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

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. This paper was accepted by Elena Katok, operations management. Funding: L. Yang acknowledges the Berkeley Haas Center for Growth Markets [2022 Grant Award]. C. Jin acknowledges the Singapore Ministry of Education Academic Research Fund [Tier 1 Grant 251RES2101]. Z. Shao acknowledges support from the National Natural Science Foundation of China [Grant 72071188]. All authors acknowledge the Networks, Electronic Commerce and Telecommunications (NET) Institute [2022 Summer Research Grant]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4948 .