Personalized marketing: Leveraging AI for culturally aware segmentation and targeting

Franciskus Antonius Alijoyo · Nadir Omer · Nadia Yusuf · M. Dhiliphan Kumar · Zoirov Ulmas · Yousef A. Baker El–Ebiary

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Customer churn and segmentation Data Mining and Machine Learning Applications Consumer Behavior and Market Dynamics

Abstract

In today’s competitive global market, personalized marketing is crucial for businesses aiming to engage diverse audiences effectively. This study examines the potential of AI-driven methodologies, specifically K-means clustering and Local Interpretable Model-Agnostic Explanations (LIME), in designing impactful marketing strategies. Using the Mall Customer Dataset, which includes consumer attributes like gender, age, annual income, and spending scores, this research develops a segmentation framework enriched with additional features, such as purchasing behavior during specific events, individual preferences, and demographic norms. K-means clustering is employed to group customers into meaningful segments based on shared behavioral patterns, with the Elbow Method and Silhouette Score ensuring optimal cluster determination. To enhance the interpretability of clustering results, LIME is used to explain the influence of demographic and behavioral factors on cluster formation. This explainability enables marketing teams to understand the rationale behind customer segmentation, promoting transparency and ethical AI usage. By integrating AI-driven segmentation with explainable AI, the framework offers deeper customer insights while aligning strategies with diverse consumer values. The approach empowers businesses to craft ethical and personalized marketing campaigns tailored to unique consumer needs. This study not only supports more effective engagement strategies but also emphasizes the importance of transparency in AI-driven marketing, fostering trust and alignment with consumer expectations. Actionable recommendations are provided for businesses aiming to enhance market presence and create impactful, tailored campaigns that resonate with their audience. The proposed K-Means-LIME framework achieved superior performance with an MSE of 0.9212 and an MAE of 0.9874, demonstrating its effectiveness in delivering precise and interpretable customer segmentation insights.

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2018RFM ranking – An effective approach to customer segmentation 2022K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data 2022A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation 2021Maximizing Strategy Improvement in Mall Customer Segmentation using K-means Clustering 2021A review of data mining methods in RFM-based customer segmentation 2021Customer Segmentation using RFM Model and K-Means Clustering 2023A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing

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