Understanding Large-Scale Dynamic Purchase Behavior

References

• Ansari A , Mela CF (2003) E-customization. J. Marketing Res. 40(2):131–145.Google Scholar
• Ansari A , Li Y , Zhang JZ (2018) Probabilistic topic model for hybrid recommender systems: A stochastic Variational Bayesian approach. Marketing Sci. 37(6):987–1008.Google Scholar
• Baltrunas L , Ludwig B , Ricci F (2011) Matrix factorization techniques for context aware recommendation. Mobasher B, Burke R, eds. Proc. 5th ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 301–304.Google Scholar
• Bishop CM (2006) Pattern Recognition and Machine Learning (Springer, Berlin). Google Scholar
• Blei DM (2012) Probabilistic topic models. Comm. ACM 55:77–84.Crossref, Google Scholar
• Blei DM , Lafferty JD (2007) A correlated topic model of science. Ann. Appl. Statist. 1(1):17–35.Google Scholar
• Blei DM , Kucukelbir A , McAuliffe JD (2017) Variational inference: A review for statisticians. J. Amer. Statist. Assoc. 112(518):859–877.Google Scholar
• Blei DM , Ng AY , Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learning Res. 3:993–1022.Google Scholar
• Bodapati AV (2008) Recommendation systems with purchase data. J. Marketing Res. 45(1):77–93.Google Scholar
• Borg I , Groenen PJF (2005) Modern Multidimensional Scaling: Theory and Applications (Springer, Berlin). Google Scholar
• Boyd S , Vandenberghe L (2004) Convex Optimization (Cambridge University Press, Cambridge, UK).Google Scholar
• Braun M , McAuliffe J (2010) Variational inference for large-scale models of discrete choice. J. Amer. Statist. Assoc. 105(489):324–335.Google Scholar
• Bronnenberg BJ , Mahajan V , Vanhonacker WR (2000) The emergence of market structure in new repeat-purchase categories: The interplay of market share and retailer distribution. J. Marketing Res. 37(1):16–31.Google Scholar
• Bruce NI , Peters K , Naik PA (2012) Discovering how advertising grows sales and builds brands. J. Marketing Res. 49(6):793–806.Google Scholar
• Büschken J , Allenby GM (2016) Sentence-based text analysis for customer reviews. Marketing Sci. 35(6):953–975.Google Scholar
• Büschken J , Allenby GM (2020) Improving text analysis using sentence conjunctions and punctuation. Marketing Sci. 39(4):727–742.Google Scholar
• Carpenter B , Gelman A , Hoffman M , Lee D , Goodrich B , Betancourt M , Brubaker M , et al. . (2017) Stan: A probabilistic programming language. J. Statist. Software 76(1):1–32.Google Scholar
• Charlin L , Ranganath R , McInerney J , Blei DM (2015) Dynamic poisson factorization. Werthner H, Zanker M, eds. Proc. 9th ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 155–162.Google Scholar
• da Costa Hernandez JM , Wright SA , Rodrigues FF (2015) Attributes vs. benefits: The role of construal levels and appeal type on the persuasiveness of marketing messages. J. Advertising 44(3):243–253.Google Scholar
• Dew R , Ansari A (2018) Bayesian nonparametric customer base analysis with model-based visualizations. Marketing Sci. 37(2):216–235.Google Scholar
• Dew R , Ansari A , Li Y (2020) