This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Bruce, N. I. Search for Related Content

Pooling and Dynamic Forgetting Effects in Multitheme Advertising: Tracking the Advertising Sales Relationship with Particle Filters

School of Management, The University of Texas at Dallas, Richardson, Texas 75083
norris.bruce{at}utdallas.edu

Firms often use a pool or series of advertising themes in their campaigns. Thus, for example, a firm may employ some of its advertising to promote price-related themes or messages and other of its advertising to promote product-related themes. This study examines the interdependence that can occur between pairs of themes in a pool (i.e., pooling effects), the impact of these pooling effects on the allocation of advertising expenditures, and the factors that can affect forgetting rates (or, conversely, carry-over rates) in a multitheme advertising environment. The study measures pooling, wear out, and forgetting (carry-over) effects for a campaign that uses five different advertising themes. To obtain these measures, I extend the linear Nerlove-Arrow (NA) (1962) model to a nonlinear model of advertising theme quality and goodwill and estimate the extended model using Markov chain Monte Carlo (MCMC) and particle filtering ideas. Particle filtering belongs to a class of sequential Monte Carlo (SMC) methods designed to estimate nonlinear/nonnormal state space models. Results show that forgetting (or carry-over) rates may be time varying and a function of prior goodwill (past advertising) and other advertising variables. Results show, moreover, that pooling effects can reduce theme wear out and, in turn, significantly improve advertising efficiency.

Key Words: nonlinear state space model; particle filtering/smoothing; sequential Monte Carlo (SMC); sequential importance resampling (SIR); Markov chain Monte Carlo (MCMC); metropolis hastings; aggregate advertising models; pooling effects; forgetting effects
History: Received: August 30, 2006;