Online Display Advertising: Targeting and Obtrusiveness

By Avi Goldfarb, Catherine E. Tucker. Marketing science (Providence, R.I.), 2011

814 citations recorded by the source corpus.

We use data from a large-scale field experiment to explore what influences the effectiveness of online advertising. We find that matching an ad to website content and increasing an ad's obtrusiveness independently increase purchase intent. However, in combination, these two strategies are ineffective. Ads that match both website content and are obtrusive do worse at increasing purchase intent than ads that do only one or the other. This failure appears to be related to privacy concerns: the negative effect of combining targeting with obtrusiveness is strongest for people who refuse to give their income and for categories where privacy matters most. Our results suggest a possible explanation for the growing bifurcation in Internet advertising between highly targeted plain text ads and more visually striking but less targeted ads.

Subfields: Marketing, Sociology and Political Science, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/75a23a6dfbd3f6331ef8b712a9fd4f2a22ccb7f9

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Citations in the corpus

Effects of Internet Display Advertising in the Purchase Funnel: Model-Based Insights from a Randomized Field Experiment (2015, methodology): “As we have noted, selection effects can bias common correlational estimates of display advertising’s effectiveness (Goldfarb and Tucker 2011; Lewis, Rao, and Reiley 2011).”
Fair Personalization (2017, background): “Content selection algorithms take data and other information as input, and – given a user’s properties and past behavior – produce a personalized list of content to display [3, 11, 13, 17] (see Figure 1).”
The economics of advertising and privacy (2012, background): “This tradeoff has now been shown to be real and substantial in Goldfarb and Tucker (2011c). That paper studies empirically the economic effects of privacy regulation for the advertising-supported internet.”
Controlling Polarization in Personalization: An Algorithmic Framework (2019, background): “Content selection algorithms consider a user’s properties and past behavior in order to produce a personalized list of content to display [21, 27].”
Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments (2017, background): “Previous research has examined and questioned the effectiveness of display advertising (Goldfarb and Tucker 2011; Hoban and Bucklin 2015; Lambrecht and Tucker 2013; Manchanda et al. 2006; Reiley et al. 2011).”
Estimation of Treatment Effects from Combined Data: Identification versus Data Security (2015, methodology): “This is clearly shown in both the lab experiments in Gross and Acquisti (2005), Acquisti and Grossklags (2008), as well as in the real-world environment in Acquisti, Friedman, and Telang (2006), Miller and Tucker (2009) and Goldfarb and Tucker (2010).”
Predicting Pre-click Quality for Native Advertisements (2016, background): “In display advertising, several studies [10, 11] suggest that excessive animation or high level of intrusiveness can have an undesirable impact on the ad effectiveness.”
Optimized Cost per Click in Taobao Display Advertising (2017, background): “Online advertising [6, 9, 13, 14], a marketing strategy involving the use of the internet as a medium to obtain website traffic and target, and deliver marketing messages to the right customers, has experienced an exponential increase in the growth since the early 1990s.”
Social Networks, Personalized Advertising, and Privacy Controls (2013, background): “However, research has shown that consumer privacy concerns affect the performance of targeted ads (Goldfarb and Tucker, 2010a).”
Too Much Data: Prices and Inefficiencies in Data Markets (2019, neutral): “One branch of this literature focuses on the use of personal data for improved allocation of online resources (e.g., Bergemann and Bonatti [2015], Goldfarb and Tucker [2011], and Montes et al. [2019]).”