Consumer Segmentation in Household Appliances Market Using K-Means Clustering

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.