Describing the Dynamics of Attention to TV Commercials: A Hierarchical Bayes Analysis of the Time to Zap an Ad

Abstract

This paper provides insights into the dynamics of attention to TV commercials via an analysis of the length of time that commercials are viewed before being 'zapped'. The model, which incorporates a flexible baseline hazard rate and captures unobserved heterogeneity across both consumers and commercials using a hierarchical Bayes approach, is estimated on two datasets in which commercial viewing is captured by a passive online device that continually monitors a household's TV viewing. Consistent with previous findings in psychology about the nature of attentional engagement in TV viewing, baseline hazard rates are found to be non-monotonic. In addition, the data show considerable ad-to-ad and household-to-household heterogeneity in zapping behavior. While one of the datasets contains some information on characteristics of the ads, these data do not reveal any firm links between the ad heterogeneity and the ad characteristics. A number of methodological and computational issues arise in the hierarchical Bayes analysis.Heterogeneity, hierarchical Bayes, marketing,

Interactive Advertising: The Case of Skippable Ads

“…We know from Proposition 4, part (ii) that it is optimal for the platform to raise ad levels under the skippable regime. Specifically, the optimizations in (11) and (13) imply…”

“…To establish (i) and (ii), we evaluate the optimization conditions implied by (11) and (13). Noting that V CR i is independent of N for i ∈ {skip, trad}, we know that N i maximizes:…”

“…Marketers can also make an advertisement worthy of watching (e.g Gustafson and Siddarth 2007. and Teixeira et al 2010).…”

TV Viewing and Advertising Targeting

“…Identified viewer-specific factors include household category purchase history (Siddarth and Chattopadhyay 1998) and the media weight of a campaign (i.e., the number of times that a household had previously been exposed to a commercial) (Gustafson and Siddarth 2007). Identified ad-specific factors include the frequency of the commercial (Siddarth and Chattopadhyay 1998), length and content of the commercial (Siddarth and Chattopadhyay 1998), program genre (Schweidel and Kent 2010), and commercial location (Gustafson and Siddarth 2007), as well as the congruity between the commercial and the program (Furnham and Price 2006).…”

Zapping Index:Using Smile to Measure Advertisement Zapping Likelihood

“…The term zapping implies that the receiver of a commercial is no longer interested in its content/presentation, thus opt not to continue watching the commercial. In [17] a hierarchical Bayes approach is used to analyze the dynamics of attention to TV commercials. It investigates how the likelihood of a commercial zapping varies with time and shows that across-ad heterogeneity in zapping is related to the underlying characteristics of the commercial.…”