Demandtec, Inc.

M-factor, Inc.

International Business Machines Corporation

Phillip Dennis Delurgio

Dirk Manfred Beyer

Chad Whipkey

Michael Alexander Shwe

James Drew

Steven John Peter Hillion

Nathaniel Keith Folkert

Brian Frederick Babcock

Lisa Laane

A method for updating a forecast model is disclosed. The forecast model includes parameters that receives collected data representing historical dependent and causal data. The collected data are in different units of measure from one another. Based on the collected data, the forecast model is applied to determine a total error value from a calculation performed on the parameters in the forecast model. To update the forecast model, additional parameters are added, existing parameters are modified, or parameters are removed from the forecast model to determine a modified total error value. The modified total error value is then compared to the original total error value. The parameters are also converted to normalized values to be in a common unit of measure to facilitate the application of the forecast model to determine a total error value.

Updating a forecast model

Methods and apparatuses for computing a variance between two business metrics is described. In one embodiment, the method computes a variance for each of a first set of activities based on the corresponding reference state of that activity, wherein the variance for an activity is the change in contribution for that activity between the first and second business metrics and with each of the first set of activities having a reference value. Furthermore, the method computes a variance for each of a second set of activities based on the corresponding start and end values of that activity with each of the second set of activities having a start and end value.

Method and apparatus for creating compound due-to reports

Methods and apparatuses for computing a variance for the difference between two business metrics. In one embodiment, the method accesses a response model and a plurality of activities with start and end values for each of the plurality of activities. Furthermore, the method computes a variance for the difference between the first and second business metrics for each of the plurality of activities using the response model by setting that activity to one of the corresponding starting and ending values and setting others of the plurality of activities to the value state opposite of that activity, wherein the variance for an activity is the change in contribution for that activity between the start and end sales volumes.

Method and apparatus for creating due-to reports for activities that may not have reference value

Methods and apparatuses for decomposing a business metric based on a plurality of activities and a response model are described. In one embodiment, the method accesses the response model and the plurality of activities, the plurality of activities each having a reference and executed value. The method computes a contribution to the business metric based on setting one of the plurality of activities to one of the corresponding reference and executed value and setting the other activities to the value state opposite of that activity. Furthermore, the method computes each of the contributions independent of the response model type.

Method and apparatus for configurable model-independent decomposition of a business metric

Methods and apparatuses for predicting set of multi-dimensional dependent data and non-measurable data from a set of multi-dimensional historical dependent and causal data are described. In one embodiment, the method comprises receiving input data that comprises multi-dimensional historical dependent data and causal data and anticipated activity data, determining a set of multi-dimensional predicted dependent data using a predictive model and the input data, creating non-measurable data based on the set of multi-dimensional predicted dependent data and the input data.

Creation and aggregation of predicted data

Methods and apparatuses for updating a forecast model quantifying the marketing of to the demand for a product and/or service are described. An original forecast model created at a reference M is used. An error of the forecast model is determined based on data including an original data and an additional data. At least one parameter is identified to be changed in value in, added to, or removed from the original forecast model. The forecast model is then modified to reduce the error of the forecast model by changing the value of the identified parameter in the forecast model, adding the identified parameter to the original forecast model, and/or removing the identified parameter from the original forecast model.

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Foster City, CA, United States of America

Phillip Dennis Delurgio