Companies invest millions of dollars in various forms of marketing communications to impact customers’ awareness, attitudes, purchases, and, ultimately, profitability. An important question for marketers and shareholders alike is: what effects do marketing investments have on market performance?

To assess these effects, marketers estimate marketing-mix models by using regression analysis. However, Professor Prasad Naik and co-authors Don Schultz from Northwestern University and Shuba Srinivasan from UC Riverside show that the estimation of marketing-mix models via regression analysis (i.e., ordinary least squares, OLS) yields severely biased estimates of marketing effects.

To mitigate such severe biases, the authors present an alternative approach, called the Wiener-Kalman filter, that provides reasonable estimates that are much closer to the true parameters than the corresponding OLS estimates. In addition, they analyze Corolla brand’s multimedia campaign and furnish results based on marketplace data that corroborate the simulation findings. Finally, they discuss both the implications of these results for brand managers and the opportunities that lie ahead for advertising researchers.