Customer-Based Market Segmentation in E-Commerce Using Hybrid Clustering

Utilizing predictive modeling and data mining, this study develops 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 behaviors, enhancing understanding of customers' demographics and dynamic behavior.

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 behavior. This study makes use of data mining techniques to categorize products based on recent sales, frequency of sales, and total amount spent.

A novel k-Means methodology for RFM analysis is introduced, aiming to improve customer segmentation and lower marketing expenses while raising customer satisfaction. The output is compared with existing RFM models, assessing the efficiency of the suggested methodology.

Overall, predictive modeling and BD are leveraged to create targeted marketing initiatives based on customer segmentation, ultimately enhancing sales efforts and resource allocation for companies, particularly in e-commerce platforms.