Analyzing Search Engine Advertising: Firm Behavior and Cross-Selling in Electronic Markets

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1 WWW 008 / Refereed Track: Internet Monetzaton - Sponsored Search Aprl -5, 008 Beng, Chna Analyzng Search Engne Advertsng: Frm Behavor and Cross-Sellng n Electronc Markets Anndya Ghose Stern School of Busness New York Unversty New York, NY-00 aghose@stern.nyu.edu Sha Yang Stern School of Busness New York Unversty New York, NY-00 syang0@stern.nyu.edu ABSTRACT The phenomenon of sponsored search advertsng s ganng ground as the largest source of revenues for search engnes. Frms a dfferent ndustres have are begnnng to adopt ths as the prmary form of onlne advertsng. Ths process works on an aucton mechansm n whch advertsers bd for dfferent keywords, and fnal rank for a gven keyword s allocated by the search engne. But how dfferent are frm s actual bds from ther optmal bds? Moreover, what are other ways n whch frms can potentally beneft from sponsored search advertsng? Based on the model and estmates from pror work [0], we conduct a number of polcy smulatons n order to nvestgate to what extent an advertser can beneft from bddng optmally for ts keywords. Further, we buld a Herarchcal Bayesan modelng framework to explore the potental for -sellng or spllovers effects from a gven keyword advertsement a multple product categores, and estmate the model usng Markov Chan Monte Carlo (MCMC) methods. Our analyss suggests that advertsers are not bddng optmally wth respect to maxmzng profts. We conduct a detaled analyss wth product level varables to explore the extent of -sellng opportuntes a dfferent categores from a gven keyword advertsement. We fnd that there exsts sgnfcant potental for -sellng through search keyword advertsements n that consumers often end up buyng products from other categores n addton to the product they were searchng for. Latency (the tme t takes for consumer to place a purchase order after clckng on the advertsement) and the presence of a brand name n the keyword are assocated wth consumer spendng on product categores that are dfferent from the one they were orgnally searchng for on the Internet. Categores and Subject Descrptors J. [Socal and Behavoral Scences]: Economcs General Terms: Performance, Measurement, Economcs. Keywords: Onlne advertsng, Search engnes, Web.0, Herarchcal Bayesan modelng, Pad search advertsng, Electronc commerce.. INTRODUCTION Search engnes lke Google, Yahoo and MSN have dscovered that as ntermedares between users and frms, they are n a Copyrght s held by the Internatonal World Wde Web Conference Commttee (IWC). Dstrbuton of these papers s lmted to classroom use, and personal use by others. WWW 008, Aprl 5, 008, Beng, Chna. ACM /08/0. unque poston to try new forms of advertsements wthout annoyng consumers. In ths regard, the advent of sponsored search advertsements the delvery of relevant, targeted text advertsements as part of the search experence, makes t ncreasngly possble for frms to attract consumers to ther webstes. These keyword advertsements are based on customers queres and are thus consdered far less ntrusve than onlne banner advertsements or pop-ups. In many ways, one could magne that ths enabled a shft n advertsng from mass advertsng to more targeted advertsng. By allottng a specfc value to each keyword, an advertser only pays the assgned prce for the people who clck on ther lstng to vst ts webste. Because lstngs appear when a keyword s searched for, an advertser can reach a more targeted audence on a much lower budget. Hence, t s now consdered to be among the most effectve marketng vehcles avalable n the onlne world. Despte the growth of search advertsng, we have lttle understandng of how consumers respond to sponsored search advertsng on the Internet. In ths paper, we focus on two prevously unexplored questons: () For a gven set of keywords, what s the spread between the optmal bd prces and the actual cost-per-clck ncurred by the advertser n the aftermath of an aucton? () Can frms beneft from -sellng or spllovers n pad search advertsng? Whle an emergng stream of theoretcal lterature n sponsored search has looked at ssues such as mechansm desgn n keyword auctons, no pror work has emprcally analyzed these questons. We adopt the model and estmates from [0] to explore ths dvergence between actual cost per clcks and the bd prce that would maxmze advertser profts. Further, usng a panel dataset of several hundred keywords collected from a large natonwde retaler that advertses on Google, we emprcally estmate the mpact of keyword attrbutes (such as the presence of retaler nformaton, brand nformaton and the length of the keyword) on consumer purchase propenstes a dfferent categores after clckng on a specfc keyword. Ths enables us to evaluate the -sellng potental of sponsored search by trackng the -category spllover effects of a clck-through on a gven keyword advertsement.. DATA Our data s the same as [0] and contans weekly nformaton on pad search advertsng from a large natonwde retal chan, 9

