An Empirical Analysis of the Auction Bids for. Keyword Types in Sponsored Search

Transcription

1 An Empirical Analysis of the Auction Bids for Keyword Types in Sponsored Search By Gabe Rich Faculty Advisor: Igal Hendel Senior Honors Thesis Mathematical Methods in the Social Sciences Northwestern University Spring, 2008

2 Rich 2 Acknowledgements I would first like to acknowledge Randy Heeb whom working with the past summer inspired my research question. Inviting me to work with him and our team got me interested in search advertising and sparked the questions I attempt to answer in this thesis. I would also like to thank my advisor, Professor Igal Hendel, whose advice and experience was invaluable as I worked through the analysis of the thesis. His patience and helpfulness through the entire thesis writing process helped me keep everything in perspective. Lastly, I would like to thank my parents for always supporting and encouraging me through the writing of this paper and my entire life. Their need to always stay on my case was crucial to my ability to have completed these tasks.

3 Rich 3 Abstract This paper evaluates a way advertisers use internet advertising. It focuses on how much advertisers are willing to pay for a given click from a search engine user. Advertisers can pick specific searches on which they would like their advertisement to appear by choosing different keywords. When a keyword is searched, an auction occurs, the advertisers per-click bids are compared, and the advertisements are placed along a sponsored search results area of the web page. Only when an advertisement is clicked by the search user will the advertiser pay the search engine. This paper uses historical bid data given by Google to see what prices and advertisement positions an advertisement would have when advertising on a list of keywords. Using this information, the paper analyses whether advertisers pay more for keywords that are more complex and keywords that include brand names. These factors are ways the advertiser can focus his consumer base. This paper will show that advertisers pay more for specific keywords and less for keywords that include a brand name. This paper also shows that advertisers pay more for keywords that are searched more often for advertisements that are in the highest position than for advertisements in the lower positions. This result might be because only the top advertisement is seen and has a benefit even when not being clicked. So the amount advertisers pay to be viewed in addition to being clicked from that position is shown even greater on keywords with higher search volumes.

4 Rich 4 Introduction When one is researching a potential future purchase on the internet, he often starts by simply typing what he is looking for into a search engine and pressing return. What he doesn t know is how much goes into deciding what comes up on the next page. This statement is not referring to the search results he sees going down the middle of the page. The process of how these are decided and placed is often through a very complicated and secret algorithm. The topic of this paper refers to the little section often placed on the right side of the page titled Sponsored Links or Sponsor Results. The sponsored search industry is an extremely important source of revenue for search engines, and has become one of the largest sources of income on the internet. The system works by providing additional sponsored search results to every search engine user generally along the right side of the search results page. These results are placed differently than the natural results that go down the middle of the page which are chosen to best respond to the user s search inquiry. The search engines make a large portion of their revenue, in most cases, by auctioning off the spots in the sponsored links section to companies to advertise and hyperlink their websites. Because advertisers pay the search engine every time a search user clicks their ad, they, of course, want users who are interested in the products that they have to offer. They control this by choosing a list of keywords on which they would like to advertise. When a search user searches on one of their keywords, the advertiser is put into an instantaneous auction which decides if and where his advertisement will be placed in the sponsored search results. What this paper evaluates is how much advertisers are willing to pay for different keywords. Advertisers, of course, choose keywords that relate to the product or service

5 Rich 5 they offer on the internet because they want users who are searching for that product. This is a simple way that the advertisers segment the population of search engine users into two groups: those that are looking for what the advertiser is trying to sell and those that are not. They can then target their advertising to the former. This paper researches whether advertisers pay more for specific and complex keywords because those keywords can segment and target the population even further. Keywords that are more specific and complex are of more value to any given advertiser because of their ability to further target potential consumers. They will still advertise only on the specific keywords that relate to their products. Therefore they will be even more likely to match the search user s desired search results than when using a less specific keyword relating to their products. Advertisers that realize that advertising on more specific keywords is more valuable to them will bid higher amounts of money for the clicks of people searching on the specific and complex keywords. In addition to this, the paper will also ask whether advertisers pay more for their advertisements that land in the best position (the one at the top of the sponsored search results links) for keywords that are searched more often. One hypothesis might be that the advertisers who are in the first position are most benefited by the search user reading the advertisement, even if it is not clicked. This may have added value to keywords with high search volumes because those advertisements will be seen most often. Introduction to Google s AdWords Google s AdWords is the program that Google uses to sell advertising spots on the internet. Advertisers subscribe to the service to advertise on Google and other

