Help-and-Haggle: Social Commerce Through Randomized, All-or-Nothing Discounts

• 成果类型: Article
• 署名作者: Yang, Luyi; Jin, Chen; Shao, Zhen
• 署名单位: University of California System; University of California Berkeley; National University of Singapore; Chinese Academy of Sciences; University of Science & Technology of China, CAS
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4948
• 发表日期: 2024
• 页码: 6026-6044
• 关键词: customer referrals; social commerce; gamification; dynamic programming

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 nonmonotone 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. Additionally, 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.