From Generic to Branded: A Model of Spillover in Paid Search Advertising

Transcription

1 OLIVER J. RUTZ and RNDOLPH E. BUCKLIN* In Inerne paid search adverising, markeers pay for search engines o serve ex adverisemens in response o keyword searches ha are generic (e.g., hoels ) or branded (e.g., Hilon Hoels ). lhough sandalone merics usually show ha generic keywords have higher apparen coss o he adveriser han branded keywords, generic search may creae a spillover effec on subsequen branded search. Building on he Nerlove rrow adverising framework, he auhors propose a dynamic linear model o capure he poenial spillover from generic o branded paid search. In he model, generic search adverisemens serve o expose users o informaion abou he brand s abiliy o mee heir needs, raising awareness ha he brand is relevan o he search. In urn, his can induce addiional fuure search aciviy for keywords ha include he brand name. Using a Bayesian esimaion approach, he auhors apply he model o daa from a paid search campaign for a major lodging chain. The resuls show ha generic search aciviy posiively affecs fuure branded search aciviy hrough awareness of relevance. However, branded search does no affec generic search, demonsraing ha he spillover is asymmeric. The findings have implicaions for undersanding search behavior on he Inerne and he managemen of paid search adverising. Keywords: Inerne adverising, paid search, spillover, awareness, Nerlove rrow model, Bayesian dynamic linear model From Generic o Branded: Model of Spillover in Paid Search dverising Consider he following scenario: raveler is planning a vacaion o Los ngeles and is unfamiliar wih he hoel opions available. The raveler begins his planning wih an Inerne search for he generic erm hoels Los ngeles. He clicks on a sponsored ex adverisemen from Hilon, visis is Web sie, bu akes no furher acion. The nex day he raveler coninues his planning; recalling ha Hilon operaes in Los ngeles, he now searches for he branded erm Hilon Los ngeles. The search engine again reurns a sponsored ex adverisemen from Hilon. When he user *Oliver J. Ruz is ssisan Professor of Markeing, Yale School of Managemen, Yale Universiy ( [email protected]). Randolph E. Bucklin is Peer W. Mullin Professor, nderson School of Managemen, Universiy of California a Los ngeles ( [email protected]. edu). The auhors hank a collaboraing firm for providing he daa used in his sudy. They also hank Prasad Naik for valuable commens and he paricipans of he Markeing Dynamics Conference a Universiy of California Davis and Universiy of California a Los ngeles for helpful suggesions and insighs. Pradeep Chinaguna served as associae edior for his aricle. clicks hrough, he is aken o he Hilon Web sie, where he places a reservaion. Figure 1 provides an illusraion of he search resuls reurned in response o he generic query hoels Los ngeles. In he hypoheical example, he iniial generic search creaes awareness ha Hilon migh be able o mee he raveler s needs, which in urn helps lead o a subsequen search for he branded erm Hilon Los ngeles. 1 In his scenario, a spillover effec has occurred from generic o branded search. The quesion we pose is his: To wha exen does such spillover ake place, and if so, wha are he implicaions for he managemen of paid search ad campaigns and researchers undersanding of consumer search behavior? The rapidly growing role of paid search adverising in he markeing communicaions mix (e.g., Pricewaerhouse- Coopers 2008) poins o he need o carefully examine he performance merics for i. These merics are derived from 1We hank an anonymous reviewer for suggesing he use of an illusraive scenario. 2011, merican Markeing ssociaion ISSN: (prin), (elecronic) 87 Journal of Markeing Research Vol. XLVIII (February 2011),

2 88 JOURNL OF MRKETING RESERCH, FEBRURy 2011 Figure 1 SERCH RESULTS PGE EXMPLE FOR GOOGLE Sponsored Links Paid search Tex ad in 1s posiion Organic Links 3 he progression of aciviy hrough he consumer search funnel. The lef panel of Figure 2 illusraes his for he case of a hoel chain. s he figure shows, searches lead o impressions, which could lead o clicks, which could lead o reservaions. To reurn o our example, hoels compeing in Los ngeles migh find generic search erms of ineres and bid for placemen of heir ex adverisemens in he sponsored search lising. Search erms, including brand names such as Hilon Los ngeles, arac more limied ineres primarily from he brand owner and channel inermediaries ha broker or resell he named service or produc. Indeed, legal resricions on rademark use ypically preclude compeiors from hijacking a branded search erm o promoe heir own producs or services. Consequenly, we migh expec ha branded keywords will have a lower cos per click han generic keywords. If branded erms are used more heavily closer o purchase imes, conversion raes from click o reservaion migh also be higher for branded versus generic erms. Tables 1 and 2 give descripive saisics from our daa se for he paid search campaign run for a major lodging chain on he Google and Yahoo search engines.2 The merics dif2daa from oher campaigns we analyzed show similar paerns, bu confidenialiy resricions do no allow us o repor deails. Conversaions wih many praciioners indicae ha his paern is also a widespread phenomenon. fer sharply beween he generic and he branded keywords in he campaign. On Google, click-hrough raes (e.g., 13.68% versus.26%) and conversion raes (e.g., 6.03% versus 1.05%) are subsanially higher for branded versus generic keywords, and he Yahoo daa show a similar paern. Furhermore, he cos per click for branded keywords is subsanially lower han ha for generic keywords (e.g., $.18 versus $.55 for Google). The resuling difference in he apparen cos per reservaion for he wo classes of keywords is sriking (e.g., $2.94 versus $51.84 on Google). This difference iniially led managers we worked wih o quesion wheher he use of generic keywords in he campaign should be heavily curailed in favor of branded erms. SPILLOVER EFFECTS IN PID SERCH One drawback of he merics described in he preceding secion is ha hey do no accoun for he poenial dynamic ineracion beween generic and branded search aciviy. We propose ha generic search can creae awareness ha he brand is relevan o he goals of he search and consequenly spill over o influence subsequen branded search. This awareness can hen lead o fuure branded keyword searches in which he user researches he brand s offering in more deail or perhaps goes on o complee a purchase.

3 Spillover in Paid Search dverising 89 Figure 2 ILLUSTRTION OF THE SERCH PROCESS Company s Perspecive Consumer s Perspecive Search Impression Search Engine Click Third Pary Reservaion 4 Our lodging company daa show a paern of modes spikes in generic search aciviy followed by increases in branded search. s an illusraion, we ake he hree-week period leading up o he July 4 holiday weekend. Three weeks before he holiday, search aciviy on generic keywords ran a 114% of average, and branded search aciviy ran a 92%. Two weeks previous, generic aciviy dropped o 93% of average while branded aciviy rose o 110%. In he week jus before he holiday, generic was 90% of average while branded was 98%. similar paern is presen in he daa for oher preholiday periods. We propose ha a consumer planning a rip who searches using a generic keyword may no be aware ha a specific brand (e.g., he anonymous lodging company we worked wih) is relevan for his or her curren search. Conversely, a consumer using a branded keyword is likely o be aware ha he brand is relevan o he search. This difference in awareness of relevance should hen ranslae ino differences in he likelihood of purchase (in our case, a hoel reservaion), given click-hrough. Noe ha we are no proposing ha generic search makes a user aware of he brand for he firs ime. This may or may no be he case. Raher, we propose ha i is he generic search ha makes he user aware ha he brand offers a poenial soluion relevan o he user s goals. This is how we disinguish beween awareness and awareness of relevance. Our objecive is o develop a saisical model o deermine wheher he previously described spillover occurs in paid search adverising and, if so, o wha exen. Our modeling approach builds on he so-called leaky bucke approach o adverising (e.g., Naik, Raman, and Srinivasan 2009; Nerlove and rrow 1962), which posis a laen, decaying, unmeasured consruc for awareness as par of he model. The premise of our approach is similar: Exposure o brandrelaed informaion ensuing from generic search increases awareness of relevance; his awareness also decays over ime (i.e., leaks ou of he bucke). In urn, greaer awareness of relevance leads o an increase in subsequen branded search aciviy. Because we do no have measures for awareness of relevance, we need a model ha can accommodae such a laen consruc. Therefore, we urn o he Nerlove rrow approach. To handle he dynamic naure of his process, we specify our proposed model in a mulivariae ime-series framework. Specifically, we use a dynamic linear model (DLM) esimaed in a Bayesian framework following he procedures of Wes and Harrison (1997). We noe ha oher mulivariae ime-series models, such as he vecor auoregressive (VR) model, are no se up o handle laen consrucs. In his sudy, we es he performance of our DLM model versus a VR model. The es assesses wheher incorporaing laen awareness of relevance adds significanly o model fi, and we find ha i does.

