IDEAS home Printed from https://ideas.repec.org/a/inm/ormsom/v18y2016i1p69-88.html
   My bibliography  Save this article

Analytics for an Online Retailer: Demand Forecasting and Price Optimization

Author

Listed:
  • 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)

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%].

Suggested Citation

  • Kris Johnson Ferreira & Bin Hong Alex Lee & David Simchi-Levi, 2016. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 69-88, February.
    • Handle: RePEc:inm:ormsom:v:18:y:2016:i:1:p:69-88
    DOI: 10.1287/msom.2015.0561
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/msom.2015.0561
    Download Restriction: no
    File URL: https://libkey.io/10.1287/msom.2015.0561?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ravi Anupindi & Maqbool Dada & Sachin Gupta, 1998. "Estimation of Consumer Demand with Stock-Out Based Substitution: An Application to Vending Machine Products," Marketing Science, INFORMS, vol. 17(4), pages 406-423.
    2. Choi, Tsan-Ming, 2007. "Pre-season stocking and pricing decisions for fashion retailers with multiple information updating," International Journal of Production Economics, Elsevier, vol. 106(1), pages 146-170, March.
    3. Andrés Musalem & Marcelo Olivares & Eric T. Bradlow & Christian Terwiesch & Daniel Corsten, 2010. "Structural Estimation of the Effect of Out-of-Stocks," Management Science, INFORMS, vol. 56(7), pages 1180-1197, July.
    4. Dr. Nikolai Ostapenko, 2013. "Online Discount Luxury: In Search of Guilty Customers," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 3(2), pages 60-68, February.
    5. Felipe Caro & Jérémie Gallien, 2012. "Clearance Pricing Optimization for a Fast-Fashion Retailer," Operations Research, INFORMS, vol. 60(6), pages 1404-1422, December.
    6. Wedad Elmaghraby & P{i}nar Keskinocak, 2003. "Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions," Management Science, INFORMS, vol. 49(10), pages 1287-1309, October.
    7. Gustavo Vulcano & Garrett van Ryzin & Richard Ratliff, 2012. "Estimating Primary Demand for Substitutable Products from Sales Transaction Data," Operations Research, INFORMS, vol. 60(2), pages 313-334, April.
    8. Martin Natter & Thomas Reutterer & Andreas Mild & Alfred Taudes, 2007. "—An Assortmentwide Decision-Support System for Dynamic Pricing and Promotion Planning in DIY Retailing," Marketing Science, INFORMS, vol. 26(4), pages 576-583, 07-08.
    9. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    10. Russell S. Winer, 1985. "Technical Note—A Price Vector Model of Demand for Consumer Durables: Preliminary Developments," Marketing Science, INFORMS, vol. 4(1), pages 74-90.
    11. Little, John D C & Shapiro, Jeremy F, 1980. "A Theory for Pricing Nonfeatured Products in Supermarkets," The Journal of Business, University of Chicago Press, vol. 53(3), pages 199-209, July.
    12. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    13. Bacel Maddah & Ebru K. Bish, 2007. "Joint pricing, assortment, and inventory decisions for a retailer's product line," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(3), pages 315-330, April.
    14. Stephen A. Smith & Dale D. Achabal, 1998. "Clearance Pricing and Inventory Policies for Retail Chains," Management Science, INFORMS, vol. 44(3), pages 285-300, March.
    15. Dr. Nikolai Ostapenko, 2013. "Online Discount Luxury: In Search of Guilty Customers," International Journal of Business and Social Research, LAR Center Press, vol. 3(2), pages 60-68, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Georgia Perakis & Melvyn Sim & Qinshen Tang & Peng Xiong, 2023. "Robust Pricing and Production with Information Partitioning and Adaptation," Management Science, INFORMS, vol. 69(3), pages 1398-1419, March.
    2. Gonca P. Soysal & Lakshman Krishnamurthi, 2012. "Demand Dynamics in the Seasonal Goods Industry: An Empirical Analysis," Marketing Science, INFORMS, vol. 31(2), pages 293-316, March.
