报告题目：
Designing Reward Programs for Modern Platforms: Insights from Two-Sided Markets and Coalition Loyalty Programs
报 告 人：美国科罗拉多抢庄牛牛 博尔德分校利兹商抢庄牛牛 张丹教授
报告时间：2025年11月23日（周日） 9:00-12:00
报告摘要：
Reward and loyalty programs have become essential tools for shaping consumer behavior across a wide range of modern marketplaces, from digital platforms to multi-firm retail ecosystems. Yet their design and impact vary substantially depending on the underlying market structure. This talk brings together insights from two analytical frameworks: reward programs in two-sided markets such as ride-hailing, freelance, and accommodation-sharing platforms, and coalition loyalty programs (CLPs) formed by multiple firms offering nondurable products.
In two-sided platforms, we show that reward programs interact deeply with both customer heterogeneity and supply constraints. By jointly optimizing customer pricing, provider compensation, reward rules, and priority matching schemes, platforms can use rewards to exert fine-grained control over consumption patterns. This alignment of scarce supply with high-value customers yields significant profit gains and can generate “win-win-win” outcomes for platforms, providers, and customers - effects that are stronger than in traditional one-sided settings.
In contrast, CLPs demonstrate how multi-firm collaboration in loyalty programs can expand the market conditions under which reward systems are effective. Even when proprietary loyalty programs (PLPs) fail to deliver benefits, CLPs can improve performance by enabling more powerful forms of price discrimination. The effectiveness and optimal size of a CLP, however, depend critically on market composition: smaller coalitions are more attractive when heterogeneity in customer shopping intensity dominates, whereas valuation-based heterogeneity favors larger coalitions. These insights help explain why some CLPs struggle to scale despite positive network effects.
Taken together, our work highlights how reward programs serve as strategic levers across diverse market architectures. We conclude by discussing design guidelines, welfare implications, and robustness across multiple model extensions, illustrating the broader principles that govern the success of reward.
报告人简介：
Dan Zhang is Associate Dean for Research and Academics and MediaOne Professor of Operations Management at Leeds School of Business, University of Colorado Boulder. Dr. Zhang’s primary research interests include revenue management, pricing, and data-driven decision making. He regularly consults in his area of expertise and is a frequent speaker at academic and industry conferences, companies, and academic institutions. He formerly served as chair of INFORMS Pricing and Revenue Management Section. He held editorial positions at several journals, including Production and Operations Management (Department Editor of Revenue Management, Senior Editor), Decision Sciences (Department Editor of Revenue Management), Operations Research (Associate Editor), Manufacturing and Service Operations Management (Associate Editor), and INFORMS Journal on Computing (Associate Editor). Dr. Zhang taught operations management, statistics, and data analytics courses in undergraduate, MBA, and specialized master programs in in-person, online, and hybrid formats. Together with two colleagues, he has been offering a five-course specialization Data Analytics for Business on the MOOC platform Coursera since 2016. He also offered research courses on Operations Management and Revenue Management to PhD students at several universities. Dr. Zhang previously served on the faculty at Desautels Faculty of Management, McGill University and held visiting appointments at Cornell University, University of Michigan, and City University of Hong Kong. He was awarded the Changjiang Chaired Professorship by the Chinese Ministry of Education. Dr. Zhang served on the advisory board of several technology startups and is a co-founder of Ramsi, a company aiming at revolutionizing pricing in the hospitality industry.
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