Factors Affecting Website Visit Duration: A Cross-Domain Analysis

Transcription

1 Factors Affectng Webste Vst Duraton: A Cross-Doman Analyss Peter J. Danaher* Guy W. Mullarey* Sander Essegaer** *Department of Maretng Faculty of Busness and Economcs The Unversty of Aucland Prvate Bag 9019 Aucland NEW ZEALAND Ph: Fax: Emal: p.danaher@aucland.ac.nz **The Wharton School Unversty of Pennsylvana Phladelpha, PA September 004

2 Factors Affectng Webste Vst Duraton: A Cross-Doman Analyss ABSTRACT In ths study we examne factors that mpact on webste vst duraton, ncludng user demographcs, text and graphcs content, type of ste, presence of functonalty features, advertsng content and the number of prevous vsts. A random effects model s used to determne the mpact of these factors on ste duraton and the number of pages vewed. The proposed model accounts for three dstnct sources of heterogenety arsng from dfferences among ndvduals, webstes and vst-occasons to the same webste by the same person. Our model s ft usng one month of user-centrc panel data and encompasses the 50 most popular stes n a maret. The results show that of the demographc varables, only user age and gender are sgnfcant, wth older people and women vstng a ste for longer. Entertanment and aucton stes have sgnfcantly longer duraton than all other ste types, whle stes wth too much advertsng have shorter duratons. 1

3 INTRODUCTION Havng a large number of vstors s crucal for many webstes, as a maor part of ther revenue derves from advertsng (East 003, p.85). An almost equally mportant performance measure, unque to webstes, s user retenton sometmes referred to as stcness (Bhat et al 00). It s defned as the tme a user s on a webste or a partcular page wthn the ste (Demers and Lev 001) and s routnely reported by nternet audence measurement agences such as comscore/meda Metrx, Htwse and Nelsen/Netratngs. A related webste measure s the depth of vst, measured by the number of pages vewed (Dreze and Zufryden 1997). Webste vst duraton s mportant for several reasons. Frst, even though clc through rates for banner ads have declned n the last fve years, there s stll value derved from mere exposure to the ads (Brggs and Holls 1997; Flores 00). Bucln and Ssmero (003) and Danaher and Mullarey (003) fnd that exposure to web advertsng s more lely for longer page duratons, as happens analogously for longer exposure tmes to televson ads (Rosster et al. 001). For example, Danaher and Mullarey (003) report that n gong from 0 to 40 to 60 seconds n page exposure duraton the unaded recall for a banner ad ncreases from 6 to 43 to 50 percent of vstors, respectvely. Second, longer page duraton also helps to mantan user nterest n a ste (Bucln and Ssmero 003; Hanson 000) and gves users more tme to consder and complete purchase transactons (Bucln and Ssmero 003). Moe and Fader (004b) show that enhanced user nterest helps generate repeat vsts followed by greater long-term sales. Thrd, from a busness nvestment pont of vew, Demers and Lev (001) show that stes wth longer vst duraton also have hgher monthly stoc returns. Whle vst duraton may not drve stoc prces n a causal sense, some nvestors use webste duraton as an ndcator of future earnngs. Ths fndng perssted even after the nternet stoc maret crash n the sprng of 000 (Demers and Lev 001).

4 Gven the endurng and well-ustfed mportance of webste vst duraton, the purpose of ths study s to examne factors that affect vst duraton and the number of pages vewed. These factors nclude demographc characterstcs of vstors, the type of ste, such as entertanment or news, and the ste content, such as text, graphcs and navgaton features, as well as the number of prevous vsts. Ours s the frst study to examne the characterstcs of users and stes and how they mpact on vst behavor. Moreover, rather than restrctng ourselves to ust one or two stes, we broaden the scope to the 50 maor webstes n a maret. We develop a random effects lnear model for vst duraton and depth that taes nto account ndvdual-level, product (.e., webste) and vst-occason heterogenety by generalzng a model developed by Ansar, Essegaer and Kohl (000) for move ratngs. Our data come from a Nelsen/Netratngs panel of over 3000 web-enabled people, all of whom provde personal demographc nformaton. The Nelsen software measures the tme panelsts spend on a webste and the number of pages vewed. Measures of webste characterstcs are obtaned from a separate group of udges who assess each of the top 50 stes n terms of ther text, graphcs and advertsng content as well as ste features, such a the ablty to customze pages, feedbac provson, navgaton ads and avalablty of chat rooms. RELEVANT LITERATURE Prevous research nto webste browsng behavor s lmted. To date, studes have nvestgated repeat vsts (Chatteree et al 003; Moe and Fader 004b) and purchase converson rates (Moe and Fader 004), whle others have looed at the depth of search (Johnson et al 000). Some nterestng fndngs have emerged from these studes, such as web users engagng n only a lmted amount of search across stes (Johnson et al 000; Zauberman 003), despte the ease wth whch a wde search s possble on the nternet. Moe and Fader (004b) fnd that even though aggregate fgures for customer loyalty (as measured by vsts per vstor) show an ncrease over tme for Amazon.com and CDNow.com, the 3

