Competitive Analytics of Multi-channel Advertising and Consumer Inertia

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1 Abstact Competive Analytics of Multi-channel Advetising and Consume Inetia Yiyi Li UT Dallas (Full pape) Eic Zheng UT Dallas Ying Xie UT Dallas The sputs and multiplicy of Intenet advetising have made multi-channel attibution an immediate challenge to maketing pactiones. We popose an integated individual-level choice model that consides thee stages of a consume s puchase funnel awaeness, altenative evaluation and puchase - acoss all competos to analyze the effects of touches on (1) consumes choice of enty se, (2) thei subsequent seach decisions about othe webses in the awaeness set, and (3) thei subsequent puchases at one of the seached webses. Keywods Multichannel maketing, puchase funnel, advetising esponse. Intoduction Advancement of digal technologies has enabled fims to each customes though a vaiety of ad channels such as seach engine, , efeal ses and display ads. As a esult, spending on digal advetising has eached $42.3 billion in 2013 in the U.S. (emakete.com, 2013). Ad attibution, the analytics ads effectiveness acoss multi-channels and multi-touchpoints (MCMT), has become an immediate challenge to maketing pactiones (Dalessando et al. 2012). Howeve, most companies evaluate the effectiveness of diffeent Intenet advetising fomats based on the sequence of the ads in the convesion path unde vaious heuistics ules. Examples include the single souce attibution models such as the last inteaction model and the fist inteaction model, and the factional attibution models such as the linea model, the time decay model and the posion based model. These attibution models commonly weight the advetisements in an ad-hoc manne, e.g. simply assigning weights based on thei appeaance sequence in the puchase paths. These heuistic attibution ules have been cicized to be lagely inconsistent and inconclusive, and they only conside the paths esulting in convesions while disegading the majoy of paths that do not, leading to biased insights (Li and Kannan 2014). Fo these easons, the industy calls fo a moe scientific measue that is data-diven (Econsultancy 2012, Shao et al. 2011). Although the academic leatue in this aea has been spase, an inceasing numbe of algohmic attibution models have been poposed in ecent yeas (Li and Kannan 2014, Abhishek et al. 2014, Xu et al. 2014). These models pedominantly focus on consumes inteaction wh the focal webse only, disegading the impact of competive actions on the convesion pobabily, a cical deteminant of ad effectiveness. As is common in today s hype-competive online etailing wold, customes ae likely to be exposed to vaious foms of digal ads acoss all the competing fims in a paticula poduct categoy. Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

2 Failing to account fo customes acoss-competo inteactions may yield biased estimate of ad effectiveness, as we demonstate in this pape. This study aims to fill the gap by poviding a new ad pefomance measue in a competive online shopping envionment. We achieve ou objective by modeling consume s multinomial choice decisions acoss all competing webses in contast to a binay choice between convesion and no convesion wh espect to a focal se only. Futhe, we examine the entie funnel of a custome s puchase path. The leatue suggests that the effectiveness of vaious digal ads depends on a consume s eadiness stage in his/he online shopping jouney (Abhishek et al. 2014). In geneal, a consume goes though thee stages in a puchase session: 1) awaeness stage, in which she peceives a need and fom a consideation set; 2) altenative evaluation stage, in which she viss one o seveal stoes to seach fo elevant infomation; 3) the final puchase stage. In the online puchasing funnel, Ratchfod (2008) documents that the amount of online seach is limed despe geatly loweed seach costs. Fo example, the aveage numbe of unique ses seached by each household in the ai-tavel industy is aound 1.8 and 42% of the tavel shoppes vis only one webse (Johnson et al. 2004). This phenomenon indicates that being the enty se (i.e., the fist webse vised by the consume duing a puchase jouney) is val. Fom the makete s pespective, is ideal fo a consume to discontinue he seach pocess and convet at the enty se. Ou model adopts the funnel view of consume s online puchase path, and specifically models customes enty decisions. Lastly, consumes loyalty plays a cical ole in undestanding the funnel. Repeat buys account fo ove half of e-tailes sales (CybeAtlas, 2002). Consume s seach and puchase decisions ae affected not only by vaious foms of advetising but also by past inteactions wh the webses. Theefoe, is essential fo ou model to account fo state dependence in ode to captue the dynamics of consumes online behavios. In paticula, we need to identify 1) whethe the obseved inetia is due to stuctual state dependence o simply induced by heteogeney in consume pefeence, and 2) if is the fome, what the souce of such inetia is. In this pape, we popose an integated thee-stage choice model that consides individual custome s seach and puchase decisions acoss all competos to analyze the effects of ad clicks on (1) consume seach, whee we specifically examine each custome s choice of enty se and vis decision fo othe webses they ae awae of, and (2) thei subsequent puchases at one of the seached ses. This model is estimated using a unique disaggegated individual level panel dataset that ecods consume inteactions wh all elevant webses in the online ai ticket booking industy though vaious online advetising channels including seach engine, , display ads and efeals (non-paid links fom othe webses) along a convesion path. Ou esult shows that diffeent types of digal ads affect consumes diffeently based on the stages in thei puchase funnel. and efeals affect vis decision posively, but have limed impact on convesion pobabily, while seach engine and display ads have a posive impact on both decisions. Ou poposed model suggests that the diect effect of advetising on puchase is smalle in size in geneal compaed wh the benchmak model that focuses solely on puchase decision. Secondly, we find that inetia of seach and puchase is obust to flexible contols fo pefeence heteogeney, and we quantify the long-tem magins of advetising contacts. Ou model bette pedicts individual consume s online behavio based on thei past behavioal data compaed wh the model that does not conside consume dynamics. Thid and moe impotantly, wh the cuent model, we ae able to compute the own- and coss-maginal impact of ad channels fo each webse and find that the effectiveness of ad channels vaies acoss webses. Empiical Setting Ou data comes fom an online panel povided by a leading Intenet analytics company. The aw data includes detailed online bowsing and tansaction data fom a goup of Intenet uses chosen andomly by the data povide fo the entie yea of Each use s online activies ae ecoded by the poxy seves to fom a click-steam dataset that includes the use id, domain name, date, time, duation, numbe of pages viewed, ediecting webse, and tansaction elated infomation including poduct desciption, categoy, pice, quanty and basket total if a puchase is obseved. This dataset is unique as contains consume s evey inteaction wh all elevant webses in a puchase session. In addion, we ae able to infe the advetising channel used by the consume fom the ediecting webse. A thid Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

