Various methods of estimating and testing the relative effectiveness of advertising campaigns using split-cable TV consumer panel data are examined. Sensitivities of the methods to missing observations, serial correlation, and the pattern of the expected advertising campaign impact over time are considered. The maximum likelihood estimators for both the case of no missing observations and the missing-observations case are derived, and a nonlinear model which allows for a gradual diffusion of the relative impact of the campaign is developed. For the data analyzed, the nonlinear model is best for discerning a significant difference between campaigns.