Abstract: A computer-based system connected to a remote user device and a plurality of services. A data collection engine having an input adapted to receive a set of collection data from a set of services, the set of collection data comprising a set of brand data for comparison against a control set of data stored in a database and having a set of fields and via the digital communications network a query.

Abstract: One embodiment is a method that predicts a probability of users responding to a second email campaign based on data gathered from a first email campaign. Data from the first email campaign is applied to different models to identify attributes that are predictive of users who responded to the first email campaign. These attributes are used to predict a probability of users responding to the second email campaign.

Abstract: A computer-based system connected to a remote user device and a plurality of services. A data collection engine having an input adapted to receive a set of collection data from a set of services, the set of collection data comprising a set of brand data for comparison against a control set of data stored in a database and having a set of fields and via the digital communications network a query.

Abstract: An apparatus for providing relative optimized pricing for markdown items is provided. A financial engine for determining revenue for determining sales costs is provided. An optimization engine receiving input from the financial engine and uses the determined sales costs to provide relative optimized pricing for markdown items and provides pricing and a promotion calendar for non-markdown items.

Abstract: Embodiments of the present invention pertain to determining top combinations of items to present to a user. According to one embodiment, data that includes information describing a plurality of combinations of records is accessed. Each record describes a plurality of items. The data is analyzed using a branch and bound search procedure to determine top combinations of items based on a specified metric and a specified number. According to one embodiment, the metric is value enabled and the specified number determines how many combinations of items are associated with the top combinations of items.

Abstract: A predetermined distribution type of a number of selections of an advertisement within a predetermined time period for a predetermined phrase and having a predetermined advertisement location is specified. A parameterization of a mean of the predetermined distribution type is also specified. The mean is determined using a hierarchical Bayesian model, based on the predetermined distribution type, the parameterization, and historical data regarding a number of actual selections of the advertisement for each of a number of phrases similar to the predetermined phrase. The mean corresponds to an average number of selections of the advertisement within the predetermined time period for the predetermined phrase and having the predetermined advertisement location, as predicted by the model.

Abstract: One embodiment is a method that predicts a probability of users responding to a second email campaign based on data gathered from a first email campaign. Data from the first email campaign is applied to different models to identify attributes that are predictive of users who responded to the first email campaign. These attributes are used to predict a probability of users responding to the second email campaign.

Abstract: A method for creating a sales model for a plurality of products is provided. Generally a plurality demand groups are created, where each demand group is a set of at least one product and wherein at least one of the demand groups is a set of at least two products. A sales model is created for each demand group. A market share model is created for each product in each demand group. In addition, an econometric engine for modeling sales as a function of price is provided. An imputed variable generator and a coefficient estimator coupled to the imputed variable generator form the econometric engine, where imputed variables generated by the variable generator are used by the coefficient estimator to create a sales model.

Abstract: A method for computing a preferred set of prices for products in a subset of a plurality of products is provided. Generally, initial prices for a plurality of products are stored. A subset of products of the plurality of products is designated, where the number of products in the subset of products is less than the number of products in the plurality of products. A demand model is created based on Bayesian modeling. Prices for the products in the subset of products are optimized, while maintaining the initial prices of products of the plurality of products that are not in the subset of products, wherein the optimization uses a grid method to determine the optimized prices.

Abstract: A method for computing a preferred set of prices for products in a subset of a plurality of products is provided. Generally, initial prices for a plurality of products are stored. A subset of products of the plurality of products is designated, where the number of products in the subset of products is less than the number of products in the plurality of products. Prices for the products in the subset of products are optimized, while maintaining the initial prices of products of the plurality of products that are not in the subset of products.

Abstract: A preferred set of prices for a plurality of products, generally demand coefficients is provided. Cost data including activity-based costs is also received. A preferred set of prices that will provide a local optimum for the preferred set of prices which maximizes profit is then determined. In one embodiment, a sales model and a cost model are created. A plurality of rules is specified. The preferred set of prices is then determined based on the sales model, the cost model, and the plurality of rules.

Abstract: Methods and apparatus for creating a promotional event calendar are provided. A sales model and a cost model are created The value of offers and promotional events using the sales model and cost model is determined. Combinations of offers and promotional events based on determined value are selected to create a promotion event calendar subject to the conditions from at least one store. The apparatus includes an econometric engine for modeling sales as a function of price to create a sales model, a financial model engine for modeling costs to create a cost model, a promotional engine coupled to the econometric engine, and financial model engine to receive input from the econometric engine and financial model engine. The promotional engine analyzes a plurality of offers and a plurality of promotional events to optimally match offers with promotional events to create a promotional event calendar.

Abstract: A method for modeling cost is provided. Sales data is received. Cost data is also received. Cost per unit of product is estimated from the sales data and the cost data. In addition, an apparatus for modeling costs, useful in association with an optimization engine, is provided. Generally, an econometric engine for receiving sales data, cleaning the data and generating imputed variables is combined with a financial engine for receiving imputed variables from the econometric engine, receiving cost data, generating a cost model, and outputting the cost model to the optimization engine.

Abstract: A price optimization system computes a preferred set of prices for a plurality of products. The optimization system stores a plurality of rules. The system may also prioritize the plurality of rules, and further relax lower priority rules to allow higher priority rules to become feasible. The feasible set of rules may then be utilized to generate the preferred set of prices.

Abstract: A method for determining a preferred set of prices for a plurality of products is provided. Generally, a sales model is created. A cost model is also created. A preferred set of prices for the plurality of products based on the sales model and cost model is then generated. An apparatus for computing a preferred set of prices for a plurality of products is provided. An econometric engine for modeling sales as a function of price to create a sales model is provided. A financial model engine for modeling costs to create a cost model is provided. An optimization engine is coupled to the econometric engine and financial model engine to receive input from the econometric engine and financial model engine, wherein the optimization engine generates the preferred set of prices.

Abstract: Embodiments of the present invention pertain to determining top combinations of items to present to a user. According to one embodiment, data that includes information describing a plurality of combinations of records is accessed. Each record describes a plurality of items. The data is analyzed using a branch and bound search procedure to determine top combinations of items based on a specified metric and a specified number. According to one embodiment, the metric is value enabled and the specified number determines how many combinations of items are associated with the top combinations of items.

Abstract: A method for forming a plurality of stores into a plurality of clusters is provided. Store specific information is collected. Optimized combinations for each individual store are provided based on the store specific information. Clusters are created based on the closeness of the optimal combinations.

Abstract: A preferred set of prices for a plurality of products, generally demand coefficients is provided. Cost data including activity-based costs is also received. A preferred set of prices that will provide a local optimum for the preferred set of prices which maximizes profit is then determined. In one embodiment, a sales model and a cost model are created. A plurality of rules is specified. The preferred set of prices is then determined based on the sales model, the cost model, and the plurality of rules.

Abstract: An apparatus for providing relative optimized pricing for markdown items is provided. A financial engine for determining revenue for determining sales costs is provided. An optimization engine receiving input from the financial engine and uses the determined sales costs to provide relative optimized pricing for markdown items and provides pricing and a promotion calendar for non-markdown items.

Abstract: A preferred set of prices for a plurality of products, generally demand coefficients is provided. Cost data including activity-based costs is also received. A preferred set of prices that will provided a local optimum for the preferred set of prices which maximizes profit is then determined. In one embodiment, a sales model and a cost model are created. A plurality of rules is specified. The preferred set of prices is then determined based on the sales model, the cost model, and the plurality of rules.