Sequential search models are popular in marketing for studying consumer search behavior. Current search models use parametric assumptions regarding different aspects of the models, such as random shocks, search costs, and consumer preferences. These assumptions can be restrictive. The authors develop a novel Bayesian nonparametric framework for sequential search to flexibly model unknown distributions. They also develop Markov chain Monte Carlo methods for inferring the model unknowns. The authors conduct simulation studies to demonstrate that currently popular parametric search models can yield incorrect estimates and inferences when the data-generating process deviates from their assumptions. In contrast, the proposed model accurately recovers these quantities for various data-generating processes. The methodology is then applied to online search and purchase data from a Japanese retailer. The results show that several heterogeneity distributions have complex patterns, such as multimodality and skewness, that are not captured by a parametric benchmark model. The authors also estimate the monetary value of search costs, the price elasticities, and a counterfactual profit gain under a personalized couponing strategy and find substantial differences in the results from the two models.
AlbertJames H.ChibSiddhartha (1993), “Bayesian Analysis of Binary and Polychotomous Response Data,” Journal of the American Statistical Association, 88 (422), 669–79.
3.
AnsariAsimIyengarRaghuram (2006), “Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis,” Psychometrika, 71 (4), 631–57.
4.
AnsariAsimMelaCarl F. (2003), “E-Customization,” Journal of Marketing Research, 40 (2), 131–45.
5.
AntoniakCharles E. (1974), “Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems,” Annals of Statistics, 2 (6), 1152–74.
6.
BarriosErnestoLijoiAntonioNieto-BarajasLuis E.PrünsterIgor (2013), “Modeling with Normalized Random Measure Mixture Models,” Statistical Science, 28 (3), 313–34.
7.
Ben-AkivaMosheLermanSteven R. (1985), Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press.
8.
BrancoFernandoSunMonicVillas-BoasJ. Miguel (2012), “Optimal Search for Product Information,” Management Science, 58 (11), 2037–56.
9.
BrancoFernandoSunMonicVillas-BoasJ. Miguel (2016), “Too Much Information? Information Provision and Search Costs,” Marketing Science, 35 (4), 605–18.
10.
BraunMichaelBonfrerAndré (2011), “Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes,” Marketing Science, 30 (3), 513–31.
11.
BruceNorris I. (2019), “Bayesian Nonparametric Dynamic Methods: Applications to Linear and Nonlinear Advertising Models,” Journal of Marketing Research, 56 (2), 211–29.
12.
BurdaMartinHardingMatthewHausmanJerry (2008), “A Bayesian Mixed Logit–Probit Model for Multinomial Choice,” Journal of Econometrics, 147 (2), 232–46.
13.
CarrollRaymond J.HallPeter (1988), “Optimal Rates of Convergence for Deconvolving a Density,” Journal of the American Statistical Association, 83 (404), 1184–86.
14.
ChenYuxinYaoSong (2017), “Sequential Search with Refinement: Model and Application with Click-Stream Data,” Management Science, 63 (12), 4345–65.
15.
ChintaguntaPradeep K.JainDipak C.VilcassimNaufel J. (1991), “Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data,” Journal of Marketing Research, 28 (4), 417–28.
ChungJae HyenChintaguntaPradeepMisraSanjog (2024), “Simulated Maximum Likelihood Estimation of the Sequential Search Model,” Quantitative Marketing and Economics(published online May 29), https://doi.org/10.1007/s11129-024-09281-4.
19.
CompianiGiovanniLewisGregoryPengSidaWangPeichun (2024), “Online Search and Optimal Product Rankings: An Empirical Framework,” Marketing Science, 43 (3), 615–36.
20.
DelaigleAuroreHallPeter (2016), “Methodology for Non-Parametric Deconvolution When the Error Distribution Is Unknown,” Journal of the Royal Statistical Society Series B: Statistical Methodology, 78 (1), 231–52.
21.
DewRyanAnsariAsimLiYang (2020), “Modeling Dynamic Heterogeneity Using Gaussian Processes,” Journal of Marketing Research, 57 (1), 55–77.
HensherDavid A.RoseJohn M.GreeneWilliam H. (2015), Applied Choice Analysis: A Primer. Cambridge University Press.
27.
HongHanShumMatthew (2006), “Using Price Distributions to Estimate Search Costs,” RAND Journal of Economics, 37 (2), 257–75.
28.
HonkaElisabeth (2014), “Quantifying Search and Switching Costs in the US Auto Insurance Industry,” RAND Journal of Economics, 45 (4), 847–84.
29.
HonkaElisabethChintaguntaPradeep (2017), “Simultaneous or Sequential? Search Strategies in the U.S. Auto Insurance Industry,” Marketing Science, 36 (1), 21–42.
30.
IshwaranHemantJamesLancelot F. (2001), “Gibbs Sampling Methods for Stick-Breaking Priors,” Journal of the American Statistical Association, 96 (453), 161–73.
31.
IshwaranHemantZarepourMahmoud (2000), “Markov Chain Monte Carlo in Approximate Dirichlet and Beta Two-Parameter Process Hierarchical Models,” Biometrika, 87 (2), 371–90.
32.