5 ndvdual-level data reveal that someone mang more frequent vsts to these stes does so at a decreasng rate. Ths fndng has an mpact on downstream sales as Moe and Fader (004b) subsequently show that more frequent shoppers have a hgher probablty of eventual purchase. Whle the above studes loo at nternet browsng, banner ad exposure and purchasng behavor, only one pror study has drect relevance to ours, beng that of Bucln and Ssmero (003). They develop a model for analyzng nternet clcstream data for vstors to an automotve webste. Ther study ontly taes nto account a user s decson to select another page wthn a ste or to ext the ste and also models page duraton, when another page s selected. Ther page vst duraton covarates are largely techncal measures of the ste. For example, ther results show that longer page duraton s assocated wth hgher bytes transferred, greater cumulatve pages vewed pror to the current page (.e., vst depth), a reload request for a page, an error n a page transfer and longer server response tme. Shorter page vews are assocated wth havng dynamc content (e.g., requrng a call to a ste s database). Whle these measures are of techncal nterest to a webmaster, they are somewhat naccessble to everyday web desgners, web advertsers and e-commerce nvestors. For ths reason we use webste characterstcs that are more user-frendly, such as graphcs, text and advertsng content. Our model of webste duraton dffers from that of Bucln and Ssmero (003) n several addtonal ways. As they have the browsng behavor for ust one ste collected by that ste s server, there s no demographc nformaton on ther vstors. Indeed one of the frustratons for webmasters s that they often now very lttle about ther vstors, wth data beng lmted to ust the browsng behavor for ther own ste. Potental demographc factors that affect webste duraton nclude gender, age, educaton and occupaton (Dreze and Hussherr 003). By contrast our data come from a panel of webenabled people montored unobtrusvely for a month, wth demographc data collected at 4

6 recrutment. Moreover, we have the ste duraton and number of pages vewed for all stes vsted that month, not ust detaled clcstream data for a sngle ste. Ths allows us to broaden our study to the top 50 webstes n a maret and develop a cross-webste analyss, n contrast to prevous studes that examne only one or two stes n detal. THE MODEL In ths secton we develop a class of models sutable for modelng webste vst duraton. As ths class of models belongs to the broader class of generalzed lnear models, wth a sutable change of dependent varable and ln functon, the same model structure can also be used for the number of pages vewed. To motvate our model we frstly dscuss the data, as ths llustrates the complexty and challenges of the modelng problem. Data Prevew Our data come from a panel of homes recruted and mantaned by ACNelsen s Netratngs servce 1. The panel comprses over 3000 people and s based on a user-centrc methodology, very much le that also used by ComScore Meda Metrx (Coffey 001). More detals on the data are gven later. Although Nelsen s user-centrc method montors a panelst s entre browsng behavor, several qualty control checs and aggregatons are appled pror to the data beng provded to us. The ey aggregaton s that all URLs vsted wthn the same doman are aggregated up to the doman name. The tme perod for our data s ust the month of November 000. Our ntal nspecton of the data revealed several features that must be accounted for n a model of webste duraton. These nclude 1 Note that only home-based browsng s montored by the panel. Worplace web actvty was not beng montored at the tme we obtaned these data. For nstance, a person mght vst aol.com, drll down several pages then leave to vst weather.com. The data provded to us has ust the total tme spent on the aol.com doman (not separate URLs wthn aol.com) and the total number of pages vewed. 5

7 ) Dfferent panelsts do not vst the same repertore of webstes. For nstance, n our data, the average number of dfferent stes a person vsts from the top 50 stes s ust 4.3 ) Panelsts do not use the web every day from ther homes and there mght be a lengthy tme separaton between web use ) v) The same person may vst the same ste more than once n a month There s heterogenety n vst duraton among people, wth some users tendng to have consstently shorter or longer vsts to all stes v) Stes that are smlar n purpose often have smlar duraton tmes across dfferent people. We see ths, for example, wth google.com, whch usually has short onepage vsts. An addtonal modelng consderaton that s not evdent from the data s that webstes may not loo the same at each vst, due to dynamcally created pages, or a dfferent path beng traced by a vstor. Ths gves rse to product heterogenety, somethng not often consdered n the maretng lterature, but recently dentfed by Ansar et al (000) as an mportant ssue when modelng a person s evaluaton of a move and by Ansar and Mela (003) as germane to emal maretng. Le moves and emal, webstes have a number of ntangble features over and beyond observed attrbutes. Snce our measured varable s duraton tme, t s natural to ntally consder a survval model, whch has prevously been appled n the maretng lterature (e.g., Jan and Vlcassm 1991). Cox s (197) proportonal hazard model seems le a reasonable startng model as t ncorporates covarates. However, to accommodate the antcpated ndvduallevel heterogenety noted n pont (v) above, Cox s model must be stratfed by each person, whch then precludes the estmaton of demographc effects as they are constant wthn a person-based stratum (Allson 1995). Ths maes Cox s model unsutable for our applcaton. 6

8 An alternatve method for handlng ndvdual-level heterogenety s a random effects survval model nown as the gamma fralty model (Hougaard 001). Whle ths model s sutable for person-level heterogenety, t cannot be extended to ncorporate product-level heterogenety, a requrement dentfed above as potentally mportant n ths applcaton. Rather than restrctng ourselves to survval models, smply because the dependent varable s tme, a much more flexble class of models becomes avalable f we nstead model the log of duraton. Snce we are worng wth duraton data that s rght sewed t s natural to log transform the duraton tme (Mosteller and Tuey 1977). Indeed, a plot of log(duraton) for the 3,64 webste vsts n our database loos very much le a normal dstrbuton. Usng the log transform on duraton enables us to employ a log-normal model (Allson 1995) that accommodates the heterogenety, unequal personal webste repertores and multple vsts by the same person to the same webste that characterze our data. A log-normal formulaton for page duraton was also used by Bucln and Ssmero (003) n ther model. Notaton Let y be the log of the tme that person spends on webste for the th vst. Snce person does not vst each webste, we denote the ndex set of the stes they vst as W, wth { 1,, K } W =, n. Each webste W, wth n beng the total number of dfferent webstes vsted by person, = 1,, K, n. Snce there are potentally multple vsts to webste by person, ranges from 1 to n, where n s the number of vsts that person maes to ste. Hence the total number of observatons s N = n. n n = 1 = 1 Model Development As mentoned above, and smlar to the case of moves and musc, webstes cannot be completely descrbed n terms of a few observable attrbutes. Vst duraton at a webste s shaped by a multtude of complex ste attrbutes that mpact ts attractveness, but 7

