Predictive modeling of consumer color preference: using retail data and merchandise images,Journal of Forecasting

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Predictive modeling of consumer color preference: using retail data and merchandise images
Journal of Forecasting ( IF 2.627 ) Pub Date : 2020-05-06 , DOI: 10.1002/for.2689
Songtao Li ₁ , Ruoran Chen ₁ , Lijian Yang ₂ , Dinglong Huang ₃ , Simin Huang ₁

Affiliation

Funding information China Postdoctoral Science Foundation, Grant/Award Number: 2019M650751; National Natural Science Foundation of China, Grant/Award Number: 11771240 Abstract The popularity of a fashion item depends on its color, shape, texture, and price. For different items (with all attributes identical except color) of a specific product, fashion retailers need to learn consumer color preference and decide their order quantities accordingly to match their products to consumer demand. This study aims to predict consumer color preference using the knowledge learned from merchandise images, historical retail data, and fashion trends. In our work, merchandise images are analyzed to extract color features, and the retail data of a sportswear retailer are used to reveal consumer choices among items with various colors. Choice behavior is described by a multinomial logit model, whose utility function captures the relationship between color features and popularity. Both linear functions and neural networks are applied to represent the utility function, and their out-of-sample prediction performances are compared. According to the out-of-sample performance test, our model shows reasonable predictive power and can outperform order decisions made by fashion buyers.

更新日期：2020-05-06

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