Abstract: A prescriptive data model for stores of a retailer is maintained. The data model comprises clusters of benchmarks and benchmark values for successful stores and unsuccessful stores. A machine-learning model (MLM) is trained on the data model to predict Key Performance Indicator (KPI) values. An interface is provided that permits an end user to override a given current benchmark value with a changed value. The changed value along with unchanged current benchmark values are provided as input to the MLM and the MLM produces as output a set of current predicted KPI values. The set of current predicted KPI is rendered within the interface to the end user as a predicted impact the changed value will have on a given store or a given department of the given store.

Abstract: Metrics are captured from a variety of systems associated with stores of a retailer. Values for factors or benchmarks are calculated per store from their corresponding metrics. Each of the stores are labeled as successful or unsuccessful. Factors for which high values are correlated with successful stores and low values are correlated with unsuccessful stores are clustered together. Similarly, factors for which low values are correlated with successful stores and high values are correlated with unsuccessful stores are clustered together. A set of clustered factors associated with the success, or the failure of stores are reported to the retailer in a data model that also comprises the various degrees to which the various clusters of the factors relate to or correlate with both the successful stores and the unsuccessful stores. Prescriptive recommendations are derived from the data model to improve metrics associated with successful factors.

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: 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: Average processing times associated with components of a transactions are maintained as performance metrics for the transactions. Ratios associated with the average processing times are normalized based on calendar dates, days of week, and times of day. A given transaction is analyzed in view of the averages and ratios and a bottleneck is identified within a time slice of the given transaction. A type of bottleneck detected within the time slice is further identified based on identifiers associated with resources of the time slice and a pattern associated with one or more of the identifiers. The transaction, time slice, type of bottleneck, and resource identifiers are reported to a retailer associated with a transaction terminal that processed the transaction through an interface.

Abstract: An ecommerce application (app) or a transaction interface of a transaction terminal are enhanced to call a Purpose of Purchase (POP) service during a user session with the app or the terminal. The POP service integrates a question posed to the user regarding the user's POP for the session. A machine-learning model is trained on customer answers, transaction data, history data, and/or loyalty data to predict a customer's POP profile classification. When another customer is engaged in a session and fails to provide an answer, the model predicts a POP profile for the customer and the session. When the customer provides an answer, the appropriate POP profile that corresponds to the answer is assigned. During the session, the POP service provides the assigned or predicted POP profile to a recommendation service to use as a factor in making a product recommendation to the user during the session.

Abstract: A checkout state for different types of transaction terminals of a store as a whole is determined during a given analyzed interval of time based on transaction logs for transactions processed during the interval. Each transaction is classified into a basket size based on that transaction's total number of items during the interval. Metrics are recorded for the checkout state by basket size and terminal type relative to the total number of transactions within the interval for all terminal types. Trends are derived from the metrics between adjacent analyzed intervals of time. The metrics and trends are custom provided by checkout state per basket size of each terminal type over configurable periods of time within graphs, charts, and tables rendered within a user interface.

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: 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: The probabilities of transitioning between states of a given transaction sequence for a given transaction are calculated and a non-fraud score is calculated from the probabilities. The non-fraud score is provided to a fraud-detection system for determining whether the transaction sequence is more likely or less likely to be associated with fraud.

Abstract: The probabilities of transitioning between item states for a given item sequence of a given transaction are calculated and item non-fraud scores are calculated from the probabilities for each item of the given transaction. The item non-fraud scores for the items of the transaction are provided to a fraud-detection system for determining whether any of the item non-fraud scores is more likely or less likely to be associated with sweethearting fraud by a cashier that performed the 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 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: An ecommerce application (app) is enhanced to call an optimizer service during a user session with the app. The optimizer service calls a recommendation service used by the app and returns recommended products to display during the user session within the app. A machine-learning model of the optimizer service is called with the session contexts or states for which the app is permitting recommendations along with the physical space that the app is permitting for recommendations within each context. The model returns specific recommendations and specific types of recommendations selected from the recommended products returned by the recommendation service and identifies a total number of recommendations and recommendation types for each context within the allotted space permitted by the app. The determined recommendations within their corresponding contexts are communicated from the optimizer service to the app and displayed to the user during the session.

Abstract: A cross-entity and cross-retailer platform is provided that captures transaction data (associated with both in-store or on-line transactions), indexes, and stores the data in a cloud-accessible data store. For any given store of a given retailer, a Virtual Planogram (VP) is maintained for that store. The VP comprises metrics, relationships derived from the metrics, and inferences drawn from the relationships based on item sales and the corresponding transaction data for those item sales. The relationships show the rate of change in item sales over different intervals of time vis-a-vis sales of item categories/departments within the store. The inferences drawn from the relationships show a logical product placement mapping of the items or the proximity of the items to one another within the store. The VP is provided to the retailers and/or entities associated with items of the retailers as an interactive graph within retailer-provided and entity-provided interfaces.

Abstract: A cross-entity and cross-retailer platform is provided that captures transaction and item return 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 and return transactions using the data store. 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. In an embodiment, a given entity workflow causes the service on a given return to calculate a custom aggregation of returns for a given item or a given set of items across different retailers within a given geographical region for a select period of time and provide selective data about the returns automatically to an inventory system and/or a schedule/delivery system of the given entity through an Application Programming Interface (API).

Abstract: A cross-entity and cross-retailer platform is provided that captures transaction data (associated with both in-store or on-line transactions), inventory data, and store planogram data for stores, indexes, and stores the data in a cloud-accessible data store. For any given store of a given retailer, metrics, aggregated totals for each metric, and correlations between the aggregated totals over intervals of time are maintained. Stocked inventory levels for items of the store and promotional compliance levels for promotions of the items within the stores are monitored and tracked using the metrics, aggregated totals, correlations, planograms, and promotion conditions associated with the promotions. When the stocked inventory levels and/or the compliance levels fall below thresholds select data and/or alerts are reported to the retailers and/or the entities. In an embodiment, select data is reported out to the retailers and/or entities at an end of each interval of time.

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: 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.