Using the clickstream data recorded in Web server log files, the authors develop and estimate a model of the browsing behavior of visitors to a Web site. Two basic aspects of browsing behavior are examined: (1) the visitor's decisions to continue browsing (by submitting an additional page request) or to exit the site and (2) the length of time spent viewing each page. The authors propose a type II tobit model that captures both aspects of browsing behavior and handles the limitations of server log-file data. The authors fit the model to the individual-level browsing decisions of a random sample of 5000 visitors to the Web site of an Internet automotive company. Empirical results show that visitors' propensity to continue browsing changes dynamically as a function of the depth of a given site visit and the number of repeat visits to the site. The dynamics are consistent both with “within-site lock-in” or site “stickiness” and with learning that carries over repeat visits. In particular, repeat visits lead to reduced page-view propensities but not to reduced page-view durations. The results also reveal browsing patterns that may reflect visitors' time-saving strategies. Finally, the authors report that simple site metrics computed at the aggregate level diverge substantially from individual-level modeling results, which indicates the need for Web site analyses to control for cross-sectional heterogeneity.
Get full access to this article
View all access options for this article.
References
1.
AdarEytan, and HubermanBernardo A., (2000), “The Economics of Surfing,”Quarterly Journal of Electronic Commerce, 1(3), 203–214.
2.
AllenbyGreg M., and RossiPeter E., (1999), “Marketing Models of Consumer Heterogeneity,”Journal of Econometrics, 89(March–April), 57–78.
3.
AmemiyaTakeshi, (1985), Advanced Econometrics.Cambridge, MA: Harvard University Press.
4.
AnsariAsim, EssegaierSkander, and KohliRajeev, (2000), “Internet Recommendation Systems,”Journal of Marketing Research, 37(August), 363–75.
5.
AnsariAsim, and MelaCarl, (2002), “E-Customization,”Journal of Marketing Research, 40(May), 131–45.
6.
BellDavid R., HoTeck-Hua, and TangChristopher S., (1998), “Determining Where to Shop: Fixed and Variable Costs of Shopping,”Journal of Marketing Research, 35(August), 352–69.
7.
CatledgeLara D., and PitkowJames E., (1995), “Characterizing Browsing Behaviors on the World Wide Web,”Computer Networks and ISDN Systems, 27(6), 1065–73.
8.
CraigSamuel, GhoshAvijit, and McLaffertySara, (1984), “Models of the Retail Location Process: A Review,”Journal of Retailing, 60(Spring), 5–36.
9.
DellaertBenedict, and KahnBarbara E., (1999), “How Tolerable Is Delay? Consumers' Evaluations of Internet Web Sites After Waiting,”Journal of Interactive Marketing, 13(Winter), 41–54.
10.
DeSarboWayne S., and ChoiJungwhan, (1999), “A Latent Structure Double Hurdle Regression Model for Exploring Heterogeneity in Consumer Search Patterns,”Journal of Econometrics, 89(1–2), 423–55.
11.
DeSarboWayne S., and JedidiKamel, (1995), “The Spatial Representation of Heterogeneous Consideration Sets,”Marketing Science, 14(3), 326–42.
12.
DeSarboWayne S., KimYoungchan, and FongDuncan, (1999), “A Bayesian Multidimensional Scaling Procedure for the Analysis of Revealed Choice Data,”Journal of Econometrics, 89(1–2), 79–108.
13.
DeSarboWayne S., KimYoungchan, WedelMichel, and, DuncanK.H. Fong, (1998), “A Bayesian Approach to the Spatial Representation of Market Structure from Consumer Choice Data,”European Journal of Operational Research, 111(2), 285–305.
14.
DeyDipak K., ChangHong, and RaySubhash C., (1996), “Bayesian Approach in Model Selection for the Binary Response Data,”Advances in Econometrics, 11(Part A), 145–75.
15.
DrèzeXavier, and ZufrydenFred, (1998), “Is Internet Advertising Ready for Prime Time?”Journal of Advertising Research, 38(3), 7–18.