4 Table 1 DESIPTIVE STTISTICS FOR GOOGLE DT Impressions/ Reservaions/ verage Click- Conversion Cos/ Impressions Clicks Reservaions Cos Day Clicks/Day Day Cos/Day Posiion Through Rae Rae Cos/Click Reservaion Generic 37,059,020 98, $53, , $ % 1.05% $.55 $51.84 Branded 4,925, ,971 40,671 $119, , $ % 6.03% $.18 $2.94 Toal 41,984, ,133 41,704 $173, , $ Table 2 DESIPTIVE STTISTICS FOR yhoo DT Impressions/ Reservaions/ verage Click- Conversion Cos/ Impressions Clicks Reservaions Cos Day Clicks/Day Day Cos/Day Posiion Through Rae Rae Cos/Click Reservaion Generic 2,118, $2, $ % 1.93% $.42 $21.97 Branded 3,378, ,828 25,889 $118, , $ % 7.16% $.33 $4.56 Toal 5,497, ,436 25,997 $120, , $ JOURNL OF MRKETING RESERCH, FEBRURy 2011

5 Spillover in Paid Search dverising 91 3We use simply awareness in place of awareness of relevance for he remainder of he aricle. We srucure he aricle as follows: fer a brief lieraure review, we presen our model specificaion, describe our daa se in deail, and discuss he empirical resuls we obained. Nex, we quanify he exen of spillover and es for possible reverse causaliy. concluding secion summarizes our work, noes limiaions of our approach, and discusses fuure research opporuniies. LITERTURE REVIEW Much of he previous empirical research on online adverising in markeing has focused on display or banner adverising. Markeing and economics journals have only recenly begun publishing work on paid search adverising. For example, heoreical sudies have jus recenly focused on he paid search aucion mechanism (e.g., Edelman and Osrovsky 2007; Edelman, Osrovsky, and Schwarz 2007), he role of paid search adverising in produc differeniaion (Chen and He 2009), and click fraud (Wilbur and Zhu 2009). Empirical work has largely aken a keyword perspecive and has reaed paid search as a direc markeing channel. focus is on esimaing he response rae o paid search in erms of boh click-hrough and conversion. premise of his work is ha consumers who do no purchase immediaely afer clicking are considered los ; ha is, hey do no generae any revenue (Ghose and Yang 2009a; Goldfarb and Tucker 2009; Ruz and Bucklin 2009; Yao and Mela 2009). These sudies do no address he poenial spillover from generic o branded search. In a relaed sudy, Ghose and Yang (2009b) invesigae wihin-session spillover as he amoun of cross-caegory purchase ha occurs afer an iniial search brings a consumer o a Web sie. For example, consider a consumer searching on a keyword relaed o he kichen caegory. Ghose and Yang invesigae he propensiy wih which his consumer buys a kichen iem as well as an iem from anoher caegory (e.g., bedding) in he session begun by he kichen-relaed keyword. They do no consider ha he search could generae searches/visis in fuure periods ha migh lead o purchase. MODELING PPROCH We ground our modeling approach on he concep of awareness of relevance. We propose ha such awareness parsimoniously capures he effecs of exposure o brandrelaed informaion during search. Consumers who conduc a generic search migh no be aware of he brand, or when hey are, hey migh no be aware ha he brand is relevan for he search. Generic search leads o brand-relaed exposures in he form of impressions (i.e., he ex adverisemens in he sponsored secion of he search resuls page) and clicks (i.e., he searcher clicking on he adverisemen and being aken o he adveriser s Web sie). These brandrelaed exposures may creae and/or increase he consumers awareness ha he brand is relevan for heir search. 3 We noe ha he impac of a ex-ad impression versus a Web sie exposure (following a click-hrough) migh differ as well. n impression is a passive exposure o he brand s ex adverisemen, whereas a click is an acive opin ha leads o furher informaion exposure a (and possibly afer) he landing page. In our model, we can invesigae wheher generic impressions versus generic clicks produce differen spillover effecs. ccording o one indusry sudy, 70% of searches begin wih a generic, inclusive keyword, and as he search process coninues, i ends o become increasingly specific (Search Engine Wach 2006). For example, consider a consumer search for a cruise vacaion (Enquiro 2006). The observed user began his search using he keyword cruise a generic keyword ha reurned a broad se of resuls. fer reviewing he iniial resuls, he user searched Caribbean cruise. In his iniial phase, he user may develop awareness for new opions and begin o narrow he scope of ineres. He may also see ha paricular cruise lines, some of which he already knows, are offering Caribbean cruises. Conversion raes a his sage are usually low, consisen wih hese ypes of searches occurring early in he decisionmaking process. The iniial broad search is followed by narrower searches in which users research opions in deail, review hird-pary esimonials, and choose a brand ofen over muliple days or weeks. In his example, he observed user narrowed his search by reading hird-pary reviews on Panama cruises. fer reading he reviews, he conduced a final search for he branded keyword Princess Panama cruise. This ype of final, argeed search has a much higher conversion rae, possibly 30% 40%, according o he Enquiro sudy. Had Panama cruises and he availabiliy of a Princess cruise no been inroduced early in he awareness sage, he user would have been unlikely o have narrowed his search in ha direcion. Search engines provide adverisers wih daa on impressions, clicks, posiions, and coss on an aggregae level, ypically repored on a daily basis. The daa are aggregaed on he basis of keywords. For each search keyword (e.g., hoels Los ngeles), campaign managers have daily informaion on cos (in U.S. dollars), average posiion served (given by daily average placemen rank, e.g., 2.3), number of impressions and clicks, and number of sales or, in our case, reservaions. We build our model using hese paid search daa. 4 Researchers have modeled he effecs of adverising wih aggregae daa in a wide variey of ways. One popular approach holds ha adverising creaes goodwill or ad sock for he firm or brand bu ha he goodwill decays over ime. Originally described by Nerlove and rrow (1962; hereinafer N-), his so-called leaky-bucke model provides an elegan and parsimonious way o capure he effecs of adverising over ime. The model has served as he basis for recen empirical work in markeing. For example, Naik, Raman, and Srinivasan (2009) find ha corporae adverising generaes goodwill, which increases boh brand sales due o direc spillover effecs and brand adverising effeciveness due o indirec spillover effecs. Bass and col- 4The major search engines (Google, Yahoo, and MSN) do provide adverisers wih daa on heir own campaign performance (i.e., impressions, clicks, posiion, and cos). These daa are aggregaed on a daily keyword level. None of he major search engines provide compeiive daa or allow adverisers o backrack which compeiors were lised ogeher wih heir own adverisemens. We spoke wih execuives a boh Google and Yahoo, and neiher plan o make more deailed daa available o eiher companies or academic insiuions because of severe privacy concerns.

