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

Ning Zhu

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

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Keywords


data mining; apparel retailing; customer relationship segmentation; cluster analysis; recency; frequency; and monetary model

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Full Text: PDF

DOI: 10.28991/HIJ-2023-04-02-05

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