3 advantage of ou dataset is that ecods each consume s complete bowsing and puchasing histoy wh all webses, so we ae able to captue the dynamics in consume decisions. We focus on the ai-tavel industy. Ou sample compises of 1697 puchase sessions made by 1002 panelists. We define a puchase session as all viss to elevant webses in the industy whin 7 days pio to a puchase, following De Los Santos et al. (2014). A webse is consideed elevant if thee is at least one ai-tavel puchase occued at the webse duing ou data time peiod. We exclude Southwest.com fom ou analysis because is distinct fom the est of majo tavel ses. Table 1 pesents the complete list of webses included in ou analysis. These webses ae gouped into: 1) online diect channel of ailine companies, 2) online agents that sell tickets of multiple ailines. To avoid the cuse of dimensionaly in estimation, we focus on the competion among online agents, and goup all ailine companies togethe unde the label diect channel. We also goup ameicanexpess-tavel.com, sabesonicweb.com and wwte1.com togethe and label them as small agents since the numbe of viss and tansactions of these thee agents ae significantly smalle than the othes. We goup all the ediecting webses into fou channels: seach engine, , display ads, and efeals (advetising on thid-paty webses that the fims do not pay fo). The null option is diect vis by typing the webse addess. The top five ediecting webses in each channel ae shown in Table 2. Table 1: List of Webses in Ai Ticket Categoy Code Webses # of Seaches # of Puchases 1 Ailine diect 1306 (35.70%) 1027 (60.52%) 2 cheaptickets.com 259 (7.08%) 86 (5.07%) 3 expedia.com 604 (16.51%) 201 (11.84%) 4 hotwie.com 180 (4.92%) 29 (1.71%) 5 obz.com 451 (12.33%) 154 (9.07%) 6 piceline.com 403 (11.02%) 86 (5.07%) 7 Small agents 53 (1.45%) 9 (0.53%) 8 tavelocy.com 402 (10.99%) 105 (6.19%) Table 2: Top Rediecting Webses in Each Advetising Channel Seach Engine Display Ads Refeals google.com 28.98% live.com 2.29% hotels.com 0.33% kayak.com 2.04% yahoo.com 21.21% comcast.net 1.57% clxtk.com 0.28% lowfaes.com 1.62% aol.com 9.13% msn.com 0.56% condu.com 0.26% bookingbuddy.com 1.11% bing.com 5.28%.com 0.51% alot.com 0.21% tavelzoo.com 0.93% ask.com 0.91% att.net 0.47% potcolumbus.com 0.21% cheapflights.com 0.87% Table 3: Channel Usage fo One Puchase Session Code Aveage seach engine (1.61) (0.82) (0.84) (1.00) (0.75) (0.74) (0.11) (0.79) (1.15) (0.35) (0.24) (0.22) (0.24) (0.33) (0.18) (0.00) (0.17) (0.28) display (1.40) (0.16) (0.45) (0.20) (0.20) (0.16) (1.02) (0.53) (0.87) efeals (0.36) (0.34) (0.36) (0.36) (0.55) (0.60) (0.37) (0.32) (0.42) diect vis (2.87) (1.22) (1.99) (1.31) (1.37) (1.18) (0.79) (1.64) (2.12) Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

