We empirically study the spatiotemporal location problem motivated by an online retailer that uses the Buy-Online-Pick-Up-In-Store fulfillment method. Customers pick up their orders from trucks parked at specific locations on specific days, and the retailer’s problem is to determine where and when these pickups occur. Customer demand is influenced by the convenience of pickup locations and days. We combine demographic and economic data, business location data, and the retailer’s historical sales and operations data to predict demand at potential locations. We introduce a novel procedure that combines machine learning and econometric techniques. First, we use a fixed effects regression to estimate spatial and temporal cannibalization effects. Then, we use a random forests algorithm to predict demand when a particular location operates in isolation. Based on the predicted demand and cannibalization effects, we solve the spatiotemporal integer program using a quadratic program relaxation to find the optimal pickup location configuration and schedule. We estimate a revenue increase of at least 51% from the improved location configuration and schedule.