    3. Nathan C. Craig & Ananth Raman, 2016. "Improving Store Liquidation," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 89-103, February.
    4. Pol Boada-Collado & Victor Martínez-de-Albéniz, 2020. "Estimating and Optimizing the Impact of Inventory on Consumer Choices in a Fashion Retail Setting," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 582-597, May.
    5. Felipe Caro & Jérémie Gallien, 2012. "Clearance Pricing Optimization for a Fast-Fashion Retailer," Operations Research, INFORMS, vol. 60(6), pages 1404-1422, December.
    6. Alfred Taudes & Christian Rudloff, 2012. "Integrating inventory control and a price change in the presence of reference price effects: a two-period model," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 75(1), pages 29-65, February.
    7. Marshall Fisher & Marcelo Olivares & Bradley R. Staats, 2020. "Why Empirical Research Is Good for Operations Management, and What Is Good Empirical Operations Management?," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 170-178, January.
    8. Wan, Mingchao & Huang, Yihui & Zhao, Lei & Deng, Tianhu & Fransoo, Jan C., 2018. "Demand estimation under multi-store multi-product substitution in high density traditional retail," European Journal of Operational Research, Elsevier, vol. 266(1), pages 99-111.
    9. Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.
    10. Pavithra Harsha & Shivaram Subramanian & Joline Uichanco, 2019. "Dynamic Pricing of Omnichannel Inventories," Service Science, INFORMS, vol. 21(1), pages 47-65, January.
    11. Bernardo Bertoldi & Chiara Giachino & Alberto Pastore, 2016. "Strategic pricing management in the omnichannel era," MERCATI & COMPETITIVIT?, FrancoAngeli Editore, vol. 2016(4), pages 131-152.
    12. Victor Martínez‐de‐Albéniz & Arnau Planas & Stefano Nasini, 2020. "Using Clickstream Data to Improve Flash Sales Effectiveness," Production and Operations Management, Production and Operations Management Society, vol. 29(11), pages 2508-2531, November.
    13. Fleischmann, M. & Hall, J.M. & Pyke, D.F., 2005. "A Dynamic Pricing Model for Coordinated Sales and Operations," ERIM Report Series Research in Management ERS-2005-074-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    14. Stephen A. Smith & Narendra Agrawal, 2017. "Optimal Markdown Pricing and Inventory Allocation for Retail Chains with Inventory Dependent Demand," Manufacturing & Service Operations Management, INFORMS, vol. 19(2), pages 290-304, May.
    15. Ibrahim, Michael Nawar & Atiya, Amir F., 2016. "Analytical solutions to the dynamic pricing problem for time-normalized revenue," European Journal of Operational Research, Elsevier, vol. 254(2), pages 632-643.
    16. Garrett van Ryzin & Gustavo Vulcano, 2015. "A Market Discovery Algorithm to Estimate a General Class of Nonparametric Choice Models," Management Science, INFORMS, vol. 61(2), pages 281-300, February.
    17. Netessine, Serguei, 2006. "Dynamic pricing of inventory/capacity with infrequent price changes," European Journal of Operational Research, Elsevier, vol. 174(1), pages 553-580, October.
    18. Ding, Xiaohui & Chen, Caihua & Li, Chongshou & Lim, Andrew, 2021. "Product demand estimation for vending machines using video surveillance data: A group-lasso method," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    19. Ming Chen & Zhi-Long Chen, 2018. "Robust Dynamic Pricing with Two Substitutable Products," Manufacturing & Service Operations Management, INFORMS, vol. 20(2), pages 249-268, May.
    20. Joonkyum Lee & Vishal Gaur & Suresh Muthulingam & Gary F. Swisher, 2016. "Stockout-Based Substitution and Inventory Planning in Textbook Retailing," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 104-121, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormsom:v:18:y:2016:i:1:p:69-88. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.