Click-through rate (CTR) is the most important measure in Internet advertising, and it represent for the percentage of users click on a giving list which can be formulated as Clicks/Impressoions . For example, one ad is delivered 100 times, and one user clicks it, the CTR will be 1%.
Due to the important role CTR plays in paid search, many researchers started to research what users’ behavior when they evaluate Internet contents, and also they studied the influencing factors and forecast model of CTR. Tombros, Ruthven, and Jose (2005) reported that there were 5 categories (text, structure, quality, non-textual items, and physical properties) used by the searchers to determine the utility of Web documents. Matthew et al. (2007) considered term, ad, order, and external features as factors to CTR, and used these variables in predicting CTR for new ads. In their model, the CTR has been improved 42% under over 1000 views. Furthermore, In 2008, Anindya et al. (2008) found in their experiments that the presence of retailer specific information in the keyword increases click-through rates, and the presence of brand-specific information in the keyword increases conversion rate .
Although there many related researches, most of them develop the algorithms in experimental environment and ignoring the complicated scenario in reality. In this research, we will depict in current market and analysis that outcomes of its bidding cost and exposures and CTR performance.
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Yahoo! and Google have traditionally employed different mechanisms to determine the placement of the advertisement (Animesh et al., 2007), and Yahoo! adopted rank by bid” (RBB) mechanism and Google adopts rank by revenue” (RBR) (Lahaie, 2006). It has been found that ranking paid placement links by the product of willingness to pay and relevance is better, in most cases, than ranking by willingness to pay alone, which performs best only when the correlation between the provider’s relevance and willingness to pay is large (Juan et al, 2006). Lahaie (2006) proposed an equilibrium analysis revealed that RBB has much weaker informational requirements than RBR, because bidders need not know any information about relevance to play the Bayes-Nash equilibrium. However, RBR leads to an efficient allocation in equilibrium, whereas RBB does not.
According the latest release of Yahoo’s new search marketing platform in 2007, the ranking differs because they start to consider both of the two factors. One is how much advertisers’ willing to pay and the other is quality of advertisements which consists of predicted and previous performances. It allows more relevant advertisements could have upper exposure opportunities even with lower bid.
Although it seems this market is blooming, ranking providers like Google and Yahoo even offer substantial signup bonuses to new advertisers (Abrams, 2006), the influence of increasing bidders in this market is studies by Edelman, when a new advertiser arrives, a search engine’s revenue increases, but the author suggested that adding another bidder is not always preferable to setting an optimal reserve, in multi-unit context. Subsequently, it is easy to realize that too competitive bidding market will not result in a better income bring-in for ranking providers; on the other hand, advertisers also can not be benefited whenever the bid is spurred up to irrational price. The best way to prevent this vicious price competition is to spread constrained budget on various different keywords. Unfortunately, this topic has never been discussed in previous researches.
Posted in Academic Study on Keyword Advertising | Tagged ads rank, CPC (Cost Per Click), CTR (Click-Through-Rate), keyword advertising | Leave a Comment »
Keyword selections are the most critical process when launching advertisements on CPC platform, because it affects the total times of impression and the probability of clicking on. A correct keyword selection can increase ads exposure to target audience and estimate Click-through rate and conversion rate even under limited budget. In general, keyword agency suggests selecting hundreds of keyword a time to when launching ads.
As the result, many researchers started to study on keyword recommendation algorithms and develop keyword selection systems. Dwihananto et al (2007) recommended a new system that learns how to extract keywords from Web pages for keyword advertising, but their system only consider search volume of keywords as their main criteria of selection. Vibhanshu and Kartik (2007) used a web based kernel function to establish semantic similarity between terms and develop a method for generation of several terms from a seed keyword. Yifan and Gui-Rong (2008) proposed a novel keyword suggestion method that fully exploits the semantic knowledge among concept hierarchy. However, these two researches only use algorithms to generate a bundle of keywords without evaluating the performance of each word.
Rusmevichientong and Williamson developed an algorithm that adaptively identifies the set of keywords to bid based on historical performance. The algorithm prioritizes keywords based on a prefix ordering, sorting of keywords in a descending order of profit-to-cost ratio (Paat et al., 2007). This algorithm can increase profits by about 7%. Their formulation assumed that the bid price of each keyword remains constant and the ad will always appear in the same spot on search result page. In other words, they did not put cost into consideration, even cost is one of the main issues advertisers care about. Therefore, we will provide a cost and performance perspective to research this topic.
Posted in Academic Study on Keyword Advertising | Tagged CPC (Cost Per Click), CTR (Click-Through-Rate), keyword advertising, Keyword Recommendation | Leave a Comment »