6 Rich 6 websites within Google s advertising network, the Search Network and the Content Network 1. For the purpose of this study, I will only refer to the part of system that involves advertising on Google s Search Network, specifically on search results pages. This means that advertisements are placed on specific search results pages chosen by the advertiser. To advertise in the Search Network the advertiser chooses which search results pages it would like to advertise on by choosing a list of keywords. When these keywords are searched, the advertisement is eligible to be shown on the sponsored results of that user s search. Because each search results page has spots for only up to eleven advertisements 2, Google uses a mechanism to determine which advertisements get positions, and which positions they get. The way Google, in addition to most of the other search engines, choose which eligible advertisements are shown is through an auction. However, its auction uses a combination of factors to determine which advertisements get which position on the search results page. (The top position, being the best, will be seen and, probably, clicked most often.) Although different search engines use different ranking criteria for the placement of ads within the auction, the one that Google AdWords uses has been shown by previous researchers (Feng, Bhargava, and Pennock, and Lahaie) to be the most stable and best for the revenue of the search engine. The way the advertisements are ordered on the page is by an Ad Rank, which is the product of two factors. The first is an advertiser s maximum cost-per-click (CPC) bid, which is the most an advertiser is willing to pay for each time a user clicks his 1 This Content Network involves advertising on partner sites with Google by matching an advertisement with the contents of websites. Though this also uses bidding, it does not involve bidding on keywords to land on search results pages. 2 More spots are allocated on subsequent search results pages, but each spots is viewed less as it is placed further from the front of the search.

7 Rich 7 advertisement. (Maximum CPC bids can be placed for an advertiser s entire group of keywords or be set individually for each keyword.) The other factor is a quality score that uses a combination of the advertiser and, specifically, the to-be-displayed advertisement s AdWords history and how well the advertisement matches the search query of the search engine user. Because the Ad Rank for each advertisement is created in real time, a new order may be made for each search that happens on Google. An important distinction is that the bid is on a cost-per-click and not a cost-perimpression basis. This means that the advertiser s bid will only be paid when the search user clicks the advertisement and is redirected to the website to either learn more about the product or service or make a purchase. In this manner, advertisers are only charged when a potential customer accesses their websites. This also means that advertisements that are never clicked will not have to pay Google. For this reason, Google also uses a quality score as part of the advertiser s bid. The quality score that is used is determined by a few factors. Though Google does not explain exactly how the score is made it does explain the main contributors to it. The most important factor is the advertisement s historical click-through-rate (CTR), which is the proportion of how many times the advertisement is clicked to how many times it is shown. The other major factor is the relevance of the search query to the advertisement itself and the keyword the advertiser chose. Because most advertisers choose their keywords to broad match the search queries, the advertisement will show on any search using a combination of the words in the keyword the advertiser chose to advertise on. For this reason, search queries may match better with some advertisers keywords than others. For example, an advertiser choosing to advertise on the keyword

8 Rich 8 car parts will be eligible for the search query What parts of the world does my driver s license allow me to drive a car?, but will have a worse keyword match component of the quality score than an advertiser with the keyword world driver s license. Google uses each of the main factors in the quality score for a reason related to the probability a user will click the advertisements. The reason for using the CTR is so the ad rank score is closer to ranking the advertisement by expected revenue (Feng, Bhargava, and Pennock.) This is because using the product of the CTR and the CPC bid would make an estimate of how often it is clicked times how much the search engine makes per click. In fact, this method is sometimes referred to as a rank-by-revenue method. Using relevancy factors ensures the search results will be attractive to the specific searchers. Google needs to make sure that their searchers continue to find the sponsored search results relevant to what they are looking for, or users will no longer click or look to the sponsored results. The relevancy factor is another way that Google tries to increase the chance that the user will click the sponsored results and generate revenue. Another concept that Google uses in its auctions is what it calls the AdWords Discounter. This service that Google offers to advertisers works in effect to turn the auction into a generalized second price auction. In a normal generalized second price auction, each bidder pays a marginally higher amount than the bid of the bidder below him. (The highest bidder would get the first prize and pay the amount of the second highest bidder. The second highest bidder would get the second prize and pay the amount of the third highest bidder. Etc. [Edelman, Ostrovsky, and Schwarz.]) The