6 92 JOURNL OF MRKETING RESERCH, FEBRURy 2011 leagues (2007) link he N- model wih a demand model for elephone services. In heir model, goodwill helps explain how differen adverising hemes affec demand for elephone services and inerac wih each oher. Building on his research sream, we use an N- ype model adaped o handle he spillover problem in paid search adverising. In paricular, our goal is o capure he changes in awareness of relevance similar o how previous work has capured changes in goodwill. Model Specificaion In our model, changes o awareness (of relevance) depend on generic search aciviy (he adverising inpu) and a carryover effec (which capures decay). Following prior formulaions (e.g., Naik, Manrala, and Sawyer 1998; Nerlove and rrow 1962), we specify he dynamic evoluion of changes in awareness as follows: d ( 1) = βgengen α, d where is awareness a ime, GEN is a vecor of generic search aciviy variables a ime, and b gen and a ~ are parameers o be esimaed. In discree ime, his model can be rewrien as follows: (1a) = b gen GEN + a 1, where a = (1 a ~ ) and a is he carryover rae of awareness (e.g., Naik, Manrala, and Sawyer 1998). 5 We can measure daily generic search aciviy GEN by he dollar amoun spen or he number of daily impressions and clicks for generic keywords. This enables us o invesigae wheher he acual exposure informaion provides a beer measure han dollar spending. We can also sudy possible sauraion effecs by modeling, for example, he log of impressions and he log of clicks. The logic underlying our adapaion of he N- model in Equaion 1 is as follows: consumer who begins his or her search wih a generic keyword will be exposed o muliple brands ha offer a service/produc ha migh fi his or her needs. This aciviy creaes or enhances an awareness ha hese brands offer he service for which he consumer is looking (and ha migh be relevan). If he search is a mulisage process, a brand ha has increased is awareness by bidding on a generic erm migh benefi in he laer sages of he search. In conras, if he searcher has never learned ha a cerain brand has an offering ha fis his or her needs, chances are lower ha he or she will search his brand laer in he process versus brands ha have increased awareness. In he aggregae, consumers search and ulimaely decide o buy or no (hus dropping ou of he marke), and new consumers ener. The N- ype model specificaion allows us o describe he aggregaion of his individual behavior and o es wheher he laen consruc of awareness (of relevance) increases our abiliy o explain observed consumer behavior. Equaion 1 specifies he dynamics for how generic search aciviy affecs awareness. Nex, o capure he poenial spillover, we need o specify he dynamics of how awareness affecs branded search. The search aciviy of consumers can be measured along hree dimensions: average 5In discree ime, D = C a ~, where D = 1 and C represens all oher erms. Thus, = C + (1 a ~ ) 1. I follows ha = C + a 1 and a is called he carryover rae. number of searches per keyword in a caegory (e.g., branded) per day (), click-hrough rae (), and conversion rae (). The number of searches is recorded in he daa as impressions. The clicks are he number of impressions served imes he click-hrough rae. Las, he number of sales is he number of clicks imes he conversion rae. In modeling search, we also need o ake he firm s acions ino accoun; hese are refleced in he average posiion served for he branded keywords () and he adveriser s bidding policy, summarized by he average cos per click (), for branded keywords. The sysem for describing branded search aciviy includes dynamic models for each of he five oucome variables:,,,, and. We use a simulaneous equaion framework ha allows for boh endogenous relaionships and exogenous effecs hrough variables such as seasonal effecs, lagged branded search aciviy, and laen awareness. The srucure of our formulaion closely follows he procedures ha Naik, Manrala, and Sawyer (1998), Naik, Raman, and Srinivasan (2009), and Bass and colleagues (2007) develop as well as he lieraure on srucural VR models (e.g., Hamilon 1994). Firs, we model he evoluion in for branded keywords as follows: (2) = b I a imp 1 + g + e, where is he average number of searches a ime, I is a vecor of indicaor variables accouning for day of week and monh (i.e., seasonaliy), and is he laen awareness from Equaion 1. The coefficien a is he carryover rae, and he coefficien g reflecs he spillover effec from generic search as capured hrough he impac of awareness,. If here is no spillover from generic o branded, he g coefficien should no be significan. The model allows for a relaively sable over ime (if a is close o 1), a behavior we would expec from a well-known brand. Moreover, varies according o seasonal effecs (e.g., mos searches occur during he week and no on he weekend; Pauwels and Dans 2001). Mos imporan for our purposes, migh increase as a resul of a posiive change in awareness of relevance. Second, we model he evoluion in he for branded erms as follows: (3) = b I a 1 + g + d + e. Changes in can come from a change in posiion because consumers reacion o a search adverisemen may differ depending on he posiion. Seasonaliy could also be a facor in. For a well-esablished brand, should be sable over ime, implying a close o 1. wareness could poenially affec. For example, on he one hand, a consumer migh be more prone o click on he adveriser s paid adverisemen if awareness of relevance is greaer. On he oher hand, he narrower se of resuls ypically associaed wih branded search migh leave lile room for variabiliy in click-hrough rae. Thus, we expec ha g will be eiher posiive or zero bu no negaively signed. Third, we model he evoluion in as follows: (4) = b I a 1 + g + e. We apply he same logic o he specificaion of Equaion 4 ha we applied for. Seasonaliy and awareness migh

7 Spillover in Paid Search dverising 93 affec. However, for a well-esablished brand should be sable over ime, implying a close o 1. gain, we make no specific predicion for g oher han ha i is nonnegaive. Our objecive is o sudy he naure and exen of spillover effecs in paid search, no o opimize bidding sraegy. Noneheless, o model his phenomenon, i is poenially imporan o accoun for he adveriser s bidding and he resuling posiions of he ex adverisemens. In paid search, he underlying aucion is no a pure second price aucion as modeled in heory research (e.g., Edelman and Osrovsky 2007; Edelman, Osrovsky, and Schwarz 2007). From he adveriser s perspecive, i is somehing of a black box ha combines he bid amoun wih pas performance of he adverisemen as generally measured by previous. The acual algorihms are proprieary and no shared wih adverisers. s we noed previously, firms do no observe compeiors bids (see he subsequen discussion on he issue of missing compeiive daa). In ligh of hese facors, we approximae he aucion mechanism by modeling he change in average posiion as a funcion of carryover (o accoun for he firm s posiion sraegy), curren, las period s (as an approximaion of he search engine s pas performance measure), and indicaors for seasonaliy. Noe ha Ghose and Yang (2009a, b) employ a similar approach o modeling he aucion in heir sudies, hough heirs is based only on weekly daa. This is given by he following: (5) = b I a 1 + j + h 1 + e. Las, we need o conrol for he company s adverising spending, which in his case is equal o imes clicks. We model changes in as a funcion of he curren average posiion and pas. Pas affecs hrough he aucion mechanism (as modeled in Equaion 5). more successful adverisemen in he pas (i.e., higher pas ) will require a lower for a fixed posiion han a less successful adverisemen. We also include carryover o accoun for he firm s sraegy as well as indicaors o accoun for seasonaliy: (6) = b I a 1 + i + k 1 + e. Esimaing a sysem ha includes Equaions 5 and 6 requires us o accoun for he conemporaneous relaionship beween curren and curren. We discuss how his can be accomplished in he nex secion. Our daa do no include informaion on compeior adverising. Search engines do no share compeiors bids or provide he names of he firms bidding on keywords. Google acively discourages crawling Google resuls pages o obain compeiive informaion and has lised doing so as a violaion of is erms of service. Despie he daa limiaions, we believe ha compeiive informaion would be unlikely o change he subsanive naure of our resuls. I is possible ha changes in compeiive bids for he same generic keywords (e.g., hoels Los ngeles ) could affec he posiion of he firm s generic ex adverisemens, leading o lower click-hrough raes. We are sudying he link beween generic search aciviy and given he impression of he firm s ex adverisemen and, possibly, click-hrough subsequen search aciviy for is branded keywords. Thus, he decline in generic search aciviy would simply be passed along in he form of a proporional reducion in awareness and fewer branded searches. In his sense, he spillover phenomenon we sudy occurs regardless of compeiion, bu compeiion can scale i up or down. For several reasons, we believe ha he exen of compeiive ineracion in his environmen is acually fairly mued. Firs, currenly a Google and Yahoo, ex adverisemens are placed in posiion rank order as a funcion of clickhrough raes, landing page qualiy scores, and bid amouns. This means ha compeiive bidding is no he sole deerminan of ex ad posiion. Second, he carryover effecs we find are of shor duraion (mos of he effec occurs wihin a few days). Even wihin he fas-paced world of paid search adverising, firms face many consrains on how quickly and how ofen hey can rese heir budges, doing so on monhly or quarerly bases (as in he case of our collaboraing firm). Third, o he exen ha i occurs, compeiive reacion is no head on. Firms mainain differeniaed liss of keywords (i.e., hey bid on differen ses of search erms). In he case of our midprice lodging chain, i faced a hos of differen compeiors in erms of price poin, posiioning, and geography (i.e., many of he search erms were locaion specific). Finally, recen empirical lieraure suggess ha compeiive reacion is infrequen. Seenkamp and colleagues (2005) find ha he mos common reacion is no reacion. They also find ha no all reacion is aggressive. In relaed lieraure, Pauwels (2004, 2007) finds ha compeiive harm is raher limied in magniude he ne effec being abou 10% of he iniiaing markeing acion. Las, Srinivasan and colleagues (2004) repor ha when compeiive acion arises afer a promoion, in he majoriy of cases, incremenal revenue is generaed by he end of he dus-seling period. Bayesian DLM We use a DLM implemened in a Bayesian framework o inegrae he models in Equaions 1 6. In markeing research, DLMs have been used o deal wih scenarios in which a key componen of he daa is unobserved (e.g., Bass e al. 2007; Naik, Raman, and Srinivasan 2009), as is rue in our model, and o handle ime-varying parameers (e.g., aman, Mela, and Van Heerde 2008; Van Heerde, Mela, and Manchanda 2004). We esimae he model using Markov chain Mone Carlo mehods (Wes and Harrison 1997). n appealing feaure of he DLM is ha i simulaneously capures he dynamic evoluion of he branded search aciviies, he underlying mechanics (as defined previously), and laen awareness (allowing us o quanify he effec of generic search on branded search). Equaions 1 6 form a srucural DLM given by he following 6 : ( 7) δ 1 ϕ 1 1 6In ime-series lieraure, he erm srucural refers o he noion ha we esimae a sysem given by y = By 1 + Xb + e. This sysem is generally unidenified, and idenificaion resricions mus be imposed (e.g., Hamilon 1994).