2 WWW 008 / Refereed Track: Internet Monetzaton - Sponsored Search Aprl -5, 008 Beng, Chna whch advertses on Google. The data span all keyword advertsements by the company durng a perod of three months n the frst quarter of 007, specfcally for the calendar weeks from January to March. Unlke most datasets used to nvestgate on-lne envronments whch usually comprse of browsng behavor only, our data are unque n that we have ndvdual level stmulus (advertsng) and response (purchase ncdence). Each keyword n our data has a unque advertsement ID. Once the advertser gets a rank allotted (based on the bd prce) to dsplay ts textual ad, these sponsored ads show up on the top left, rght and bottom of the computer screen n response to a query that a consumer types on the search engne. The servng of a text ad n response to a query for a certan keyword s denoted as an mpresson. If the consumer clcks on the ad, he s led to the landng page of the advertser s webste. Ths s recorded as a clck, and advertsers usually pay on a per clck bass. In the event that the consumer ends up purchasng a product from the advertser, ths s recorded as a converson. The tme between a clck and an actual purchase s kn as latency. Ths s usually measured n days. In the majorty of cases the value of ths varable s 0, denotng that the consumer placed an order at the same tme as when they landed on a frm s webste. Our data conssts of the number of mpressons, number of clcks, the average cost-per-clck (CPC), the rank of the keyword, the number of conversons, the total revenues from a clck (revenues from converson) and the average order value for a gven keyword for a gven week. Gven that these are second prce auctons, the CPC s lkely to be hghly correlated wth the actual bd prce n the case of a successful bd. Whle a search can lead to an mpresson, and often to a clck, t may not lead to an actual purchase (defned as a converson). The product of CPC and number of clcks gves the total costs to the frm for sponsorng a partcular advertsement. Thus the dfference n revenues and costs gves the profts accrung to the retaler from advertsng a gven keyword n a gven week. Our dataset ncludes 57 observatons from a total of 799 unque keywords that had at least one postve mpresson.. Keyword Characterstcs As descrbed n [0], there are three mportant keyword specfc characterstcs for a frm (the advertser) when t advertses on a search engne. Ths ncludes whether the keyword should have () retaler-specfc nformaton, () brand-specfc nformaton, () and the length (number of words) of the keyword. A consumer seekng to purchase a dgtal camera s as lkely to search for a popular brand name such as NIKON, CANON or KODAK on a search engne as searchng for the generc phrase dgtal camera on the same search engne. Smlarly, the same consumer may search drectly for a retaler such as BEST BUY or CIRCUIT CITY on the search engne. In recognton of these electronc marketplace realtes, search engnes do not merely sell generc dentfers such as dgtal cameras as keywords, but also wellkn brand names that can be purchased by any thrd-party advertser n order to attract consumers to ts Web ste. The length of the keyword s also an mportant determnant of search and The frm s a Fortune-500 frm but due to the nature of the data sharng agreement between the frm and us, we are unable to reveal the name of the frm. purchase behavor but anecdotal evdence on ths vares a trade press reports. Some studes have sh that the percentage of searchers who use a combnaton of keywords s.6 tmes the percentage of those who use sngle-keyword queres [9]. To nvestgate the mpact of the length of a keyword, we constructed a varable that ndcates the number of words n a keyword that a user quered for on the search engne (n response to whch the pad advertsement was dsplayed to the user). The dataset was enhanced by ntroducng some keyword-specfc characterstcs such as Brand, Retaler and Length. For each keyword, we constructed two dummy varables, based on whether they were () branded or unbranded keywords and () retalerspecfc or non-retaler specfc keywords. To be precse, for creatng the varable n () we looked for the presence of a brand name (ether a product-specfc or a company specfc) n the keyword, and labeled the dummy as or 0, wth ndcatng the presence of a brand name. For (), we looked for the presence of the advertsng retaler s name n the keyword, and then labeled the dummy as or 0, wth ndcatng the presence of the retaler s name. There were no keywords that contaned both retaler name and brand name nformaton. Ths enabled a clean classfcaton n our data. Ths classfcaton s smlar n noton to [, ] who classfy user queres n search engnes as navgatonal (searchng for a specfc frm or retaler), transactonal (searchng for a specfc product) or nformatonal (longer keywords). Table : Summary Statstcs (Keyword level) Varable Mean Std. Dev. Mn Max Impressons Clcks Orders Clck-through Rate (CTR) Converson Rate Cost-per-Clck (CPC) Lag Rank Log (Lag Proft) Rank Lag CTR Retaler Brand POLICY SIMULATIONS A prmary goal of research s to evaluate and recommend optmal polces for marketng actons. One way of dong ths s to assess current decson-makng behavor and compare them wth optmal decson-makng behavor. Towards ths objectve, we estmate the optmal bd prce for each keyword and assess how much the advertser s decson (actual bd prce) devates from the optmal bd prce based on our model estmates. We adopt the model from [0] and estmate t usng Markov Chan Monte Carlo (MCMC) methods (see [6] for a detaled revew of such models). We use the Metropols-Hastngs algorthm wth a random walk chan to generate draws. ([]). Rather than descrbe the entre Herarchcal 0

3 WWW 008 / Refereed Track: Internet Monetzaton - Sponsored Search Aprl -5, 008 Beng, Chna Bayesan model, we refer nterested readers to [0] and provde a summary descrpton of the theoretcal framework below. Assume for search keyword at week j, there are n clckthroughs among N mpressons (the number of tmes an advertsement s dsplayed by the retaler), where n N. Suppose that among the n clck-throughs, there are m clckthroughs that lead to purchases, where m n. Let us further assume that the probablty of havng a clck-through s p and the probablty of havng a purchase s q. In our model, a consumer faces decsons at two levels one, when she sees a keyword advertsement, she makes decson whether or not to clck t; two, f she clcks on the advertsement, she can take any one of the followng two actons make a purchase or not make a purchase. Thus, there are three types of observatons. Frst, a person clcked through and made a purchase. The probablty of such an event s p q. Second, a person clcked through but dd not make a purchase. The probablty of such an event s p (- q ). Thrd, an mpresson dd not lead to a clck-through or purchase. The probablty of such an event s - p. Then, the probablty of observng (n, m ) s gven by: f(n,m,p,q ) = N! m!(n m )!(N n )! m n m N n {p q } {p ( q )} { p } Thereafter, clck-through rates, converson rates, cost-per-clck, and keyword ranks are analyzed by jontly modelng the consumers search and purchase behavor, the advertser s bd prcng behavor, and the search engne s keyword rank allocatng behavor. The decson of whether to clck and purchase n a gven week s modeled as a functon of the probablty of advertsng exposure (for example, through the Rank of the keyword) and ndvdual dfferences, both observed and unobserved heterogenety (for example, through the keywordspecfc attrbutes lke Retaler, Brand and Length). The advertser s bd prce decson s modeled as a functon of keyword attrbutes and other varables such as lagged values of Rank and Proft. The search engne s rankng decson s modeled as a functon of keyword attrbutes and other factors such as CPC and lagged values of CTR n accordance wth nsttutonal practces. The Metropols-Hastngs algorthm wth a random walk chan s adopted to generate draws n the MCMC methods ([]). Usng the parameter estmates from the clck-through, converson and rank models from [0] and the data on clck-through rates, converson rates, revenues and actual CPC of each advertsement, we estmate the expected proft of the frm. We assume the advertser determnes the optmal bd prce for each keyword to maxmze the expected proft ( Π ) from each consumer mpresson of the advertsement: Π = p (qr CPC ) () In equaton (), p s the expected clck-through rate for keyword at week j, q s the expected converson rate condtonal on a clck through, r s the expected revenue from a converson that s () observed from our data, and CPC s the actual cost per clck pad by the