9 Rich 9 Discounter works to have this effect in the auction using the advertisement s quality scores. The way it works is by lowering the amount an advertiser pays enough so that its ad rank would still be higher than the ad rank of the advertiser in the next position. This way an advertiser never pays more than it needs to maintain its same position. This works by lowering the product of the CPC payment and quality score to the amount of the bidder below it and then dividing the quality score from it to determine the actual cost the advertiser would pay for a click on that search. This function allows the advertisers to bid their actual value of a click without worrying about paying too much money because their payment will only be what is necessary to reach the highest possible position. Literature Review The majority of the literature that studies the sponsored search industry has been developing and evaluating theoretical models that correspond to the different auction methods that search engines use to determine the positioning and prices for the advertisers. This research is helpful in showing exactly what advertisers bid prices represent. Previous research has shown that the auction method Google uses weighted by CTR is the most stable in showing bids that are closest to the true values of clicks to advertisers. The research done by Feng et al. explains that the method Google uses with Click-Through-Rates to determine ordering is close to inducing truthful bids from advertisers. Their findings are that when using past CTRs for an advertiser, they should control for the position in which an ad is placed. By doing this the search engine can fairly attribute CTRs to ads despite wherever they are initially placed along searches. By

10 Rich 10 modeling this mechanism, Feng et al. show that the best way to evaluate how much revenue ads will make for a search engine will take an advertisers CTR into account when ranking ads. This assurance demonstrates the importance of rank-by-revenue system that Google uses and reinforces the use of Google estimates in this study of advertisers willingness to pay for a given keyword. Lahaie also discusses the closer relationship with the Google method to having bidder s true values, while explaining that with some circumstances there are no equilibriums for bidder s acting within the auctions rules. In the two papers Edelman and Ostrovsky have published they explain that the second price auction that is used does not have an equilibrium of bidders truth-telling their values in the bids. They explain how the outcome of the generalized second-price auction is different than the Vickrey-Clarke Groves mechanism because the pricing methods differ. They go on to say that the VCG method would induce truthful bids in Internet Advertising and the Generalized Second-Price Auction and continue to discuss how bidders dynamically change their bids depending on their competitors for each keyword in Strategic Bidder Behavior in Sponsored Search Auctions. This second publication developed a theory of bidders moving along a spectrum of bid values depending on where their competitor was and observed this behavior among different keywords in both major search engines. Thus, bid amounts fluctuate over cycles of time as the competitors respond to each others actions and move continually. Though this knowledge would skew the results if this analysis were using real, instantaneous bid data, I assume that the bid estimates Google gives to prospective advertisers averages out these effects. Because the website claims that some of the estimates they give for CPCs will

11 Rich 11 reach the top position 85% of the time, I will assume that these estimates are based over a long enough time period to be confident that the numbers accurately represent an average of what advertisers are paying for the given keyword. I also assume that these effects would not be different over different keywords or would correlate directly with the level of competition within each keyword. Because Google gives a metric of competitive level for each keyword, whatever effect that the bid fluctuation will have on the data will be accounted for by using that rating as a variable in the regression on each keyword s value. Another study done by Zhou and Lukose that might skew the results of this study show that advertisers might act vindictively and choose bids that would not necessarily directly benefit themselves, but do more harm to their competitors. This works by raising ones own bid to increase the CPC of the advertiser in a higher position. Because the auction is a generalized second price auction, the vindictive bidder will not see higher CPCs because the advertiser below has not changed its bid, but the advertiser above will see a raised CPC effect 3. Therefore a competitor can raise its price to incur a higher cost to the advertiser who has the spot above him. Not only does this make the competitor lose money, but because many advertisers put spending limits on their accounts, the victim advertiser may reach its limit and no longer display ads. The vindictive bidder then benefits by having the better advertising spot without having to pay a larger amount for it, because the original advertiser is no longer bidding for a position. Knowing that advertisers bid this way would change Google s estimates for bids at lower positions, and 3 This is different than click fraud, which is clicking many times on someone else s ad to run up their advertising bill and reach their account cap. Though this can be done, search engines explain that there are systems in place to monitor and stop click fraud from occurring and advertisers accounts from being charged for this.

12 Rich 12 possibly from the top position if many advertisers reach their limit often. Though vindictive bidding would have an affect on a study using historical bid estimates, I make two assumptions about why the historical estimates used in this study can be representative of advertisers true value of a click from different keywords. The first assumption is that vindictive bidding is very hard to accomplish on keywords with many bidders. It is difficult to target where your opponents bids are 4 and to consistently be in an advertising position to have the desired effect. I also make the assumption that if vindictive bidding does occur and would have an affect on historical bid data compared to advertisers true values, then this will be consistent with how much advertiser competition there is for a given keyword. Again, by including the factor for the level competition there is for each keyword as another independent variable, this effect on CPC prices can hopefully be taken out of the regression. These studies which create and use theoretical models demonstrate that the generalized second price auction works in search engines advertisement sales and that the bids advertisers offer is the assumed value of a click to the advertisers. This paper attempts to determine some of the reasons behind why advertisers bid certain amounts for different keywords, and what makes clicks from some keywords more valuable than others. Data Being Used The data used for this paper was gathered from tools in Google AdWords which give information on the performance of keywords within the AdWords product. The data 4 Google explains that advertisers cannot see their competitors bids.

