Investigating Purchase Conversion by Uncovering Online Visit Patterns

References

• Abe M (2009) Counting your customers one by one: A hierarchical Bayes extension to the Pareto/NBD model. Marketing Sci. 28(3):541–553.Link, Google Scholar
• Ansari A, Mela CF, Neslin SA (2008) Customer channel migration. J. Marketing Res. 45(1):60–76.Crossref, Google Scholar
• Bakos JY (1997) Reducing buyer search costs: Implications for electronic marketplaces. Management Sci. 43(12):1676–1692.Link, Google Scholar
• Barry D, Hartigan JA (1993) A Bayesian analysis for changepoint problems. J. Amer. Statist. Assoc. 88(421):309–319.Google Scholar
• Bernasco W, Nieuwbeerta P (2005) How do residential burglars select target areas? A new approach to the analysis of criminal location choice. British J. Criminology 45(3):296–315.Crossref, Google Scholar
• Bucklin RE, Sismeiro C (2003) A model of Web site browsing behavior estimated on clickstream data. J. Marketing Res. 41(3):249–267.Crossref, Google Scholar
• Cliff AD, Ord JK (1981) Spatial Processes: Models and Applications (Pion Ltd., London).Google Scholar
• Danaher PJ (2007) Modeling page views across multiple websites with an application to Internet reach and frequency prediction. Marketing Sci. 26(3):422–437.Link, Google Scholar
• Ehrenberg ASC (1988) Repeat-Buying: Facts, Theory and Applications (Oxford University Press, New York).Google Scholar
• Fader PS, Hardie BGS (2009) Probability models for customer-base analysis. J. Interactive Marketing 23(1):61–69.Crossref, Google Scholar
• Fader PS, Hardie BGS, Huang CY (2004) A dynamic changepoint model for new product sales forecasting. Marketing Sci. 23(3):280–303.Link, Google Scholar
• Fader PS, Hardie BGS, Lee KL (2005) Counting your customers the easy way: An alternative to the Pareto/NBD model. Marketing Sci. 24(2):275–284.Link, Google Scholar
• Forrester Research (2014) The state of retailing online 2014: Key metrics and initiatives. Forrester Research, Cambridge, MA.Google Scholar
• Forrester Research (2015) Forrester research eCommerce forecast, 2014 to 2019 (US). Forrester Research, Cambridge, MA.Google Scholar
• Gelman A, Rubin DB (1995) Avoiding model selection in Bayesian social research. Sociol. Methodology 25:165–173.Crossref, Google Scholar
• Gilovich T, Vallone R, Tversky A (1985) The hot hand in basketball: On the misperception of random sequences. Cognitive Psych. 17(3):295–314.Crossref, Google Scholar
• Gupta S (1988) Impact of sales promotions on when, what, and how much to buy. J. Marketing Res. 25(4):342–355.Crossref, Google Scholar
• Gupta S (1991) Stochastic models of interpurchase time with time-dependent covariates. J. Marketing Res. 28(1):1–15.Crossref, Google Scholar
• Hansotia B, Rukstales B (2002) Incremental value modeling. J. Interactive Marketing 16(3):35–46.Crossref, Google Scholar
• Hauser JR, Urban GL, Liberali G, Braun M (2009) Website morphing. Marketing Sci. 28(2):202–223.Link, Google Scholar
• Jain DC, Vilcassim NJ (1991) Investigating household purchase timing decisions: A conditional hazard function approach. Marketing Sci. 10(1):1–23.Link, Google Scholar
• Johnson EJ, Moe WW, Fader PS, Bellman S, Lohse J (2004) On the depth and dynamics of world wide Web shopping behavior. Management Sci. 50(3):299–308.Link, Google Scholar
• Manchanda P, Ansari A, Gupta S (1999) The “shopping basket”: A model for multicategory purchase incidence decisions. Marketing Sci. 18(2):95–114.Link, Google Scholar
• Manchanda P, Rossi PE, Chintagunta PK (2004) Response modeling with nonrandom marketing-mix variables. J. Marketing Res. 41(4):467–478.Crossref, Google Scholar
• Manchanda P, Dubé JP, Goh KY, Chintagunta PK (2006) The effect of banner advertising on Internet purchasing. J. Marketing Res. 43(1):98–108.Crossref, Google Scholar
• Miller HJ, Han J (2009) Geographic Data Mining and Knowledge Discovery (CRC Press, Boca Raton, FL).Crossref, Google Scholar
• Moe WW (2003) Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. J. Consumer Psych. 13(1–2):29–39.Crossref, Google Scholar
• Moe WW, Fader PS (2004) Dynamic conversion behavior at e-commerce sites. Management Sci. 50(3):326–335.Link, Google Scholar
• Montgomery AL, Li S, Srinivasan K, Liechty JC (2004) Modeling online browsing and path analysis using clickstream data. Marketing Sci. 23(4):579–595.Link, Google Scholar
