Abstract:
A report indicating a user-reported probability of a successful outcome is received. A behavior and information model is estimated based on the report. The behavior and information model includes a behavior model component having a bias parameter and a consistency parameter. The behavior and information model includes an information model component having a first user-believed probability of a successful outcome and a second user-believed probability of a successful outcome. The behavior and information model is used to yield a model-determined probability of a successful outcome that more accurately reflects a probability of a successful outcome than the user-reported probability of a successful outcome does.
Abstract:
Planning a publication includes receiving information about an event, accessing a database of profiles that relate in general to content usage, and using a search engine to match the profiles with the event information to estimate parameters for using content for the event. The publication is planned according to the estimated usage parameters.
Abstract:
A method of determining the behavioral outcome resulting from a business rule includes the step of defining at least one player, business rules, and an environment that defines actions that the player can take in accordance with the business rules. The definitions are translated into a codified script. The behavioral outcome resulting from player-selected actions during execution of the codified script are determined.
Abstract:
Planning a publication includes receiving information about an event, accessing a database of profiles that relate in general to content usage, and using a search engine to match the profiles with the event information to estimate parameters for using content for the event. The publication is planned according to the estimated usage parameters.
Abstract:
A method to elicit a customer's product preference propensities among sub-groups, with each of the sub-groups having multiple members based on at least one common attribute, begins when customer action data is collected through the actions of the customer in the sub-group. The actions include the customer's propensities to purchase a product while within the sub-group and the customers' responses to displayed marketing messages or surveys while within the sub-group. The customer action data collected in the sub-group is analyzed to determine a customer's product preference propensities in the sub-group. The customer is targeted, when within the sub-group, with an electronic display that includes at least one product that corresponds to the customer's product preference propensities within the sub-group.
Abstract:
Methods, systems, and computer-readable and executable instructions are provided for determining a product price. Determining a product price can include determining an initial market attraction value, a market price sensitivity, and cost information for a product. Determining a product price can also include receiving a market constraint with respect to the product and pricing the product based on the initial market attraction value, the market price sensitivity, the cost information, and the market constraint.
Abstract:
A computer system for multi-channel campaign planning includes a digital processor, and computer readable instructions to plan and manage a multi-channel campaign. The instructions are embedded on a non-transitory, tangible memory device and executable by the processor. The instructions include a scenario outcome predicting module to predict an outcome for a scenario having a set of parameters defined for each channel of a phase of a plurality of iterative phases of the multi-channel campaign. The instructions include an adaptive learning module to generate an optimized learning component of the multi-channel campaign. The instructions include a decision optimization module to optimize the multi-channel campaign over the plurality of iterative phases. The instructions include a campaign execution module to execute the multi-channel campaign and collect outcome data. An initial phase of the plurality of phases is executed without prior outcome data for the scenario of the initial phase.
Abstract:
A computer-implemented automated decision support system for designing an auction for a given item includes a structure extractor that estimates unknown elements of market structure of the auction based on auction characteristics data extracted from historical auctions for similar items and a bidding model matching the extracted auction characteristics data. The decision support system also includes a bidding behavior predictor that predicts bidding behaviors of bidders in the auction based on the estimated unknown elements of market structure and characteristics of the auction. In addition, the system includes an optimizer that employs an evaluation criterion to generate an evaluation of the auction based on (1) the estimated unknown elements of market structure and (2) the predicted bidding behavior of bidders. A method of providing an automated auction analysis is also described.