13 Rich 13 comes from two different outputs on the site that are used to help potential advertisers who might be interested in advertising on Google AdWords. They both provide estimates about what advertisers can expect to pay to advertise on the Google Search Network. Because these estimates are created from historical AdWords data, I assume that the data is an accurate representation of what advertisers paid and experienced while advertising on these keywords. The words chosen for this analysis were also provided by the keyword tools. In addition to the data about the keywords, the AdWords tools also suggest keywords for the advertiser to use. The paper uses these keywords to use the best sample of keywords that are advertised on. The keywords that were used in the analysis come from the AdWords External Keyword Tool located at This program will, when given a keyword or list of keywords, produce additional keywords (up to a total of 200) related to and including the initial listed words. This page also reports, for each keyword, normalized ratings (from zero to one 5 ) of advertiser competition, search volume of the past month, average search volume over the past year, a search volume for each month of the past year, and which month of the last year had the highest search volume for that keyword. When given a maximum Cost-per-Click bid, the External Keyword Tool will also provide an average CPC price the advertiser will pay, and a range of ad positions (first to third, fourth to sixth, or seventh to tenth) where the advertisement will most likely land. This data can be used to determine what advertisers have been bidding and paying per click to have their advertisements shown in certain 5 On the website display the normalized ratings for competitive level and volume are shown in the form of a bar chart, however, when the tables are downloaded, they are each converted to numbers with two decimal places.

14 Rich 14 position groups. These tables of data can be downloaded directly into.csv format from the Google website. A concern of using the normalized data about search volume and level of competition is that the values may only be normalized by the list of keywords being retrieved during any singular use of the External Keyword Tool. In order to make sure the values would at least be consistent over the entire system of keywords, and not just those whose data was being collected in the specific retrieval, I checked keywords on the keyword tool to make sure normalized values for competition and advertiser competition were normalized against the whole system, and not the keywords just in the set. I did this by using some of the same keywords in two different sets of keywords, and then compared the values of the advertiser competition and search volume from each set. Because the numbers were the same for each keyword, this means that the tool does not base these values on a scale within the given keyword set, but rather always gives a consistent value for the estimations. The other source of keyword data provided by Google is provided by the Sandbox Traffic Estimator, which can be found at this site Within this tool potential advertisers can suggest a list of keywords and a maximum CPC bid for the list. Though the site does not provide the user with additional keyword suggestions, it does provide additional information about each keyword and CPC bid combination including a visual metric of keyword traffic, minimum and maximum values for estimated CPC, a range of ad placements similar to the first tool, a range of how many clicks the ad will get at that position, and a range of the cost per day of running the ad at that CPC on that keyword.

15 Rich 15 This tool differs slightly from the first in that the potential advertiser does not need to provide a maximum CPC bid. When that input is left blank, the Estimator will give a maximum CPC estimate that will reach the ad position 85% of the time. This is an important function because it gives a consistent estimate for the highest CPCs advertisers have been paying to reach the top position on the search results page. This estimate will be used as a representative of the CPC price to reach the top position. The dependent variables used for the main regressions in the analysis are all the suggested CPC payments the advertiser would pay. They come from how much the potential Google advertiser would pay to reach the provided position for the given keyword. Though these are explicitly stated as how much current advertisers pay to advertise, they result from historical usage that Google has of their advertisers history. The effect the AdWords Discounter has is that the advertiser would not have to pay more than advertiser below him would pay for the spot, so I assume that these values are on average what advertisers are bidding to advertise on each keyword. Using these as how much advertisers bid, we know how much the advertisers value advertising in each spot for the given keywords. The data actually used for this analysis is taken from three different keyword groups. Each of the three lists is provided from the External Keyword Tool and is generated from one keyword. The initial keywords are stereo, insurance, and therapist; the Keyword Tool provided a list of 200 words for each of these three. I chose these three to be a small sample of different products that people might research online. A stereo being an actual tangible product someone could purchase online. Insurance is a financial service that people can research and purchase online. Thirdly, a therapist is a

