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  • https://doi.org/10.1142/s0129156425407429Copy DOI Icon

Customer Loyalty Evaluation Model Based on Improved Particle Swarm Algorithm

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Abstract

Customer loyalty means that customers identify with products and services and have the performance of repeated purchase, which is presented through behavior and emotion. To achieve higher customer loyalty, specific analysis of the factors affecting customer loyalty is needed. Currently, most scholars believe that the decisive influencing factors on customer loyalty are: customer satisfaction, customer conversion cost, trust, service quality, and corporate image. In this paper, the [Formula: see text]-nearest neighbors (KNN) clustering evaluation model optimized by improved particle swarm algorithm is used for customer loyalty evaluation. The paper particleizes the eigenvalues and thresholds of the factors influencing loyalty, realizes the dynamic change of inertia weights by introducing particle swarm evolution degree and particle swarm aggregation degree, and utilizes the global searchable feature particles and threshold updates of particle swarm algorithm to enhance the algorithm’s ability to deal with nonlinear problems and accelerate the model. The results show that our model can evaluate customer loyalty using multiple parameters, which is more comprehensive, reasonable, and accurate than other evaluation methods, and can consider all parameters and their interrelationships, so as to obtain more reasonable evaluation conclusions. Experimental results demonstrate a 3.35% average error, surpassing KNN (5.28%) in loyalty prediction accuracy.

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