2018/06/28 by Aniket Jain, Jain, Aniket, Yadunath Gupta +5
Arts and Humanities · Business, Management and Accounting · #Consumer Behavior in Brand Consumption and Identification #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Fashion and Cultural Textiles #Information Retrieval (cs.IR) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1806.11424
openalex publication_date 2018/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We use customer demand data for fashion articles on Myntra, and derive a fashionability or style quotient, which represents customer demand for the stylistic content of a fashion article, decoupled with its commercials (price, offers, etc.). We demonstrate learning for assortment planning in fashion that would aim to keep a healthy mix of breadth and depth across various styles, and we show the relationship between a customer's perception of a style vs a merchandiser's catalogue of styles. We also backtest our method to calculate prediction errors in our style quotient and customer demand, and discuss various implications and findings.