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The Advertising Research Foundation and Warc are calling for nominations and applications for the position of Executive Editor of the Journal of Advertising Research. For details, click here!
Celebrating 50 years, the Journal of Advertising Research 50th Anniversary Special Edition is packed with analysis and insights from over 40 internationally renowned academics and industry leaders.
Geoffrey Precourt, Volume 49, No. 4, December 2009, pp.395-396
In his editorial, Geoffrey Precourt discusses the use of metrics to marketers and introduces the articles for this issue of the Journal of Advertising Research.
Provides a bullet-point summary of key learnings and conclusions from the main articles in JAR issue 49,4.
Joel Rubinson, Volume 49, No. 4, December 2009, pp.399-400
In his Viewpoint column, Joel Rubinson discusses the effect of curiosity on humans and how marketers can engage it. He presents factors to consider for building curiosity into products, brand communications and retail activation including creating places to explore or using rich digital media.
Ben Edelman, Volume 49, No. 4, December 2009, pp.401-403
In his commentary, Benjamin Edelman writes on industry fair practice on the use of metrics. He presents the rights of advertisers: the right to know where its ads are shown; the right to meaningful, itemized billing; the right to use its data as it sees fit; the right to enjoy the fruits of its advertising campaigns; and the right to resolve disputes fairly and transparently.
George Lekakos, Volume 49, No. 4, December 2009, pp.404-418
Digital television technology developments provide the unprecedented opportunity to personalize television advertisements enhanced with interactive features on the basis of viewers’ preferences or interests. Existing personalization techniques applied over interactive platforms such as the Web provide the framework for the development of novel personalization approaches that take into account the particular television domain characteristics. In this article, we examine the exploitation of lifestyle as a predictor of consumers’ behavior in combination with dynamic behavioral user-driven data for the development of an efficient personalization approach. The focus is on the extraction of a limited set of variables that model membership in lifestyle segments easily collectible in this environment. The lifestyle indicators are then utilized as a key element in a personalization algorithm for digital television advertisements.
Dan Zigmond, Sundar Dorai-Raj, Yannet Interian and Igor Naverniouk, Volume 49, No. 4, December 2009, pp.419-428
In recent years, there has been an explosion of interest in collecting and analyzing television set-top box (STB) data (also called "return-path" data). As U.S. television moves from analog to digital signals, digital STBs increasingly are common in American homes. Where these are attached to some sort of return path (as is the case in many homes subscribing to cable or satellite TV services), these data can be aggregated and licensed to companies wishing to measure television viewership. Whereas previous television measurement relied on panels consisting of thousands of households, data can now be collected and analyzed for millions of households. This holds the promise of providing accurate measurement for much of the niche TV content that eludes current panel-based methods in many countries. Past attempts to provide quality scores for TV ads have typically relied on smaller constructed panels and focused on programming with very large audiences. This article defines a rigorous measure of audience retention for TV ads that can be used to predict future audience response for a much larger range of ads. The primary challenge in designing such a measure is that many factors appear to impact STB tuning during ads, making it difficult to isolate the effect of the specific ad itself on the probability that a STB will tune away. Several ways of modeling such a probability are proposed.
Eva A. van Reijmersdal, Peter C. Neijens and Edith G. Smit, Volume 49, No. 4, December 2009, pp.429-449
This literature review presents a quantitative synthesis of 57 studies on product placement and shows which factors are most effective. It shows that placement characteristics, such as placement commerciality, modality, and prominence, have a strong impact on audience reactions. Audience characteristics, such as attitudes and beliefs about brand placement, advertising, and media, also shape audience reactions to brand placement. Advertising and psychological theories provide valuable explanations for the majority of the effects. However, the authors call for development of theories on capacity constraints and implicit processing as these can explain effects that are specific to brand placement.
Robert G. Heath, Agnes C. Nairn and Paul A. Bottomley, Volume 49, No. 4, December 2009, pp.450-463
Emotive creativity is generally believed to facilitate communication by increasing attention. However, during relaxed TV viewing, psychology suggests we may pay less not more attention to emotive ads. An experiment conducted in a realistic viewing environment found that ads that were high in emotive content correlated with a 20 percent lower level of attention and that attention toward these ads was unlikely to decline on repeat viewing. This supports the idea that TV advertising is not systematically processed but is automatically processed in response to the stimuli presented. We speculate that emotive creativity may benefit brand TV advertising by lowering attention and inhibiting counter-argument.
Robert Walker, Raymond Pettit and Joel Rubinson, Volume 49, No. 4, December 2009, pp.464-485
This article introduces a measure of television ad quality based on audience retention using logistic regression techniques to normalize such scores against expected audience behavior. By adjusting for features such as time of day, network, recent user behavior, and household demographics, we are able to isolate ad quality from these extraneous factors. We introduce the model used in the current Google TV Ads product and two new competing models that show some improvement. We also devise metrics for calculating a model’s predictive power and variance, allowing us to determine which of our models performs best. We conclude with discussions of retention score applications for advertisers to evaluate their ad strategies and as a potential aid in future ad pricing.
"I absolutely loved the JAR issue on Andrew Ehrenberg.
I read the entire thing."
George Terhanian – Toluna