An Empirical Model of Advertising Dynamics

Abstract

This paper develops a model of dynamic advertising competition, and applies it to the problem of optimal advertising scheduling through time. In many industries we observe advertising “pulsing”, whereby firms systematically switch advertising on and off at a high-frequency. Hence, we observe periods of zero and non-zero advertising, as opposed to a steady level of positive advertising. Previous research has rationalized pulsing through two features of the sale response function: an S-shaped response to advertising, and long-run effects of current advertising on demand. Despite considerable evidence for advertising carry-over, existing evidence for non-convexities in the shape of the sales-response to advertising has been limited and, often, mixed. We show how both features can be included in a discrete choice based demand system and estimated using a simple partial maximum likelihood estimator. The demand estimates are then taken to the supply side, where we simulate the outcome of a dynamic game using the Markov perfect equilibrium (MPE) concept. Our objective is not to test for the specific game generating observed advertising levels. Rather, we wish to verify whether the use of pulsing (on and off) can be justified as an equilibrium advertising practice. We solve for the equilibrium using numerical dynamic programming methods. The flexibility provided by the numerical solution method allows us to improve on the existing literature, which typically considers only two competitors, and places strong restrictions on the demand models for which the supply side policies can be obtained. We estimate the demand model using data from the Frozen Entree product category. We find evidence for a threshold effect, which is qualitatively similar to the aforementioned S-shaped advertising response. We also show that the threshold is robust to functional form assumptions for the marginal impact of advertising on demand. Our estimates, which are obtained without imposing any supply side restrictions, imply that firms should indeed pulse in equilibrium. Predicted advertising in the MPE is higher, on average, than observed advertising. On average, the optimal advertising policies yield a moderate profit improvement over the profits under observed advertising. Copyright Springer Science + Business Media, Inc. 2005advertising, dynamic oligopoly, Markov perfect equilibrium, pulsing,

Goodwill and dynamic advertising strategies

“…Firms tend to advertise for a certain number of weeks, and cease advertising activity for a few months and then start again. This pattern seems to hold for a variety of product groups such as the liquid detergents which we analyze here, or frozen food items as analyzed in Dube, Hitsch and Manchanda (2003). Viewed at a low frequency, i.e.…”

“…Closest to our paper is Dube, Hitsch and Manchanda (2003) (henceforth DHM) which is very similar in its modeling approach. Both papers start by estimating the parameters of a goodwill production and an advertising response function using a demand specification and both papers adopt logit based discrete choice models.…”

“…It is well know that when dynamic games are considered, the potential equilibrium outcomes are many, thus there is an equilibrium selection problem. In this paper, we will adopt Markov Perfect Equilibrium (MPE) as our equilibrium concept as in Dube et al (2003) and Doraszelski and Markovich (2004) (see also Maskin and Tirole, 1988;Basar and Oldser, 1982).…”

“…In fact, this is the very reason Dube et al (2003) and Doraszelski and Markovich (2004) employ numerical methods to derive and describe the shape of advertising policy functions. In this paper, we will instead assume that firms' observed advertising activities are the result of a MPE.…”

Optimal Advertising and Promotion Budgets in Dynamic Markets with Brand Equity as a Mediating Variable

“…To this end, following Dube, Hitsch, and Manchanda (2005), we compute the Markov Perfect Equilibrium (MPE) advertising and sales promotion strategies, which use dynamic programming based numerical solution techniques (Pakes and McGuire 1994). However, in addition to the optimal advertising budget allocation that Dube et al (2005) consider, we also investigate the impact of sales promotions on the long-term profitability of a brand and its implications for optimal sales promotion budgets for myopic versus forward-looking decision makers.…”