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: 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: 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: 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 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: 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 (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 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: 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.