Enhancing Reputation via Price Discounts in E-Commerce Systems: A Data-Driven Approach

Reputation systems have become an indispensable component of modern E-commerce systems, as they help buyers make informed decisions in choosing trustworthy sellers. To attract buyers and increase the transaction volume, sellers need to earn reasonably high reputation scores. This process usually takes a substantial amount of time. To accelerate this process, sellers can provide price discounts to attract users, but the underlying difficulty is that sellers have no prior knowledge on buyers’ preferences over price discounts. In this article, we develop an online algorithm to infer the optimal discount rate from data. We first formulate an optimization framework to select the optimal discount rate given buyers’ discount preferences, which is a tradeoff between the short-term profit and the ramp-up time (for reputation). We then derive the closed-form optimal discount rate, which gives us key insights in applying a stochastic bandits framework to infer the optimal discount rate from the transaction data with regret upper bounds. We show that the computational complexity of evaluating the performance metrics is infeasibly high, and therefore, we develop efficient randomized algorithms with guaranteed performance to approximate them. Finally, we conduct experiments on a dataset crawled from eBay. Experimental results show that our framework can trade 60% of the short-term profit for reducing the ramp-up time by 40%. This reduction in the ramp-up time can increase the long-term profit of a seller by at least 20%.