16 Rich 16 service most people probably would not purchase online, but could be researched for purchase of the service in the near future and could therefore be profitable to advertise online. These are to represent a small cross section of commercial searches people make on the internet. For each of these 600 keywords, the analysis will use estimates from the traffic estimator and keyword tool for a list of 23 maximum CPC bids ranging from $50 to $.10 to cover the spectrum of where advertisers bid. The increments between the highest and lowest bids are not even but are formulated to best retrieve precise data about CPC payments and advertisement positioning by becoming smaller and smaller as the bids approaches $.10. See appendix for full list of keywords and bids used in collecting the data. It is important to note that the variables provided by the tools do overlap, and the choice of variables from which tool to use can be important. The variables provided by the External Keyword Tool are: average CPC, estimated bin of advertisement position (The bins given suggest the advertisement will be from the first third position, fourth to sixth, or seventh to tenth.), normalized search volumes for the past month, each month in the past year, and the entire past year, and a normalized metric of advertiser competitiveness. The variables given by the traffic estimator are a measure of the estimation on a one to five scale of the search volume of the keyword and minimums and maximums for the estimated CPC, advertisement position location (The bin ranges from the traffic estimator uses the same bins as the keyword tool, but does not stop at three bins. This data goes up to seven including bins for the eleventh to fifteenth spot, sixteenth to twentieth, twenty-first to thirtieth, and thirty-first to fortieth.), expected clicks per day on that keyword, and expected cost per day on the keyword. The appendix

17 Rich 17 includes a list of the position locations that the advertisements would land, and how each is estimated in the regression. Each analysis done in this paper uses variables from both of the sources about each keyword and bid observation, but uses each for a reason described for the specific analysis. Because AdWords ranks and places advertisements in positions based on more than just the CPC bid, the Traffic Estimator explains that the estimates are all based on average historical CTRs. This means that the number of clicks per day the estimator provides is based on the assumption that search users will click the advertisement in the given position in question at the same rate as they had on average in the past. It also means when evaluating what position bin the advertisement will land for each keyword and bid observation, it uses a quality score based on the keyword history. Because historical averages are used to generate the data, I assume that the prices advertisers actually bid and pay are similar to the CPC estimates given by the External Keyword Tool and the Traffic Estimator. In addition to the variables provided by Google, a few are created to be used in the regressions to account for other factors. The first three dummy variables are made to distinguish from which keyword group each keyword came: stereo, insurance, or therapist. By using these variables in the regressions, the inherent differences in keyword value are taken into account. For instance, insurance advertisers might value the click of someone searching for insurance more than stereo advertisers value stereo searchers clicks, because the profit of selling insurance policy is much more than that of selling a stereo. This effect is shown in the regression, and does not influence the regression s measurement of the value of keyword complexity. Another variable added is a word

18 Rich 18 count of how many words are in each keyword. This is a simple measure of the complexity and specificity of the keywords advertisers choose. The last variable added is a brand variable. For each of the six hundred keywords, the keyword was flagged on whether or not it is a brand name of a certain company. This dummy variable determines if the keyword the advertisers are bidding on is a keyword that is an existing company. The variable for how many words the keyword has and the brand dummy variable are the measures of keyword complexity and specification. The number of words does this because with searches in general, each additional word used in the search query narrows the search results to those including the additional word. Therefore someone who enters a search query with more words is looking for something more specific in their search results. The brand dummy variable has this same effect of narrowing the possible results to those produced or sold by the brand name used in the search query. Analysis Max CPC to Reach Top Position The first regression in this paper is on the prices suggested by AdWords to reach the top position. The CPC variable used for this regression is the upper bound of the estimates from the Traffic Estimator when the keywords were input without a maximum CPC bid. The reason for using this variable for the CPC payment and not the estimate from the External Keyword Tool or the lower bound from the Traffic Estimator is because the site describes that the estimates given when no maximum CPC bid is entered will put the advertisement in the top position for searches on that keyword 85% of the

19 Rich 19 time. Because this is explicitly stated on the Google page, the regression uses this as the most consistent price estimator for CPC bids of the top position. In this regression a group of independent variables are used to explain the CPC of the top position for each keyword. The first is the average of the upper and lower bound of the estimation of clicks the advertisement might receive. As explained by Edelman, Ostrovsky, and Schwarz, the actual advertisement location may matter less to the advertiser than the number of clicks it receives, so by using the estimated number of clicks an advertisement would receive in part of the regression, that is an estimate of one of the reasons advertisers might pay a certain amount, even though what they pay is on a per-click basis. The second independent variable is the External Keyword Tool s volume estimator. It does not give an actual number of how often the keywords are searched but is a metric used to represent this factor. This is an important aspect of how much advertisers are willing to pay because the volume, of course, will affect the number of clicks an advertisement will receive. It could also be important to an advertiser because a search user just seeing the advertisement might be of a value, albeit a lesser value than the user clicking it. The next two variables used are measures of the competitive levels on keywords. The first is the variable given by External Keyword Tool to describe the competitive situation in the bidding of the keyword observation. The other is a different variable generated from the AdWords data. Because the data set has observations for each keyword of bids ranging from $50 to $.10, there is a variety of advertisement position location estimates for lower bids. As the bids get lower, different keywords get moved to worse and worse position bins. The second variable that estimates the level of