8 94 JOURNL OF MRKETING RESERCH, FEBRURy 2011 α = α η κ where he drif vecor, d..., for,,,, and is given by ( 8a) d... I... = DY λ + I λ H days and for is given by ( 8b) d = GEN IMPλ + GEN λ. α d d d + d d d α DY IMP GEN monhs MONTH CL CL GEN... MONT, In Equaions 8a and 8b, I DY is an indicaor for weekday (e.g., Monday), I MONTH is an indicaor for monh (e.g., July), GEN IMP are he generic impressions a ime, and GEN CL are he generic clicks a ime. The correlaed error erms, e ṫ.., capure he effec of oher facors no included in he model and e ~ N(0, V e ), where V e is a full covariance marix o be esimaed. To esimae he model, we need o ransform he srucural DLM given in Equaion 7 ino a reduced-form DLM (for deails, see ppendix Web pppendix a hp://www. markeingpower.com/jmrfeb11): ( 9) α = α ε ε ε + ε ε ε γ γ γ α, αˆ CT β24 β25 γ α γ β42 αˆ β45 β52 β54 αˆ + dˆ dˆ dˆ dˆ dˆ ˆ d εˆ εˆ εˆ + εˆ εˆ εˆ γ α. 1 1 To complee he seup of he DLM, we specify he observaion equaion linking he sae variables o he observed quaniies branded impressions (IMP), branded clicks (CL), branded reservaions (RES),, and oal branded cos (COST) 7 : ( 10) NW = IMP CL 1 IMP CL RES COST CL where NW is he number of keywords in he campaign, IMP is he number of impressions, CL is he number of clicks, and he v ṫ.. are error erms and v ~ N(0, V v ). 8 We esimae he DLM (Equaions 9 and 10) using sequenial Gibbs sampling. For a complee descripion of he esimaion procedure following he DLM framework, see Web ppendix B (Wes and Harrison 1997). For deails on he recovery of he srucural parameers from Equaion 7, see Web ppendix (hp:// We base our empirical idenificaion on he variaion in he daa ha sems from he number of daily searches consumers conduc on he company s generic keywords. (The company mainained a fixed lis of keywords over he observaion period.) This produces he daily variaion in generic clicks (see Figure 3). Exploraory daa analysis reveals srong correlaion beween lagged generic aciviy and branded aciviy, as in he July 4 holiday example discussed previously. In a regression, we find significan effecs of lagged generic aciviy on branded aciviy, for boh Google and Yahoo. We also fail o find he reverse effec (i.e., lagged branded aciviy did no significanly affec generic aciviy). s anoher check, we also run a series of Granger causaliy ess. Generic impressions did no Granger-cause any branded search aciviy (impressions, clicks, or resuling reservaions), bu generic clicks did Granger-cause branded impressions and branded clicks (bu no branded reservaions). Branded aciviy did no Granger-cause any generic aciviy (impressions, clicks, or reservaions). In addiion o hese ess, we underake wo addiional analyses o provide furher confidence in he empirical idenificaion of our resuls. Firs, we invesigae wheher a reverse spillover effec exiss from generic o branded and es his using our proposed model. s described subsequenly, we find ha reverse spillover is no presen. Second, our daa from wo search engines, Yahoo and Google, V V + V V V IMP CL RES BID, 7Impressions are number of words imes average number of searches per word, clicks are impressions imes, reservaions are clicks imes, and oal cos is clicks imes. 8lhough i is linear in parameer specificaion, he DLM does no force a linear srucure on he daa. each poin, he DLM would approximae a nonlinear funcion wih a linear piece. This provides for a flexible evoluion paern of he search aciviies and he laen awareness.

9 Spillover in Paid Search dverising 95 allow us o es for a poenial idenificaion problem by leveraging informaion from he nonfocal search engine. Specifically, we invesigae wheher generic aciviy on he nonfocal search engine spills over ino branded aciviy on he focal search engine. We find ha spillover is no presen in hese ess, reducing he likelihood ha spillover effecs are due o an omied variable ha affecs boh generic and subsequen branded aciviy. lernaive Models One alernaive model is a radiional ime-series approach, such as an auoregressive disribued lag (RDL) model wih correlaed errors. In such a model, here would be no laen consruc for awareness presen. Branded search aciviy (i.e., impressions, clicks, reservaions, average posiion, and coss) is dependen on is own lagged values, seasonal effecs, and lagged generic search aciviy (i.e., generic impressions and generic clicks). Noe ha in using his approach, we would no be able o deconsruc search aciviies as proposed previously; ha is, we would no be able o model,, and. n advanage of our DLM is ha i allows us o esimae he underlying srucural parameers. Observed clicks are mached o, which allows us o decompose he effecs of generic search on and. radiional approach needs o model impressions, clicks, and reservaions direcly ogeher wih posiion and cos o accoun for he firm s paid search sraegy. Nex, we es wheher our DLM wih awareness fis beer han an alernaive RDL specificaion. second alernaive is he VR approach. Here, we do no need o formulae how differen search aciviies inerac wih each oher, as described in Equaion 7. Raher, he sandard VR model allows he daa o reveal relaionships among he differen search aciviies by reaing each as endogenous. (For a more deailed discussion of alernaive models, see Web ppendix C a hp:// com/jmrfeb11.) Tes for Reverse Spillover I is possible ha generic search is affeced by spillover from branded search. Because he awareness concep is relaed o a brand, no a generic eniy, i is unclear how branded search aciviy would lead o greaer generic search aciviy (hrough awareness). Neverheless, we es for he possible effecs of branded search on generic search using our DLM and an RDL model analogous o he branded RDL. 9 Boh models use generic search aciviy as he dependen variable o es wheher pas generic search and pas branded search affec i. EMPIRICL PPLICTION Paid Search Lodging Daa Our daa include daily informaion on a paid search campaign for a major lodging chain. For each search keyword (e.g., hoels Los ngeles ), we know is generic or branded designaion, daily informaion on cos (in U.S. dollars), average posiion served (given by daily average placemen rank, e.g., 2.3), and number of impressions, clicks, and 9The specificaion simply exchanges branded and generic in Equaion 11. Generic search aciviies become he dependen variables, and we use branded impressions and clicks as predicors. reservaions. The daa se includes campaign informaion from boh Google and Yahoo. The Google daa run from March 1, 2004, o December 20, 2004, and he Yahoo daa run from May 6, 2004, o ugus 31, Boh campaigns included several hundred generic and branded keywords. (The exac number is proprieary bu sable over he sample.) Tables 1 and 2 give summary saisics. Impressions, clicks, and reservaions peraining o boh generic and branded search flucuae by day of week. In Figure 3, we presen a represenaive ime-series snapsho from he Google daa for four weeks. (The Yahoo daa show he same paern.) The poin of greaes aciviy is usually on Monday. civiy declines modesly up o Thursday. Beginning on Friday, he weekend brings a seep drop. This paern is consisen, wih mos online raffic coming from he workplace (Pauwels and Dans 2001); herefore, we include indicaor variables for day of week in he model. In addiion o he day-of-week flucuaion, here is also subsanial variaion in he level of generic search aciviy over ime (e.g., from preholiday rip planning). Model Comparison: Wihin Sample We esimae he DLM, RDL, and VR models in a Bayesian framework and compare hem using log Bayes facors. We es for auocorrelaion using a Durbin Wason es and regress he residuals on heir own lags. Using boh mehods, we find ha here is no auocorrelaion in he residuals. 10 (This holds for boh he Google and he Yahoo daa.) In Table 3, Panel, we repor he resuls of he model comparisons for he Google daa. 11 For boh he RDL and he VR models, he formulaion wih one lag erm for boh branded and generic aciviy provides he bes fi. Table 3, Panel, shows he bes-fiing RDL model (one lag generic and one lag branded; log marginal densiy: 10Serial correlaion also can be modeled in he DLM framework. Consider he example model, given by y = x b + q + e and q = dq 1 + n, where y and x are observed, is laen, and n ~ N(0, s 2 n). If serial correlaion is presen, i follows ha e = re 1 + x, where x = N(0, s 2 x). To handle i, we can augmen he sae space as follows: y = x b + q + e θ δ θ ν ε ρ = 0 1 ε ξ. We can esimae he augmened model in he DLM framework and, if necessary, incorporae higher-order serial correlaion as well (Naik and Raman 2003). s Naik and Raman (2003, p. 384) illusrae in heir Equaions 25, 26, and 27, boh he lagged dependen variable and he serial correlaion can be incorporaed in an appropriae DLM formulaion. s a special case of heir equaions, we consider he following model: y = ly 1 + x b + q + e. Then we can express i in DLM as follows: α y = Za + z, y λ y 1 1 = θ + 1 = δ 0 0 θ ε ρ ε x β where Z = [1 0 0]. 11The resuls for he Yahoo daa are similar and available on reques. 1 0 ν ξ,