4 We obseve ad click-thoughs but not ad exposues, and so we aggegate the numbe of click-thoughs fom each ad channel to epesent the infomation stock fo each webse. The aveage numbe of clickthoughs pe puchase session fo each ad channel/webse combination is pesented in Table 3 (along wh the standad deviation in paenthesis). On aveage, seach engine is the most widely used ediecting channel, followed by display ads. The Model Ou model adopts the puchase funnel view in the context of online puchases of high- involvement poducts in a competive envionment. The puchase funnel is gounded in the infomation pocessing theoy that descibes the way consumes make thei puchase decisions, fom peceiving a need all the way to post-puchase behavios (Bettman et al., 1998). Figue 1 illustates how a consume moves though the thee stages duing a puchase session. Figue 1 A Funnel View of Online Puchase Diffeent ad fomat affects consumes diffeently based on s stage in a puchase funnel. In ode to captue the effectiveness of diffeent ad fomats at diffeent stages, we popose a thee-level model which explicly accounts fo the fomation of awaeness set, the vis decision and the puchase decision in a competive envionment. The details of each stage ae discussed as follow. Stage 1: Awaeness At this stage, the consume conceives a puchase need and etieves a set of webses that may satisfy the need fom memoy. Such awaeness of these webses due to past ad exposues, inteactions wh the webses o wod-of-mouth. Such awaeness affects subsequent decisions such as which webses to vis and whee to make the final puchase. Tadional discete-choice models usually assume that consumes ae awae of all available altenatives in the maketplace and they make decisions out of identical choice sets. Howeve, pevious studies have shown that choices ae often limed to a subset of all altenatives and that band awaeness vaies acoss consumes (Daganska and Klappe 2011). Fo example, the suvey data by Daganska and Klappe (2011) eveals that most gound coffee shoppes ae only able to ecall two to fou bands out of the five majo national bands. In ou case, the online ai-tavel industy is composed of dozens of webses heteogeneous in maket shae and investment in advetising. Theefoe, will be even hade fo the consumes to be awae of all possible choices and thus, making the assumption of full infomation set moe poblematic. Failue to account fo the heteogeney in consume s awaeness sets will lead to biased estimates of band pefeences and esponses to maketing activies (Goeee 2008). Theefoe, moe pactically we allow awaeness set to vay acoss consumes and model the fomation of each individual at each puchase session explicly in the fist stage. We use = ( K ) = { } A 1,, 0,1 J ai t a ijt to indicate consume i s awaeness set at time t fo the total J competing webses in the industy whee a ijt denotes each se. We assume that awaeness set membeship is independent acoss webses, so the pobabily that consume i s awaeness set equals A Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

5 is given by Φ = P( A) = φijt ( 1 φijt) j A j A webses pobabily of belonging to the awaeness set as φ ijt, whee φ ijt is the pobabily that a ilt=1. We model a ( λ0, ij + Iijtλi) ( λ0, ij Iijtλi) exp = 1 + exp + (1) whee λ 0,ij is a webse-specific constant vaying acoss individuals and I ijt {0,1} indicates whethe the consume i has any past inteaction (vis o puchase) wh webse j befoe the cuent puchase session. This specification allows a consume s awaeness set to be affected by both population-level populay of the webse as well as individual-level histoy wh the webse. All webses that ae vised by the consume late in the second stage should be included in he awaeness set automatically because the consume can only vis the webses that she is awae of. Theefoe, these webses ente into the likelihood function as ϕ ijt. Howeve, is not obsevable to eseaches whethe the consume is awae of any othe webses that ae not vised. Theefoe, we need to account fo all possible combinations. If thee ae J competing webses and the consume seaches s of them, then thee will be 2 J-s potential awaeness sets. If J is lage is s is small, will be infeasible to compute all the possible pobabilies since the numbe of potential awaeness sets inceases exponentially. To addess this issue, we adopt the method poposed by Goeee (2008) that simulate the choice set facing consume i fist and theeby educe the elevant numbe of awaeness sets to only one. Stage 2: Altenative Evaluation A consume begins to vis webses fo infomation seach and altenative evaluation in the second stage afte ecognizing a puchase need. We use S to indicate the set of webses that ae vised by the consume in this stage. S is a subset of A because the consume has to be awae of the webse to vis the webse. We pos that consume i s vis to a specific webse depends on the peceived utily, which is affected by he pio inteactions wh the webse including ad clicks, vis and puchase histoy. The utily of vising webse j is given by: = + ε = α + α + ε, (2) Uijt U ijt ijt 0, ij xij, t 1 i ijt Whee x ij,t-1 denotes all the factos that may impact consume i s peceived utily of vising webse j at time t, α 0,ij captues consume i s intinsic pefeence fo webse j that cannot be explained by x ij,t-1, and ε ijt follows a genealized exteme value distibution. The coefficients α s ae assumed to follow Multivaiate Nomal distibution to account fo individual heteogeney in webse pefeences. The seach theoy assets that a consume stats his/he seach pocess wh the altenative wh the highest utily (Ratchfod 2008). Given the utily specification, the condional pobabily to obseve webse k being chosen as the enty se by consume i at time t given his/he awaeness set A and individual paametes is given by ( F k A α ) P =, i = exp k ' A ( α0, ik + xik, t 1αi) ( α0, ik ' + xik ', t 1αi) Ideally, we would like to model a consume s subsequent webse viss in sequence. Howeve, consumes vis the webses back and foth and may dop an unattactive webse at any time befoe the convesion takes place, thus making difficult to define the eal sequence of viss. In compomise, we model the seach decision fo the emaining webses in a consume s awaeness set as a numbe of independent binay choices. A consume will vis a webse if the peceived value of that vis is posive. Given the awaeness set A, the enty se F. And he individual paametes, the condional pobabily of obseving a consume s seached set S except the enty se is given by exp (3) Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