20 Rich 20 competition in a given keyword is an estimate of how many advertisers bid on that keyword, based on the lowest position bin the advertisement could be in. The number used for the variable is the estimate of the average number of advertisements that are bidding on a keyword, given by the worst bin an advertisement could be placed in for that word. These keywords are both included to best take out the effects of the number of competitors bidding to advertise on certain keywords. The next two variables are the word count and the brand dummy variable. The word count is a measure of the complexity and specificity of the keyword observation. The effect of this variable will be a gauge on how valuable complex and specific keywords are to advertisers. The brand variable is another way the keywords are specified even more, this measure is of specification and uniqueness. Because, theoretically, only one website (the brand s) will actually match the search query, it is interesting to see how much all advertisers are willing to pay to advertise at the top of that keyword search. Because these variables specify and narrow the searches, they are likely to have a much smaller search volume. This is shown in Regression A of the Appendix. For this reason, the prices of keywords that correlate with them could be lower, but by using the search volume variables, the regression counters this affect to actually see how much more valuable more specific keywords are to advertisers independent of how often they are searched. The last variables used in the regression are the dummy variables for what keyword group the observation comes from, insurance, stereo, or therapist. Because the actual value of a customer clicking through to an internet provider of one of these things

21 Rich 21 differs greatly, these variables were included to remove that effect from the analysis of the keyword construction. By using these dummy variables the regression overcomes the inherent differences in values of clicks from people searching for insurance, a stereo, or a sort of therapy. Only the variables for stereo and therapist were included because using all three would cause co-linearity. The constant used in the regression represents the value of advertising on an insurance keyword. The regression is as so: regress cpcmax dayclicksavg avgvol comp num_advts words brand stereo therapist cpcmax Coef. Std. Err. P>t dayclicksavg avgvol comp num_advts words brand stereo therapist _cons The R-squared of this regression is.531. The variables that are significant (at the 95% confidence level) in this regression are the number of clicks, the search volume, the number of words in the keyword, the brand dummy variable, and, of course, the identifying variables from what keyword list each came. The two insignificant variables are the two measures of the level of advertiser competitiveness in the keyword. The positive coefficient for search volume shows that advertisers who are paying for the first advertisement position value how often the keyword is searched. This contrasts with the negative coefficient for the number of clicks an advertisement receives. It shows that advertisers want the ads shown more often, but not necessarily clicked the most when they are in the top position. This could make sense because as the advertiser tries to reach the top position, his CPC bid and therefore CPC payments will rise. So

22 Rich 22 both the CPC payment and number of clicks or times he would pay the CPC both increase, making the total payment to the search engine much larger. Because of this larger overall increase in payment, some advertisers may not find paying for the top position of a keyword whose advertisements get clicked very often worth its price. An interesting observation of the explanatory variables used to measure word specificity is the positive coefficient for the number of words but the negative coefficient for the brand variable. This shows that advertisers are paying more for keywords with more words, but less for the brand names. Because advertisers want clicks form people who are genuinely interested in the products offered, the more specific a search, the more likely a purchase will be made, and the more valuable the click is. This is why advertisers are willing to pay more for keywords that results in specific search queries. The reason the specificity of a consumer s search for a specific brand may not be valuable to advertisers is probably for reasons related to what the consumer is actually looking for. One reason is that the consumer is looking for that brand, so he might not be interested in a competitor s advertisement. In addition to that, his search may result in the brand s website being given by the search engine s natural results. Specifying a certain type of product through using specific keywords will get customers with more specific and concrete purchasing intentions. If a customer uses a brand name, however, the natural search results are likely to bring up exactly what the search user is looking for. Because of this sponsored results become obsolete compared to the natural search results. This next regression of the number of clicks on a brand name and the volume of a search shows that a brand name in the keyword significantly reduces the number of clicks advertisements receive in the highest position.

