Abstract: Valuable media balancing data and transaction data for valuable media accounts are tagged into categories and a machine-learning model is derived by training the model with the data and actual actions taken to rectify account discrepancies. The model is provided real-time data and produces as output a probability that a given account can have a discrepancy rectified along with recommended actions for resolving the discrepancy and specific transactions that should be investigated with the actions. If different actions are taken to resolve the discrepancy and/or if different transactions were identified as a cause of the discrepancy, the different actions and different transactions are provided as feedback to the model for subsequent training sessions of the model.

Abstract: Features attributable to non-deliberate cashier shrink are identified within a given store's historical transaction data. A machine-learning model is trained on the features to predict shrink events over a future interval of time. Each prediction is also associated with a specific prescriptive recommendation, which if followed, eliminates or otherwise mitigates the likelihood that the predicted shrink event occurs during the corresponding time interval. Each prediction can be specific to a given cashier for the future interval of time. The predictions and corresponding prescriptive recommendations can be provided to store managers in advance of a start of the future interval of time, which allows the manager to follow the prescriptive recommendations and potentially avoid the shrink events altogether.

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 retailer's 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: A system and methods for fine-grain predictive guidance for item markdowns are provided. The predictive guidance indicates whether an item should or should not be marked down, a markdown level for any markdown, a total number of units for any markdown, a time duration for any markdown, a store location to place an item associated with the markdown, and a listing of customers who are likely to purchase a marked down item when provided a targeted promotion. The predictive guidance utilizes, as input, the output produced by multiple predictive services and weighs those respective outputs to optimize item markdown sales and overall sales of the store.

Abstract: Transaction, customer, employee, security, and sales forecast data are obtained for a store. Known actions to prevent shrink events are maintained. Transactions associated with shrink events are identified and features are derived. The data is labeled and used to train a machine-learning model to produce, as output, scores for the features, combinations of the features, and prescriptive action identifiers. Each score represents a likelihood of shrink for a given feature or a given combination of features. The output scores and prescriptive action identifiers are predicted at intervals over a period of future time. At each interval, the output for remaining intervals is updated based on real-time store data generated for transactions at the store in a previous interval. An action identifier can be provided to a security application causing the application to increase sensitivity of security detection on a terminal based on the predicted likelihood of shrink.

Abstract: Transaction data, customer data, employee data, and security data are obtained for a store. Transactions associated with shrink events are identified and features are derived. The data is labeled and used to train a machine-learning model to produce, as output, scores for the features and combinations of the features, where each score represents a likelihood of shrink for a given feature or a given feature combination. The scores are mapped to a heatmap data structure that includes labels or icons for resources associated with the features and indicia such as colors that are indicative of the scores. The heatmap data structure is overlaid onto a map or a physical layout for the store to show locations of the resources within the store. The labels or icons are user-selectable within the heatmap and the indicia corresponding to a selected combination of icons is adjusted based on the combination's assigned score.

Abstract: An unrecognized item code is identified during a transaction at a transaction terminal. When the unrecognized code is determined to be associated with a typographical error, alternative corrected item codes are supplied to the transaction terminal to replace the unrecognized code. When the unrecognized code is determined to be missing from a product catalogue, an ordered list of most likely corrected item codes for the unrecognized code is provided to the transaction terminal for selection of one of the corrected item codes by an operator.

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: 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: 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 network-based service is provided that utilizes a machine learning model (MLM) trained on a variety of data from disparate systems of a retailer to generate predictive guidance/parameters for a price markdown process. The predictive guidance includes a markdown prediction that indicates whether an item should or should not be marked down. For an item designated for markdown, the MLM also generates markdown parameters including a markdown level for the item and a quantity of the item to be marked down. The predictions of the MLM are optimized to reduce item shrink, reduce item spoilage, increase item sales, and increase item margins. The service can be integrated into existing retailer systems and services to provide optimal markdown instructions for perishable items that are data-driven and objective.

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 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: 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: A cross-entity and cross-retailer platform is provided that captures transaction data (and/or browser history data associated with online browser-based transactions), indexes, and stores the data in a cloud-accessible data store. A cloud service is provided that custom processes retailer and entity-defined workflows based on purchase transactions using the data store. The service discovers and updates trends and patterns associated with item sales for a given retailer or for a given entity across channels associated with in-store and online item sales. The trends and patterns are dynamically reported to the corresponding entity or the corresponding retailer. The entities may comprise manufacturers of an item, a supplier of the item, a distributor of the item, and a Consumer Packaging Goods (CPG) company of the item.

Abstract: A cross-entity and cross-retailer platform is provided that captures transaction data, indexes, and stores the data in a cloud-accessible data store. A service is provided that custom processes retailer and entity-defined workflows based on purchase transactions using the data store. The service also generates and maintains correlations between items, the transactions, geographical locations, retailers, and stores of the retailers. The correlations are provided as timely retail recommendations to the retailers and entities for suggested changes that are likely to optimize purchase transactions. The entities may comprise manufacturers of an item, a supplier of the item, a distributor of the item, and a Consumer Packaging Goods (CPG) company of the item.

Abstract: An image of a transaction receipt is obtained and optical character recognition (OCR) is processed on the image to identify a transaction identifier, a transaction total, and a handwritten amount associated with a transaction. When a confidence value associated with the OCR is above a threshold value, the handwritten amount is processed automatically through a transaction interface for the transaction to update the transaction total for the transaction.

Abstract: Historical transaction data, events, replenishment activities, and terminal statuses for terminals of a given store are obtained. The data is preprocessed to include calculated terminal traffic patterns, cash usage patterns, and elapsed time until a replenishment activity for a given interval of time on per terminal. A machine-learning model (MLM) is trained on the modified data to configure itself for predicting the elapsed time per terminal. Real-time transaction data, events, and terminal statuses for the terminals of the store are obtained at the interval of time, and current traffic and usage patterns are inserted into the real-time data and fed as input to the MLM. The MLM returns terminal identifiers for the terminals and corresponding elapsed periods of time until a projected replenishment activity is likely to occur. The terminal identifiers and corresponding elapsed periods of time are rendered within an interface accessible to the store for media management.

Abstract: A real-time and dynamic media baseline for a transaction terminal is calculated when requested. The baseline is optimized to minimize media activities on the terminal while at the same time to minimize a total media volume level associated with each of the terminals for an enterprise as a whole. Real-time and dynamic conditions at the terminals are accounted for in any calculated baseline at the time the baseline is requested to optimally minimize the media activities of the corresponding terminal and to optimally minimize the total media volume level of the terminals as a whole.

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.