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Despite a 20-year trend toward integrated marketing communications, advertisers seldom coordinate television and search advertising campaigns. We find that television advertising for financial services brands increases both the number of related Google searches and searchers' tendency to use branded keywords in place of generic keywords. The elasticity of a brand's total searches with respect to its TV advertising is 0.17, an effect that peaks in the morning. These results suggest that practitioners should account for cross-media effects when planning, executing, and evaluating both television and search advertising campaigns. This paper was accepted by Pradeep Chintagunta, marketing.

As with all forms of advertising, exposure is a necessary prerequisite for Internet banner ad effectiveness. However, exposure does not guarantee a user's attention, an issue especially relevant to the Internet, where ad avoidance occurs most frequently. And if an ad is noticed, the message may or may not remain in the consumer's memory after cognitive processing. However, even if the advertising message is not consciously remembered, the exposure can be unconsciously processed and subsequently change the user's affective state. To investigate how attention levels influence users, this study uses eye tracking to measure the level of attention that results from an advertisement exposure and explores how different levels of attention influence users in conscious and unconscious ways. Also, we examine the effect of animation-one of the most popular attention-grabbing tools-on attention and how it moderates cognitive processing. By measuring and analyzing users' actual eye-movement data, we found that animation in banner ads not only attracts less attention than static ads but also reduces the positive effect of attention on memory. In addition, although more than half of the participants could not recognize the advertised brand, the animated banner ad was unconsciously processed and did influence attitudes toward the brand. The results suggest that animation in banner ads does not necessarily increase user attention, but that even if a user does not consciously notice a banner ad, the user's attitude toward the brand is influenced.

\"As comprehensive as its predecessor was for print and TV, this ... handbook dives deep into the digital ecosystem, discusses how to best collect and utilize data--the currency of the digital age--to convert sales specifically on screen (phone, tablet, smart watch, computer, etc.), breaks down when and how to market to millennials, highlights the top five current industry giants, suggests best practices from brand response to social media, and offers 13 trend predictions for the future\"--Amazon.com.

It is clear from the growing role of ad exchanges in the real-time sale of advertising slots that Web publishers are considering a new alternative to their more traditional reservation-based ad contracts. To make this choice, the publisher must trade off, in real-time, the short-term revenue from ad exchange with the long-term benefits of delivering good spots to the reservation ads. In this paper we formalize this combined optimization problem as a multiobjective stochastic control problem and derive an efficient policy for online ad allocation in settings with general joint distribution over placement quality and exchange prices. We prove the asymptotic optimality of this policy in terms of any arbitrary trade-off between the quality of delivered reservation ads and revenue from the exchange, and we show that our policy approximates any Pareto-optimal point on the quality-versus-revenue curve. Experimental results on data derived from real publisher inventory confirm that there are significant benefits for publishers if they jointly optimize over both channels. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.2017 . This paper was accepted by Dimitris Bertsimas, optimization.

We consider variants of the online stochastic bipartite matching problem motivated by Internet advertising display applications, as introduced in Feldman et al. [Feldman J, Mehta A, Mirrokni VS, Muthukrishnan S (2009) Online stochastic matching: Beating 1 − 1/ e . FOCS '09: Proc. 50th Annual IEEE Sympos. Foundations Comput. Sci. (IEEE, Washington, DC), 117-126]. In this setting, advertisers express specific interests into requests for impressions of different types. Advertisers are fixed and known in advance, whereas requests for impressions come online. The task is to assign each request to an interested advertiser (or to discard it) immediately upon its arrival. In the adversarial online model, the ranking algorithm of Karp et al. [Karp RM, Vazirani UV, Varirani VV (1990) An optimal algorithm for online bipartite matching. STOC '90: Proc. 22nd Annual ACM Sympos. Theory Comput. (ACM, New York), 352-358] provides a best possible randomized algorithm with competitive ratio 1 − 1/ e 0.632. In the stochastic i.i.d. model, when requests are drawn repeatedly and independently from a known probability distribution over the different impression types, Feldman et al. [Feldman J, Mehta A, Mirrokni VS, Muthukrishnan S (2009) Online stochastic matching: Beating 1 − 1/ e . FOCS '09: Proc. 50th Annual IEEE Sympos. Foundations Comput. Sci. (IEEE, Washington, DC), 117-126] prove that one can do better than 1 − 1/ e . Under the restriction that the expected number of request of each impression type is an integer, they provide a 0.670-competitive algorithm, later improved by Bahmani and Kapralov [Bahmani B, Kapralov M (2010) Improved bounds for online stochastic matching. ESA '10: Proc. 22nd Annual Eur. Sympos. Algorithms (Springer-Verlag, Berlin, Heidelberg), 170-181] to 0.699 and by Manshadi et al. [Manshadi V, Gharan SO, Saberi A (2012) Online stochastic matching: Online actions based on offline statistics. Math. Oper. Res. 37(4):559-573] to 0.705. Without this integrality restriction, Manshadi et al. are able to provide a 0.702-competitive algorithm. In this paper we consider a general class of online algorithms for the i.i.d. model that improve on all these bounds and that use computationally efficient offline procedures (based on the solution of simple linear programs of maximum flow types). Under the integrality restriction on the expected number of impression types, we get a 1 − 2 e −2 ( 0.729)-competitive algorithm. Without this restriction, we get a 0.706-competitive algorithm. Our techniques can also be applied to other related problems such as the online stochastic vertex-weighted bipartite matching problem as defined in Aggarwal et al. [Aggarwal G, Goel G, Karande C, Mehta A (2011) Online vertex-weighted bipartite matching and single-bid budgeted allocations. SODA '11: Proc. 22nd Annual ACM-SIAM Sympos. Discrete Algorithms (SIAM, Philadelphia), 1253-1264]. For this problem, we obtain a 0.725-competitive algorithm under the stochastic i.i.d. model with integral arrival rate. Finally, we show the validity of all our results under a Poisson arrival model, removing the need to assume that the total number of arrivals is fixed and known in advance, as is required for the analysis of the stochastic i.i.d. models described above.

Researchers and practitioners devote substantial effort to targeting banner advertisements to consumers, but they focus less effort on how to communicate with consumers once targeted. Morphing enables a website to learn, automatically and near optimally, which banner advertisements to serve to consumers to maximize click-through rates, brand consideration, and purchase likelihood. Banners are matched to consumers based on posterior probabilities of latent segment membership, which are identified from consumers' clickstreams. This paper describes the first large-sample random-assignment field test of banner morphing—more than 100,000 consumers viewed more than 450,000 banners on CNET.com. On relevant Web pages, CNET's click-through rates almost doubled relative to control banners. We supplement the CNET field test with an experiment on an automotive information-and-recommendation website. The automotive experiment replaces automated learning with a longitudinal design that implements morph-to-segment matching. Banners matched to cognitive styles, as well as the stage of the consumer's buying process and body-type preference, significantly increase click-through rates, brand consideration, and purchase likelihood relative to a control. The CNET field test and automotive experiment demonstrate that matching banners to cognitive-style segments is feasible and provides significant benefits above and beyond traditional targeting. Improved banner effectiveness has strategic implications for allocations of budgets among media.