9 unfortunately these attrbutes are often dffcult to observe and measure. A webste s unobserved attrbutes contrbute to ts feel and touch and lead to dfferences n appeal across domans. In our modelng approach we therefore not only account for ndvdual-level heterogenety, but also account for webste-level heterogenety to allow for dfferences n webste appeal and the downstream effect on webste vst duraton. In dong so, we buld upon the methodology frst proposed by Ansar, Essegaer and Kohl (000). Our model has three components. In the frst component, the dependent varable (logduraton) s modeled as a functon of observed webste attrbutes (denoted Z, beng a w 1 vector of webste s characterstcs). These webste attrbutes have dfferent regresson weghts for each person (denoted Z β ) reflectng ndvdual-level heterogenety and result n a lnear model, wrtten as Z β Z. The second component arses from consderng the dependent varable to be a functon of observed personal characterstcs (denoted X, beng a d 1 vector of person s demographc characterstcs), whch have dfferent regresson weghts for X each webste (denoted β ), reflectng ste-level heterogenety. The resultant lnear model for X ths component s. β X Unle the Ansar et al (000) case, where respondents rated each move at most once, webstes n our dataset are often vsted multple tmes by the same person. In fact about 47% of ntal vsts to one the top 50 webstes n our data are followed by another vst to the same ste later n the month, wth an average of. return vsts n a month. As a result, n our applcaton, we need to capture not only the unobserved person and webste heterogenety, but also the observed and unobserved dfferences across multple vst occasons to the same webste by the same person. Hence, a thrd component arses from consderng the dependent varable to be a functon of observed characterstcs of a partcular vst occason to the same webste by the 8

10 same person (denoted M, beng a m 1 vector of the characterstcs of the -th observed vst, such as the day of the wee or the number of prevous vsts to the ste). To reflect occason-level heterogenety across multple vsts, we assgn dfferent regresson weghts for each person-webste par (denoted ). The resultant lnear model for ths component 3 M β M s β M. Hence, our model s a trple heterogenety model: capturng person, product and vst-occason heterogenety, whle the Ansar et al (000) model s a double heterogenety model that captures only person and product heterogenety. Combnng the three components gves the full model, X Z M (1) y = β X + β Z + β M + ε, where ε s a random error beng..d. normal wth mean zero and varance σ, ntended to capture any remanng unexplaned varaton. In the sprt of the herarchcal Bayes method, the random effect regresson coeffcents n equaton (1) can be decomposed nto fxed and random parts as follows, () β β β X Z M = β Z = β = β X M + γ, + λ, + µ, γ ~ N(0, Φ), λ ~ N(0, Ω), µ ~ N(0, Ψ). Substtutng equaton () nto equaton (1) gves X Z M (3) y = β X + β Z + β M + λz + γ X + µ M + ε For the three random effect terms n equaton (3) we can wrte (4) λ Z γ X µ M ~ N(0, Z ~ N(0, X ' ' ~ N(0, M ΦZ ΩX ' ), ), ΨM ). 3 Strctly speang, M should be wrtten as to reflect that the th vst s nested wthn the ()th person-webste par, but we use the M M ( ) notaton for smplcty. 9

11 By settng the frst element of X, Z and M to be 1 to permt an ntercept term and parttonng Φ, Ω and Ψ nto ' φ 1 φ Φ = * φ Φ ' ' ω 1 ω ψ 1 ψ, Ω = and Ψ =, * * ω Ω ψ Ψ the random effect lnear combnatons n equaton (4) can be rewrtten as (5) λ Z γ X µ M ~ N(0, φ + φ Z 1 ~ N(0, ω + ω X 1 ~ N(0, ψ + ψ M 1 * + Z Φ * * * * * + M Z + X Ω * * * X Ψ ), * * ), M * ), X * * * where, and are, respectvely, the demographc profle for person, the vector of Z M descrptors for webste and the vector of vst occason descrptors for the -th vst of person to webste (each vector excludng the ntercept term). Now denote the left hand sde of the respectve terms n equaton (5) as τ = λz, δ = γ X and η = µ M. Hence, an alternatve way to wrte equaton (3) s 4 X Z M (6) y = β X + β Z + β M + τ + δ + η + ε, where τ ~ N(0, σ ( )), δ ~ N(0, σ ( )) and η ~ N(0, σ ( )), notng that the three τ Z δ X varances are functons of Z, X and M, respectvely. Notce that f the demographc, webste and vst-occason nformaton s gnored n the varances of these random effects dstrbutons (that s, only the frst term n the varances of equaton (5) s taen nto account) then we smply have τ ~ N ( 0, φ1), δ ~ N(0, ω 1 ) and η ~ N(0, ψ 1), whch results n a standard mxed effects model wth homoscedastc varances (Lard and Ware 198). Therefore, the dfference between the Ansar et al (000) model and a standard mxed effects η M model s ther model has heteroscedastc random effects (whch are functons of X and Z, 4 Equaton (6) can also be wrtten wth an explct fxed effect ntercept term as X* * Z* * M* * X Z M y = α + β X + β Z + β M + τ + δ + η + ε, where α = β 0 + β 0 + β 0. 10