advertser to the search engne for each keyword. p, q and Rank are predcted based on equatons (.), (.6) and (.5) respectvely n [0], usng the estmates obtaned from the proposed model. We conduct the optmzaton routne to maxmze the expected proft from each consumer mpresson of the advertsement for each keyword at each week usng the grd search. Our smulaton results hghlght that there s a consderable amount of spread n the optmal bd prces and the actual cost per clck for a gven keyword, wth the average devaton beng. cents per bd. Gven that ths s a second prce aucton and the frm actually pays the bd prce of the next hghest bdder plus a small ncrement of cent, we fnd that a vast majorty of the keyword CPCs actually hghlght that the frm s overbddng relatve to the optmal bd prce. Specfcally, 6% of the CPCs are below the optmal bd prces wth the average dfference beng 67 cents, whle the remanng 9% of the CPCs (and thus the frm s bd prce) are above the optmal bd prce wth the average dfference beng 8.7 cents. We also examned the devaton from the optmal bd prces based on whether the keyword advertsement had retaler or brand nformaton. On an average, the frm was underbddng by. cents for each ad that had retaler nformaton n t and was overbddng by 6. cents for each ad that had brand nformaton n t. For those keywords that dd not have retaler or brand nformaton n them the frm was generally overbddng wth the range gong from 5. cents to 7.7 cents. These results are very ntutve: the lack of competton for retaler-specfc keywords s lkely to be drvng the underbddng behavor whle the presence of ntense competton n branded or generc keywords would be drvng the overbddng behavor. Consequently, there s a sgnfcant amount of dvergence between optmal expected profts and actual profts accrung to the frm from ther current bd prces, wth the average dfference beng. tmes the expected profts wth actual bd prces. Next we examned the sample based on overbddng or underbddng behavor. We found that the average dfference n profts s.5 tmes the expected profts wth actual bd prces when the frm s overbddng. When the frm s underbddng, the rato s.05. When the frm s underbddng, the rato s.05. Fgure a and b hghlght the dfferences from the optmal and actual bd prces. Frequency Dfference Between Optmal and Actual Bd Prce Fgure a: Dstrbuton of the Dfference between Optmal and Actual Bds

4 WWW 008 / Refereed Track: Internet Monetzaton - Sponsored Search Aprl -5, 008 Beng, Chna Frequency about the ndvdual products wthn these categores. Snce, our analyss s about the -sellng potental of a gven productbased advertsement, we exclude advertsements that only have the retaler nformaton n them but no product nformaton. Hence, we focus on the 66 keywords that have some product or product category nformaton mbedded n them. Table reports the summary statstcs of the data. As sh, the average spendng s 79 dollars on the searched product category, and.8 dollars on the non-searched product category. The average latency s about one day. These statstcs provde some evdence suggestng that keyword advertsng can lead to purchases on a non-searched product category, and consumers may wat for a whle after startng the search to complete an order Dfference n Expected Profts from Optmal and Actual Bd Prce Fgure b. Dstrbuton of the Log of Dfference n Expected Profts usng Optmal and Actual Bds In order to nvestgate how the three keyword level covarates are assocated wth optmal bd prces, we ran OLS regressons wth keyword-level random effects. The dependent varable was the optmal bd prce. Our analyss reveals that the presence of retaler-specfc nformaton (Retaler) or brand-specfc (Brand) nformaton leads to an ncrease n the optmal bd prce, whle longer keywords (Length) s assocated wth a lower optmal bd prce. Specfcally, the presence of retaler and brand nformaton should lead to an ncrease n the optmal bd prces by.5% and.9%, respectvely whle an ncrease n the length of the keyword by one word should lead to a decrease n the bd prce by.