Analytics for an Online Retailer: Demand Forecasting and Price Optimization

Kris Johnson Ferreira (), Bin Hong Alex Lee () and David Simchi-Levi ()
Additional contact information
Kris Johnson Ferreira: Technology and Operations Management Unit, Harvard Business School, Boston, Massachusetts 02163
Bin Hong Alex Lee: Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, Massachusetts 02193
David Simchi-Levi: Engineering Systems Division, Department of Civil and Environmental Engineering, Institute for Data, Systems, and Society, and the Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02193

Manufacturing & Service Operations Management, 2016, vol. 18, issue 1, 69-88

Abstract: We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts on designer apparel and accessories. One of the retailer’s main challenges is pricing and predicting demand for products that it has never sold before, which account for the majority of sales and revenue. To tackle this challenge, we use machine learning techniques to estimate historical lost sales and predict future demand of new products. The nonparametric structure of our demand prediction model, along with the dependence of a product’s demand on the price of competing products, pose new challenges on translating the demand forecasts into a pricing policy. We develop an algorithm to efficiently solve the subsequent multiproduct price optimization that incorporates reference price effects, and we create and implement this algorithm into a pricing decision support tool for Rue La La’s daily use. We conduct a field experiment and find that sales does not decrease because of implementing tool recommended price increases for medium and high price point products. Finally, we estimate an increase in revenue of the test group by approximately 9.7% with an associated 90% confidence interval of [2.3%, 17.8%].

Keywords: online retailing; flash sales; initial pricing; revenue management; price optimization; machine learning; regression trees; demand forecasting; demand interdependency; model implementation (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (60)

Downloads: (external link)
http://dx.doi.org/10.1287/msom.2015.0561 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:18:y:2016:i:1:p:69-88

Access Statistics for this article

More articles in Manufacturing & Service Operations Management from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().