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  • Winter 2016
  • Article
  • Manufacturing & Service Operations Management

Analytics for an Online Retailer: Demand Forecasting and Price Optimization

By: Kris J. Ferreira, Bin Hong Alex Lee and David Simchi-Levi
  • Format:Print
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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 multi-product 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 do not decrease due to 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

Internet and the Web; Price; Forecasting and Prediction; Revenue; Sales; Retail Industry

Citation

Ferreira, Kris J., Bin Hong Alex Lee, and David Simchi-Levi. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization." Manufacturing & Service Operations Management 18, no. 1 (Winter 2016): 69–88.
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About The Author

Kris Johnson Ferreira

Technology and Operations Management
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    • 2025
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    Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers

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    ReUp Education: Can AI Help Learners Return to College?

    By: Kris Ferreira, Christopher Thomas Ryan and Sarah Mehta
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    Demand Learning and Pricing for Varying Assortments

    By: Kris Ferreira and Emily Mower
More from the Authors
  • Warnings and Endorsements: Improving Human-AI Collaboration in the Presence of Outliers By: Matthew DosSantos DiSorbo, Kris Ferreira, Maya Balakrishnan and Jordan Tong
  • ReUp Education: Can AI Help Learners Return to College? By: Kris Ferreira, Christopher Thomas Ryan and Sarah Mehta
  • Demand Learning and Pricing for Varying Assortments By: Kris Ferreira and Emily Mower
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