Customer Segmentation and Clustering Techniques: An Analysis for Product Recommendation

Customer segmentation is an important approach in the ever-evolving world of marketing and data-based business operations, allowing organizations to develop a better understanding of distinct customer segments and act on this information. The proposed review elaborates on established approaches to customer segmentation through the integration of Principal Component Analysis (PCA), RFM (Recency, Frequency, Monetary) analysis, and K-Means clustering. PCA reduces data dimensions by removing redundancy, improving clustering efficiency. RFM provides a quantifiable measure of customer value and engagement. K-Means then segments customers based on behavioural patterns. The review outlines the theoretical aspects, their application, challenges, and each techniques relative performance. Lastly, the paper outlines future areas for research and notes the need to implement hybrid models, interfacing with deep learning techniques, as well as addressing inherent limitations such as interpretability and dynamic customer behaviour.