23 Rich 23 regress dayclicksavg brand avgvol dayclicksavg Coef. Std. Err. P>t brand avgvol _cons These results show how the number of clicks can drop by an estimation of 645 clicks a day if the keyword being searched has a brand name. This regression also takes into account how often the advertisements are shown by including the average search volume of the keywords, so it focuses of the click-through-rate of advertisements on brand keywords. CPCs Over All Advertisement Positions Another way to see how the complexity of keywords can affect how much advertisers pay for them is to examine how much advertisers paid across the board to advertise on keywords, not just how much they paid to reach the top position. The main difference between this regression and the one done before is that instead of having one observation for each keyword to reach the top bid, this regression includes up to 23 for each keyword to explore advertisers CPC payments over all of the position spectrum and uses their estimated advertisement position as an additional independent variable. The dependent variable for this regression is estimated CPC payment. For consistency s sake, the same variable is used from the data as the first regression, the maximum estimated CPC value provided by the Traffic estimator. This allows direct comparisons of the results from this regression to those from the regression using only the values to reach the top advertisement position.

24 Rich 24 In order to run this regression two more new variables needed to be created. This first is an independent variable, ad-position. This variable is calculated very similarly to the number of advertisements from the previous regression; however, it uses the estimated position given by AdWords for the advertisement with that specific bid observation. It uses the lower and upper bound for estimated advertisement position to create an average position at which the advertisement would land, given that observation s bid. This variable is added to control for the wide variance in CPC values across the bids ranging from $50 to $.10. Because of the inclusion of this variable, I did not include the bottom advertisement number as an additional measure of competition in the regression. At lower bids, those two variables would begin to converge, because the lower bids would cause the advertisement position to drop to its lowest position. For this reason, the only measure of advertiser competition is the one provided by External Keyword Tool. The second variable used for this regression is used as a filter. Because the bids used to make the data observations begin at $50 and move down, a portion of them are extremely high bids, even for the most expensive keywords. The average CPC of the keywords over this bid range is $1.80, and the maximum of any keyword is under $37. For these reasons, many of the bids on keywords are extremely high, and including them in the regression might skew the results. In order to compensate, a variable filter is used to prevent the over-bids from being included in the regression. This was done by identifying what the highest maximum CPC payment estimate for each keyword is, and then tagging each of the bids higher than it. For example, say the CPC estimates for the keyword acceptance insurance provided by AdWords for the bids of $50, $35, $25, and

25 Rich 25 $20 are all $5.84, but the CPC estimate for the $15 bid is $5.79. This converted to the observations whose bids are $50, $35, and $25 are all tagged as over-bids. This way the $20 at $5.84 bid is still included in the regression. This rest of the variables in the regression are the same as the previous regression of the CPCs to reach the top position. regress cpcmax dayclicksavg estadpos avgvol comp words brand stereo therapist if overbid == 0 cpcmax Coef. Std. Err. P>t dayclickavg 7.62E E estadpos avgvol comp words brand stereo therapist _cons The R-squared value for this regression is This means that even including the variable for the expected advertisement position, these variables explain much less about the CPCs across the spectrum of advertiser bidding than the regression on just the payments to get to the top position. The regression shows similarly that the value of a click over all the bids are affected by the word count and brand factor in the same direction as they affect the top bid. However their coefficients have, on average, a little less than half the effect on all CPCs as they do on the ones to reach the top positions. This most likely has to do with the smaller average of CPCs of the bids over than the ones to reach the top. This effect can also be seen in the decrease in the size of the coefficients for the stereo and therapist identifiers. Though they both still decrease the value of the word (from a keyword tagged

26 Rich 26 in the insurance category) the coefficients have a smaller effect on the entire range of CPCs. A major difference in this regression is that search volume is no longer significant and that the number of clicks is now significant and positive. For positions other than the first, a larger number of clicks becomes more important, whereas at just the first position a large number of clicks makes the keyword less valuable. It seems that beyond being in the top position of the sponsored search results, the amount people are willing to pay to advertise on keywords does not depend on how often the keyword is searched as much as how often they are clicked. These differences, in the value of number of clicks and search volume, between the advertisers paying for the top position and the advertisers paying for the other positions could result from the added benefit of a search user viewing advertisements in the top position. It is possible that the only advertisement that gets any benefit to being on a search results page that is viewed but receives no clicks on the sponsored advertisements is the advertisement in the top position. This makes sense because a search user might look at the first advertisement on the list without deciding to click any of them. The search user would still have seen the first advertisement, and that is a value to advertisers. The top position gains an additional value to high volume search words because those are more times the advertisement is viewed. In this way, the amount the advertiser pays for each click could be more than his actual value of click, because the value of being in the position is from both the clicks he pays for and the unrecorded impressions that he receives.

