doi:10.3850/978-981-08-7303-5_1534

ABSTRACT

This study measures the stability of the retail market segment based on store images using fuzzy clustering method. Various approaches to segment the market were analysed in literature and it was found that the fuzzy method of segmentation has a potential advantage to measure the stability of the market segment. Data collected through mall intercept interviews were used for empirical investigation. K-means and fuzzy c-means clustering algorithms were initially used to segment the market and later the membership grades of both the methods were compared to measure the stableness of market segments. The most stable segment and the volatile segment were identified. The membership grade obtained by fuzzy cluster methods provides a higher resolution to read the real market situation and it helps the marketers to visualize an individual’s level of multiple preferences in multiple segments. Further, Managers will be equipped to find and target the most stable and promising segments.

Keywords: Market segmentation, Segmentation stability, Shoppers’ segmentation.

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