12 and addtonally M n our case), whereas the usual mxed effects model has homoscedastc random effects. Indeed a further generalzaton of the Ansar et al (000) model s to have τ ~ N(0, σ ), δ ~ N(0, σ ) and η ~ N(0, σ ), where the varances are not constraned to be functons of observed varables. Later we shall emprcally examne the ft of model (6) under these alternatve varance specfcatons n the random effect terms. Correlaton Structure Under the model n equaton (6) we can derve the correlaton n webste duraton wthn people and wthn webstes. Knowng these correlatons helps us gauge the way our model handles the antcpated person and webste heterogenety. Frstly, consder the correlaton n duraton for person across two dfferent webstes and ', whch s 5, σ τ (7) corr( y,, '. + σ + σ y ' ) = σ τ + σ δ η Ths correlaton results from ndvdual-level heterogenety and ndcates the degree to whch duraton tmes are smlar as a result of unobserved personal characterstcs. The second correlaton s that between duraton tmes for the same webste, but for two dfferent people. Here, equaton (6) gves σ (8) corr( y,, '. + + σ y ' ) = σ τ + σ δ δ σ η It s apparent that duraton tmes for the same webste may be correlated even for dfferent people. Such a scenaro s reasonable n our applcaton, snce webste duraton wll often be 5 Note n equaton (7) that we are usng the homoscedastc form of the varance for τ, δ and η. The correlaton can easly be generalzed to the stuaton where the varance of τ s dfferent for each person and the varance of δ dffers by webste, but t s slghtly more complcated. 11

13 determned by ts functon, features and layout, not all of whch can be explctly accounted for by observed varables. The thrd correlaton we consder s one that derves from the same person vstng the same webste multple tmes, namely, vst replcaton or repetton. Ths correlaton s (9) corr σ τ + σ δ + σ η ( y, y ' ) =, '. σ + σ + σ + σ τ δ It s reasonable to expect that duraton tmes for vsts to the same ste by the same person are correlated, perhaps hghly correlated. Notce that ths thrd correlaton s hgher than ether of the prevous two, as would be expected, snce, for example, two vsts by the same person to aol.com are more lely to have smlar duraton than one vst to aol.com and another to amazon.com by that person. Returnng to our lst of model requrements above, we see that the model proposed n equaton (6) has all the necessary features for ths applcaton to webste vst duraton. These features nclude the ablty to test for the mpact of observed characterstcs of web-users, webstes and vst occasons. Moreover, the model captures heterogenety across ndvduals and webstes and allows for the possblty of multple vsts to a ste by the same person. The downstream mpled correlaton structure from these sources of heterogenety also seems reasonable. Lastly, there s no requrement n our model that each person vsts the same subset of webstes over the observaton perod. Estmaton Method Whle equaton (6) can be thought of as a mxed effects model, a number of data ssues mae ts estmaton by standard maxmum lelhood rather challengng. Gelfand et al (1995) show that a typcal requrement for estmablty by maxmum lelhood n mxed n effect models s that n > 1+ n,.e., there are more observatons than parameters for each = 1 person. However, there are many nstances n our dataset where a person vsts one or two η 1

14 webstes only once a month. In such cases the Gelfand et al (1995) crteron s not met. One soluton s to elmnate people wth nfrequent and lght web actvty, but ths would sew the sample towards medum and heavy nternet users. Estmaton of models wth challengng data requrements has been made possble by recent developments n Bayesan estmaton (Allenby and Ross 1999; Gelfand and Smth 1990), prncpally Gbbs samplng, an teratve method for parameter estmaton whch pools nformaton across respondents. Ths permts parameter estmaton even n stuatons where data may be sparse at the ndvdual level. We use the versatle WnBUGS software (Spegelhalter et al 003) to mplement the Bayesan estmaton. In our applcaton we use 10,000 burn-n teratons and 0,000 estmaton teratons, wth three chans. Convergence of the parameter estmates s assessed va the Gelman and Rubn (199) statstc. Model for Pages Vewed Earler we stated that we want to model the number of pages vewed as well as the ste duraton. A hstogram of the dstrbuton of pages vewed mmedately shows the Posson dstrbuton to be approprate, as also found by Dreze and Zufryden (1997). However, the observed number of pages always starts at 1 rather than 0, snce at least 1 page must be vewed by a webste vstor. Thus we modfy equaton (6) so that the dependent varable s now the number of pages vewed less 1 and ths dependent varable has a Posson rather than a normal dstrbuton. In the sprt of generalzed lnear models (McCullagh and Nelder 1989), we now apply the log ln functon to the rght hand sde of equaton (6). Congdon (003, p.93) shows that ths can also be estmated by Gbbs samplng. Data n Detal EMPIRICAL ANALYSIS 13

15 As mentoned above, the data used to ft our models come from Nelsen s Netratngs servce n New Zealand, whch has a user centrc methodology. Nelsen s servce s avalable n many countres, ncludng the U.S. ( Johnson et al (1999), Par and Fader (004) and Moe and Fader (004a; 004b) also use user-centrc data to ft ther e- commerce models, but ther data are suppled by comscore Meda Metrx ( Coffey (001) detals much of the Meda Metrx user-centrc web measurement methodology, whch s broadly smlar to Nelsen s. The panel used n ths study comprses 384 people recruted n such a way as to represent people wth nternet access n New Zealand. Data were obtaned for ust the month of November 000. Durng that month some 185 panelsts (56%) used the nternet at least once 6. The Nelsen/Netratngs software (called Insght ) s actvated each tme a panelst uses an nternet browser at home. As homes may have multple panelsts, each person n the home logs on when they open ther browser by selectng ther name from a lst of household members aged or more. Demographc nformaton on age, gender, occupaton and educaton are obtaned at recrutment for each panelst, as well as the educaton and occupaton of the man ncome earner n the home. Table 1 shows the demographc profle of the panel. Compared wth the general populaton, ths panel of nternet users tends to be slghtly younger and s better educated, wth a correspondng sew towards students and professonal employment. Such upscale demographc sews have also been observed for nternet users n the U.S. (Degeratu, Rangaswamy and Wu 000) and the U.K (Emmanouldes and Hammond 000). The Nelsen software captures each URL and vst duraton for that URL as the panelst proceeds through ther nternet sesson. However, as mentoned above, the data suppled to us are aggregated to the doman level, and report the total doman vst duraton and the total 6 It s worth notng that n ths maret n November 000 broadband penetraton was less than %, wth almost all panelsts usng a 14, 8 or 56 phone modem for ther nternet connecton. In a maret wth hgher broadband penetraton, duraton tmes would tend to be lower due to faster downloadng. Ths would dfferentally affect only stes wth hgh graphcs content. 14

