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Granular Computing

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19.07.2022 | Original Paper

Dynamic customer segmentation: a case study using the modified dynamic fuzzy c-means clustering algorithm

verfasst von: M. Sivaguru

Erschienen in: Granular Computing | Ausgabe 2/2023

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Abstract

Dynamic customer segmentation (DCS) is a useful tool for managers to adjust their marketing strategies from time to time. However, no study in the literature has attempted to develop a DCS framework until now. To fill the research gap, a DCS framework is proposed. To improve the effectiveness of the proposed framework, the existing dynamic fuzzy \(c\)-means clustering (dFCM) algorithm is modified owing to certain limitations found in it. Extensive experiments were conducted using the retail supermarket dataset to assess the performance of the modified dFCM (MdFCM) algorithm. Experimental results prove that the MdFCM algorithm performs better than the existing algorithm. The experimental results are validated by fuzzy clustering evaluation measures such as Xie–Beni index (XBI), partition coefficient (PC), modified partition coefficient (MPC), partition entropy (PE), and fuzzy silhouette index (FSI). A statistical significance test, MANOVA Pillai's statistics, is carried out to prove that clusters obtained from the MdFCM algorithm are significant. Finally, a case study on a retail supermarket has been conducted using the proposed DCS framework. The study has shown that the proposed DCS framework extracts useful information for managers to support strategic decision-making.

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Titel: Dynamic customer segmentation: a case study using the modified dynamic fuzzy c-means clustering algorithm
verfasst von: M. Sivaguru
Publikationsdatum: 19.07.2022
Verlag: Springer International Publishing
Erschienen in: Granular Computing / Ausgabe 2/2023
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI: https://doi.org/10.1007/s41066-022-00335-0

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