%. Note that these results are n contrast to the results from [0] wheren usng actual bd prces we found that the frm s actually ncurrng a lower CPC when t has ether retaler or brand nformaton n the keywords, and ncurs a hgher CPC for longer keywords. To summarze, whle the frm s exhbtng some learnng behavor over tme n terms of decdng on bd prces based on ts rank and proft n the prevous perod, our smulatons suggest that t can mprove ts profts dramatcally by bddng optmally. Further, t would be better off by placng hgher bds on keyword advertsement that ether have retaler or brand nformaton n them, and lower bds as keywords become longer. Moreover, we also fnd that expected profts from retaler-specfc keywords are lkely to be much hgher than those from brand-specfc keywords.. ECONOMETRIC MODEL: IMPACT OF SPONSORED SEARCH ON CROSS- SELLING In ths secton, we nvestgate the mpact of sponsored search advertsng n a gven category on consumer s propensty to buy products a other categores. Our dataset has detaled nformaton on the varous categores of products that were eventually purchased by consumers after they had clcked on any gven pad advertsement. There are sx product categores n our data: bath, beddng, electrcal applances, home décor, ktchen and dnng. Due to the confdentalty agreement wth the frm that gave us the data, we are not able to reveal any more detals Table : Summary Statstcs of the Cross-Sellng Data Varable Mean Std. Dev. Mn Max Order Value Own Order Value Cross Latency Rank Brand Length We cast our model n a herarchcal Bayesan framework and estmate t usng Markov chan Monte Carlo methods (see [9] for a detaled revew of such models). We use the Metropols- Hastngs algorthm wth a random walk chan to generate draws. ([]). Each order can lead to a purchase from the searched product category and/or from any of the other fve non-searched product categores. We model the consumer purchase behavor as a twostage decson process. In the frst stage, the consumer decdes on how much to spend on the searched product category. We adopt the Tobt model specfcaton to account for a large number of zeros n consumer spendng on ether the searched product category or non-searched product categores. Let s denote as the money spent on the searched product category n order j for the searched keyword. We assume there s latent spendng ntenton ( z ) that determnes how much to spend on the searched product category, that s, y = z f y = 0 f z > 0 (.) z 0 (.) We model the latent buyng ntenton of the searched category as: z K + k k = α = Brand + Search + k Length+ ε Latency + Rank + y (.) where Search k = f the searched category s the k th product category for keyword, and Search k = 0 f the searched category s not the k th product category for keyword. Latency

5 WWW 008 / Refereed Track: Internet Monetzaton - Sponsored Search Aprl -5, 008 Beng, Chna s the tme duraton n number of days between the search and the order j for keyword. Rank s the average rank of keyword for order j. Brand s a dummy varable ndcatng whether a brand name s ncluded n the search keyword. Length s the number of words ncluded n the search keywords. We have a total of 6 product categores, that s, K=6 and wthout loss of generalty, we use category 6 as the baselne. To complete the model specfcaton, we assume the followng dstrbutons regardng the error term and ntercept term: ε ~ N(0, σ ) (.) α ~ N( α, τ ) (.5) In the second stage, the consumer decdes on how much to spend on the non-searched product categores n total condtonal on the spendng on the searched product category. Let s denote y as the money spent on the non-searched product category n order j for the searched keyword. We assume there s latent spendng ntenton ( z ) that determnes how much to spend on the nonsearched product category, that s, y = z f z > 0 (.6) y = 0 f z 0 (.7) We model the latent buyng ntenton of the non-searched category as follows: z K = α + k k = Brand + Search k + Length + Latency y ε Rank (.8) To complete the model specfcaton, we assume the followng dstrbutons regardng the error term and ntercept term: ε ~ N(0, σ ) (.9) α ~ N( α, τ ) (.0) Equatons (.) (.), and (.6) (.8) lead to a non-lnear fully non-recursve smultaneous equatons model. Note that k, k as well as 5 are modeled as fxed effects due to the emprcal dentfcaton wth our data.. Results and Analyss We next dscuss the fndngs from our analyss. In table a, the coeffcent, s negatve and sgnfcant suggestng that consumer average spendng on the searched category s lower n category than category 6. On the other hand, the coeffcent, s postve and sgnfcant suggestng that the consumer average spendng on the searched category s hgher n category + than category 6. The coeffcents,,, and 5 are statstcally nsgnfcant suggestng that on an average, and consumers spend the same amount n each of these categores (, and 5) as they do n category 6 when they search for a product n each of these categores. What are the man factors that affect ths knd of consumer behavor? Based on the estmates n Table a and b, we fnd that Latency tends to decrease consumer spendng