The first concept this week for me is an empirical analysis of search engine advertising from Ghose and Yang's article that our team presentation is based on it. The phenomenon of sponsored search advertising—where advertisers pay a fee to Internet search engines to be displayed alongside organic (nonsponsored) Web search results—is gaining ground as the largest source of revenues for search engines. For example, using a unique six-month panel data set of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different sponsored search metrics such as click-through rates, conversion rates, cost per click, and ranking of advertisements. I have found this very useful because it makes me to think about choosing to use brand specific keywords or retailer specific keywords. But as the writers said in this article, it still has some limitations, such as lack of data. So are the results that they get persuasive?
The second most important concept I learned is the Google Display Network from the case article of Jordan Brand. The Google Display Network offers text, image, rich media, and video advertising on Google properties, YouTube, and millions of web, domain, video, gaming, and mobile partner sites. From mass media to niche sites, advertisers can find the most engaged audiences, place ads on the most relevant pages, and achieve performance at scale through our innovative targeting technology. Using our tools, advertisers can build ads, measure results, optimize campaigns, and expand their advertising reach to specific audiences all over the web. I thought this example is useful because it shows the positive impact of Google's Display Network on businesses and our team can learn from it. So are there any more good examples for us to follow or understand?
The most important skill is the Google Analytics that is a service offered by Google that generates detailed statistics about a website's traffic and traffic sources and measures conversions and sales. The product is aimed at marketers as opposed to webmasters and technologists from which the industry of web analytics originally grew. It is the most widely used website statistics service. It's useful because it can track visitors from all referrers, including search engines and social networks, direct visits and referring sites. It also displays advertising, pay-per-click networks, email marketing and digital collateral such as links within PDF documents. Will any groups that wined the campaign in the past want to share their experience about observe the data?
This week's article is more difficult for me to understand, especially the equations and calculations in Ghose and Yang's article. But the good thing is the quiz associated with this article I did was satisfactory. In team works, I think I really pay less than my team members who did a lot of favors for me. With their helps, I can easily finish our work before the due time. Thank you for their works. Besides, I think I need to pay more attention on Google analytic because I was confused about it at first, so I believe we could get a positive outlook in the near future.
