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Assistant Professor (Senior Grade), Faculty of Management, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, India
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Associate Professor, Department of Management Studies, St. Joseph’s College of Engineering, OMR, Chennai, Tamil Nadu, India
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Assistant Professor, Department of Management Studies, St. Joseph’s Institute of Technology, OMR, Chennai, Tamil Nadu, India
Professor, Department of Management Studies, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India
As digital marketing platforms evolve, there is an increasing need for intelligent customer segmentation methods to comprehend complex customer behaviors and enable personalized marketing strategies. The conventional approaches to customer segmentation are mainly based on demographics and transactions of customers, which limit the effectiveness of understanding the dynamic customer preferences, behavior variations, and marketing needs in real-time. To overcome these challenges, this study introduces Artificial Intelligence-Based Intelligent Customer Segmentation for Precision Marketing (AI-ICSPM). The uniqueness of the suggested approach is the combination of customer behavioral feature learning, dimensionality reduction via PCA, the K-Means Clustering algorithm for segmentation, and predictive decision-making via the random forest algorithm within a precision marketing framework. This research suggests a customer data collection, preprocessing, feature extraction and optimization, segmentation, and marketing strategy development process. Evaluating the framework is done based on a set of 5,000 customer records that contain demographic, transactional, purchase, and engagement information. The analysis was done based on comparative analysis between the framework and demographic segmentation, RFM segmentation, conventional k-means algorithm segmentation, and machine learning segmentation techniques. As a result, the experimental results show that the suggested AI-ICSPM framework outperforms other frameworks in terms of clustering performance, with a Silhouette Score of 86.5%, a Davies–Bouldin Index of 0.214, and a Calinski–Harabasz Index of 5428.6. Moreover, the framework enhances the results of predictive marketing through improved customer profiling, recommendation, and retention activities.
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