【学术研讨】Help and Haggle: Social Commerce Through Randomized, All-or-Nothing Discounts

Help and Haggle: Social Commerce Through Randomized, All-or-Nothing Discounts 助力砍价: 数字经济下的社交商务

Guest Speaker: Prof. Yang, Luyi (杨路怡)

The University of California, Berkeley

Time/Date:   10 AM, Thursday 7^th, Dec. 2023

Classroom:    Room 402, Tongji Building A

ABSTRACT | 讲座摘要

This paper studies a novel social commerce practice known as “help-and-haggle,” whereby an online consumer can ask friends to help her “haggle” over the price of a product. Each time a friend agrees to help, the price is cut by a random amount, and if the consumer cuts the product price down to zero within a time limit, she will get the product for free; otherwise, the product reverts to the original price. Help-and-haggle enables the firm to promote its product and boost its social reach as consumers effectively refer their friends to the firm. We model the consumer’s dynamic referral behavior in help-and-haggle and provide prescriptive guidance on how the firm should randomize price cuts. Our results are as follows. First, contrary to conventional wisdom, the firm should not always reduce the (realized) price-cut amount if referrals are less costly for the consumer. In fact, the minimum number of successful referrals the consumer must make to have a chance to win the product can be non-monotone in referral cost. Second, relative to the deterministic-price-cut benchmark, a random-price-cut scheme improves firm payoff, extracts more consumer surplus, and widens social reach. Besides, in most instances, it also reduces the promotion expense while increasing profit from product sales at the same time. Third, help-and-haggle can be more cost-effective in social reach than a reward-per-referral program that offers a cash reward for each successful referral. However, using the prospect of a free product to attract referrals cannibalizes product sales, potentially causing help-and-haggle to fall short. Yet, if consumers are heterogeneous in product valuations and referral costs or face increasing marginal referral costs, help-and-haggle can outperform the reward-per-referral program.

GUEST BIO | 讲者介绍

Luyi Yang is an assistant professor in Operations and Information Technology Management and Barbara and Gerson Bakar Faculty Fellow at the University of California, Berkeley, Haas School of Business. His research interests include service operations, marketplace operations, and sustainable operations, with a focus on innovative business practices and emerging policy initiatives. His award-winning research has appeared in leading journals and outlets such as Management Science, Operations Research, Manufacturing & Service Operations Management, Information Systems Research, Production and Operations Management, and Harvard Business Review. His work was presented at major companies and organizations such as Uber Technologies, IBM Research, and the Federal Trade Commission and featured in major media outlets such as Forbes, Fox, NBC, and the South China Morning Post. He has taught courses in business analytics, data mining, and operations management. He is the author of the book Innovative Priority Mechanisms in Service Operations - Theory and Applications. Prior to joining Berkeley Haas, he was an assistant professor of operations management and business analytics at Johns Hopkins University’s Carey Business School. He received his PhD and MBA from the University of Chicago, Booth School of Business, and his BS in Industrial Engineering and BA in English, both from Tsinghua University.