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

Luyi Yang (), Chen Jin () and Zhen Shao ()
Additional contact information
Luyi Yang: The University of California, Berkeley, Haas School of Business, 2220 Piedmont Ave, Berkeley, CA 94720 USA
Chen Jin: National University of Singapore, School of Computing, Department of Information Systems and Analytics, 13 Computing Drive, Singapore 117417, Republic of Singapore
Zhen Shao: University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, Anhui, 230026, P.R.China

No 22-01, Working Papers from NET Institute

Abstract: This paper studies a novel social e-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 widens social reach and often lowers promotion expense while increasing profit from product sales at the same time. Third, help-and-haggle is 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 in the total firm payoff. Yet, if consumers are heterogeneous in product valuation and referral cost or if they face increasing marginal referral costs, help-and-haggle can outperform the reward-per-referral program.

Keywords: Customer referrals; social e-commerce; gamification; dynamic programming (search for similar items in EconPapers)
Pages: 62 pages
Date: 2022-09
New Economics Papers: this item is included in nep-pay and nep-sea
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