Abstract
Customer segmentation, a critical strategy in marketing, involves grouping consumers based on shared characteristics like age, income, and geographical location, enabling firms to effectively establish different strategies depending on the target group of customers. Clustering is a widely utilized data analysis technique that facilitates the identification of diverse groups, each distinguished by their unique set of characteristics. Traditional clustering techniques often lack in handling the complexity of consumer data. This paper introduces a novel approach employing the Flying Fox Optimization algorithm, inspired by the survival strategies of flying foxes, to determine customer segments. Applied to two different datasets, this method demonstrates superior capability in identifying distinct customer groups, thereby facilitating the development of targeted marketing strategies. Our comparative analysis with existing state-of-the-art as well as recently developed clustering methods reveals that the proposed method outperforms them in terms of segmentation capabilities. This research not only presents an innovative clustering technique in market segmentation but also showcases the potential of computational intelligence in improving marketing strategies, enhancing their alignment with each customer’s needs.
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All authors contributed to the study conception and design. Material preparation, software development, data collection and analysis were performed by Konstantinos Zervoudakis and Stelios Tsafarakis. The first draft of the manuscript was written by Konstantinos Zervoudakis and Stelios Tsafarakis revised the manuscript. All authors read and approved the final manuscript.
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Zervoudakis, K., Tsafarakis, S. Customer segmentation using flying fox optimization algorithm. J Comb Optim 49, 5 (2025). https://doi.org/10.1007/s10878-024-01243-6
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DOI: https://doi.org/10.1007/s10878-024-01243-6