Understanding Fashionability: What drives sales of a style?
This addresses assortment planning for fashion retailers by analyzing style perception, but it appears incremental as it builds on existing data and methods.
The paper tackled the problem of quantifying fashionability by decoupling style demand from commercial factors, using customer data from Myntra to derive a style quotient and applying it to assortment planning, with results including prediction errors for style quotient and customer demand.
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.