6 ( S\ F F A βi) P,, Stage 3: Puchase Stage = exp( β0, il + xil, t 1βi) 1 \{ } 1 exp( 0,, 1 ) (4) + β il + xil t βi exp \ ( β0, il + xil, t 1β i) l S F l A S In the final stage of the puchase funnel, the custome decides which webse to puchase fom. Customes have to vis the webse befoe they can puchase fom, theefoe, the choice is made among vised webses in contacts to all webses in the maket o the consume s awaeness set. As we diectly obseve the entie seach pocess made by each consume, we ae able to captue eveyone s choice set whout any uncetainty. Thee is no need to model the heteogeney in consume s choice set in a pobabilistic manne like stage 1. In addion, as has been explained in the Data section, final puchase at one of the competing webses is equied fo a session to be included in ou estimation sample. Theefoe, no outside option is allowed in the puchase stage. The utily of puchasing fom webse j is given by the fomula as follow: U U x ijt = ijt + εijt = γ0, il + iltγ i + ξijt, (5) Whee x ilt ae the covaiates that may affect consume s puchase utily and γ 0,il is consume i s intinsic webse-specific pefeence that cannot be explained by othe covaiates.γ 0 s ae assumed to be Multivaiate Nomal distibuted to allow fo consume heteogeney in pefeences. ξ ijt is the unobseved pat of utily and follows a genealized exteme value distibution Given the consume i s seached set at puchase session t: S =(s i1t,,s ijt) {0,1} J and he individual tastes, the condional pobabily of puchasing fom webse l is given by ( P l S) P = = l ' S exp exp ( γ 0, il + xiltγi ) silt ( γ il + x γ ) s 0, ' il ' t i il ' t. (6) In paticula, if s ilt=0, the pobabily that webse l is chosen tuns zeo. If the consume viss only one webse, the pobabily that the enty se is chosen is one. Covaiates The covaiates that may affect the consume s utily of vising and puchasing fom a webse ae gouped into thee blocks. The fist block involves the numbe of viss to the webse though diffeent online channels, i.e., numbe of ad clicks. Wheneve a consume viss one of the competing webses though a ediecting webse, we label the vis as one of the fou following ad channels: 1) seach engine, in which the consume entes a keywod and ediects to the webse by clicking ehe a sponsoed o oganic link (fo which ou data does not diffeentiate); 2) , in which the consume links to the webse by clicking an fom his/he mailbox; 3) display ads, which we define as thid paty webse that uns banne ads o video ads; 4) efeal engines, which we define as thid paty webse that povide a list of webses that competes in the same industy and may o may not povide eviews o atings on the webses, such as TipAdviso.com. If one vis does not have a ediecting webse, we label as diect vis, which means the consume viss the webse by typing in the URL diectly in the bowse. We then count the numbe of viss though diffeent ways to evey seached webse in a puchase session. We pos that the numbe of clicks on diffeent ad channels in the pevious puchase session affects consume s utily of vising a webse, while the numbe of ad click in the cuent puchase session affects the utily of puchasing on a webse. This is because the moe a consume inteacts wh a webse though an advetising channel, the moe cumulative infomation stock she may accue about the webse. The infomation stock collected though diffeent channels can take many functional foms. To simplify the estimation, we simply assume that is a linea function of the numbe of viss to the webse though a specific channel. But ou model can be eadily adapted to incopoate othe fomations of the latent infomation stock. Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

7 The second block of covaiates is consume s long-un histoy wh the webse, which involves the decayed cumulative amount of time and money spent on the webse in the past. The decayed cumulative time (money) spent on the webse j at puchase session n is calculated as n 1 tijn tijh Cml _ time( money) = 0.7 ijn time( money), (7) ijh h= 1 whee t ijn-t ijh is the elapsed time (in month) between two puchase sessions and time(money) ijh is the time (money) spent on bowsing the webse at puchase session h. The decay ate 0.3 indicates that only 50% of infomation is etained afte two months. We tied othe decay ates and the estimation esults do not change qualatively. The consume leans about a webse by bowsing and puchasing on. The moe s/he uses the webse, the lowe the costs of making sense of infomation souces and thing about the infomation gatheed will be, which in tun lead to educed time in finding elevant infomation and a faste check-out pocess (Shugan 1980). Theefoe, cumulative time and money spent on the webse will incease s attactiveness and thus, esult in a highe utily of seach and puchase. The thid block of covaiates is consume s shot-un histoy wh the webse whethe the consume vised and puchased fom the webse in the last puchase session o the so called state dependence. Consume s past seach and puchase can lead to an intinsic incease in pefeence fo a webse if stuctual state dependence exist, thus incease the seach and convesion pobabily. We include two lagged indicatos, one fo seach and one fo puchase in the model to account fo the state dependence of consume choice. The distibution of pefeence paametes is fully accounted fo, so if the state dependence coefficient is posive, indicates that the webse vis choices exhib stuctual state dependence (Dubé et al. 2008). Thee ae two altenative explanations fo inetia in seach - loyalty vesus leaning. Consumes may be unfamilia wh the webse in tems of s layout (ease of use) o the potential benef (discount, loyalty pogam, etc.), in which case, vising the webse povides elevant infomation. If leaning, instead of loyalty, is the cause of state dependence, the effect of state dependence should diminish as the consume accumulates moe bowsing expeience wh the webse, because thee is less and less infomation to be leaned. The cumulative time spent on the webse seves as a good poxy fo webse-level bowsing expeience. Theefoe, we include an inteaction tem of the state dependence vaiable and cumulative bowsing time as a means to test fo the leaning explanation. Unde the leaning assumption, the coefficient of the inteaction tem should be negative, which implies that state dependence is educed as consumes band expeience inceases. Fo the puchase stage, we also contol fo the webse s posion in the sequence of seach pocess. In paticula, we contol fo the fist and last webse vised duing the cuent puchase session and use the in-between ones as baseline. Endogeney and Instuments In ou model, unobseved puchase utily and the numbe of ad clicks in the cuent puchase session may be coelated because consumes who ae moe likely to convet on the webses ae those who ae exposed to moe advetisements as well, and may click the ads moe times than othes. In ode to addess the potential endogeney issue, we adopt the contol function appoach and use 1) the custome s numbe of viss to the webse though the same channel duing the past 3 months except the viss due to the puchase of ai tickets, and 2) the total numbe of viss to the webse though the same channel by all panelists duing the past 7 days as ad-click instuments fo each channel. Instument 1 eflects the consume s ad click popensy, thus is coelated wh numbe of clicks on flight-elated ads. But such popensy is wh othe poduct categoies offeed by the webse, thus is not diectly linked to the utily of the focal puchase. Instument 2 eflects the webse s investment in a paticula ad channel in the shot-un and the highe is, the moe likely that a consume will see an ad and click on. Howeve, the investment is not diectly coelated wh the attactiveness of puchasing on the webse, because puchase decision is nomally affected by pice, poduct availabily o custome loyalty instead of how heavy the advetising effot is. Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

