The eectiveness of monetary promotions has been well reported in the literature to aect shopping decisions for products in real life experience . Nowadays, e-commerce retailers are facing more fierce competition on price promotion in that consumers can easily use a search engine to find another merchant selling an identical product for comparing price. We study e-commerce data — shopping receipts collected from email accounts, and conclude that for non-urgent products like books or electronics, buyers are price sensitive and are willing to delay the purchase for better deals. We then present a real-time promotion framework, called the RTP system: a one-time promoted discount price is oered to allure a potential buyer making a decision promptly. To achieve more eectiveness on real-time promotion in pursuit of better profits, we propose two discount-giving strategies: an algorithm based on Kernel density estimation, and the other algorithm based on Thompson sampling strategy. We show that, given a pre-determined discount budget, our algorithms can significantly acquire better revenue in return than classical strategies with simply fixed discount on label price. We then demonstrate its feasibility to be a promising deployment in e-commerce services for real-time promotion.