16 number of pages vsted 7. In addton, f several nternet browsng sessons occurred on the same day, data are aggregated across that day. Par and Fader (004) and Moe and Fader (004a; 004b) also use data aggregated to a one-day level. Over 3,000 dfferent stes were vsted n November, wth two-thrds of those stes vsted ust once. Due to ths low vst ncdence for the maorty of stes and because we later content analyze each ste, we select ust the top 50 webstes 8. Moreover, owng to the mportance of advertsng revenue for most webstes, we consder only stes that carry advertsng. Ths elmnated several bans and government webstes, for example. Our fnal sample sze based on vstors to at least one of these 50 stes s 1665 people, who had a total of 3,64 vsts over the month. Table gves an alphabetcal lstng of each webste. Not surprsngly the top ffty stes are domnated by portals, Internet Servce Provders (ISPs) and search engnes. Other frequently vsted stes are web hostng servces, entertanment and software products. Table also gves the medan ste duraton (measured n seconds) and the medan number of pages vewed. There s much varaton n vst duraton and depth across the stes. For example, the portal gohp.com has a medan duraton tme of only 43 seconds, whle the medan duraton tme for games/entertanment stes le Imperalconflct.com, neopets.com and swrve.com all exceed 1000 seconds (about 16 mnutes). Lewse for the number of pages vewed, where search engnes le altavsta.com and google.com average between 1 and pages vewed, whereas many of the entertanment stes average over 10 pages vewed. Content Analyss of the Top 50 Webstes In addton to characterstcs of nternet users, we also study webste features to see f they have any mpact on vst duraton. Understandng the effect of webste desgn and 7 Our data also have the number of hts, whch s dstnct from the number of pages vewed. 8 These 50 stes had the hghest total count of the number of pages vewed over the month. 15

17 content on a user s vst duraton mght help webmasters talor ther stes to retan vstors for longer, wth the resultng downstream benefts mentoned above. Some potental webste desgn features that have been examned prevously n the context of vst duraton nclude the text and graphcs content and bacground complexty (Dreze and Zufryden 1997), advertsng content (Dreze and Zufryden 1997; Hofacer and Murphy 000) and functonalty, for example, content customzaton, search functons and dscusson boards (Bezan-Avery et al 1998; Ghosh and Dou 1998). All of these features are easy for a web user to assess and are smlarly easy for a webmaster to manpulate. For nstance, f a web user notes that there s a lot of advertsng on a ste s home page, resultng n unappealng ad clutter (Kent 1993) that lowers vst duraton, then the webmaster can attempt to reduce the clutter wthout maredly sacrfcng advertsng revenue. The assessment of each of the top 50 webstes was made by three udges, who were nstructed to vst each doman and examne the ste for fve mnutes by clcng across pages. Durng ths surfng perod, udges rated the ste s textual, graphcs and bacground complexty, advertsng content as well as the functonalty tems. Ths made the content analyss more detaled than merely usng the homepage (as done by Ha and James 1998), but not so tme consumng as to mae the evaluaton too arduous. The codng nstructons and codng forms are based on the wor of Grenfell (1998). The nstructons specfed how the analyses were to be conducted, as well as defnng all of the techncal terms used n the codng sheet. Text and graphcs content, as well as bacground complexty, are measured on a fve pont scale, where 1 denotes smple and 5 denotes complex. Advertsng content s coded so that codes 1 through 5 denote 1, -3, 4-5, 6-7 and 8 or more ads, respectvely, on a typcal page 9. Functonalty s measured va 19 tems based largely on the measures used by Grenfell (1998) and Ghose and Dou (1998) 9 Obvously there are dfferng numbers of ads per page, so udges later reported that they used the home page as an ntal ndcaton of ad quantty, then modfed ther assessment (f necessary) after the 5 mnute browsng perod. 16

18 ncludng features such as onlne help, search functons, ste maps, user regstraton, emal contact avalablty, chat rooms and message boards. Table 3 gves the complete lst. Each of these tems s coded on a two pont scale (yes=1/no=0). An overall functonalty score between 0 and 1 s obtaned for each webste by averagng the 19 tems. Inter-udge relablty s assessed by usng Rust and Cool s (1994) Proportonal Reducton n Loss (PRL) ndex, whch s a generalzaton of Cronbach s alpha that taes nto account the number of udges and the number of scale categores for each tem. Rust and Cool (1994) recommend that PRL values should be hgher than 0.7 for adequate nter-udge relablty. The PRL values obtaned n our study were generally very hgh, beng.79 for text content,.65 for graphcs content,.75 for bacground complexty,.91 for advertsng content, whle the average PRL across the 19 functonalty tems was.89. Therefore, we can reasonably conclude that the assessment of the content of the top ffty stes s relable. The mddle columns of Table dsplay the ratngs for each of the stes on the text, graphcs, bacground and advertsng attrbutes, as well as the average functonalty score, whle Table 3 shows the percentage of stes that were rated as a 4 or 5 (.e., hgh) on these attrbutes. We see that over 40% of the stes are udged to have hgh text content, whle very few have hgh bacground complexty and advertsng content. The overall average functonalty score s 49%. Model Varables Our model for webste duraton n equaton (6) contans three broad groups of varables: demographc characterstcs of users; webste characterstcs; and varables related to vst occasons. We now gve more detals on the actual varables used n our emprcal applcaton of the model n equaton (6). Demographc Descrptors. Table 1 gves the four demographc varables that are measured on each panelst. In the model, gender s bnary coded wth a 1 for males and for females. We 17

