Machine Learning Algorithms for Customer Segmentation in Data-Driven Marketing Strategies

In order to divide a company's customer base and retain customers while generating revenue, customer segmentation has grown in popularity. In every industry, client segmentation has grown dramatically within the last ten years. Many methods have been developed to examine consumer behavior with regard to money, regularity, loyalty, purchases, and closeness in order to create effective marketing tactics that are tailored to each customer's specific needs. The rivalry grows along with the supply of goods and services. Due to the increasing prevalence of automation as well as its affordability and accessibility, all companies have equipped themselves with the personnel and equipment needed to carry out large-scale trials, including consumer segmentation.In the suggested study, features are extracted from preexisting properties to alter the datasets. RFM is a widely used technique that calculates each customer pair's Recency, Frequency, and Monetary values. This study discusses validation, preliminary processing, clustering, and marketing techniques for client retention while presenting a novel way to customer segmentation. Two datasets, the Mall Customers relationships Dataset and the Online Retail and e-commerce Set, are modeled in order to arrive at a generalized view. Subsequently, the analytic outcomes for both datasets are presented and compared.