27 Rich 27 This effect is clear from the data in this and the first regression. Advertisers value the added benefit of more impressions when they have the highest position. In this manner, the advertisements in the top positions are advertising similarly to that of more traditional advertising mediums like radio, print, and television. The goal of those types is to get exposure of the product and the name of the company, compared to the added new benefit of advertising by linking to the website. Though the benefit of these impressions is much less traceable, it can still be of great value to advertisers. Conclusions The results of this analysis show three main findings. Advertisers do in fact value more specific keywords for all different advertisement positions, when controlled for how often the keywords are searched and clicked. Specificity brings customers who are more likely to be interested in the products the company offers and are, therefore, more valuable to reach than those searching less specific keywords. Search users who search a specific brand, however, are less valuable. This is most likely because the sponsored ads are clicked less often because of the likelihood of the natural search results containing the desired websites. These effects are shown in both the CPCs to reach the highest advertisement position and in the CPCs to reach any position. The effects of the keyword complexity and brand name are smaller on the CPCs to reach any position. This can be explained by the much larger CPC values to reach the highest position than to reach any position. The number of words in a keyword and whether it is a brand name both have smaller effects on the smaller CPC values.

28 Rich 28 The third finding is that advertisers are willing to pay more, specifically for the top position, when there is a large search volume. An explanation for this is because the top position gets the added benefit of being seen much more often than the other advertisements. This fact makes having the best position much more valuable for keywords that are searched often, because this means the keywords are seen more often. This effect makes the top-position advertisements (when they are not clicked) function much more like traditional advertising through television or print. The user only sees the name of the brand and it registers in their memory. What this means for potential advertisers is a couple of things. Though this research does not discuss the success of the companies who are advertising on more specific keywords, it shows that previous advertisers do know that specific keywords are more valuable. Though new advertisers would have to pay more for these, the knowledge that other advertisers have done this shows that specific keywords might be worth the extra cost. The impact to advertisers is that it might be more important to advertise to specificity to find the right customers, rather than to generality for a larger amount of clicks. Recommendations for Future Research Some of the ways this topic could be researched further would involve better data measuring some of the variables. Because the data was retrieved using only the tools Google provides to potential advertisers, more accurate measures were not possible. One of these would be more developed measurements of the keywords specificity and complexity. By using a linguistics algorithm or an expert opinion to determine how

29 Rich 29 specific the keywords are, one would have a much more accurate variable describing how specific keywords are. Instead of just using how many words are in a keyword, the actual words could be measured on how specific they are to the keyword s group, and that would be a better way to analyze the effect of specific keywords. Another variable that could be more precise is the variable for search volume. The one used in this analysis was on a one to one hundred scale. If the actual search volume of the keywords were used, many more variables could be more accurate. One could create a theoretical click-through-rate for the words, and this could be used as a measure of the quality score. Using this value, a researcher might be able to delve further into the details of Google s ad rank auction system. A last variable of data which could be very informative but would be hard to retrieve is the conversion rate for advertisers. This is the percentage of times the ad is clicked that the search user actually moves through with the process and makes a purchase from the advertising company. With this data, one might be able to tell more about how the way the user searches relates to how he follows through with the advertising company. By analyzing these success rates, better recommendations could be made to advertisers to actually see how keyword complexity affects their return on investment and profitability of advertising.

30 Rich 30 Appendix Word List Insurance Stereo Therapist acceptance insurance american hi fi acne treatment aetna health insurance american hi fi lyrics addiction counseling aetna insurance amp adolescent counseling affordable health insurance amplifier alternative therapy affordable insurance amplifiers american physical therapy association all state insurance amps anger management american family insurance apples in stereo animal assisted therapy american insurance appliances anxiety annuities audio anxiety therapist annuity audio adrenaline apartment therapy auto audio amplifier aquatic therapy auto insurance audio bible aroma therapy auto insurance companies audio book art therapist auto insurance company audio books art therapy auto insurance quote audio cable asian massage auto insurance quotes audio cables behavior therapy auto owners insurance audio clips behavioral therapy automobile audio codec cancer treatment automobile insurance audio codecs chelation therapy boat insurance audio com chemo therapy business insurance audio control child counseling buy insurance audio converter child counselor california department of insurance audio device child therapist california insurance audio driver child therapy car audio drivers cognitive behavior therapy car ins audio editing cognitive behavioral therapy car insurance audio editor cognitive therapist car insurance quote audio engineering cognitive therapy car insurance quotes audio equipment colon therapy car insurance rates audio express color therapy car quote audio files consumer credit counseling cars audio hijack counseling center cash advance audio interface counseling psychologist cheap audio mixer counseling services cheap auto insurance audio recorder counseling therapy cheap car insurance audio recording counsellors cheap health insurance audio research counselor cheap insurance audio review counselors chubb insurance audio software couple counseling citizens insurance audio speakers couples counselor claims audio systems couples therapist cna insurance audio technica couples therapy cobra insurance audio visual cranial sacral therapy