Abstract: Systems, methods, and machine readable media are provided for targeted advertising. One or more images of individuals in a location in a facility are captured with an imaging device. Face detection is performed on the one or more images. Based on a face detection result, Convolutional Neural Network models are used to determine a gender and estimate an age of each detected face. A targeted audience is determined, based on pre-determined criteria using the age estimation and gender identification of each detected face. A relevant advertisement is selected for the targeted audience for display on a display device. The relevant advertisement may be selected based at least in part on a footfall score (fscore) indicative of customer traffic in the facility.

Abstract: The system and method described herein enable generating inventory placement recommendations for a store based on transaction data. Inventory data associated with the individual store is obtained. The inventory data includes container location data, item location data, item category data, and transaction data. A store layout model of the store is generated based on the container location data and item location data. Item category relationships are calculated based on the transaction data and the item category data. Inventory layouts are generated based on the store layout model and the calculated item category relationships. Average distance values for each inventory layout are calculated, and an inventory placement recommendation is generated based on the average distance values of the inventory layouts. The generated inventory placement recommendation enables arrangement of the inventory of the store based on past transactions to reduce the average travel distance of customers when shopping there.

Abstract: Examples provide demand transference modeling for item assortment management. A demand prediction component analyzes item attribute data using a demand transference model to calculate a magnitude of demand transfer between items in a set of substitute items associated with a proposed item assortment. The proposed item assortment includes at least one assortment change. The assortment change includes a set of one or more items to be added to a current item assortment and/or a set of one or more items to be removed from the current item assortment. The demand prediction component generates a demand transference result including the calculated magnitude of demand transfer for each item in the set of substitute items and/or a predicted walk-off rate associated with lost demand. An assortment recommendation component generates an accept recommendation and/or a reject recommendation based on the demand transference result, the predicted walk-off rate, and/or a demand transference score.