on the searched category, but ncrease ther average spendng on the non-searched category. Recall that latency s the tme between when consumers clck on an advertsement and when they actual purchase the product from the webste. Intutvely, ths result suggests that f consumers delay the fnal purchase of the product after the ntal clck on the ad, they are lkely to dgress from ther orgnal spendng ntenton n the searched category and ncreasng ther purchase of products n other non-searched categores. Note also that the coeffcent of y s negatve suggestng that f a consumer has already spent a lot on the category that they had orgnally searched for, then they are lkely to spend less on the other categores. Table a: Estmates on Consumer Spendng on the Searched Product Category Intercept Latency Rank Brand Length α (.97) (0.079) (0.5) (.96) (0.900) Search Search Search Search Search (.55) (.50) (.658) (.6) (.00) σ τ.6.67 (6.90) (.70) Table b: Estmates on Consumer Spendng on Non-Searched Product Category Intercep Latency Rank Brand Length y t α (.96) (0.) (0.7) (.5) (.86) (0.06) Search Search Search Search Search (.767) (.78) (.86) (.9) (.8) σ τ (7.00) (.6)

6 WWW 008 / Refereed Track: Internet Monetzaton - Sponsored Search Aprl -5, 008 Beng, Chna Interestngly, we fnd that the presence of Brand nformaton n the search keyword advertsement does not affect the amount that consumers spend on the category that they orgnally searched for on the search engne. However, note from Table b that t does sgnfcantly ncrease consumers spendng n the other categores. Ths mples that the presence of a brand name n a keyword advertsement can have a strong swtchng effect on consumer s purchasng propenstes. It has a smlar flavor to the bat and swtch strateges used by retalers, when they attract consumers to ther stores based advertsements n one category and then nduce them to buy a product n a dfferent category addton to the orgnal product, perhaps through some marketng promoton. Thus, our analyss ndcates a strong -sellng potental of a sponsored search advertsement that contans a brand name n t. The statstcally sgnfcant estmates of,, and n Table b ndcate that there are complementary demands for three product categores at each purchase ncdence. In partcular, we see n Table b that categores,, and (bath, beddng and electrcal applances) exhbt the strongest opportuntes for -sellng. We fnd that nether Rank nor the Length has any mpact on consumers spendng ether on the searched category or the nonsearched category. Ths s not too surprsng. Both these attrbutes are lkely to nfluence consumer clck-through behavor but are unlkely to affect ther latent spendng ntenton once they have already landed on the retaler s web page. As a robustness check, we also ft a model that controls for the potental endogenety n Rank. We found smlar results on the coeffcent estmates. We also ncluded dummes for dfferent categores of landng pages such as search page, shop, home page, nformaton page, product page and category page. Ths dd not affect the qualtatve nature of the results, and moreover the estmates on the dummes were not statstcally sgnfcant. 5. RELATED WORK Our paper s related to several streams of research. Frst, t contrbutes to recent research n onlne advertsng n economcs and marketng by provdng the frst kn emprcal analyss of sponsored search keyword advertsng. Much of the exstng academc (e.g., [5], [6], [7]) on advertsng n onlne world has focused on measurng changes n brand awareness, brand atttudes, and purchase ntentons as a functon of exposure. Ths s usually done va feld surveys or laboratory experments usng ndvdual (or cooke) level data. In contrast to other studes whch measure (ndvdual) exposure to advertsng va aggregate advertsng dollars ([]), we use data on ndvdual search keyword advertsng exposure. [7] looks at onlne banner advertsng. Because banner ads have been perceved by many consumers as beng annoyng, tradtonally they have had a negatve connotaton assocated wth t. Moreover, t was argued that snce there s consderably evdence that only a small proporton of vsts translate nto fnal purchase ([], [6], [8]), clck-through rates may be too mprecse for measurng the effectveness of banners served to the mass market. Interestngly however, [] found that banner advertsng actually ncreases purchasng behavor, n contrast to conventonal wsdom. These studes therefore hghlght the mportance of nvestgatng the mpact of other knds of onlne advertsng such as search keyword advertsng on actual purchase behavor, snce the success of keyword advertsng