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The objective of this paper is to study consumer purchase timing and product returns for daily deal e-commerce where products are often sold in a short window of time (usually one to three days). Leveraging a unique proprietary data set from a leading Chinese daily deal website, we find two interesting patterns: (1) consumers generally buy earlier rather than later in sales events; and (2) product return rates are higher for consumers who purchase earlier. These empirical patterns may suggest a potential problem to daily deal merchants: a sales promotion to encourage consumers to buy earlier may actually increase consumer return probabilities and possibly hurt profits. To understand such tradeoffs, we develop an integrative model of consumer purchase timing and product return decisions. In a post-purchase stage, consumer knowledge of the product fit gets realized, and the consumer can return the product with some cost. In a purchase (order) stage, the consumer makes the purchase decision based on her expected utility considering the return probability. A forward-looking consumer solves an optimal stopping problem for a finite-horizon time period game to decide when to purchase in a sales event. Delaying purchase allows the consumer to see newly posted offers and have more time to consider her purchase. We estimate our structural model using a panel data of the purchase and return histories of 5,000 consumers of women’s clothing from January to June 2017. We find that the proposed model fits the data well and that competition significantly increases a consumer’s probability of buying later. In the counterfactual analysis, we adjust the product price over days of a sales event and compare the merchants’ profits under different pricing schedules. Our counterfactual results reveal important managerial insights and can help daily deal merchants select a pricing schedule to improve profit.