8 We assume that the endogenous vaiables ae addive in thei obseved and unobseved covaiates. Theefoe, the numbe of clicks made by consume i on webse j though channel k at time t can be wten as a function of all exogenous vaiables enteing the puchase utily as well as the two instuments discussed above: (, ; ) ad W x z ψ µ ijkt = ijt ijkt + ijkt (8) whee µ ijkt is the contol function fo channel k and affects ad ijkt but is independent of the unobseved puchase utily. Likelihood Function We assume that all the webse-specific intecepts follow a Nomal distibution, in ode to captue unobseved heteogeney in consume pefeence while letting all othe coefficients be identical fo each individual. In addion, one s awaeness about a webse and the likelihood to seach can be coelated wh some unobseved common shocks such as webse design, offline advetising, etc. We tackle wh this by allowing fo coelations in andom pats of the (webse) intecept of the fist two stages. We assume that λ 0,ij and β 0,ij ae jointly distibuted as Let ϑ { σλ, σα, σβ, σγ, σλβ} σ σ λ0, j λβ λ0, j j N, β 2. 0, j σλβ σ j β 0, j = denote all the standad eos to be estimated. The uncondional pobabily of obseving consume i s puchase seies is given by T i ( i = ( i1 K it )) = ( ) ( ) ( \ ) ( ) ( ) i = j = = P P l,, l P A P F k A P S F k, A P P l S f ϑ dϑ (9) t= 1 A Stage Stage Stage If S \ F =, then the pobabily of puchasing fom the enty se is one and thus thee is no need to estimate the following stages. Othewise, the consume chooses fom all seached webses in stage 3. The complete likelihood function to be maximized is ( Pi ( li1 lit i) ) N L= P =, K,. (10) i= 1 We use simulated maximum likelihood method to estimate all the paametes in ou model next. Empiical Analysis Paamete Estimates Table 9 pesents the paamete estimates using the MSL estimato. The numbe of andom daws taken is set to be Awaeness Stage Ou esult shows that moe than 99.9% of consumes in ou sample have moe than 50% chance to be awae of the following webses even whout pio inteaction wh them: ailine diect, Expedia.com, Piceline.com, Obz.com, and Tavelocy.com. In contast, moe than 99.9% consumes have a high pobabily of not knowing about small agents. This esult is consistent wh these webses numbe of seaches and puchases made by the consumes in the sample. The coefficient fo indicato of pio vis (10.128) is posive and significant, meaning that the consume s pio inteaction wh the webse significantly inceases the awaeness pobabily in the cuent puchase session. This is even tue wh the case of small agents, because the magnude of I(pio vis) is lage than that of small agents (- Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

