Leveraging Experienced Consumers to Attract New Consumers: An Equilibrium Analysis of Displaying Deal Sales by Daily Deal Websites

• 成果类型: Article
• 署名作者: Subramanian, Upender; Rao, Ram C.
• 署名单位: University of Texas System; University of Texas Dallas
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2015.2298
• 发表日期: 2016
• 页码: 3555-3575
• 关键词: customer acquisition; daily deals; Observational learning; online intermediaries; promotions; signaling; strongly undefeated perfect Bayesian equilibrium; Two-sided Market

摘要

Daily deal websites help small local merchants to attract new consumers. A strategy adopted by some websites is to continually track and display the number of deals sold by a merchant. We investigate the strategic implications of displaying deal sales and the website's incentive to implement this feature. We analyze a market in which a merchant offering an experience good is privately informed of its type. Whereas daily deals cannibalize a merchant's revenue from experienced consumers, we show that, by displaying deal sales, the website can transform this cannibalization into an advantage. Displaying deal sales can leverage discounted sales to experienced consumers to help a high-quality merchant signal its type and acquire new consumers at a higher margin. Signaling is supported through observational learning from displayed deal sales since it reveals how experienced consumers respond to the deal. Nevertheless, the website may not implement this feature if signaling entails too much distortion in the merchant's deal price. We also find that it can be optimal for the website to offer the merchant an up-front subsidy, but only if the website displays deal sales. Our analysis leads to managerial insights for daily deal websites.