19 use the exact panelst age (rangng from to 83 years) rather than code age nto categores. Table 1 lsts three educaton categores, whch we code as two dummy varables, wth the baselne beng grammar school or some hgh school (low educaton), medum educaton s those wth hgh school or some college and the second dummy varable s those wth a college degree (hgh educaton). The occupaton categores lsted n Table 1 are smlarly dummy varable coded, wth retred/unemployed as the baselne. Webste Descrptors. The observed webste characterstcs are lsted n Table 3. Ste type s dummy varable coded, wth portals beng the baselne. The ste attrbute scores for text, graphcs, bacground complexty and advertsng content gven n Table are used drectly n the model. As some of the 19 functonalty tems are ether hghly correlated among themselves (e.g., tems -4 and 17-19) or correlated wth partcular ste types, we use ust the average functonalty score for each webste, as reported n Table. Vst Occason Descrptors. These varables pertan to the condtons under whch a partcular vst taes place. The frst varable s called Weeend Vst and ndcates whether a partcular vst occurs on a weeday or weeend, beng coded as a 1 f the observed vst occurs on a Saturday or a Sunday. Followng Bucln and Ssmero (003), the second varable measures the cumulatve number of prevous vsts to a gven webste by a gven person 10. We operatonalze ths by creatng a varable called CVst, whch s the cumulatve number of prevous vsts to a partcular ste by a panelst pror to the occurrence of the present vst, but only from November 1, 000 whch s the begnnng of our observaton wndow. For example, f a person vsts google.com on, 7, 11 and 0 November, then CVst has a value of 0 on November, but ncrements to 1, then 3, 10 Snce our data are restrcted to ust the month of November, we have no way of nowng when a panelst frst vsts a partcular webste,.e., the data are left censored. It s, therefore, very hard to clam that such a varable captures any potental learnng or fatgue effects due to multple vsts by the same person to the same webste. 18

20 respectvely, on 7, 11 and 0 November 11. Average values of CVst, for ust the fnal vst n November to a ste by a person, are gven for each webste n the last column of Table. It can be seen that entertanment and games stes, for example, generally have more multple vsts wthn a month. Model Comparson RESULTS Several alternatve models were dscussed above, whch we now compare. The frst s a model wth fxed effects only (equaton (6) wthout the τ, δ and η terms), whch s equvalent to an OLS regresson model. The second model s Ansar et al s (000) random effects model where the random effects are, n turn, lnear functons of demographc and webste covarates 1. The remanng two models are based on equaton (6), one where the random effects are homoscedastc and the other where they are heteroscedastc. We compare models va ther log-lelhood and Bayes nformaton crteron, whch taes nto account the number of estmated parameters. When usng Gbbs samplng for Bayesan estmaton (as we do), Spegelhalter et al (003) recommend ther DIC crteron, whch s smlar to BIC, and also does not requre alternatve models to be nested wthn each other. The model wth the lowest BIC and DIC values s deemed to be the best. As an addtonal model comparson, we also splt our data nto calbraton and valdaton data sets. In our case we use the frst 1000 people (correspondng to ste vsts) for calbraton, leavng 665 people (wth 970 vsts) n the valdaton dataset. We compare across the four models for both datasets usng three crtera: relatve absolute devaton (RAD), beng the average of the absolute value of the dfference between the estmated and actual log duraton dvded by the estmated log duraton; the mean absolute 11 Snce CVst ranges from 0 to 30, and s heavly sewed to the rght, we follow Bucln and Ssmero (003) by tang a log transformaton and usng log(1+cvst) n the model. M β M 1 Note that Ansar et al s (000) model does not have the term n equaton (1). 19

21 devaton (MAD), beng the absolute value of the dfference between the estmated and actual log duraton; and the root mean squared error (RMSE), beng the square root of the averaged squared dfferences between the estmated and actual log duraton. Table 4 shows that the hghest log-lelhood occurs for the mxed effects model wth heteroscedastc random effects 13. However, ths model also has the most parameters. When the number of parameters s taen nto account the BIC and DIC crtera both show that the mxed effects model wth homoscedastc random effects s the best model. Notce that both mxed effects models are an mprovement over Ansar et al s (000) model, showng the beneft of ncludng fxed and random effects for vst occasons to the same ste by the same person. The RAD, MAD and RMSE crtera for the calbraton show that the mxed effects model wth homoscedastc random effects performs as well as the model wth heteroscedastc random effects and s better than the other two models. For the valdaton data all the models do worse than for the calbraton data, but perform smlarly. Hence, on balance, the mxed effects model wth homoscedastc random effects performs as well or better than the alternatve models. Ths demonstrates that a relatvely smple random effects model that ncorporates personal and product heterogenety can perform ust as well as a more complex ones n ths class of models. Therefore, from now on we report results for ust ths model. Parameter Estmates for the Vst Duraton Model Table 5 gves the parameter estmates for the mxed effects model wth homoscedastc random effects. Ths tme we use the entre sample of 1665 people. Only two demographc varables are statstcally sgnfcant, gender and age. The postve estmated coeffcents for these two varables shows that n general women vst webstes for longer and that vst 13 Due to convergence problems we had to constran the number of varance parameters for η to be 1,.e., ust σ η, rather than the 7189 varance parameters permssble under full heterogenety. Smlarly for number of varance parameters for δ s the requred 50, however. τ. The 0

