Abstract: A machine-learning algorithm is trained with features relevant to a visual/video analysis performed on subjects conducting transaction at transaction terminals. The algorithm is also trained on weather data known at the time of the transactions and on selective details of the transactions. The algorithm produces as output predictions relevant to: whether a given subject for a current transaction is likely to enter a store, likely items that the given subject might purchase if the subject were to enter the store and likely amount of money that the subject would spend in the store, an effectiveness of providing an incentive for the subject to enter the store, and what type of incentive would most likely entice the subject to enter the store.

Abstract: A machine-learning algorithm is trained with features relevant to a modeled set of input directed to patterns of activities specific to a given behavior. The trained algorithm is also trained on success and failures of remediation actions that change or do not change the given behavior. The trained algorithm is then provided the modeled set of input at predefined intervals of time and supplies as output expected deviations/changes that are predicted for the given behavior along with an indication as to whether the remediation actions are likely to prevent or change the expected behaviors.

Abstract: Actual item prices and actual baskets of item prices are aggregated for a configured region across multiple retailers within the region based on transaction data for the multiple retailers. The prices are analyzed for benchmarks, trends, and abnormalities in view of item and basket prices for a requesting retailer and in view of the requesting retailer's defined pricing goal. The benchmarks and trends are delivered to the requesting retailer via a pull technique and any abnormal pricing is provided to the requesting retailer via a push technique.

Abstract: A culture is defined that spans multiple retailers. Transaction data from the multiple retailers are processed to map barcoded item codes to a culture item vector space. Any non-barcoded item for a given retailer associated with the culture is linked to a most similar barcoded item of that retailer based on a retailer-specific item vector space. The distances between the mapped barcoded item codes of the culture item vector space are processed to cluster the barcoded item codes into classifications within the culture vector space. Each retailers non-barcoded items are associated to the classifications of the culture item vector space based on their linkages to the retailers' specific barcoded items, which are already mapped within the culture item vector space. Each item code of a given retailer's item catalogue is linked to its corresponding classification.

Abstract: Transaction items for a transaction are received during a transaction. Any non-barcoded items are identified and processed within a retailer-specific vector space to identify a most-similar barcoded item offered by a corresponding retailer to the non-barcoded item. The transaction items are revised to include the most-similar barcoded item as a replacement for the non-barcoded item. The revised transaction item list is used to identify a recommended item based on a segment-specific vector space associated with a segment assigned to the transaction. The recommended item is provided in real time to a transaction service that processes the transaction for delivery to a customer associated with the transaction.

Abstract: An item price that is noted in a transaction for an item is identified. Any discount or item price override is identified for the transaction. A catalogue price for the item is obtained. Similar items associated with the item are determined based on mapped transaction contexts for the item and the similar items within a multidimensional space. Similar item prices are obtained and a median price for the item and similar items is calculated. A real-time price alert is sent to a resource that is associated with processing or handling the transaction when the item price, adjusted for any discount or item price override, deviates (above or below) from the median price by a threshold amount.

Abstract: Item vectors representing transaction contexts for items are mapped to multidimensional space. A request is received for an alternative to a given item from a resource. The multidimensional space is evaluated to identify closest candidate items to the given item based on the corresponding item vectors. An optimal candidate item is selected from the candidate items based on the request. The association between the given item and the optimal candidate item is injected within a process workflow associated with the resource.

Abstract: Item codes for items are mapped to multidimensional space as item vectors based on transaction contexts. Similar items are clustered together based on distances between the items within the multidimensional space to create item clusters. Basket clusters are derived from the item clusters and each basket cluster is scored. Prepackaged baskets are derived from the scored basket clusters, each prepackaged basket comprising at least 1 item selected from each of the item clusters of a given basket cluster. The prepackaged baskets are recommended to services or systems via an interface.

Abstract: A transaction is identified for a partial rescan security check based at least in part on basket items of the transaction. A total number of rescan items from the basket items is identified for rescan; the total number of rescan items selected for rescan is less than a total number of the basket items in the transaction. Based on the basket items and transaction features for the transaction, item categories or item departments are identified from which the total number of rescan items are to be selected from the basket items for the rescan security check. The total number of rescan items and the item categories for selection are provided to an attendant terminal for the rescan security check. The rescan security check is processed to determine whether the transaction was associated with theft or not associated with theft.

Abstract: Item codes for items are mapped to multidimensional space as item vectors based on transaction contexts. Similarities between item codes are based on distances between the item codes within the multidimensional space. Substitute items for out-of-stock items are automatically identified based on the item similarities and based on collected feedback from transactions. The substitute items are provided in real time to customers during transactions, item picking services during item fulfillment, and shelf management services for item shelf stocking. In an embodiment, the substitute items are further determined based on a specific transaction history for a given customer and specific feedback collected for the given customer from the specific transaction history.

