incorporating demographic information, behavioural patterns, and purchasing histories to group consumers into distinct clusters matching their preferences. By embedding an explainability module, the method ensures that segmentation outputs are interpretable and accurate, helping enterprises tailor marketing strategies and improve sales outcomes. DeepLimeSeg was evaluated against conventional recency, frequency, and monetary analysis paired with K-means clustering. Testing on two real-world datasets, covering mall customer data and an e-commerce dataset, demonstrated superior predictive performance across standard evaluation metrics, recording a mean squared error of 0.9412 and a mean absolute error of 0.9874 when predicting spending scores.
Businesses rely on customer segmentation to deliver targeted marketing, but complex machine learning models often lack transparency. By combining deep learning with explainable artificial intelligence, this methodology provides both accurate predictions of spending behaviour and clear interpretations of why customers are placed into specific groups, allowing organisations to design more informed, data-driven promotional campaigns.
The framework is designed for commercial targeted marketing and retail analytics, enabling businesses to predict customer spending scores and cluster buyers by preferences. Having been validated on real-world mall customer and e-commerce datasets, the approach appears to be applied and tested, though the abstract does not describe direct commercial software tools or an active pathway to deployment.
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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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DOI: 10.3390/math11183930
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