s also based on consumer clckthrough rates. A large lterature n economcs sees advertsng as necessary to sgnal some form of qualty ([]). There s also an emergng theoretcal stream of lterature exemplfed by [9, 0] that examnes aucton prce and mechansm desgn n keyword auctons. Despte the emergng theory work, very lttle emprcal work exsts n onlne search advertsng. The handful of emprcal studes that exst n search engne advertsng have manly analyzed publcly avalable data from search engnes. [] looks at the presence of qualty uncertanty and adverse selecton n pad search advertsng. [] classfes queres as nformatonal, navgatonal, and transactonal based on the expected type of content destnaton desred and analyze clck through patterns of each. In a paper related to our work, [0] studed the converson rates of hotel marketng keywords to analyze the proftablty of dfferent campagn management strateges. Fnally, n our pror work [0], we only analyzed the mpact of keyword attrbutes on consumer and frm behavor. Ths paper goes well beyond t by conductng polcy smulatons to examne the optmalty of advertser strateges. We also substantally extend that work n ths paper by examnng the potental for -sellng products through sponsored search advertsement and quantfyng the actual mpact of specfc varables lke brand and latency. Our paper s also related to the stream of work n -sellng. Amongst the frst papers that formally model sequental orderng and the -sellng opportuntes s []. Ther research apples latent trat analyss to poston fnancal servces and nvestors along a common contnuum. [5] present next product-topurchase models that can be used to predct what s to be purchased next and when. [6] model consumers sequental acquston decsons for multple products and servces, a behavor that s common n servce and consumer technology ndustres. We thus contrbute to the lterature by demonstratng the -sellng potental of pad search advertsng n an onlne context, thereby supplementng the exstng stream of work on -sellng. 6. CONCLUSIONS AND FUTURE WORK The phenomenon of sponsored search advertsng s ganng ground as the largest source of revenues for search engnes. In ths research, we am to analyze how advertser s actual cost per clck may dffer from optmal bd prces. In addton to ths, our second objectve s to enhance our understandng of how sponsored search advertsng affects consumer purchasng patterns on the Internet by analyzng ts -sellng potental. Usng a unque panel dataset of several hundred keywords collected from a natonwde retaler that advertses on Google, we emprcally model the relatonshp between dfferent keyword attrbutes and consumer search and purchase behavor a multple product categores. We use a Herarchcal Bayesan modelng framework and estmate the model usng Markov Chan Monte Carlo (MCMC) methods. We conduct smulatons to assess the relatve proft mpact from changes n bd prces, and fnd that despte some learnng, the advertser s not bddng optmally. What are some of the mplcatons? Retaler-name searches are navgatonal searches, and are analogous to a customer fndng the retaler's phone number or address n the Whte Pages. These searches are drven by brand awareness generated by catalog malngs, TV ads, etc, and are lkely to have come from more loyal consumers. Even

7 WWW 008 / Refereed Track: Internet Monetzaton - Sponsored Search Aprl -5, 008 Beng, Chna though the referral to the retaler s webste came through a search engne, the search engne had very lttle to do wth generatng the demand n the frst place. On the other hand, searches on product or manufacturer specfc brand names are analogous to consumers gong to the Yellow Pages they know they need a product or servce, but don't yet know where to buy t [0]. These are lkely to be compettve searches. Even for loyal buyers, a branded search means the searcher s surveyng the market and s vulnerable to competton. If the advertser wns the clck and the order, that mples they have taken market share away from a compettor. Thus, retaler-specfc keywords are lkely to be searched and clcked by 'loyal' consumers who are nclned towards buyng from that retaler whereas brand-specfc keywords are lkely to be searched and clcked by the 'shoppers or searchers who can easly swtch to competton. Our polcy smulatons suggest that the average proftablty from conversons generated by 'retaler' keywords s much hgher than that from brand' keywords. Our results thus provde some manageral nsghts for an advertser of sponsorng such retal store keywords (retaler-specfc keywords) wth natonal-brand keywords (brand-specfc keywords). We have sh some evdence that although the average clckthrough and converson rates are typcally low n sponsored search, there are other potental benefts from such advertsng. Specfcally, retalers can not only refne ther keyword purchases on search engnes, but also set up relevant -sellng opportuntes on ther webstes by advertsng brandspecfc keywords. The strategy s that when a consumer searches for a specfc product and lands deep wthn the retaler s webste by clckng on ts keyword advertsement, the retaler can par that product wth other products that sell well wth that keyword and promnently feature them on ts webste. Ths provdes a retaler wth an opportunty to not only convert someone on the product they had searched for, but also get other opportuntes for -sellng. From the retaler s perspectve, there could be synerges n promotng both categores smultaneously rather than separately. Indeed anecdotal evdence suggests that retalers are engagng n the practce of lookng up the most-searched and the top-convertng keywords on ther webstes, and bddng for them on search engnes. They are takng -sellng reports from other marketng mx campagns and puttng up the top -sellng product for the searched product on the same page. Interestngly, we fnd that latency n purchases s not necessarly detrmental for a frm that s sponsorng the keyword advertsement. Whle t s n general assocated wth a reducton n product purchases n the category that the consumer was orgnally searchng for, t ncreases consumers spendng n other product categores. In a way, t has an mpact smlar to a bat and swtch strategy. Ths effect s partcularly strong n keywords that have a brand name n t, snce consumers who clck on branded keywords typcally tend to spend more on other categores than the one they were orgnally searchng for. Thus, onlne advertsers can focus on nvestng more often n such keywords relatve to the generc keywords, especally f the cannbalzaton effect of drawng out consumers from one category s smaller relatve to revenue expanson effect. From the pont of vew of the manufacturer, such dependences a categores may be exploted by runnng cooperatve promotons wthn brands but a categores. Of course, such decsons would need a detaled proftablty analyss based not only on the potental from sellng n other product categores but also the performance of the keyword n ts category. We are cognzant of the lmtatons of our paper. These lmtatons arse prmarly from the lack of nformaton n our data. For example, we do not have data on competton. That s, we do not know the keyword aucton ranks or other performance metrcs such as clck-through rates and converson rates of the keyword advertsements of the compettors of the frm whose data we have used n ths paper. Future research can use data on competton and hghlght some more nsghts on how frms should manage a pad search campagn by runnng more detaled polcy smulatons that ncorporate compettve bd prces. Further, we do not have any knowledge of the other marketng varables such as any promotons durng consumers search and purchase vsts. Future work can nvestgate the value to frms from partcpatng n such sponsored search advertsng by comparng the performance of sponsored searches wth natural searches usng a common pool of keywords durng the same tme perod. By collectng nformaton on the ranks and page numbers of the natural search lstngs for the same keywords as those n pad search, one can study the mpact of natural search lstngs on pad search advertsements and vce-versa. We hope that ths study wll generate further nterest n explorng ths mportant emergng area n web search. 7. ACKNOWLEDGMENTS The authors would lke to thank the anonymous company that provded data for ths study. Ths work was partally supported by a grant from the NET Insttute and Marketng Scence Insttute. Anndya Ghose also acknowledges the generous fnancal support of the Natonal Scence Foundaton through CAREER Award IIS The usual dsclamer apples. 8. REFERENCES [] Anmesh A., Ramachandran, V., and Vswanathan, S. Qualty uncertanty and adverse selecton n sponsored search markets. Proceedngs of the 006 NET Insttute Conference. [] Broder, A. Taxonomy of web search, SIGIR Forum, vol. 6, 00, -0. [] Chatterjee, P., Hoffman, D., and Novak, T. Modelng the clckstream: mplcatons for web-based advertsng efforts. Marketng Scence: (), 00, [] Chb, S., and Greenberg. E. Understandng the Metropols- Hastngs algorthm. The Amercan Statstcan, 9, 995, 7-5. [5] Cho, C., Lee, J., and Tharp, M. 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