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Clearance Pricing Optimization for a Fast-Fashion Retailer

Author

Listed:
  • Felipe Caro

    (UCLA Anderson School of Management, Los Angeles, California 90095)

  • Jérémie Gallien

    (London Business School, London NW1 4SA, United Kingdom)

Abstract

Fast-fashion retailers such as Zara offer continuously changing assortments and use minimal in-season promotions. Their clearance pricing problem is thus challenging because it involves comparatively more different articles of unsold inventory with less historical price data points. Until 2007, Zara used a manual and informal decision-making process for determining price markdowns. In collaboration with their pricing team, we since designed and implemented an alternative process relying on a formal forecasting model feeding a price optimization model. As part of a controlled field experiment conducted in all Belgian and Irish stores during the 2008 fall-winter season, this new process increased clearance revenues by approximately 6%. Zara is currently using this process worldwide for its markdown decisions during clearance sales.

Suggested Citation

  • Felipe Caro & Jérémie Gallien, 2012. "Clearance Pricing Optimization for a Fast-Fashion Retailer," Operations Research, INFORMS, vol. 60(6), pages 1404-1422, December.
    • Handle: RePEc:inm:oropre:v:60:y:2012:i:6:p:1404-1422
    DOI: 10.1287/opre.1120.1102
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    References listed on IDEAS

    as
    1. Gérard P. Cachon & Robert Swinney, 2009. "Purchasing, Pricing, and Quick Response in the Presence of Strategic Consumers," Management Science, INFORMS, vol. 55(3), pages 497-511, March.
    2. 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.
    3. Victor F. Araman & René Caldentey, 2009. "Dynamic Pricing for Nonperishable Products with Demand Learning," Operations Research, INFORMS, vol. 57(5), pages 1169-1188, October.
    4. Guillermo Gallego & Garrett van Ryzin, 1997. "A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management," Operations Research, INFORMS, vol. 45(1), pages 24-41, February.
    5. Dimitris Bertsimas & Ioana Popescu, 2003. "Revenue Management in a Dynamic Network Environment," Transportation Science, INFORMS, vol. 37(3), pages 257-277, August.
    6. Murali K. Mantrala & Surya Rao, 2001. "A Decision-Support System that Helps Retailers Decide Order Quantities and Markdowns for Fashion Goods," Interfaces, INFORMS, vol. 31(3_supplem), pages 146-165, June.
    7. Marshall Fisher & Kumar Rajaram, 2000. "Accurate Retail Testing of Fashion Merchandise: Methodology and Application," Marketing Science, INFORMS, vol. 19(3), pages 266-278, June.
    8. Omar Besbes & Robert Phillips & Assaf Zeevi, 2010. "Testing the Validity of a Demand Model: An Operations Perspective," Manufacturing & Service Operations Management, INFORMS, vol. 12(1), pages 162-183, June.
    9. Gabriel Bitran & René Caldentey & Susana Mondschein, 1998. "Coordinating Clearance Markdown Sales of Seasonal Products in Retail Chains," Operations Research, INFORMS, vol. 46(5), pages 609-624, October.
    10. Constantinos Maglaras & Joern Meissner, 2003. "Dynamic Pricing Strategies for Multi-Product Revenue Management Problems," Working Papers MRG/0002, Department of Management Science, Lancaster University, revised Nov 2005.
    11. John D. C. Little, 1966. "A Model of Adaptive Control of Promotional Spending," Operations Research, INFORMS, vol. 14(6), pages 1075-1097, December.
    12. James D. Dana, Jr. & Nicholas C. Petruzzi, 2001. "Note: The Newsvendor Model with Endogenous Demand," Management Science, INFORMS, vol. 47(11), pages 1488-1497, November.
    13. Eric Anderson & Duncan Simester, 2003. "Effects of $9 Price Endings on Retail Sales: Evidence from Field Experiments," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 93-110, March.
    14. Felipe Caro & Jérémie Gallien, 2010. "Inventory Management of a Fast-Fashion Retail Network," Operations Research, INFORMS, vol. 58(2), pages 257-273, April.
    15. Lingxiu Dong & Panos Kouvelis & Zhongjun Tian, 2009. "Dynamic Pricing and Inventory Control of Substitute Products," Manufacturing & Service Operations Management, INFORMS, vol. 11(2), pages 317-339, December.
    16. 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.
    17. Felipe Caro & Victor Martínez-de-Albéniz, 2010. "The Impact of Quick Response in Inventory-Based Competition," Manufacturing & Service Operations Management, INFORMS, vol. 12(3), pages 409-429, January.
    18. Nicole DeHoratius & Adam J. Mersereau & Linus Schrage, 2008. "Retail Inventory Management When Records Are Inaccurate," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 257-277, November.
    19. Narendra Agrawal & Stephen A. Smith, 1996. "Estimating negative binomial demand for retail inventory management with unobservable lost sales," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(6), pages 839-861, September.
    20. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
    21. 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.
    22. Saravanan Kesavan & Vishal Gaur & Ananth Raman, 2010. "Do Inventory and Gross Margin Data Improve Sales Forecasts for U.S. Public Retailers?," Management Science, INFORMS, vol. 56(9), pages 1519-1533, September.
    23. 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.
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