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

This article analyzes the "help-and-haggle" (HH) social commerce practice, where consumers ask friends to help reduce a product’s price through randomized discounts, aiming to cut the price to zero within a time limit to obtain the product for free. The study models consumers’ dynamic referral behavior and shows that randomizing price cuts enables firms to better extract consumer surplus, widen social reach, and often reduce promotion expenses while increasing profits compared to deterministic price-cut schemes. Compared to a reward-per-referral (RR) program that offers cash rebates for each referral, HH can be more cost-effective in generating referrals but may cannibalize product sales; however, HH outperforms RR when consumers are heterogeneous in valuations and referral costs or face increasing marginal referral costs. The paper provides theoretical characterizations, a heuristic for general cases, and discusses implications for firms balancing short-term sales and long-term growth in social commerce.