Work

Customer Segmentation Using K-Means Clustering in Unsupervised Machine Learning

Year: 2025

Type: article

Abstract: Customer segmentation is essential for improving marketing strategies and customer satisfaction. This study uses K-Means clustering to group consumers based on average shopping expenditure and annual store visits, incorporating demographic, geographic, psychographic, and behavioural data. Unlike traditional methods that rely mainly on demographics or past purchases, this approach offers a more comprehensive and data-driven solution. A Python-based model was developed using real-world delivery company data. The segmentation results enable more targeted marketing and support better business decisions, showing improved customer engagement and performance.

Source: International Journal of Science and Research (IJSR)

DOI: 10.21275/sr25417125301

Authors: Aparna S Nair, Sindhu Daniel

Language: English

Topic: Customer churn and segmentation

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: diamond

OpenAlex ID: W4409651009

FWCI:

Cites: 8

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Related to: 10