22 duraton ncreases wth age. Educaton and occupaton do not have a sgnfcant mpact on the length of a ste vst. Ths fndng on the age of web users s supported by a prevous study by Dreze and Hussherr (003) where eye fxaton tmes on web pages are longer for older people. However, they dd not fnd a sgnfcant gender effect. Not surprsngly, entertanment stes have sgnfcantly longer vst duratons than portals (the baselne ste type) at the.1% level of sgnfcance. Addtonally, at the 5% level of sgnfcance, aucton stes also have longer vst duratons than portals. Of the webste characterstcs, graphcs content s sgnfcant at the 10% level and advertsng content s statstcally sgnfcant at the 5% level. The longer duraton for stes wth hgh graphcs content s lely due to the combned effect of many entertanment stes havng hgh graphcs content and the longer download tmes for graphcs va a phone modem. The negatve coeffcent for advertsng shows that hgher levels of advertsng on the ste s assocated wth shorter ste vsts. Dreze and Hussherr (003) fnd that many web users actvely avod banner ads (even though they may be perpherally exposed to the ad). Moreover, Schlosser et al (1999) fnd that web ads are dsled more than ads n conventonal meda. Ths s consstent wth our fndng, where stes wth too much advertsng may be drvng away vstors 14. The estmates of the varance components n equaton (6) are also gven n Table 5. The largest component corresponds to the vst occason effects by the same person to the same webste, followed by webste effects and then ndvdual-specfc effects. The estmated correlatons obtaned va equatons (7) through (9) are, respectvely, 0.0, 0.04 and 0.3. Ths ndcates reasonable correlaton n vst duraton tmes for the same person to the same webste, but near zero correlaton wthn ndvduals and wthn webstes. The large estmated 14 We examned possble nteracton effects between demographcs, webste type and ad content. Unfortunately, some of these nteractons are nestmable due to no varaton n advertsng levels across a webste type. Also, the age*ad content nteracton s hghly collnear wth the separate effects of age and ad content, whch mass the sgnfcance of these man effects. 1

23 value of σ relatve to the other varance components s ndcatve of the hgh varablty n duraton tmes even when explanatory factors and heterogenety are accounted for. Havng sad ths, the value of σˆ goes from 1.6 up to.4 when the three random effect terms are omtted from the model, showng that the addton of random effects maes a bg mprovement n model ft. Parameter Estmates for the Vst Depth Model Table 5 also gves the parameter estmates for the model of the number of pages vewed. As explaned above, our model for pages vsted s easly adapted from equaton (6) as a generalzed lnear model wth a Posson-dstrbuted dependent varable and a log ln functon. Ths model s also ft usng WnBUGS. Table 5 shows that none of the demographc varables are statstcally sgnfcant. Furthermore, only entertanment stes have sgnfcantly more pages vewed than all other webste types. Of the ste attrbutes, graphcs, advertsng content and functonalty are sgnfcant at the 5% level. As wth vst duraton, the sgnfcance of graphcs content may not be so much owng to the graphcs themselves, but more because of the hgh graphcs content of most entertanment stes. The negatve coeffcent for advertsng s consstent wth the webste duraton model and demonstrates that too much advertsng s an mpedment to further depth of a ste vst, probably for the same reason t mght be an mpedment to a longer vst. The reason for the negatve coeffcent for functonalty s less obvous. However, t s noteworthy from Table that 6 of the 8 stes wth functonalty scores greater than 0.65 nclude four portals, a news ste and a search engne, each wth a medan number of pages vewed of one or two. Hence, the ssue may not be so much that havng lots of functonalty features s a deterrent to deeper search, but that the type of ste wth lots of features does not typcally nduce deep vsts.

24 We have a sgnfcant postve coeffcent at the 10% level for the Weeend Vst varable. Ths demonstrates that more pages are vewed on weeends compared wth weedays, an ntutvely reasonable fndng, as resdental web users have more tme avalable n the weeend. Lastly, note that cumulatve number of prevous vsts has no sgnfcant nfluence on duraton or pages vewed. Whle Bucln and Ssmero (003) and Johnson et al (003) found learnng effects across repeat webste vsts, as we mentoned before, due to the left-censorng of our observatonal data, t s dffcult to use the CVst varable to mae nferences about potental learnng effects across multple webste vsts. CONCLUSION The purpose of ths research s to examne factors that mght mpact on webste vst duraton and depth. Ths s of nterest to both advertsers and webmasters. Advertsers are een to now the demographc profle of the audence for ther chosen meda and some broad comparsons of webste types, whle webmasters want to now how to redesgn ther webste to attract vstors for longer. Regardng demographcs, only gender and age of a vstor have any mpact on vst duraton, wth women and older users stayng for longer. As Danaher and Mullarey (003) show that longer vsts result n hgher banner advertsng recall, the mplcaton s that web advertsng n general s more suted to women rather than men and older rather than younger people. Ths mght come as a surprse to web advertsers, who generally target younger males due to ther hgh nternet use (Gershberg 004). Educaton and occupaton have no affect on vst duraton or the number of pages vewed. Of the webste types, entertanment stes have sgnfcantly longer vst duraton and more pages vewed than other webstes. Aucton stes have sgnfcantly longer duraton (but not pages vewed) than other webstes (except entertanment stes of course). Of the webste attrbutes, too much advertsng results n sgnfcantly shorter vst duraton and fewer pages vewed. User ntolerance of webste advertsng has also been found by Dreze and Hussherr 3