JaraAlejandro (2017), “Theory and Computations for the Dirichlet Process and Related Models: An Overview,” International Journal of Approximate Reasoning, 81, 128–46.
33.
JiangZhenlingChanTatCheHaiWangYouwei (2021), “Consumer Search and Purchase: An Empirical Investigation of Retargeting Based on Consumer Online Behaviors,” Marketing Science, 40 (2), 219–40.
34.
JindalPranavAribargAnocha (2021), “The Importance of Price Beliefs in Consumer Search,” Journal of Marketing Research, 58 (2), 321–42.
35.
KamakuraWagner A.RussellGary J. (1989), “A Probabilistic Choice Model for Market Segmentation and Elasticity Structure,” Journal of Marketing Research, 26 (4), 379–90.
36.
KeT. TonyShenZuo-Jun MaxVillas-BoasJ. Miguel (2016), “Search for Information on Multiple Products,” Management Science, 62 (12), 3576–603.
37.
KeaneMichaelWasiNada (2013), “Comparing Alternative Models of Heterogeneity in Consumer Choice Behavior,” Journal of Applied Econometrics, 28 (6), 1018–45.
38.
KimJun B.AlbuquerquePauloBronnenbergBart J. (2010), “Online Demand Under Limited Consumer Search,” Marketing Science, 29 (6), 1001–23.
39.
KimJun B.AlbuquerquePauloBronnenbergBart J. (2017), “The Probit Choice Model Under Sequential Search with an Application to Online Retailing,” Management Science, 63 (11), 3911–29.
40.
KimJin GyoMenzefrickeUlrichFeinbergFred M. (2004), “Assessing Heterogeneity in Discrete Choice Models Using a Dirichlet Process Prior,” Review of Marketing Science, 2 (1), 1–39.
41.
LiYangAnsariAsim (2014), “A Bayesian Semiparametric Approach for Endogeneity and Heterogeneity in Choice Models,” Management Science, 60 (5), 1161–79.
42.
LouviereJordan J.HensherDavid A.SwaitJoffre D. (2000), Stated Choice Methods: Analysis and Applications. Cambridge University Press.
43.
McCullochRobert E.PolsonNicholas G.RossiPeter E. (2000), “A Bayesian Analysis of the Multinomial Probit Model with Fully Identified Parameters,” Journal of Econometrics, 99 (1), 173–93.
44.
McCullochRobertRossiPeter E. (1994), “An Exact Likelihood Analysis of the Multinomial Probit Model,” Journal of Econometrics, 64 (1), 207–40.
45.
MorozovIlya (2023), “Measuring Benefits from New Products in Markets with Information Frictions,” Management Science, 69 (11), 6988–7008.
46.
MorozovIlyaSeilerStephanDongXiaojingHouLiwen (2021), “Estimation of Preference Heterogeneity in Markets with Costly Search,” Marketing Science, 40 (5), 871–99.
47.
MurrayIainAdamsRyanMacKayDavid (2010), “Elliptical Slice Sampling,” Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research, 9, 541–48.
48.
PitmanJimYorMarc (1997), “The Two-Parameter Poisson-Dirichlet Distribution Derived from a Stable Subordinator,” Annals of Probability, 25 (2), 855–900.
49.
RossiPeter E.AllenbyGreg M. (2003), “Bayesian Statistics and Marketing,” Marketing Science, 22 (3), 304–28.
50.
SeilerStephan (2013), “The Impact of Search Costs on Consumer Behavior: A Dynamic Approach,” Quantitative Marketing and Economics, 11 (2), 155–203.
SonnierGarrettAinslieAndrewOtterThomas (2007), “Heterogeneity Distributions of Willingness-to-Pay in Choice Models,” Quantitative Marketing and Economics, 5 (3), 313–31.
53.
TrainKenneth E. (2009), Discrete Choice Methods with Simulation. Cambridge University Press.
54.
UrsuRaluca M. (2018), “The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions,” Marketing Science, 37 (4), 530–52.
55.
UrsuRalucaSeilerStephanHonkaElisabeth (2023), “The Sequential Search Model: A Framework for Empirical Research,” SSRN (February 16), https://doi.org/10.2139/ssrn.4357557.
56.
UrsuRaluca MihaelaSimonovAndreyAnEunkyung (2024), “Online Advertising as Passive Search,” Management Science(published online April 30), https://doi.org/10.1287/mnsc.2022.02154.
WeitzmanMartin L. (1979), “Optimal Search for the Best Alternative,” Econometrica, 47 (3), 641–54.
60.
YangMinganDunsonDavid B. (2010), “Bayesian Semiparametric Structural Equation Models with Latent Variables,” Psychometrika, 75 (4), 675–93.
61.
YangMinganDunsonDavid B.BairdDonna (2010), “Semiparametric Bayes Hierarchical Models with Mean and Variance Constraints,” Computational Statistics & Data Analysis, 54 (9), 2172–86.
62.
YavorskyDanHonkaElisabethChenKeith (2021), “Consumer Search in the U.S. Auto Industry: The Role of Dealership Visits,” Quantitative Marketing and Economics, 19 (1), 1–52.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