9 4.069). But we need to notice that even wh pio vis, we still cannot asset that one webse is sue to be included in the awaeness set. This is because consumes have impefect ecall each time they peceive a puchase need and may foget some stoes o bands they had past expeience wh. Altenative Evaluation Stage This stage is composed of two sub-decisions: 1) choose an enty se among all webses in the awaeness set, 2) decide whethe o not to vis othe webses in the awaeness set. The intecepts in the fist subdecision captue the elative unobseved attactiveness of all competing webses. We find that even though small agents have a low pobabily of being awae of, enjoys a highe pefeence than any othe competing webses once is included in the awaeness set. Consumes also favo ailines diect etail webses, expedia.com and Tavelocy.com compaed to othe webses. As shown in Table 9, decayed cumulative bowsing time inceases a webse s pobabily of being chosen as the enty se. This could be due to the eduction in cost of e-vising because consumes lean about the webse by spending moe time on in the past and become poficient in using the webse in the futue. We also find that convesion on the webse in the last puchase session does not affect the choice of enty se, but vising the webse last time inceases the choice pobabily, which means consumes exhib inetia in seach behavio. Moeove, such inetia is due to an intention to lean moe about the webse because the inteaction tem between lag seach and cumulative bowsing time is negative and significant, which indicates that state dependence has a smalle impact on the choice of enty se as the consume accumulates moe bowsing expeience wh the webse and thee is less infomation to be leaned. Last but not least, we find that thee out of fou advetising channels have posive impact on the choice of enty se, in which seach engine and display ads ae most effective. Howeve, thei maginal effectiveness is not as stong as the numbe of viss to the webse by typing in the addess diectly, because the late indicates the stongest familiay wh the webse. Puchase Stage The fist two columns Table 8 display the estimates fo the puchase stage. We find that the cumulative amount of money spent on the webse incease the convesion pobabily significantly, while the cumulative amount of time spent on bowsing the webse has no significant impact. We also find that if the consume viss moe than one webse, being the fist webse vised does not incease the convesion pobabily, but being the last one does. Ou findings also eveal inteesting state dependence pattens. Once the custome eaches the thid stage, the state dependence in seach does not exist anymoe as does in the altenative evaluation stage. This means pevious viss to the webse only affect seach decision, but has no significant impact on the puchase behavio fo those deal seekes. Howeve, state dependence in puchase still pesists, in that last puchase inceases the chance of cuent puchase. The infomation stock gained by vising the webse though vaious channels also affects the convesion choice. Among the fou advetising channels, display ads exet the lagest impact on the choice of convesion se, followed by seach engine. Viss though the othe two ad channels do not appea to affect puchase pobabily significantly. We analyze the maginal impact of ad channels on convesion pobabily implied by the coefficients in the next session. We also compae the estimates of the puchase stage of ou poposed model wh two benchmak models that do not conside state dependence o the entie funnel. The thid and fouth columns of Panel C of Table 8 pesent the estimate of the benchmak model 1 in which inetia and histoy wh the webses ae not consideed. We find that omting these vaiables will not change the coefficients qualatively, but does lead to an ove-estimation of ad effectiveness fo seach engine and display ads. The diffeence should be attibuted to consume s intinsic band loyalty due to past inteactions wh the webse o offline advetising. The fifth and sixth columns of Panel C of Table 8 display the estimates of benchmak model 2 that only consides the puchase stage. In this model, we mimic the scenaio that which webses have been vised duing the seach stage ae unknown, so the consumes make puchase decisions fom all eight altenatives wheeas in ealy, they can only puchase fom a webse that has been seached. Thee ae two main diffeences in findings accoding to this model. Fist, the effect of state dependence in seach is significant, which means pevious vis to the webse affects cuent puchase decision. This model also shows that being the enty se significantly inceases puchase pobabily. These two changes can be contibuted to the ignoance of a lage goup of consumes who only seach one webse and Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

10 convet on the enty se. But this benchmak model confims ou findings on the effectiveness of advetising: only seach engine and display ads have significant impact on convesion pobabily and display ads have a stonge effect. In geneal, ou model fs the data much bette than the two benchmak models as s log-likelihood is the lowest. Modeling the consideation set is moe impotant than inetia because benchmak model 2 fs the data bette than benchmak model 1. These compaative analyses demonstate that incognizant of the intinsic state dependence and the puchase funnel of consumes can not only lead to infeio models, but moe seiously, manageial conclusions dawn fom such popula benchmaking model can be eoneous and misleading. Table 8: Paamete Estimates Awaeness Stage Mean Std Dev ailine diect cheaptickets.com expedia.com hotwie.com obz.com piceline.com small agents tavelocy.com I(pio vis) Altenative Evaluation Stage Enty Se Othe Awae Ses Mean Std Dev Mean Std Dev ailine diect cheaptickets.com expedia.com hotwie.com obz.com piceline.com small agents tavelocy.com Decayed cum. spending ($100) Decayed cum. bowsing time (10 min) Lag puchase (0/1) Lag seach (0/1) LagSeach*CumBowsing No. of viss: seach engine at (t-1) No. of viss: at (t-1) No. of viss: display at (t-1) No. of viss: efeal engine at (t-1) No. of viss: self type-in at (t-1) Puchase Stage Ou Model Benchmak 1 Benchmak 2 Mean Std Dev Mean Std Dev Mean Std Dev cheaptickets.com expedia.com Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

11 hotwie.com obz.com piceline.com small agents tavelocy.com Decayed cum. spending ($100) Decayed cum. bowsing time (10 min) Lag puchase (0/1) / Lag seach (0/1) / Enty se (0/1) Last webse (0/1) No. of viss: seach engine No. of viss: No. of viss: display No. of viss: efeal engine No. of viss: self type-in CF: seach engine CF: CF: display CF: efeal CF: self type-in Log-Likelihood AIC Ad Effectiveness Ads impact on convesion is two-folded: fist, past ad-clicks incease a webse s pobabily of being chosen as the enty se o being seached, and theeby incease convesion pobabily indiectly; second, the numbe of viss though diffeent advetising channels inceases consume s puchase pobabily diectly in the puchase stage. In the section, we compute the own- and coss- maginal impact of ad clicks on these two aspects to show the effectiveness of diffeent advetising channels on diffeent webses. Modeling consumes choice among all competing webses allows us to analyze the self and competive advetising effects fo each webse simultaneously. To compute the maginal impact of ad click on choice pobabily, we fist calculate the simulated mean of individual-level intecept by β i P( yi xi, β ) = β, P( yi xi, β ) whee β is a andom daw fom the population s distibution and T iy t e ( i xi, β ) =. P y t= 1 β x j e β x ijt Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