10 96 JOURNL OF MRKETING RESERCH, FEBRURy 2011 Figure 3 DILy CTIVITy FOR BRNDED ND GENERIC KEyWORDS FOR GOOGLE DT (FOUR-WEEK PERIOD) : Branded x Impressions Sa Sa Sa Sa Clicks Sa Sa Sa Sa Reservaions Sa Sa Sa Sa B: Generic x Impressions Clicks Reservaions Sa Sa Sa Sa Sa Sa Sa Sa 1 Sa Sa Sa Sa 11,958), VR model (one lag generic and one lag branded, log marginal densiy: 11,312), and DLM model (log marginal densiy: 11,245). We find ha he bes RDL and VR models are rejeced in favor of he inegraed DLM. We es for diminishing reurns in he awareness par of our model by using he log of impressions and he log of clicks as measures of generic aciviy. We find ha he models wih impressions and clicks provide beer fis han he log models (see Table 3, Panel B). We also es wheher dollars spen on generic search provides a beer fi han he observed coun of generic impressions and clicks. Table 3, Panel B, shows ha he coun daa provide a beer fi. Model Comparison: Ou of Sample We assessed he ou-of-sample forecas performance for he DLM, RDL, and VR models on he Google daa. We esimaed all models wih wo sample cuoff poins: The firs sample has 100 daa poins, and he second has 200. For boh cases, we generae an ou-of-sample forecas for en periods (Bass e al. 2007). In Table 4, we repor he mean absolue percenage error in he forecas period for each branded search aciviy for each model. In all cases, he DLM ouperforms he RDL and VR models. The seleced lag srucure for he RDL and VR models is also confirmed by he holdou es. (The bes-fiing RDL and VR models use one lag branded and one lag generic.) In summary, our DLM model ouperforms he alernaive ime-series models wihin sample and ou of sample. This suggess ha incorporaing he laen consruc of awareness (DLM) offers a superior approach o one in which generic search direcly eners he model hrough a specified lag srucure or in a VR. I is noeworhy ha we find ha measuring generic search aciviy on he basis of absolue impressions and clicks is preferred o doing so on he basis of dollars spen. Parameer Esimaes We firs discuss he parameer esimaes for he Google daa se (Table 5, Panels and B). Google had a significanly higher level of daily aciviy han Yahoo (a leas for his search campaign) and also provided a somewha longer ime series. Nex, we briefly examine he Yahoo resuls o corroborae he findings from he Google daa (Table 6, Pan-

11 Spillover in Paid Search dverising 97 Table 3 MODEL COMPRISON : In-Sample Fi Measures Model Fi Log Marginal Log Bayes Densiy Facor DLM model 11,245 RDL model 1 Lag BR 1 Lag GEN 11, VR model 1 Lag BR 1 Lag GEN 11, B: Measuremen of Generic civiy in he DLM Model Model Fi Generic civiy Log Marginal Log Bayes Measured By Densiy Facor DLM model Impressions and clicks 11,245 ln(impressions) and ln(clicks) 11, Cos 11, Noes: For Panel, generic aciviy is measured by number of impressions and number of clicks, he log Bayes facor is expressed in relaion o he bes model (i.e., he DLM), and he RDL and VR models are he bes-fiing models. Fis for oher lag formulaions are available on reques. For Panel B, he log Bayes facor is expressed in relaion o he bes model (i.e., he model ha uses impressions and clicks as measures of generic aciviy). els and B). 12 Table 5, Panel, and Table 6, Panel, show he esimaes from he reduced-form model given in Equaions 9 and 10, and Table 5, Panel B, and Table 6, Panel B, give he esimaes for he srucural DLM specified in Equaion 7 (for deails, see Web ppendix a hp:// com/jmrfeb11). Indicaor variables for day of week and monh. ll models include indicaor variables o conrol for differences in search aciviy by day of week and monh. The significan (and, herefore, reained) covariaes are he same for Google and Yahoo. s we expeced, he indicaor variables capure he lower weekend search aciviy. We find a negaive effec for Saurday (or he sar of he weekend, WE) and a posiive effec for Monday (or he sar of he week, WK). Only he indicaor variables for are significan (see Table 5, Panel B, and Table 6, Panel B). Thus, day-of-he-week effecs in branded search aciviy occur only in he number of searches (i.e., he search volume), no in he differences in click-hrough or conversion (see Table 5, Panel, and Table 6, Panel ). In oher words, search volume is affeced by day of he week, as previous research has found (Pauwels and Dans 2001). However, he propensiy o click hrough or conver is no affeced by day of he week. We find similar paerns for posiion and : There are no significan differences across days of he week (see Table 5, Panel, and Table 6, Panel ). We did no find any significan paerns of monhly seasonaliy in eiher daa se. verage number of branded searches. We urn o he resuls for repored in Table 5, Panel B. We find ha lagged has a coefficien of.8367 (a ; see Table 5, Panel B). Our daa come from a well-esablished firm in he U.S. lodging indusry. The high carryover rae indicaes ha here 12 he ime we colleced our daa, Yahoo had no inroduced is new aucion sysem Panama. Panama mimics Google s sysem (i.e., incorporaing pas ad performance) and was using a pure second price aucion sysem. Therefore, no ineracion beween pas ad performance and he aucion exiss. Translaed o our framework, his means ha h and k are zero. Table 4 MODEL COMPRISON: FORECST PERFORMNCE : = 100 MPE Impressions Clicks Reservaions DLM model RDL model 1 Lag BR 1 Lag GEN VR model 1 Lag BR 1 Lag GEN B: = 200 MPE Impressions Clicks Reservaions DLM model RDL model 1 Lag BR 1 Lag GEN VR model 1 Lag BR 1 Lag GEN Noes: MPE = mean absolue percen error. We assessed forecas performance wih wo scenarios: using he firs 100 daa poins ( = 100) and he firs 200 daa poins ( = 200). In each scenario, we forecas he nex en periods and compare models. We repor fi resuls for he bes-fiing lag specificaion for he RDL and VR models. is a significan and sable base level of searches for he firm over ime. We find ha he laen consruc for awareness posiively affecs curren (g =.0514; see Table 5, Panel B). This indicaes ha greaer awareness leads o more searches (i.e., impressions) for keywords ha include he brand name. (We discuss he effec of generic search aciviy on awareness afer findings for branded search.). The carryover from he previous period is a =.8836 (see Table 5, Panel B). gain, we find a high carryover rae for. This is because our firm is well known and offers a sable service. We expec low variaion in clickhrough across ime because he service offering is no changing. s we expeced, curren posiion has an effec on. We find ha he effec of curren posiion on is negaive (d =.0166, see Table 5, Panel B). higher posiion (e.g., 5) decreases compared wih a lower posiion (e.g., 2). Unlike, is no affeced by awareness (g is no significan; see Table 5, Panel B). In oher words, awareness (poenially generaed by exposure o branded maerials afer a generic search) leads o an increase in searches. However, i does no lead o an increase in.. We also find a high carryover rae for (a =.8149; see Table 5, Panel B), hough slighly lower han for and. gain, his resul is driven by our lodging company being well known and offering a clear value proposiion. Thus, our finding of a relaively sable over ime is no surprising. There is also no significan effec of awareness on (g is no significan; see Table 5, Panel B).. We find ha a =.7975, indicaing a sable posiioning sraegy over ime. Managers verified our finding: Posiion arges remained mainly sable over he period of our daa. Nex, h allows us o undersand beer how Google has weaked he second price aucion o include pas ad performance (generally assumed in pracice o be measured by pas ). We find ha h = In oher words, an increase in pas leads o a lower curren posiion. ( lower posiion means a beer posiion; e.g., 2 is a beer posiion han 5, all else being equal.) This is wha Google s algorihm is doing rewarding beer-argeed adverisemens (i.e., hose wih higher ) wih lower posiion a he same cos or a consan posiion a a lower cos (see he

