Abstract: Systems, methods, and other embodiments for predicting a future characteristic of a target object/product are described based on a digital target image. In one embodiment, the method includes a machine learning model identifying a set of similar known product images by comparing the target product image to a group of known product images. For each similar known product image, product attributes are retrieved including historical characteristic/event data associated with each similar known product image. A predicted characteristic model for the target product is generated which is based on a similarity score combined with the historical characteristic/event data associated with each similar known product image to generate a predicted characteristic for the target product.

Abstract: One example of computerized inventory redistribution control includes, for each location inventory record in a set of location inventory records, calculating a quantity change that will bring a current item quantity to a different item quantity for the location inventory record. Determining a cost of a minimum-cost redistribution among the physical locations to effect the quantity changes. Determining a scaling factor that maximizes total revenue when the quantity changes are scaled by the scaling factor after deducting the cost scaled by the scaling factor. Generating transfer instructions for a redistribution of the item by scaling the transfer quantities of the minimum-cost redistribution by the scaling factor. Transmitting each transfer instruction to a computing device associated with a physical location indicated in the transfer instruction.

Abstract: Systems, methods, and other embodiments associated with generating a price schedule are described. In one embodiment, for each customer segment of a plurality of customer segments, a per-segment value of an approximate objective function for the customer segment is determined by an optimizer, and a ratio of the per-segment value to a sum of all per-segment values for the customer segments is computed. The inventory quantity is allocated amongst the customer segments according to the ratio for each customer segment to form an inventory quantity for each customer segment. For each customer segment, a promotion portion of the price schedule that maximizes the objective function by the optimizer is determined. A quantity of remaining inventory allocated to the plurality of customer segments at an end of the regular season is aggregated. A markdown portion of the price schedule for the item that maximizes the objective function is determined by the optimizer.

Abstract: One example of computerized inventory redistribution control includes, for each location inventory record in a set of location inventory records, calculating a quantity change that will bring a current item quantity to a different item quantity for the location inventory record. Determining a cost of a minimum-cost redistribution among the physical locations to effect the quantity changes. Determining a scaling factor that maximizes total revenue when the quantity changes are scaled by the scaling factor after deducting the cost scaled by the scaling factor. Generating transfer instructions for a redistribution of the item by scaling the transfer quantities of the minimum-cost redistribution by the scaling factor. Transmitting each transfer instruction to a computing device associated with a physical location indicated in the transfer instruction.

Abstract: One example of computerized inventory redistribution control includes, for each location inventory record in a set of location inventory records, calculating a quantity change that will bring a current item quantity to a different item quantity for the location inventory record. Determining a cost of a minimum-cost redistribution among the physical locations to effect the quantity changes. Determining a scaling factor that maximizes total revenue when the quantity changes are scaled by the scaling factor after deducting the cost scaled by the scaling factor. Generating transfer instructions for a redistribution of the item by scaling the transfer quantities of the minimum-cost redistribution by the scaling factor. Transmitting each transfer instruction to a computing device associated with a physical location indicated in the transfer instruction.

Abstract: Systems, methods, and other embodiments associated with generating a price schedule are described. An inventory quantity for the item is allocated amongst a plurality of customer segments based on a predicted contribution of each customer segment to the objective function. For each customer segment, based on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. Remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item. In one embodiment, a price schedule may be generated that includes promotions on top of markdown prices during the clearance season.

Abstract: Systems, methods, and other embodiments associated with determining a promotion price schedule for each item in a group are described. In one embodiment, a method includes computing an item coefficient that corresponds to a change in a value of an objective function when the item is priced at the promotion price. The objective function is based on a multiple product demand model. An item coefficient is computed for each item, each time period in the price schedule, and each promotion price in a price ladder for the item. An approximate objective function is formulated that includes products of item coefficients and binary decision variables. The item coefficients, the approximate objective function, and constraints are provided to an optimizer that determines values of the decision variables that maximize the approximate objective function. A promotion price schedule is created for each item based on values of the decision variables.

