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: 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: Systems and methods are disclosed for cumulative attribute forecasting using a probability density function of a current-to-future value ratio. Some method embodiments may comprise determining an unconditional probability density function for a ratio of a cumulative attribute's current value to an associated future value for the cumulative attribute, and determining an unconditional PDF for a future value of the cumulative attribute. The unconditional probability functions are combined with a current value of the cumulative attribute in a manner that yields a conditional probability density function for the future value. The conditional probability density function may then be used to determine forecasts such as a most likely future value, a mean value, a median value, a confidence interval for the future value, and a probability of achieving a future value within some specified range.

Abstract: An embodiment in accordance with the present invention provides a method for determining a demand function for an item. For example, the method includes determining a first estimate of the demand function for the item by utilizing a first auction having a first set of auction parameters. Additionally, the method includes determining a second estimate of the demand function for the item by utilizing a second auction using auction data from the first auction. The second auction has a second set of auction parameters based on the first estimate of the demand function.

Abstract: A system and method employing an allocation process for determining the number of server machines at each tier of a multiple tiered server system. The allocation process evaluates the number of server machines at each tier sufficient to achieve an average response time of a transaction request to be processed by the server system in response to changes in the average number of transaction requests. The allocation process also identifies shadow pricing enabling analysis of the cost associated with incremental changes in the average response time or other critical system resources.

Abstract: Systems and methods are disclosed for cumulative attribute forecasting using a probability density function of a current-to-future value ratio. Some method embodiments may comprise determining an unconditional probability density function for a ratio of a cumulative attribute's current value to an associated future value for the cumulative attribute, and determining an unconditional PDF for a future value of the cumulative attribute. The unconditional probability functions are combined with a current value of the cumulative attribute in a manner that yields a conditional probability density function for the future value. The conditional probability density function may then be used to determine forecasts such as a most likely future value, a mean value, a median value, a confidence interval for the future value, and a probability of achieving a future value within some specified range.

Abstract: A system and method employing an allocation process for determining the number of server machines at each tier of a multiple tiered server system. The allocation process evaluates the number of server machines at each tier sufficient to achieve an average response time of a transaction request to be processed by the server system in response to changes in the average number of transaction requests. The allocation process also identifies shadow pricing enabling analysis of the cost associated with incremental changes in the average response time or other critical system resources.