12 Then we calculate the updated choice pobabily fo each awae (o seached) webse fo evey individual. Finally, we compute the aveage choice pobabily fo each webse acoss all consumes fo ad click change wh espect to each webse ailine cheaptickets expedia hotwie piceline obz small agents tavelocy Seach Engine ailine cheaptickets expedia hotwie piceline obz small agents tavelocy ailine cheaptickets expedia hotwie piceline obz small agents tavelocy ailine cheaptickets expedia hotwie piceline obz small agents tavelocy ailine 0.35 cheaptickets 0.3 expedia 0.25 hotwie 0.2 piceline 0.15 obz 0.1 small agents 0.05 tavelocy 0 ailine cheaptickets expedia hotwie piceline obz small agents tavelocy Display Ads Figue 2 Maginal Impacts of Ad Clicks on Pobabily of Being Chosen as Enty Se Seach engine Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

13 Figue 2 depicts each webse s aveage pobabily of being chosen as the enty se given an addional vis to self o othe webses though seach engine, and display ads. The maginal impact of efeal engine is not shown hee because s paamete estimate is not significantly diffeent fom zeo. The esult indicates that one addional click on ad inceases the webse s own pobabily of being chosen as the enty se, but the size of change vaies a lot by webses and by ad channels. Fo example, an addional vis to small agents though seach engine in the pevious puchase session will incease s pobabily of being chosen as the enty se by 8.1%, but fo cheaptickets, an addional click on seach engine advetising only leads to an incease of 1.6%. ad-clicks have a smalle impact in geneal. Fo example, an addional click on ad will only incease the pobabily fo small agents by 3.3%. Ou model stuctue also allows us to analyze the competive patten among all webses. Fom Figue 4, we also obseve the competive effects of seach engine, and display ads. Fo example, an addional vis to small agents deceases s competos chance of being chosen by 1.1% on aveage. The effect size vaies by webse as well: ailine companies etail webse is affected the most wh a decease of 2.2% seach engine display efeal engine baseline ailine cheaptickets expedia hotwie piceline obz small agents tavelocy Figue 3 Maginal Impacts of Ad Clicks on Pobabily of Being Seached Figue 3 shows the maginal impact of ad clicks on the pobabily of being seached fo a webse in the awaeness set but not chosen as the enty se. Addional clicks on any advetisement incease seach pobabily fo all webses, but the magnude of effects vay by ad channel and by webse. Fo example, an addional vis though display ads to piceline.com in the pevious puchase session incease s pobabily of being seached by 21.2%, but one moe vis though ads only incease the pobabily by 4.3%. In contast, an addional vis though display ads only incease seach pobabily fo cheaptickets.com by 2.1% ailine cheaptickets expedia hotwie piceline obz small agents tavelocy 0 ailine cheaptickets expedia hotwie piceline obz small agents tavelocy Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

14 Seach Engine ailine cheaptickets expedia hotwie piceline obz small agents tavelocy ailine cheaptickets expedia hotwie piceline obz small agents tavelocy Display Ads Figue 4 Maginal Impacts of Ad Clicks on Convesion Pobabily Figue 4 shows the maginal impact of ad clicks on convesion pobabily. Only analysis fo seach engine and display ads ae shown because the othe two advetising channels do not have a significant impact on convesion pobabily accoding to ou estimation esults. We can do simila analysis hee as what we have done fo the choice of enty se. Fo example, an addional vis to cheaptickets.com though seach engine inceases s convesion pobabily by 5.14%, but one moe vis to small online agents has baely any effect. We can also compute the coss maginal impact of ad click of one webse on s competos convesion pobabily. Fo example, an addional vis to ailine companies diect webses though seach engine deceases s competos convesion pobabily by 1.4% on aveage. And the effect size vaies by webses: expedia.com suffes the most wh a decease of 2.1%, while the convesion pobabily fo small agents baely changes. In geneal, we find that webses wh lage maket shae suffe moe fom ad-clicks on thei competos webses. Conclusion In this pape, we have developed an integated thee-stage model to measue the advetising effectiveness on consume s online seach and puchase decisions in a multi-channel, multi-touch-point envionment using individual-level advetising esponse data. We specifically model (1) consumes awaeness, (2) consumes seach by consideing both thei choice of enty se and thei decisions about continuation of seach, and (3) thei subsequent puchases at one of the seached ses. Ou model accounts fo (1) the competive effect by incopoating consume s choice among all elevant webses instead of a binay choice at one focal webse, (2) state dependence effect, (3) heteogeney acoss individuals and webses. The esults fom ou analysis shed seveal impotant insights on the effectiveness of multi-channel advetising. Fist, wh ou competive dataset, we show that the maginal impact of ad clicks vaies acoss webses. The impact of ad click on competos convesion pobabily deceases as the competo s maket shae inceases. Second, we show that diffeent ad fomats affect consumes diffeently based on the stage in puchase funnel. All fou ad fomats that we study have a posive impact on the seach stage by inceasing the pobabily of being chosen as the enty se o being seached. Howeve, only seach engine and display ads ae effective in inceasing the convesion pobabily diectly. Thid, we find that stuctual state dependence exist in both seach and puchase stage. Ignoing state dependence will lead to sevee ove-estimation of ad effectiveness. Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