Abstract: Systems, methods, and other embodiments associated with generating a price schedule are described. In one embodiment, for each customer segment of a plurality of customer segments, a per-segment value of an approximate objective function for the customer segment is determined by an optimizer, and a ratio of the per-segment value to a sum of all per-segment values for the customer segments is computed. The inventory quantity is allocated amongst the customer segments according to the ratio for each customer segment to form an inventory quantity for each customer segment. For each customer segment, a promotion portion of the price schedule that maximizes the objective function by the optimizer is determined. A quantity of remaining inventory allocated to the plurality of customer segments at an end of the regular season is aggregated. A markdown portion of the price schedule for the item that maximizes the objective function is determined by the optimizer.

Abstract: Systems, methods, and other embodiments associated with generating a price schedule are described. A inventory quantity for the item is allocated amongst a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. For each customer segment, based at least on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. A quantity of remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based at least on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item.

Abstract: Systems, methods, and other embodiments associated with predicting customer behavior are described. The method can include identifying a group comprising customers who satisfy a defined criterion, and receiving input that identifies a factor that influences a decision by the customers to purchase a product. A likelihood that the factor will induce the customers in the group to purchase the product is generated. A customer influence on the generated likelihood is estimated independently of data expressly identifying relationships between the customers in the group. The likelihood is modified by combining the likelihood and the customer influence according to a predictive model, and one or more of the customers eligible for a promotional offer related to the product is identified based, at least in part, on the modified likelihood. Transmission of the promotional offer is controlled to transmit the promotional offer to the identified customers in the group.

Abstract: Systems, methods, and other embodiments associated with generating a price schedule are described. A inventory quantity for the item is allocated amongst a plurality of customer segments based at least on a predicted contribution of each customer segment to the objective function. For each customer segment, based at least on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. A quantity of remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based at least on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item.

Abstract: Systems, methods, and other embodiments associated with generating a price schedule are described. An inventory quantity for the item is allocated amongst a plurality of customer segments based on a predicted contribution of each customer segment to the objective function. For each customer segment, based on a quantity of inventory allocated to the customer segment, a promotion portion of the price schedule is determined that maximizes the objective function. Remaining inventory allocated to the plurality of customer segments at the end of the regular season is aggregated. Based on the aggregated inventory, a markdown portion of the price schedule for the item is determined that maximizes the objective function. The promotion portion and the markdown portion are combined to create a price schedule for the item. In one embodiment, a price schedule may be generated that includes promotions on top of markdown prices during the clearance season.

Abstract: Systems, methods, and other embodiments associated with determining a promotion price schedule for each item in a group are described. In one embodiment, a method includes computing an item coefficient that corresponds to a change in a value of an objective function when the item is priced at the promotion price. The objective function is based on a multiple product demand model. An item coefficient is computed for each item, each time period in the price schedule, and each promotion price in a price ladder for the item. An approximate objective function is formulated that includes products of item coefficients and binary decision variables. The item coefficients, the approximate objective function, and constraints are provided to an optimizer that determines values of the decision variables that maximize the approximate objective function. A promotion price schedule is created for each item based on values of the decision variables.

Abstract: A system that determines markdown pricing for a plurality of items over a plurality of time periods receives a non-linear time-dependent problem, where the non-linear time-dependent problem comprises a demand model. The system determines approximate inventory levels for each item in each time period and, for a plurality of pair of items in a product category, determines coefficients for a change in demand of a first product at each of the plurality of time periods when a price of a second product is changed using initial prices and initial approximate inventory levels. The system generates an approximate MILP problem comprising a change of demand based on a sum of the determined coefficients. The system then solves the MILP problem to generate revised prices and revised inventory levels. The functionality is repeated until a convergence criteria is satisfied, and then the system assigns the revised prices as the markdown product pricing.

Abstract: A system for determining time-dependent product pricing for a product category receives a non-linear problem for the product category, in which the non-linear problem includes a demand model. For a plurality of pair of products in the product category, the system determines coefficients for a change in demand of a first product at each of a plurality of time periods when a price of a second product is changed. The system then generates an approximate Mixed Integer Linear Programming (“MILP”) problem that includes a change of demand based on a sum of the determined coefficients. The system then solves the MILP problem to obtain a MILP solution, which provides the product pricing.