Customer-Based Market Segmentation Using Clustering in Data mining

This research study aims to utilize predictive modelling and data mining to develop market and customer segments for effective marketing strategies. Market segmentation recognizes that customers have different interests, buying habits, and preferences. By creating specialized strategies for specific target groups, a company can enhance its resource management and sales. Customer segmentation involves clustering individuals with similar characteristics and behaviours, enhancing understanding of customers' demographics and dynamic behaviour. The RFM (Recency, Frequency, Monetary) approach is simple and efficient for dividing markets. RFM analysis examines how recently, frequently, and financially customers make purchases, providing insights into consumer behaviour. This study makes use of data mining techniques to categorize products based on recent sales, frequency of sales, and total amount spent. This study introduces a novel k-Means methodology for RFM analysis, aiming to improve customer segmentation and lower marketing expenses while raising customer satisfaction. The output of this study is compared with existing RFM models, assessing the efficiency of the suggested methodology. Overall, this research study focuses on leveraging predictive modelling and BD to create targeted marketing initiatives based on customer segmentation, ultimately enhancing sales efforts and resource allocation for companies, particularly in e- commerce platforms.