Abstract: A risk management system enables users to assess various probabilities of achieving certain audience and advertising delivery guarantees by computing levels of risk associated with various levels of audience guarantees. Statistical theory is applied to actual currency level audience estimation systems to support an application that implements a guarantee tool so that users may examine the specific risk levels for specific advertising schedules. In one illustrative example, both sellers and buyers of magazine advertising may utilize the risk management system to negotiate guarantees at specific ad-audience levels.

Abstract: A model-based method for reducing selection bias in Internet samples utilizes a series of sample weighting procedures that adjust the distribution of key drivers of ad noting and recognition in Internet samples to mirror the distribution of the drivers found in a full-probability sample. In the first phase of the method, a relatively large number of Starch studies are utilized to explore and understand key drivers of ad noting and recognition using multivariate regression analysis. The second phase compares the distribution of the key drivers found in Internet samples with the distribution of those drivers obtained in a full-probability sample to develop the weighting adjustment. In the third phase, the impact of the weighting adjustment is evaluated using a mean squared error model.