Uncovering Customer Archetypes in Direct-to-Consumer Apparel: A K-Means Clustering Analysis of DMart Sales Data

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, SI1 (2026), pp. 144–150
Proceedings of Selected Papers from the
National Conference on Emerging Trends in Commerce and Management
NCETCM-2K26
2026-03-30 to 2026-03-31
Vijayawada, Andhra Pradesh, India
Organized by Andhra Loyola College, Vijayawada, India
eISSN: 3107-9075

This work is licensed under a Creative Commons Attribution 4.0 International License .

Uncovering Customer Archetypes in Direct-to-Consumer Apparel: A K-Means Clustering Analysis of DMart Sales Data

P. Guru Prasad, Naziya Sultana, S N Sai Mohan

Assistant Professor, Department of MBA Analytics, P.B. Siddhartha College of Arts & Science, Vijayawada, India.

Abstract

In the evolving direct-to-consumer (DTC) apparel sector, understanding customer behavior beyond traditional demographic segmentation is essential for enabling personalized marketing and optimizing inventory decisions. This study applies K-means clustering to analyze historical sales data from DMart, focusing on uncovering actionable customer segments based on transactional behavior. The dataset includes purchase frequency, monetary value, product preferences, return rates, and channel usage. A hybrid analytical approach integrating Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Structural Equation Modeling (SEM), and K-means clustering was employed. The findings reveal four distinct customer archetypes: Practical Loyalists, Seasonal Sporadic, Value-Driven Explorers, and Comfort-First Seniors. The study demonstrates how machine learning combined with multivariate analysis enhances segmentation accuracy and supports targeted marketing strategies.

Keywords: K-Means Clustering, Customer Segmentation, Retail Analytics, Direct-To-Consumer, Sales Data Analysis.

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DOI: 10.66710/ijersem.v2si1.19

Open Access • Peer Reviewed Article

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This article is part of Volume 2, Special Issue 1 (May 2026)

2026-05-18