Consumer Segmentation in Household Appliances Market Using K-Means Clustering

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, SI1 (2026), pp. 151–157
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 .

Consumer Segmentation in Household Appliances Market Using K-Means Clustering

1Krishna Babu Sambaru, 2P. Guru Prasad, 2Sangeetha Dhanala

1Dean of Management, Department of Management Studies, Aditya Degree Colleges, Andhra Pradesh, India
2Assistant Professor, Department of MBA Business Analytics, P. B. Siddhartha college of arts and science, Vijayawada, Andhra Pradesh, India

Abstract

In the dynamic and highly competitive household appliances market, understanding diverse consumer needs is critical for effective segmentation and targeted marketing strategies. This study aims to segment consumers based on demographic, behavioural, and psychographic variables using the K-means clustering technique. Primary data were collected from 370 respondents using a structured questionnaire. Variables such as purchase frequency, brand preference, price sensitivity, product usage, and technological awareness were considered. Exploratory Factor Analysis (EFA) was conducted to identify underlying constructs, followed by Confirmatory Factor Analysis (CFA) to validate the measurement model. Structural Equation Modeling (SEM) was applied to examine relationships among variables influencing segmentation. K-means clustering was then used to classify consumers into distinct groups. The results revealed three meaningful segments: price-sensitive buyers, quality-conscious consumers, and tech-savvy premium users. The study highlights the importance of data-driven segmentation in enhancing marketing efficiency and customer satisfaction.

Keywords: Consumer Segmentation, Household Appliances, K-Means Clustering, Machine Learning, EFA.

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DOI: 10.66710/ijersem.v2si1.20

Open Access • Peer Reviewed Article

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

2026-05-18