25 (003) and Schlosser et al (1999). As many webstes are supported prmarly by advertsng (East 003), ths presents a challenge to webmasters. That s, how to attract advertsng revenue wthout smultaneously drvng away vstors. Stes wth hgher graphcs content also have longer vst duraton and more pages vewed. However, the lely reason for ths s that entertanment stes tend to have hgher levels of graphcs and we have already seen that entertanment stes have sgnfcantly greater vst duraton and depth. We also demonstrate some nsghts nto how to smultaneously model ndvdual-level and product-specfc heterogenety. Prevous models by Ansar et al (000) and Ansar and Mela (003) for moves and emal, respectvely, have used lnear combnatons of random and fxed effects. We show that these models are members of a larger class of random effects models wth heteroscedastc varances. However, our emprcal fndngs llustrate that ths addtonal complexty mght be unnecessary and a smpler homoscedastc random effects model may suffce. Nonetheless, the mportance of ncorporatng heterogenety for products as well people s corroborated. Fnally, we also hghlght the mportance of capturng vst-occason heterogenety. Ths thrd source of heterogenety arses whenever customers have multple nteractons wth the product. In such stuatons, we demonstrate how a trple-heterogenety model can be developed to smultaneously ncorporate person-specfc and product-specfc heterogenety, as well as heterogenety that s specfc to person-product combnatons. One area for future wor s the use of ndvdual-specfc parameter estmates to help customze a ste for a partcular person, wth an am to maxmze vst duraton. Ths s analogous to Ansar and Mela (003) who customze emal content to maxmze clc through rates. 4

26 Table 1: Demographc Profle of Panelsts Gender Panel Percent Populaton Percent Gender Male Female Age Educaton Grammar school or some hgh school only Hgh school graduate or some college Bachelor or postgraduate degree 1 1 Occupaton Blue collar 7 0 Admnstraton/sales Homemaer 6 9 Student 1 0 Self-employed 1 8 Professonal 3 16 Retred/unemployed/other 1 17 *Sample base s the 1665 people that accessed at least one of the top 50 stes n November 000 5

27 Table : Lst of the Top Ffty Webstes wth Average Vst Duraton and Ste Attrbutes Ste Name Ste Type Text content Graphcs content Bacground complex Advert. content Functonalty score Medan Vst Duraton Medan Pages vewed Av. CVst about.com Portal altavsta.com Search amazon.com Retal aol.com Portal as.com Search bluemountan.com Greetngs bolt.com Entertanment bonz.com Portal Cartoonnetwor.com. Entertanment clear.net.nz ISP cnet.com Servce cnn.com News ebay.com Aucton egreetngs.com Greetngs excte.com Portal ezboard.com Messagng flybuys.co.nz Servce foxds.com Games go.com Portal gohp.com Portal google.com Search homestead.com Hostng hotbar.com Software cq.com Messagng hug.co.nz ISP Imperalconflct.com Games lycos.com Portal mcrosoft.com Software msn.com Portal mtnsms.com Messagng nbc.com Portal neopets.com Entertanment netscape.com Portal nzcty.co.nz Portal nzherald.co.nz News nzoom.com Portal paradse.net.nz ISP passport.com Portal shocwave.com Entertanment stuff.co.nz News swrve.com Entertanment trademe.co.nz Aucton trpod.com Hostng webshots.com Software xtra.co.nz Portal xtramal.co.nz Servce yahoo.com Portal zdnet.com Servce zfree.co.nz ISP zone.com Games

28 Table 3: Profle of Stes and Descrpton of the Functonalty Items Top 50 Webstes Percent Ste Type Aucton 4 Entertanment 0 ISP 8 News 6 Portal 30 Servce 6 Software 6 Ste Features Hgh text content 4 Hgh graphcs content 16 Hgh bacground complexty 4 Hgh advertsng content 4 Functonalty Item Descrpton Percent wth tem 1 A button or functon that allows a user to change the ste s language? 4 A button or functon that allows a user to change the ste s graphc or text content mx? 1 3 A button or functon that allows a user to change the ste s page layout? 16 4 A button or functon that allows a user to customze the ste s content? 3 5 Are there any emal contact addresses on the ste? 74 6 Can users vew product/servce nformaton on the ste? 98 7 Is there any form of onlne help avalable? 84 8 Does the ste have a basc search functon? 80 9 Does the ste have a detaled ste map avalable? Does the ste have lns related to other relevant parts of the ste present? Can you download ste paraphernala (e.g. wallpaper) on ths webste? 36 1 Does ths ste have user regstraton as an opton? Does the ste encourage feedbac va onlne survey forms? Does the ste encourage feedbac va emal? Does the ste have onlne problem dagnostcs tools? 16 Does the ste have a clear secton that features recent updates? Does the ste have any chatrooms avalable? 3 18 Does the ste have topc-specfc dscusson forums? Does the ste have message boards avalable? 3 Average functonalty score (on a 0 to 1 scale) 48.8% 7

29 Table 4: Model comparson Measure Fxed Effects only* Model AEK** Mxed Effects homoscedastc effects Mxed Effects heteroscedastc effects Log-lelhood Parameters BIC DIC Calbraton Data (13544 obs.) RAD,% MAD RMSE Valdaton Data (970 obs.) RAD,% MAD RMSE * Equvalent to OLS regresson model **AEK s the orgnal Ansar et al (000) model Log-lelhood for the null model wth an ntercept only s

30 Table 5: Parameter Estmates for Duraton and Pages Vewed Models Duraton Model Pages Vewed Model Mxed Effects wth homoscedastc effects Posson GLM wth homoscedastc effects Estmate t-stat Estmate t-stat Intercept Gender (female) Age Moderate educaton Hgh educaton Lttle educaton* Blue collar Admnstraton/sales Homemaer Student Self-employed Professonal Retred/unemployed* Software Aucton Entertanment Servces ISP News Portal* Text content Graphcs content Bacground complexty Advertsng content Functonalty Weeend Vst Log(1+CVst) σ τ.05.3 σ δ σ η σ *Baselne dummy varables