• Moorthy S, Ratchford BT, Talukdar D (1997) Consumer information search revisited: Theory and empirical analysis. J. Consumer Res. 23(4):263–277.Crossref, Google Scholar
• Murray AT (2000) Spatial characteristics and comparisons of interaction and median clustering models. Geographical Anal. 32(1):1–18.Crossref, Google Scholar
• Neslin SA, Grewal D, Leghorn R, Shankar V, Teerling ML, Thomas JS, Verhoef PC (2006) Challenges and opportunities in multichannel customer management. J. Service Res. 9(2):95–112.Crossref, Google Scholar
• Netzer O, Lattin JM, Srinivasan V (2008) A hidden Markov model of customer relationship dynamics. Marketing Sci. 27(2):185–204.Link, Google Scholar
• Newton MA, Raftery AE (1994) Approximate Bayesian inference by the weighted likelihood bootstrap. J. Roy. Statist. Soc., Ser. B 56(1):3–48.Google Scholar
• Park CH, Park Y-H, Schweidel DA (2014) A multi-category customer base analysis. Internat. J. Res. Marketing 31(3):266–279.Crossref, Google Scholar
• Park Y-H, Fader PS (2004) Browsing behavior at multiple websites. Marketing Sci. 23(3):280–303.Link, Google Scholar
• Porter JR, Howell FM (2012) Geographical Sociology: Theoretical Foundations and Methodological Applications in the Sociology of Location (Springer, New York).Crossref, Google Scholar
• Schmittlein DC, Bemmaor AC, Morrison DG (1985) Why does the NBD model work? Robustness in representing product purchases, brand purchases and imperfectly recorded purchases. Marketing Sci. 4(3):255–266.Link, Google Scholar
• Schmittlein DC, Morrison DG, Colombo R (1987) Counting your customers: Who are they and what will they do next? Management Sci. 33(1):1–24.Link, Google Scholar
• Schwartz EM, Fader PS, Bradlow ET (2014) Model selection using database characteristics: Developing a classification tree for longitudinal incidence data. Marketing Sci. 33(2):188–205.Link, Google Scholar
• Schweidel DA, Fader PS (2009) Dynamic changepoints revisited: An evolving process model of new product sales. Internat. J. Res. Marketing 26(2):119–124.Crossref, Google Scholar
• Schweidel DA, Knox G (2013) Incorporating direct marketing activity into latent attrition models. Marketing Sci. 32(3):471–481.Link, Google Scholar
• Schweidel DA, Bradlow ET, Fader PS (2009) Portfolio dynamics for customers of a multiservice provider. Management Sci. 57(3):471–486.Link, Google Scholar
• Schweidel DA, Fader PS, Bradlow ET (2008) A bivariate timing model of customer acquisition and retention. Marketing Sci. 27(5):829–843.Link, Google Scholar
• Schweidel DA, Park Y-H, Jamal Z (2014) A multiactivity latent attrition model for customer base analysis. Marketing Sci. 33(2):273–286.Link, Google Scholar
• Seetharaman PB, Chintagunta PK (2003) The proportional hazard model for purchase timing: A comparison of alternative specifications. J. Bus. Econom. Statist. 21(3):368–382.Crossref, Google Scholar
• Sismeiro C, Bucklin RE (2004) Modeling purchase behavior at an e-commerce Web site: A task completion approach. J. Marketing Res. 41(3):306–323.Crossref, Google Scholar
• Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J. Roy. Statist. Soc., Ser. B 64(4):583–639.Crossref, Google Scholar
• Tanner MA, Wong WH (1987) The calculation of posterior distributions by data augmentation. J. Amer. Statist. Assoc. 82(398):528–540.Crossref, Google Scholar
• Telang R, Boatwright P, Mukhopadhyay T (2004) A mixture model for Internet search-engine visits. J. Marketing Res. 41(2):206–214.Crossref, Google Scholar
• Upton GJ, Fingleton B (1985) Spatial Data Analysis by Example: Point Pattern and Quantitative Data (Wiley, New York).Google Scholar
• Urbany JE, Dickson PR, Wilkie WL (1989) Buyer uncertainty and information search. J. Consumer Res. 16(2):208–215.Crossref, Google Scholar
• Van Diepen M, Donkers B, Franses PH (2009) Dynamic and competitive effects of direct mailings: A charitable giving application. J. Marketing Res. 46(1):120–133.Crossref, Google Scholar
• Venkatesan R, Kumar V (2004) Customer lifetime value framework for customer selection and optimal resource allocation strategy. J. Marketing 68(4):106–125.Crossref, Google Scholar
• Zhang Y, Bradlow ET, Small DS (2013) New measures of clumpiness for incidence data. J. Appl. Statist. 40(11):2533–2548.Crossref, Google Scholar
• Zhang Y, Bradlow ET, Small DS (2015) Predicting customer value using clumpiness: From RFM to RFMC. Marketing Sci. 34(2):195–208.Link, Google Scholar