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

F 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.