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.

客户细分是营销中的一项关键策略，涉及根据年龄、收入和地理位置等共同特征对消费者进行分组，使公司能够根据目标客户群有效地制定不同的策略。聚类是一种广泛使用的数据分析技术，有助于识别不同的组，每个组都有其独特的特征集。传统的聚类分析技术通常无法处理消费者数据的复杂性。本文介绍了一种采用 Flying Fox Optimization 算法的新方法，该方法受到 Flying Fox 生存策略的启发，以确定客户群。该方法应用于两个不同的数据集，在识别不同的客户群方面表现出卓越的能力，从而促进有针对性的营销策略的制定。我们与现有最先进的聚类方法以及最近开发的聚类方法的比较分析表明，所提出的方法在分割能力方面优于它们。这项研究不仅提出了一种创新的市场细分聚类技术，还展示了计算智能在改进营销策略、增强其与每个客户需求的一致性方面的潜力。