Abstract: Systems and methods for estimating in-store demand based on online data are disclosed. In some embodiments, a machine learning model is trained based on data of shared items that are being sold both online and in-store by a retailer. For a physical store of the retailer, inference items are determined from items being sold online but not in-store. An estimated demand is computed for each inference item to be offered for sale in the physical store in a future time period, based on the trained machine learning model and online data of the inference item. Based on the estimated demands for the inference items, recommended assortment data is generated for the physical store in the future time period, and is transmitted to a computing device associated with the physical store for assortment refresh.