After completing the Google Analytics Academy, I now feel I have a solid foundation of knowledge to experiment and monitor Analytics connected to my teams adwords campaign. The course first provides excellent foundation knowledge and the builds upon that knowledge in part two of the course.
The foundational course was basically a refresher of terminology and basic concepts that we have been hearing most of the semester. Taking this course prior to the Google Analytics Platform Principles course was useful as it "set the scene" for the more in depth information.
Again, now that the GOMC is up and running, I look forward to using Google analytics in a real-world setting.
Wednesday, March 26, 2014
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.
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
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.
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| Visual Representation of Cross-Media marketing Source: http://shawmut.me/tag/cross-media-marketing/ |
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
Wednesday, March 5, 2014
BUS 572 Session 4: Ghose and Yang Sponsered Search Research
The required reading this week was a research paper from Ghose and Yang published in 2009 entitled "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets". In this paper, Ghose and Yang attempt to explain what they call the "phenomenon" of paid advertising (pg. 1605). Although this research is nearly 5 years old, it remains useful as it provides a clear and concise picture of how companies can benefit from proper use of paid advertising. For example, Ghose and Yang's findings, which at times go against prior conventional wisdom, provide valuable insight to businesses using this technology. Two of Ghose and Yang's findings I would like to highlight are: Position profitability and Brand-Specific versus Retailer-Specific keywords.
One of Ghose and Yang's (2009) findings that went against prior conventional wisdom was position profitability, or in other words how an ads position on a web page affects the amount of clicks and therefore amount of conversions. For example, it was previously thought that the best position for ads was at the top of the page. It seems as though the higher position of an ad correlates with higher clicks. Ghose and Yang (2009) pointed out that sometimes, number of clicks is not the most important metric and therefore companies should focus on higher conversion rates. However, ad positioning becomes more expensive moving up the page and clicks become significantly reduced moving down the page. Ghose and Yang (2009) that "profits are often higher at the middle positions than at the top or bottom ones" (pg 1605). It seems as though the middle positions strike a balance between cost and conversion rate.
A second key finding by Ghose and Yang (2009) was how consumers look for brand-specific keywords versus retailer-specific keywords. In general, Ghose and Yang (2009) found that retailer-specific keywords increased click-through and conversion rates and brand-specific keywords decreased click through and conversion rates (pg. 1606). This can prove to be vital information as retailers attempt to distance themselves from their competition.
Questions I have moving forward deal with more of the specifics of running ad words. Now that my team is registered for GOMC, I am curious to see how quickly the challenge progresses. I find myself wondering how much attention is needed to keep our campaign on track. I suppose this information will become apparent as we move forward.
At this point in the class, I feel as though I have a solid foundation to manage an AdWords campaign. I am comfortable with the terminology used and the interface of AdWords. I look forward to gaining more knowledge through practice during my teams GOMC experience.
One of Ghose and Yang's (2009) findings that went against prior conventional wisdom was position profitability, or in other words how an ads position on a web page affects the amount of clicks and therefore amount of conversions. For example, it was previously thought that the best position for ads was at the top of the page. It seems as though the higher position of an ad correlates with higher clicks. Ghose and Yang (2009) pointed out that sometimes, number of clicks is not the most important metric and therefore companies should focus on higher conversion rates. However, ad positioning becomes more expensive moving up the page and clicks become significantly reduced moving down the page. Ghose and Yang (2009) that "profits are often higher at the middle positions than at the top or bottom ones" (pg 1605). It seems as though the middle positions strike a balance between cost and conversion rate.
A second key finding by Ghose and Yang (2009) was how consumers look for brand-specific keywords versus retailer-specific keywords. In general, Ghose and Yang (2009) found that retailer-specific keywords increased click-through and conversion rates and brand-specific keywords decreased click through and conversion rates (pg. 1606). This can prove to be vital information as retailers attempt to distance themselves from their competition.
Questions I have moving forward deal with more of the specifics of running ad words. Now that my team is registered for GOMC, I am curious to see how quickly the challenge progresses. I find myself wondering how much attention is needed to keep our campaign on track. I suppose this information will become apparent as we move forward.
At this point in the class, I feel as though I have a solid foundation to manage an AdWords campaign. I am comfortable with the terminology used and the interface of AdWords. I look forward to gaining more knowledge through practice during my teams GOMC experience.
Ghose , A., & Yang, S. (2009). An empirical analysis of search engine advertising: Sponsered search in electronic markets. (Master's thesis, New York University).
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