Understanding Customer Behaviour: A Comprehensive Survey of Segmentation and Classification Techniques in the Age of Big Data
Keywords:
Customer segmentation, Classification techniques, Marketing research, Machine learning, Behavioral segmentation, K-Means Clustering, Decision Trees, Support Vector Machines (SVM), Neural NetworksAbstract
The contemporary business environment necessitates an understanding of customer behavior and preferences to optimize marketing strategies and improve customer satisfaction. Customer segmentation and classification techniques have emerged as fundamental tools for organizations to differentiate their customers based on meaningful differences and create targeted marketing campaigns. This research paper provides a comprehensive survey of the latest customer segmentation and classification techniques used in marketing research. This paper examines traditional and modern approaches to customer segmentation and classification, encompassing demographic, geographic, psychographic, and behavioral segmentation. Furthermore, this paper investigates the latest advancements in customer classification techniques, such as machine learning, data mining, and artificial intelligence. Additionally, this paper discusses the challenges and limitations of these techniques and proposes future research directions. The findings of this research have significant implications for marketing practitioners and scholars interested in optimizing their marketing strategies and improving customer satisfaction.
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