Abstract: One method embodiment includes receiving a transaction dataset including data representative of transactions including data representative of at least one product purchased within the respective transactions. This method then processes the dataset according to a contextualizing algorithm to generate a data representation for at least some products included in transactions of the transaction dataset. Each generated data representation represents a context of a product with regard to each of the other products of the data representation. This method further includes processing the generated data representations according to a clustering algorithm to partition products represented by the generated data representations into a number of product clusters. A data representation of the product clusters may then be stored including data identifying products and the product clusters to which they are partitioned.

Abstract: A machine-learning algorithm is trained with features relevant to a visual/video analysis performed on subjects conducting transaction at transaction terminals. The algorithm is also trained on weather data known at the time of the transactions and on selective details of the transactions. The algorithm produces as output predictions relevant to: whether a given subject for a current transaction is likely to enter a store, likely items that the given subject might purchase if the subject were to enter the store and likely amount of money that the subject would spend in the store, an effectiveness of providing an incentive for the subject to enter the store, and what type of incentive would most likely entice the subject to enter the store.

Abstract: Item codes are mapped to multidimensional space as item vectors based on each item codes context relevant to other item codes in a product catalogue. A transaction history for a given customer is obtained and each item vector associated with a corresponding item purchase made by that customer is obtained. All item vectors per customer are summed to create an aggregated and single vector representing the purchase history of each customer. The aggregated customer-item vectors for the customers are plotted in the multidimensional space. The plotted customer-item vectors are then clustered into groupings based on their distances from one another in the multidimensional space; the groupings representing data-driven customer segments. The data-driven customer segments along with customer identifiers for the customers comprising each segment are provided as input to promotional engines and/or loyalty systems.

Abstract: A machine-learning algorithm is trained with features relevant to a visual/video analysis performed on subjects conducting transaction at transaction terminals. The algorithm is also trained on weather data known at the time of the transactions and on selective details of the transactions. The algorithm produces as output predictions relevant to: whether a given subject for a current transaction is likely to enter a store, likely items that the given subject might purchase if the subject were to enter the store and likely amount of money that the subject would spend in the store, an effectiveness of providing an incentive for the subject to enter the store, and what type of incentive would most likely entice the subject to enter the store.

Abstract: Transaction item codes for transactions are mapped to item vectors within multidimensional space. Each transaction defines a plurality of item vectors for transaction item codes that are mapped within the multidimensional space. Any given item vector's positions within the multidimensional space can have distances calculated to other specific item vectors plotted within the multidimensional space. The distances between the other specific item codes and a given item vector's positions represent probabilities that specific items are likely to be associated with the corresponding item associated with the transaction. Item vector distances below a predefined threshold for a given transaction having a given set of items represent items that are not present in the given transaction but should be recommended to be included with the given transaction.

Abstract: A transaction is identified for a partial rescan security check based at least in part on basket items of the transaction. A total number of rescan items from the basket items is identified for rescan; the total number of rescan items selected for rescan is less than a total number of the basket items in the transaction. Based on the basket items and transaction features for the transaction, item categories or item departments are identified from which the total number of rescan items are to be selected from the basket items for the rescan security check. The total number of rescan items and the item categories for selection are provided to an attendant terminal for the rescan security check. The rescan security check is processed to determine whether the transaction was associated with theft or not associated with theft.

Abstract: A machine-learning algorithm is trained with features relevant to a visual/video analysis performed on subjects conducting transaction at transaction terminals. The algorithm is also trained on weather data known at the time of the transactions and on selective details of the transactions. The algorithm produces as output predictions relevant to: whether a given subject for a current transaction is likely to enter a store, likely items that the given subject might purchase if the subject were to enter the store and likely amount of money that the subject would spend in the store, an effectiveness of providing an incentive for the subject to enter the store, and what type of incentive would most likely entice the subject to enter the store.

Abstract: A machine-learning algorithm is trained with features relevant to basket data for items of transactions. The trained algorithm is trained to predict whether a given transaction is more or less likely to be associated with theft being engaged in by a transaction operator for the transaction. The trained algorithm is then provided basket data for a given transaction and produces as output a theft prediction value. When the theft prediction value exceeds a configured threshold value, the transaction is flagged for manual intervention or the transaction is flagged for subsequent manual verification.

Abstract: A machine-learning algorithm is trained with features relevant to transaction exceptions, distributions of items in transaction mapped to product hierarchies, and operator data. The trained algorithm is trained to predict whether a given transaction requires a transaction exception for potential fraud or for management approval. The trained algorithm is then provided a set of in-progress input data for an in-progress transaction being processed on a transaction terminal. Output from the trained algorithm is used to determine whether the in-progress transaction is allowed to continue processing unabated or whether the in-progress transaction is to be suspended with a transaction exception requiring a manager override or security credential to continue processing.

Abstract: A machine-learning algorithm is trained with features relevant to a modeled set of input directed to patterns of activities specific to a given behavior. The trained algorithm is also trained on success and failures of remediation actions that change or do not change the given behavior. The trained algorithm is then provided the modeled set of input at predefined intervals of time and supplies as output expected deviations/changes that are predicted for the given behavior along with an indication as to whether the remediation actions are likely to prevent or change the expected behaviors.