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Responsive Pricing of Fashion Products: The Effects of Demand Learning and Strategic Consumer Behavior

Author

Listed:
  • Yossi Aviv

    (Faculty of Management, Tel Aviv University, Tel Aviv 6997801, Israel)

  • Mike Mingcheng Wei

    (School of Management, University at Buffalo, Buffalo, New York 14260)

  • Fuqiang Zhang

    (Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130)

Abstract

This paper studies the potential benefits of responsive pricing and demand learning to sellers of seasonal fashion goods. As typical in such markets, demand uncertainty is high at the beginning of a season, but there is a potential opportunity to learn about demand via early sales observations. Additionally, although the consumers have general preference for purchasing a fashion product earlier rather than later in the season, they may exhibit strategic behavior—contemplating the benefits of postponing their purchase in anticipation of end-of-season discounts. Our results demonstrate that the benefits of responsive pricing, in comparison with a benchmark case of a fixed-price policy, depend sharply on the nature of the consumers’ behavior. Interestingly, in stark contrast to markets of myopic consumers, when the consumers are all strategic, the benefits of responsive pricing tend to worsen when there is a higher potential for learning. We explain this counterintuitive outcome by pointing to two phenomena: the spread effect and information shaping. For example, sellers of fashion products that consider upgrading their pricing systems to incorporate “ accurate response ” strategies (i.e., integrating learning and responsive pricing) should be aware of the possibility that such action might lead them to a new and potentially worse equilibrium, particularly when there is a higher opportunity to learn. Despite the fact that price commitment completely eliminates the seller’s ability to learn, it appears to increasingly dominate responsive pricing as the portion of strategic consumers in the market increases. But, although performing better than responsive pricing, a price-commitment policy is typically limited in performing effective discrimination. Finally, we studied the potential benefits of quick response strategies—ones that embed both dynamic pricing and quick inventory replenishment during the sales season—and found that they are particularly significant under strategic consumer behavior. We explain this result by arguing that quick response provides the seller with a real option that serves as an effective implicit threat to the consumers: encouraging them to buy earlier at premium prices rather than wait for discounts at the end of the season.

Suggested Citation

  • Yossi Aviv & Mike Mingcheng Wei & Fuqiang Zhang, 2019. "Responsive Pricing of Fashion Products: The Effects of Demand Learning and Strategic Consumer Behavior," Management Science, INFORMS, vol. 65(7), pages 2982-3000, July.
  • Handle: RePEc:inm:ormnsc:v:65:y:2019:i:7:p:2982-3000
    DOI: 10.1287/mnsc.2018.3114
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    References listed on IDEAS

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    3. Zhao, Ju & Qiu, Ju & Zhou, Yong-Wu & Hu, Xiao-Jian & Yang, Ai-Feng, 2020. "Quality disclosure in the presence of strategic consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
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    6. Wu, Meng & Ran, Yun & Zhu, Stuart X., 2022. "Optimal pricing strategy: How to sell to strategic consumers?," International Journal of Production Economics, Elsevier, vol. 244(C).
    7. Yao, Yao & Zhang, Jianxiong & Fan, Xiaoqing, 2022. "Strategic pricing: An anti-encroachment policy of retailer with uncertainty in retail service," European Journal of Operational Research, Elsevier, vol. 302(1), pages 144-157.
    8. Yewon Kim & Pradeep K. Chintagunta & Bhuvanesh Pareek, 2022. "Government Policy, Strategic Consumer Behavior, and Spillovers to Retailers: The Case of Demonetization in India," Marketing Science, INFORMS, vol. 41(6), pages 1118-1144, November.
    9. Namin, Aidin & Soysal, Gonca P. & Ratchford, Brian T., 2022. "Alleviating demand uncertainty for seasonal goods: An analysis of attribute-based markdown policy for fashion retailers," Journal of Business Research, Elsevier, vol. 145(C), pages 671-681.
    10. Yash Kanoria & Hamid Nazerzadeh, 2021. "Incentive-Compatible Learning of Reserve Prices for Repeated Auctions," Operations Research, INFORMS, vol. 69(2), pages 509-524, March.
    11. Wang, Hua & Dong, Dayong & Guan, Zhenzhong & Bai, Chunguang, 2021. "The impact of quantity commitment with disappointment-averse and elation-seeking consumers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    12. Zhou, Yu & Yan, Shuangqi & Li, Gendao & Xiong, Yu & Lin, Zhibin, 2023. "The impact of consumer skepticism on blockchain-enabled sustainability disclosure in a supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    13. Li, Guo & Li, Xiaochuan & Zheng, Hong, 2023. "Discount preannouncement in the digital supply chain era," International Journal of Production Economics, Elsevier, vol. 258(C).
    14. Chenxu Ke & Bo Yan & Jingna Ji, 2023. "Pricing new and remanufactured products under patent protection and government intervention," Annals of Operations Research, Springer, vol. 324(1), pages 131-161, May.
    15. Bingsheng Liu & Wenwen Zhu & Yinghua Shen & Yuan Chen & Tao Wang & Fengwen Chen & Maggie Wenjing Liu & Shi‐Hao Zhou, 2022. "A study about return policies in the presence of consumer social learning," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2571-2587, June.
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