Data Processing and mining for customer segmentation

DOI

https://www.doi.org/10.59256/indjcst.20250402033

Published In

Volume 4, Issue 2 (2025)

Published Online

May-August 2025

Pages

239-245

Article Type

Original Article

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Machine learning (ML) and Data Mining are crucial to the analysis of big data and the drawing out of relevant insights. ML is concerned with developing algorithms that allow computers to learn from data, whereas data mining is concerned with finding patterns and relationships between data sets. Customer segmentation is one of the applications of these technologies, whereby a customer base is segmented into groups with common traits, like buying habits or demographics. Among all customer segmentation techniques, the K-Means algorithm is a well-known clustering method for datasets. It separates data points into k clusters based on similarity, enabling organizations to recognize and study various customer groups effectively. RFM (Recency, Frequency, Monetary) analysis is another effective technique for segmenting customers by measuring how recently and how often they buy and their overall spend. By combining K-Means with RFM analysis, companies will be able to understand customers better, resulting in more effective marketing and better customer relationships. Such methods highlight data-driven approaches for improving business outcomes through individualized customer interactions. In addition to that, research also deals with churn prediction, customer lifetime value, and the top customer in various categories.