Abstract: According to some embodiments, a system and method includes receiving historical data of promotional offers associated with a product or service, the promotional offers including at least one tactic effect; receiving a request to forecast a demand for the product or service, the request including an indication of a promotional offer tactic effect; generating a demand forecast including a tactic lift for the requested promotional offer tactic effect, the demand forecast based on the at least one tactic effect contributing to the demand for the product or service; and providing an output of the generated demand forecast

Abstract: According to some embodiments, a method and system provides separating demand models and forecasts into demand components thereof. The methods include receiving a demand model to forecast a demand for a product or service; decomposing the demand model into a plurality of distinct demand components, each of the demand components associated with a causal contribution to a demand for the product or service; and generating a demand forecast including an indication of the plurality of demand components associated with the causal contributions contributing to the demand forecast for the product or service. Some embodiments include a method to determine demand components of a demand model for a product or service based on historical data.

Abstract: A computer-implemented method estimates model parameters for a product. The method includes storing transaction data from customer sales in a database. The transaction data includes a product and a store. The database includes a hierarchical categorization of the products or the stores. The method includes generating a model for each product at each store in the database. The models include model parameters. The method includes aggregating first and second sets of model parameters from a first set of products occupying a first node of the hierarchical categorization and a second set of products occupying a second node of the hierarchical categorization, weighting the first and second sets of model parameters, and storing the average of the weighted first and second sets of model parameters in the database as the model parameters for a model of a product.

Abstract: A computer-implemented method transforms transactional data into a forecast of demand for controlling a commerce system. Goods move between members of a commerce system. The transactional data is recorded. Daily disaggregating parameters (DDP) are estimated by minimizing an error function of day of week and transactional data grouped according to promotion, price range, or customer. Model parameters are estimated based on the DDP and transactional data using a demand model to generate a weekly forecast of demand for the good. The weekly forecast of demand is disaggregated into daily components using the DDP. The daily components of the forecast of demand are provided to a member of the commerce system to control the movement of goods in the commerce system. Any variation in the demand model due to promotions, price changes, out-of-stock, and low selling product is taken into account when determining the daily components.

Abstract: A computer-implemented method estimates model parameters for a product. The method includes storing transaction data from customer sales in a database. The transaction data includes a product and a store. The database includes a hierarchical categorization of the products or the stores. The method includes generating a model for each product at each store in the database. The models include model parameters. The method includes aggregating first and second sets of model parameters from a first set of products occupying a first node of the hierarchical categorization and a second set of products occupying a second node of the hierarchical categorization, weighting the first and second sets of model parameters, and storing the average of the weighted first and second sets of model parameters in the database as the model parameters for a model of a product.