BUS 572 Session 5: Lewis, Rao and Reiley Paper (2013)

After reading "Measuring the Effects of Advertising: The Digital Frontier" written by Lewis, Rao, and Reiley.  It is clear, there is a lot unclear about advertising even in today's "digital frontier".  Lewis et al. (2013) argue that while digital advertising offers possible solutions to analytical problems in traditional advertising, more steps need to be taken to make better use of "big data" collection and analysis.  A few solutions offered by Lewis et al. (2013) include use of  "computational advertising" and "cross-derivative" experimentation.

Computational advertising, as described by Broder (2008), is “a principled way to find the best match between a given user in a given context and a suitable advertisement'".  Lewis et al. (2013) differentiate from the traditional idea of specifying who you want to advertise to, against computational advertising in which you "...specify outcome metrics—an end-goal supported by the system—and automated systems determine how to achieve that goal most efficiently" (pg. 19).  In other words, you decide what you what to accomplish with your advertisement campaign and a complex computer program offers solutions on how to achieve a stated goal.

Visual Representation of Cross-Media marketing Source: http://shawmut.me/tag/cross-media-marketing/

Lewis et al. (2013) point out that online advertising does not occur in a vacuum and that clicking on an ad does not show the full picture of the effectiveness of that ad (meaning influences outside of online ads may have led to the click) and conversely, not clicking on an ad does not mean a conversion did not occur.  For these reasons, Lewis et al. (2013) propose that for online advertising to reach a higher potential, "cross-derivative" experimentation needs to occur (pg. 22).  Lewis et al. refer to cross derivative as spanning multiple media sources (television, gaming devices, internet), suggesting that with a greater picture of a consumers behavior the effectiveness of online ads may increase.

With the majority of BUS572 behind me, I feel as though the foundational knowledge built in the first half of the class has led to a better understanding of the academic journals assigned in the last two classes.  It has been helpful to not get caught up on the technical terminology used in these papers, so that more focus can be turned to the general purpose of the paper.

Questions I have at this point remain similar to my last post.  What will the GOMC be like once it begins?  I am hoping for my team to receive the $250 credit in the next few days, so that I can apply the knowledge gained in BUS572 to a real word adwords campaign.

Broder, A. (2008). Computational advertising and recommender systems. In Proceedings of the 2008 ACM conference on Recommender systems, pages 1–2. ACM.

Lewis, R., Rao, J., and Reiley, D. (2013). Measuring the effects of advertising: The digital frontier. National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w19520.ack