Wearout or Weariness? Measuring Potential Negative Consequences of Online Ad Volume and Placement on Website Visits

Abstract

The global importance of online advertising calls for a detailed understanding of consumer-specific responses to online ad repetitions. A key concern for advertisers is not only whether some consumers display degrees of “wearout” but also whether they can surpass a point at which additional exposures have a negative marginal effect: “weariness.” The authors examine a large-scale advertising campaign aimed at driving viewers to a target website, which comprises more than 12,000 users across over 400 websites. These data are analyzed using a flexible discrete mixture specification that accommodates different response shapes over ad stock and timing and parcels ad viewers into response classes based on their internet usage metrics. The resulting classes display varying degrees of wearout, with one subgroup, accounting for about 24% of the sample, evincing weariness. The model also estimates differential publisher effectiveness, with the most effective publisher being nine times more effective than the one 26 places down. The authors demonstrate that the finding of weariness is robust to all the model’s main components, with one key exception: heterogeneity in users’ ad response. Analysis further suggests that an appropriate “profiling and capping” strategy can improve ad deployment by as much as 15% overall for these data.

Inefficiencies in Digital Advertising Markets

“…Advertising effects may be nonlinear in the number and types of ads a consumer sees. Marginal effects of ads vary with wear-in, wear-out, or weariness (Chae, Bruno, and Feinberg 2019; Schmidt and Eisend 2015); competitor advertising (Danaher, Bonfrer, and Dhar 2008; Shapiro 2018); and ads in other media (e.g., Joo et al 2014; Lewis and Nguyen 2015; Naik and Raman 2003). The inability to measure all of a consumer’s advertising exposures makes it difficult to obtain a fully accurate view of ad effects in many settings.…”

Platform diffusion at temporary gatherings: Social coordination and ecosystem emergence

“…For each developer in our sample, we collect data from LinkedIn and GitHub on their skills, education, and programming experience. Using these observable developer characteristics, we apply Latent Class Analysis to classify each developer into mutually exclusive categories (Chae, Bruno, & Feinberg, 2019; Rawlings & Friedkin, 2017), which yields three classes of developers, based on developer observables for their technological expertise. Novice developers are relatively unskilled, have lower levels of post‐secondary education, and have little software development experience.…”

Online Museums Segmentation with Structured Data: The Case of the Canary Island’s Online Marketplace

“…The conversion funnel model enables tracking customer behaviour throughout the sales process with the help of properly chosen metrics [5]. In academic and professional literature [43][44][45], we can find many examples of metrics and techniques to measure the different stages of the conversion process. Selection of the proper combination of metrics as criteria to segment the online marketplace is a challenging task even for the most sophisticated marketers [46].…”

“…The first stage of the conversion funnel, i.e., attraction, will be measured with one of the most efficient consumption metrics to measure brand awareness: traffic generation. Through this metric, it is also possible to know the number of visits to a website and its significance [45,47]. Measurement of visitor statistics, according to [44], is a core activity for any business.…”