12 98 JOURNL OF MRKETING RESERCH, FEBRURy 2011 : Reduced-Form DLM Parameer Esimaes for he Google Daa Se Esimae Parameer Mean 95% Coverage Inerval Carryover a.8367 (.7841,.8766) â.9198 (.7826,.9972) a.8149 (.6929,.9828) â.9741 (.8977,.9994) â.9238 (.8403,.9983) a.3354 (.3302,.3486) Ineracion b (.0294,.0102) b (.0549,.1977) b ( , ) b (1.4048, ) b ( ,.8925) b (.1076,.1552) g.0514 (.0416,.0653) g 4.20E 06 ( 2.15E 05, 3.16E 05) g 1.91E 05 ( 4.63E 07, 4.51E 05) Drif l WK ( , ) l WE ( , ) lˆ WK.0034 (.0184,.0124) lˆ WE.0110 (.0154,.0261) l WK.0028 (.0186,.0129) l WE.0001 (.0159,.0163) lˆ WK.0308 (.0265,.0920) lˆ WE.0246 (.0324,.0806) lˆ WK.0063 (.0124,.0250) lˆ WE.0061 (.0123,.0254) l IMP GEN.0012 (.0236,.1102) l CL GEN (.9812, ) Table 5 ESTIMTES FOR THE GOOGLE DT SET B: Srucural DLM Parameer Esimaes for he Google Daa Se Esimae Parameer Mean 95% Coverage Inerval Carryover a.8367 (.7841,.8766) a.8836 (.7550,.9661) a.8149 (.6929,.9828) a.7975 (.7364,.8210) a.7564 (.5706,.8210) a.3354 (.3302,.3486) Ineracion d.0166 (.0304,.0104) j ( , ,) i.0198 (.0369,.0100) h ( , ) k.1490 (.1973,.0546) g.0514 (.0416,.0653) g 4.20E 06 ( 2.15E 05, 3.16E 05) g 1.91E 05 ( 4.63E 07, 4.51E 05) Drif l WK ( , ) l WE ( , ) l WK.0029 (.0180,.0128) l WE.0114 (.0047,.0266) l WK.0028 (.0186,.0129) l WE.0001 (.0159,.0163) l WK.0986 (.1362,.3712) lˆ WE.0905 (.1292,.3444) l WK.0090 (.0122,.0296) l WE.0083 (.0116,.0306) l IMP GEN.0012 (.0236,.1102) l CL GEN (.9812, ) Noes: WK = sar week (i.e., Monday), and WE = sar weekend (i.e., Saurday). Indicaor variables for monhs were no significan, and we removed hem from he model. Parameers in boldface are significan. nex secion for resuls for ). The effec of curren on curren is j = , so a higher curren leads o a lower posiion. In urn, he effec of curren on curren is i =.0198, meaning ha an increase in reduces. s wih many ime-series models, hese parameers are no meaningful in hemselves; in oher words, comparing h and i will no lead o insighs oher han wheher he parameers are significan. Nex, we use an impulse response approach o shed ligh on heir meaning. Noe ha we did no se ou o opimize he firm s bids. We included and in our model o accoun for he firm s campaign managemen sraegy and o allow simulaneous ineracions among,, and. Given he srucure of paid search in general, and Google s sysem in paricular, a model ha invesigaes spillover effecs beween generic and branded needs o address hese ineracions.. We find ha a =.7564 and k = The high carryover rae for, a, again indicaes ha he firm s bidding sraegy was relaively sable over he period of he daa. The parameer k gives us anoher glimpse ino Google s black box: higher pas leads o a lower going forward. Google punishes a bad adverisemen (i.e., low pas ) by charging more for he same posiion. The effec of curren on curren is i =.0198, meaning ha a higher posiion is cheaper. wareness. In he leaky-bucke model formulaion, changes in awareness are a funcion of he carryover rae and changes in he brand-relaed exposure generic search aciviy. Table 5, Panel B, repors he parameer esimaes and coverage inervals for his componen of he model. We find ha approximaely 30% of curren awareness is carried over o he nex period (carryover rae a =.3354; see Table 5, Panel B). In our framework, his means ha consumers have, on average, a relaively shor search process. We believe ha our resuls using daily search daa provide a lower bound for spillover. To be sure, more spillover could be occurring inraday or even wihin a given user session. Our finding of significan spillover across days highlighs he imporance of his phenomenon. lhough some adverisers are now able o examine inraday daa (and analyzing his should be a opic for furher research), we noe ha i is likely o bring oher modeling challenges along wih i (e.g., sparse daa in he overnigh hours). 13 he hear of our sudy is he quesion wheher generic search aciviy spills over ino branded search aciviy hrough awareness. Our modeling resuls indicae ha his is 13lhough we acknowledge he limiaions of daily daa, we noe ha oher recen sudies on paid search have worked wih weekly or monhly daa (e.g., Ghose and Yang 2009a, b; Yao and Mela 2009). We conduced an analysis in which we aggregaed our daa o he weekly level and esimaed exploraory models. We found no significan effec for generic-obranded spillover in any case. Thus, access o daily daa has a leas enabled documenaion of he spillover effec, is asymmery, and an iniial esimae of is magniude and decay over ime.

