Research on Customer Relationship Segmentation of Apparel Retail Industry through Data Mining

Abstract

Objectives: This paper aims to segment customers in the apparel retail industry using data mining techniques. Methods: First, a customer segmentation model was constructed, and then the K-means algorithm was used to classify customers based on indicators from the model. The classification effectiveness was enhanced by introducing indicator feature weights. A case study was also conducted. Findings: When the value of K was 4, the K-means algorithm achieved the best classification performance. The improved K-means algorithm outperformed the traditional K-means algorithm in terms of classification effectiveness. The improved K-means algorithm categorized customers into premium customers, important customers, regular customers, and churned customers. Different marketing suggestions were proposed to manufacturers. Novelty: The novelty of this article lies in the introduction of feature weights for indicators, which allows for a distinction between their importance and improves classification effectiveness. Doi: 10.28991/HIJ-2023-04-02-05 Full Text: PDF