Abstract: Provided is a sales management system configured to: acquire: sales data on a target product sold at a shop to be targeted, the sales data indicating a plurality of time-series values relating to sales of the target product; and a period of a bargain sale of the target product at the shop to be targeted; and output an image including a graph which is based on the plurality of time-series values indicated by the acquired sales data, and in which a display mode for an area corresponding to the period of the bargain sale is different from a display mode for another area.

Abstract: An information processing system acquires an action history including at least one of a purchase history including an item purchased by a user and a browsing history including an item browsed by the user, selects one or more items from the plurality of items as a first item by a first selection method based on the acquired action history, selects one or more items from the plurality of items as a second item by a second selection method different from the first selection method based on the acquired action history, and adds the first item and the second item to the same list.

Abstract: A computer-implemented method transforms transactional data and supply data into a forecast of demand for controlling a commerce system. Goods move between members of a commerce system. Transactional data related to movement of goods between the members of the commerce system is recorded. The transactional data includes customer store, product, time, price, promotion, and merchandizing. Supply data related to movement of goods between the members of the commerce system is recorded. The supply data includes inventory, product, store, and merchandising readily available for purchase. Model parameters are estimated based on the transactional data and supply data using a model to generate a forecast of demand for the goods. The forecast of demand for the goods is provided to a member of the commerce system to control the movement of goods in the commerce system. The forecasts of demand takes into account an out-of-stock condition, price promotion, and promotional lift of the product.

Abstract: A computer-implemented method prepares data for modeling. The method comprises storing data from customer sales transactions in a database and retrieving a dataset of the data from the database. The dataset may include promotion and merchandizing entries. The method includes cleansing the dataset to remove erroneous and anomalous entries. Cleansing the dataset may include determining a threshold value from the dataset and determining whether a value of the dataset exceeds the threshold value, and determining an out-of-stock status for a product from the dataset. The method includes aggregating the dataset over a plurality of dimensions of the transactional space including store, product, and time dimensions, and analyzing the dataset following the cleansing and aggregating steps within a model to predict attributes of subsequent sales transactions.

Abstract: A computer-implemented method transforms transactional data and supply data into a forecast of demand for controlling a commerce system. Goods move between members of a commerce system. Transactional data related to movement of goods between the members of the commerce system is recorded. The transactional data includes customer store, product, time, price, promotion, and merchandizing. Supply data related to movement of goods between the members of the commerce system is recorded. The supply data includes inventory, product, store, and merchandising readily available for purchase. Model parameters are estimated based on the transactional data and supply data using a model to generate a forecast of demand for the goods. The forecast of demand for the goods is provided to a member of the commerce system to control the movement of goods in the commerce system. The forecasts of demand takes into account an out-of-stock condition, price promotion, and promotional lift of the product.

Abstract: A computer-implemented method prepares data for modeling. The method comprises storing data from customer sales transactions in a database and retrieving a dataset of the data from the database. The dataset may include promotion and merchandizing entries. The method includes cleansing the dataset to remove erroneous and anomalous entries. Cleansing the dataset may include determining a threshold value from the dataset and determining whether a value of the dataset exceeds the threshold value, and determining an out-of-stock status for a product from the dataset. The method includes aggregating the dataset over a plurality of dimensions of the transactional space including store, product, and time dimensions, and analyzing the dataset following the cleansing and aggregating steps within a model to predict attributes of subsequent sales transactions.