15 REFERENCES Abhishek, Vibhanshu, Pete S. Fade, and Katik Hosanaga (2013), Media Exposue though the Funnel: A Model of Multi-Stage Attibution, woking pape, Canegie Mellon Univesy, Ptsbugh Dalessando, B., O. Stelman, C. Pelich, and F. Povost (2012), Causally Motivated Attibution fo Online Advetising, NYU Woking Pape seies. De Los Santos, Babu, Ali Hotaçsu, and Matthijs R. Wildenbeest, (2012), "Testing Models of Consume Seach Using Data on Web Bowsing and Puchasing Behavio," Ameican Economic Review, 102(6): Daganska, Michaela and Daniel Klappe, (2011), Choice Set Heteogeney and The Role of Advetising: An Analysis wh Mico and Maco Data, Jounal of Maketing Reseach, 48(4): Dèze, Xavie and Fançois-Xavie Husshe (2003), Intenet Advetising: Is Anybody Watching? Jounal of Inteactive Maketing, 17 (4): Dubé, J., G. J. Hsch and P. E. Rossi, and M. A. Voino (2008), Categoy Picing wh State-Dependent Utily, Maketing Science, 27 (3): Econsultancy, (2012), Maketing attibution: Valuing the custome jouney, London, UK. Retieved fom Johnson, Eic J., Wendy W. Moe, Pete S. Fade, Steven Bellman, Geald L. Lohse, (2004), On the Depth and Dynamics of Online Seach Behavio, Management Science 50(3): Li, H. and P. Kannan, (2014), Attibuting Convesion in a Multichannel Online Maketing Envionment: An Empiical Model and a Field Expeiment, Jounal of Maketing Reseach, 51(1): Li, Jun, Nelson Gandos and Seguei Netessine (2014), Ae Consumes Stategic, Stuctual Estimation fom the Ai-tavel Industy, Management Science, 60(9): Petin, Amil and Kenneth Tain, (2010), A Contol Function Appoach to Endogeney in Consume Choice Models, Jounal of Maketing Reseach, 47(1): Ratchfod, Bian (2008), Consume Seach Behavio and Its Effect on Makets, Foundations and Tends in Maketing, 3 (1): Shao, X. and L. Li, (2011), Data Diven Multi-Touch Attibution Models, KDD 11, Shugan, Stephen M., (1980), The Cost of Thinking, Jounal of Consume Reseach, 7 (3), Tain, Kenneth (2009), Discete Choice Methods wh Simulation, Cambidge Univesy Pess. Xu. L., J. A. Duan and A. B. Whinston (2014), Path to Puchase: A Mutually Excing point Pocess Model fo Online Advetising and Convesion, Management Science, 60(6): Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

16 Online Appendix: Simulation We adopt the Maximum Simulated Likelihood method to estimate the thee stages of ou model jointly. MSL is consistent if the numbe of daws that ae used in the simulation ises wh sample size (Tain 2009). Ou model also allows fo consume heteogeney in awaeness set. Tadionally one has to compute all puchase pobabilies that coesponds to each possible choice set, which leads to sevee dimensionaly poblem as the numbe of altenatives inceases. To solve this poblem, we adopt the method poposed by Goeee (2009) that simulates the awaeness set facing each consume, theeby making only one puchase pobabily computation pe individual necessay. The entie pocedue of ou simulation is intoduced as follows: 1) A daw ofμ ijt is taken fom a unifom distibution fo each poduct-individual-time combination. 2) A daw of θi ( λ0, ij, α0, ij, β0, ij, γ0, ij ; j) individual. In specific, a set of andom vaiables ηi = ( ηλ, ηα, ηβ, ηγ, ηλβ ; j j j j j ) = is taken fom s assumed distibution fo each j ae dawn fom i.i.d N(0,1). Then we can compute the webse-specific intecepts in diffeent stages fo each individual as follows: 0, ij 0, j λ λ λ = λ + σ η, 0, j α = α + σ η, 0, ij 0, j α0, j α j 0, ij 0, j c j c λβ j β β = β + η + η, 2 j 3 = +, j γ γ σ η 0, ij 0, j γ 0, j γ j j j whee σ σ 0 λ λβ 0, 0, j j λ j = chol c 2 j c 2 j σ 3 λβ σ j β 0, j 2 σ. 3) Calculate the pobabily that a webse j belongs to the awaeness set A fo each individual-time combination: ( λ0, ij + Iijtλi ) ( λ0, ij Iijtλi ) exp φijt = 1+ exp + 4) Given ϕ ijt and μ ijt, constuct a J dimensional Benoulli vecto, A, which defines the awaeness set fo the th loop. The jth element is detemined accoding to 1, if φijt > uijt aijt =. 0, if φijt uijt 5) Calculate the pobabily of obseving webse k as the enty se fo each individual-time combination: ( F k A) P = = k ' A exp ( α0, ik + xik, t 1αi ) ( α0, ik ' + xik ', t 1αi ) exp.. Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

17 6) Calculate the pobabily of obseving seached set (excluding the enty webse) fo each individual-time combination: exp( β0, il + x im, t 1βi ) 1 ( S\ F F A) =. \{ } 1 exp( 0, im im, t 1 i ) m S ' \ exp( 0, im ' im ', t 1 i ) F + β + x β m A S β + x β exp( γ0, il + xijtγl ) S, calculate P ( P = l S) = ; othewise, ( P l S) exp( γ0, il ' + xil ' tγi ) P, 7) If \ F 8) Calculate l ' S φ ( 1 φ ) P ( A ) P ( \,A ) P ( S ). = ijt ijt = F = j A j A L F k S F P l P = = 1. 9) Repeat steps 1 to 8 many times and calculate the simulated pobabily as is the numbe of daws. ˆP 1 = L R, whee R 10) Calculate the simulated log-likelihood: SLL N T i =. i= 1 t= 1 ln Pˆ Twenty-fist Ameicas Confeence on Infomation Systems, Pueto Rico,

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