Multiple-perspective consumer segmentation using improved weighted Fuzzy k-prototypes clustering and swarm intelligence algorithm for fresh apricot market

Leveraging clustering technology for consumer segmentation is crucial for discerning the nuanced differences among fresh apricot consumer groups and subsequently executing precise marketing strategies. To achieve a more comprehensive and lucid consumer segmentation and identify typical characteristics of apricot consumers in different clusters, this research constructs a novel multiple-perspective segmentation indicator system for fresh apricot consumers. Given the diverse degrees of importance and types of consumer segmentation variables, and the inherent sensitivity of the original Fuzzy k-prototypes (FKP) algorithm to clustering centers, we proposed the weighted Fuzzy k-prototypes (WFKP) algorithms for mixed data (MD) optimized by the particle swarm optimization (PSO) algorithm (MDPSO-WFKP) and mixed data sparrow search algorithm (SSA) (MDSSA-WFKP), both incorporating information entropy weighting for mixed attributes. We test the proposed algorithms on four University of California Irvine machine learning repository (UCI) datasets and the consumer segmentation dataset, and the performance of all selected evaluation indexes shows significant improvement. These findings unequivocally validate the efficacy of the proposed methodologies. Since the MDSSA-WFKP algorithm has the best comprehensive effect on the evaluation indexes, we use it to conduct in-depth apricot consumer segmentation research and find that the apricot consumers can be subdivided into three groups with differentiation: 'Buddhist-like youths,' 'Upscale attribute enthusiasts,' and 'Quality-oriented consumers'. Finally, this paper gives the corresponding marketing suggestions based on the characteristics of the segmented groups.