Abstract

CASE STUDIES OF ZARA: LEVERAGE WEBSITE DATA TO IMPROVE ONLINE DEMAND FORECAST

Yunyao Gu*

ABSTRACT

This essay demonstrates that the addition of new data to a linear regression model reduces prediction error by an average of 16% for e-commerce articles experiencing censored demand during a stock out, in comparison to traditional methods. Expanding the scope to all ecommerce articles, this thesis demonstrates that incorporating easily accessible web data yields an additional 2% error reduction on average for all articles on a color and location basis. Traditional methods to improve demand prediction have not before leveraged the expansive availability of ecommerce data, and this research presents a novel solution to the fashion forecasting challenge. Besides, a case-study for companies using subscriptions or an analogous tracking tool is implemented inside the essay, as well as novel data features, in a user-friendly and implementable demand forecast model.

Keywords: Website; data analysis; forecast; user centered; AI; ZARA.