13 Spillover in Paid Search dverising 99 : Reduced-Form DLM Parameer Esimaes for he Yahoo Daa Se Esimae Parameer Mean 95% Coverage Inerval Carryover a.9396 (.9001,.9732) â.9680 (.8786,.9994) a.8911 (.8015,.9945) â.8912 (.8091,.9832) â.8773 (.7687,.9937) a.4134 (.3326,.4773) Ineracion b (.0142,.0091) b (.0131,.0570) b 42 N.. b (.0992, ) b 52 N.. b (.0017,.0341) g.0747 (.0524,.1061) g.0003 (.0001,.0008) g.4134 (.0003,.0005) Drif l WK ( , ) l WE ( , ) lˆ WK.0090 (.0291,.0489) lˆ WE.0241 (.0150,.060) l WK.0030 (.0407,.0355) l WE.0164 (.0212,.0551) lˆ WK.0773 (.1464,.3268) lˆ WE.0222 (.2002,.2524) lˆ WK.0039 (.0372,.0458) lˆ WE.0006 (.0390,.0430) l IMP GEN 6.89E-07 ( 3.48E-06, 3.28E-05) l CL GEN.1677 (.0054,.7139) Table 6 ESTIMTES FOR THE yhoo DT SET B: Srucural DLM Parameer Esimaes for he Yahoo Daa Se Esimae Parameer Mean 95% Coverage Inerval Carryover a.9396 (.9001,.9732) a.9680 (.8786,.9994) a.8911 (.8015,.9945) a.6845 (.5754,.7736) a.6696 (.5022,.7674) a.4134 (.3326,.4773) Ineracion d.0124 (.0160,.0105) j ( , ) i.0488 (.1296,.0335) h N.. k N.. g.0747 (.0524,.1061) g.0003 (.0001,.0008) g.4134 (.0003,.0005) Drif l WK ( , ) l WE ( , ) lˆ WK.0100 (.0277,.0500) lˆ WE.0243 (.0142,.0655) l WK.0030 (.0407,.0355) l WE.0164 (.0212,.0551) lˆ WK.0925 (.2155,.4234) lˆ WE.0264 (.2755,.3401) lˆ WK.0077 (.0372,.0543) lˆ WE.0017 (.0417,.0486) l IMP GEN 6.89E-07 ( 3.48E-06, 3.28E-05) l CL GEN.1677 (.0054,.7139) Noes: WK = sar week (i.e., Monday), and WE = sar weekend (i.e., Saurday). Indicaor variables for monhs were no significan, and we removed hem from he model. N.. = no applicable because Yahoo s aucion mechanism a his ime did no ake pas performance ino accoun; ha is, pas does no affec curren posiion or. Parameers in boldface are significan. indeed he case. Firs, as we discussed previously, awareness has a posiive impac on curren branded searches, (g =.0514). Second, we find ha generic clicks have a srong posiive effec on awareness (l CL GEN = ; see Table 5, Panel B). In conras, generic impressions do no have a significan effec on awareness (l IMP GEN =.0012; see Table 5, Panel B) because he 95% coverage inerval (.0236,.1102) includes zero. This means ha simply being exposed o he company s ex adverisemen afer a generic search does no spill over o increase branded search aciviy. However, if he user clicks on he adverisemen and visis he company Web sie, his leads hrough awareness o an increase in branded searches going forward. We can hypohesize ha inspecing a brand afer a generic click-hrough migh lead he consumer o become aware of is relevance for curren search goals. The user migh search for he brand again, nex ime using a query ha includes he brand name. Resuls for he Yahoo Daa We also esimaed he DLM model on he Yahoo daa se o provide a validaion es across search engines. Yahoo used a differen mehod o rank sponsored links on is sie a he ime we colleced our daa se. Yahoo had no inroduced is Panama sysem, which mimics he Google sysem, bu was using a pure second price aucion sysem wih no performance feedback hrough pas. Noe ha he feedback parameers, h and k, ha link pas o curren and are se o zero in he Yahoo model. 14 The wo search engines also differed in sie design and appearance and migh arac differen ypes of online users. (We have no direc evidence of user differences oher han managemen s belief and indusry whie papers.) Panels and B in Table 6 repor he parameer esimaes for he Yahoo daa. comparison of Table 5 (Google) and Table 6 (Yahoo) shows ha all he key findings are corroboraed. We find ha he effecs for Saurday and Monday are similar among he indicaor variables. We also find no significan seasonaliy in monhly effecs. s wih he Google daa, lagged branded aciviy has carryover coefficiens in he range (see Table 6, Panel B). In boh cases, has a somewha lower carryover rae han and. wareness also significanly affecs, bu no and. (Because awareness is dimensionless, he coefficiens are no direcly comparable beween he wo daa ses.) The esimaed awareness carryover rae for he Yahoo daa is of similar magniude (Yahoo a =.4134 versus Google a =.3354) o he one esimaed for Google. The parameer esimaes for generic impressions and clicks in 14Esimaing he model on he Yahoo daa wihou hese consrains produced similar resuls, and neiher parameer was significanly differen from zero.

14 100 JOURNL OF MRKETING RESERCH, FEBRURy 2011 he Yahoo daa also parallel he resuls in he Google daa. boh search engines, generic impressions had no significan effec on awareness, bu generic clicks did. Thus, we conclude ha he esimaes from he Yahoo daa corroborae he naure of he spillover effecs found in he Google daa. Tesing for Reverse Spillover Our resuls show ha generic search posiively affecs branded search hrough awareness. However, his finding could be due o a general correlaion beween generic and branded search: On days wih high search aciviy in he caegory, boh generic and branded search migh be similarly affeced. To address his alernaive explanaion, we also invesigae wheher branded search influences generic search. We esimae a DLM and an RDL model wih generic search aciviy measures as he dependen variables (in place of branded search measures). nalogous o he previously described models, we include daily and monhly indicaor variables along wih lagged generic and branded aciviy as covariaes. In boh he DLM and he RDL models, we do no find ha branded search aciviies affec generic search aciviy; ha is, all he relevan parameers are insignifican for boh he Google and he Yahoo daa. Table 7 presens hese parameers and heir coverage inervals. Collecively, our resuls show ha spillover is asymmeric (i.e., generic affecs branded bu no vice versa), consisen wih he concepual and modeling frameworks we propose. Omied Variable Bias noher poenial alernaive explanaion for our resuls is omied variable bias. Suppose ha here is no spillover from generic search aciviy o branded search. We sill migh find such an effec if generic search aciviy in our model proxies for an omied variable, which in urn affecs branded search. lhough we canno compleely eliminae his possibiliy, we can ake advanage of he availabiliy of aciviy daa for wo search engines. (Thus far, all our resuls have been based on models esimaed on he daa wihin a given search engine, i.e., using eiher Google or Yahoo daa alone.) Table 7 STRUCTURL SPILLOVER PRMETER ESTIMTES FOR THE GENERIC DLM : Google Esimae Parameer Mean Coverage Inerval Drif l IMP BR.0021 (.0030,.0093) l CL BR.0047 (.0004,.0085) B: Yahoo Esimae Parameer Mean Coverage Inerval Drif l IMP BR.0013 (.0006,.0022) l CL BR.0115 (.0180,.0049) Noes: For exposiional ease, we only repor he relevan parameers (i.e., he parameers ha describe spillover from branded o generic). Parameers in boldface are significan. Indusry whie papers have repored ha consumers are loyal o heir search engine and ha limied cross-engine search akes place (i.e., users become accusomed o employing one engine or anoher). If such is he case, examining wheher generic search aciviy on Yahoo creaes a spillover effec on branded aciviy on Google (and vice versa) enables us o assess he poenial for omied variable bias. This is because such an omied variable (e.g., a major offline adverising campaign) would be likely o have similar effecs on aciviy a boh search engines. We reesimaed our models by swiching he generic aciviy variables beween search engines. For boh Google and Yahoo, we find ha generic aciviy on he nonfocal search engine does no spill over o branded search aciviy on he focal search engine. Table 8 shows ha he relevan parameers ha capure he impac of generic aciviy on awareness are no significan a he 95% coverage level in eiher case. The Exen of Generic-o-Branded Spillover The foregoing resuls demonsrae ha spillover is significan and asymmeric. However, i is difficul o ell from he DLM parameers wha he exen of his effec is likely o be. To invesigae he magniude of he esimaed spillover effec, we use an impulse response approach. Because generic impressions do no have a significan effec in he model, we focus our analysis on generic clicks. Specifically, we ask how much spillover o branded search would be generaed by an addiional en generic clicks on he Google or Yahoo search engine. To do his, we shock he sysem in he firs period wih en generic clicks. (Such a shock could come from, for example, adverising hrough an addiional se of generic keywords.) Using he exising daa and our model esimaes, we hen calculae how he one-ime increase in generic clicks affecs reservaions semming from branded search aciviy. We find ha he shock in Period 1 produces an increase in branded search aciviy. In Figure 4, we plo he incremenal reservaions semming from aciviy a each search engine for he following 30 days. For Google, he effec peaks on Day 3 and decays aferward. For Yahoo, he effec peaks on Day 5. fer 12 days, nearly 95% of he spillover Table 8 STRUCTURL SPILLOVER PRMETER ESTIMTES FOR THE GENERIC DLM USING NONFOCL GENERIC CTIVITy : Google Esimae Parameer Mean Coverage Inerval Drif l IMP GE _Yahoo.0075 (.0092,.0023) l CL GE _Yahoo.0900 (.2752,.2789) B: Yahoo Esimae Parameer Mean Coverage Inerval Drif l IMP GE _Google.0678 (.3666,.2573) l CL GE _Google.0144 (.0601,.0615) Noes: For exposiional ease, we only repor he relevan parameers (i.e., he parameers ha describe spillover from branded o generic). Parameers in boldface are significan.