GelfandA.E., (1996), “Model Determination Using Sampling-Based Methods,” in Markov Chain Monte Carlo in Practice, GilksW. R., RichardsonS., and SpiegelhalterD.J., eds. London: Chapman and Hall, 145–62.
18.
GelfandA.E., and DeyDipak K., (1994), “Bayesian Model Choice: Asymptotics and Exact Calculations,”Journal of the Royal Statistics Society, B56 (3), 501–514.
19.
GelfandA.E., HillsS.E., Racine-PoonA., and SmithA.F.M., (1990), “Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling,”Journal of the American Statistical Association, 85(June), 972–85.
20.
GelfandA.E., and SmithA.F.M., (1990), “Sampling-Based Approaches to Calculating Marginal Densities,”Journal of the American Statistical Association, 85, 398–409.
21.
GilksW.R., (1992), “Derivative-Free Adaptive Rejection Sampling for Gibbs Sampling,” in Bayesian Statistics 4, BernardoJ., BergerJ., DawidA.P., and SmithA.F.M., eds. Oxford: Oxford University Press.
GilksW.R., and WildP., (1992), “Adaptive Rejection Sampling for Gibbs Sampling,”Applied Statistics, 41(2), 337–48.
24.
HeckmanJ., (1978), “Dummy Endogenous Variables in a Simultaneous Equation System,”Econometrica, 46(4), 931–59.
25.
HeckmanJ., and SingerB., (1984), “A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data,”Econometrica, 52(2), 271–320.
26.
HubermanBernardo A., PirolliPeter, PitkowJames E., and LukoseRajan M., (1998), “Strong Regularities in World Wide Web Surfing,”Science, 280(3), 95–97.
27.
JainDipak C., and VilcassimNaufel J., (1991), “Investigating Household Purchase Timing Decisions: Conditional Hazard Function Approach,”Marketing Science, 10(1), 1–23.
28.
JohnsonEric J., BellmanSteven, and LohseJerry, (2003), “Cognitive Lock-In and the Power Law of Practice,”Journal of Marketing, 67(April), 62–75.
29.
JohnsonEric J., MoeWendy, FaderPeter, BellmanSteven, and LohseJerry, (2002), “On the Depth and Dynamics of Online Search Behavior,” working paper, Department of Marketing, Columbia Business School, Columbia University.
30.
KamakuraWagner A., and WedelMichel, (2001), “Exploratory Tobit Factor Analysis for Multivariate Censored Data,”Multivariate Behavioral Research, 36(1), 53–82.
31.
MandelNaomi, and JohnsonEric J., (2002), “When Web Pages Influence Choice: Effects of Visual Primes on Experts and Novices,”Journal of Consumer Research, 29(2), 235–45.
32.
MoeWendy W., and FaderPeter S., (2002a), “Capturing Evolving Visit Behavior in Clickstream Data,” working paper, Wharton School, University of Pennsylvania.
33.
MoeWendy W., and FaderPeter S., (2002b), “Dynamic Conversion Behavior at E-Commerce Sites,” working paper, Wharton School, University of Pennsylvania.
34.
NielsenJakob, (2000), Designing Web Usability: The Practice of Simplicity.Indianapolis: New Riders Publishing.
35.
SahuSujit K., DeyDipak K., AslanidouHelen, and SinhaDebajyoti, (1997), “A Weibull Regression Model with Gamma Frailties for Multivariate Survival Data,”Lifetime Data Analysis, 3(2), 123–37.
36.
TannerMartin A., and WongWing H., (1987), “The Calculation of Posterior Distributions by Data Augmentation,”Journal of the American Statistical Association, 82(June), 528–49.
37.
WedelMichel, KamakuraWagner, AroraNeeraj, BemmaorAlbert, ChiangJeongwen, ElrodTerry, JohnsonRich, LenkPeter, NeslinScott, and PoulsenCarsten S., (1999), “Discrete and Continuous Representations of Unobserved Heterogeneity in Choice Modeling,”Marketing Letters, 10(3), 219–32.
38.
ZaubermanGal, (2003), “The Intertemporal Dynamics of Consumer Lock-In,”Journal of Consumer Research, 30(December), forthcoming.