15 Spillover in Paid Search dverising 101 Incremenal Reservaions 15Deailed resuls are available on reques. Figure 4 IMPULSE RESPOE FOR NUMBER OF RESERVTIO Google yahoo Time (Days) has been realized a Google. The oal effec of he en addiional generic clicks is o produce.68 (.51) incremenal reservaions in he Google (Yahoo) case. Of hese,.56 (.32) are spillover reservaions from branded search, and he balance are reservaions ha flowed direcly from generic search conversions. The paern of resuls in Figure 4 is consisen wih he noion ha he search for lodging is relaively shor primarily occurring over he course of a couple of days o wo weeks. We would expec spillover iming o differ across producs (e.g., for new cars, i migh be longer and more evenly spread ou). nalyss should be able o use his approach o esimae he role of spillover in a paid search adverising campaign and hen pu i ino a decision suppor sysem for opimizing paid search ad spending allocaions. We leave his endeavor as an imporan opic for furher research. Consumer Search and wareness of Relevance key ene of our approach is ha he laen consruc in he DLM represens wha we refer o as awareness of relevance; ha is, generic search informs consumers ha he brand is relevan o heir search goals. Thus, we migh expec o observe ha he effecs of generic search are more pronounced when he underlying level of brand awareness is high. lhough we lack informaion on awareness for our lodging chain, we do know ha i is viewed as a sronger brand in rural areas han major ciies. To explore his, we spli our generic search variables ino search aciviy for keywords wih locaions in major ciies and aciviy for all oher U.S. locaions. We esimae he DLM model using boh ses of new variables. We find ha he effec of generic clicks is significan in each case bu much sronger for generic searches for he rural locaions. 15 Though exploraory, his resul is consisen wih our inerpreaion of he laen consruc as awareness of relevance. CONCLUSION In his sudy, we examine wo caegories of paid search adverising: he ex adverisemens linked o generic keyword searches and hose linked o branded keyword searches. Our objecive is o invesigae he dynamic relaionship beween hese wo caegories of search aciviy. Specifically, we sudy wheher generic search aciviy spills over o branded search and, if so, wheher he effec is asymmeric (i.e., reverse spillover does no occur). To invesigae hese quesions, we develop a modeling framework based on he esablished noion ha exposure o brand-relaed informaion creaes awareness. pplying his idea o paid search, our approach holds ha he exposure o brand-relaed informaion due o paid search (e.g., impressions from ex adverisemens, company Web pages afer click-hrough) can creae an awareness of relevance ha he brand provides a soluion o he searcher s problem. s users coninue heir search process, his awareness can hen spill over o fuure branded search aciviy. Following Naik, Manrala, and Sawyer (1998), Naik, Raman, and Srinivasan (2009), and Bass and colleagues (2007), we model he dynamic evoluion of awareness of relevance using a leaky-bucke model. In place of radiional (indirec) measures of brand-relaed exposures (e.g., gross raing poin, dollar expendiures), we use he direc measures of impressions and clicks ha occur as a resul of search aciviy. We combine he awareness model wih a dynamic branded search aciviy model and esimae he wo componens ogeher in a Bayesian framework. Our approach accouns for he firm s endogenous campaign managemen sraegy by including and in he model. Our model also accouns for he Google enhanced aucion, which uses pas o eiher lower posiion (a he same ) or lower (a he same posiion). We apply he modeling approach o daily daa from paid search campaigns on Google and Yahoo run by a major lodging chain. We model five measures of branded search aciviy as dependen variables simulaneously: impressions, clicks, reservaions, posiion, and coss. We find ha generic search aciviy, specifically generic clicks, has a significan, posiive effec on awareness of relevance. In urn, we find ha his awareness significanly influences he number of branded searches, hough no he or. Thus, increases in he number of clicks and conversions come from he increased number of branded searches aking place in response o an increase in awareness of relevance. We compare our DLM wih wo ime-series models ha do no use a laen consruc for awareness. Insead, hese alernaive models a sandard VR and an RDL represen he effec of generic search on branded search direcly wihin a lag srucure. We find ha our DLM ouperforms boh alernaive models wihin sample as well as ou of sample. We also invesigae he asymmery in spillover (i.e., wheher branded search also spills over o generic search). We find ha branded search has no significan impac on generic search, showing he expeced asymmery. In addiion, we es for wheher generic aciviy poenially proxies for omied variables. Our daa allow us o es wheher generic search on Yahoo (Google) affecs branded search on Google (Yahoo). In boh cases, we find ha generic search on he nonfocal search engine does no influence branded search on he focal search engine. Las, we measure he exen of he spillover effecs over ime by employing an impulse response approach. We find ha increases in generic search generae sizable spillover o branded search. The peak impac occurs roughly wo o five days ou, and he bulk of he impac is realized wihin wo weeks. The magniude of hese effecs is large enough o

16 102 JOURNL OF MRKETING RESERCH, FEBRURy 2011 have implicaions for spending allocaions and search ad campaign managemen. We leave exploraion of his as an imporan opic for furher research. We develop and es our model using he ype of informaion ha managers use on a day-o-day basis. Thus, our key goal is o make he model useful in pracice wihou having o obain addiional daa. However, a drawback of his approach is ha i does no rack he search aciviy of individual users. Thus, we have refrained from aemping o develop a comprehensive heory of Inerne search behavior as applied o paid search adverising. Insead, we propose he more general concepualizaion of awareness of relevance for he search, disinguishing generic from branded erms by he noion ha only a brand ha has awareness of relevance will be searched using a branded erm. The exploraory resuls we obained by sraifying he keywords by geographic locaion also lend suppor o his inerpreaion of consumer search behavior. Furher research should develop and es a heory-driven model of individual-level paid search. lhough we have daa from boh Google and Yahoo, we lack informaion ha would enable us o explore, in more deail, he differences beween he search engines and heir user bases. Given he innovaion and compeiion currenly aking place among search engines, we believe ha his also represens an imporan opic for furher research. Links o he adveriser s Web sie may also appear in he organic search resuls ha are reurned along wih he paid search adverisemens. Unforunaely, we have no informaion on organic search in our daa se. This means ha we canno assess wheher i is necessary for managers o buy branded keyword adverisemens o reap he benefis of he spillover from generic search. To he exen ha organic lisings do mee his need, our approach will oversae he conribuion of generic paid search o branded paid search. In addiion, an increase in awareness could lead o an increase in direc visis o he Web sie (i.e., when visiors ype in he URL of he Web sie direcly). This could also affec he performance of generic search as we measure i. In his aricle, we evaluae performance a he level of keyword caegories (i.e., aggregaions across keywords sharing cerain characerisics). Managers also have he capabiliy o make changes a he individual keyword level if hey so choose. Developing models o help evaluae he performance of individual keywords would also be worhwhile. For example, i may urn ou ha he significance and exen of spillover from generic o branded may vary wih he characerisics of he generic search erm. Las, we use aggregae daa and a laen consruc in our modeling approach. We have labeled his laen consruc awareness of relevance and believe ha, in so doing, i is consisen wih boh he overall approach and our empirical findings. n imporan nex sep would be o obain acual measures for awareness (e.g., by using survey mehods) o validae he consruc in fuure modeling applicaions. REFERENCES aman, Berk, Carl F. Mela, and Harald J. van Heerde (2008), Building Brands, Markeing Science, 27 (6), Bass, Frank, Norris Bruce, Sumi Majundar, and B.P.S. Murhi (2007), Dynamic Bayesian Model of he dverising-sales Relaionship, Markeing Science, 26 (2), Chen, Yongmin and Chuan He (2009), Paid Placemen: dverising and Search on he Inerne, working paper, Deparmen of Economics, Universiy of Colorado Boulder. 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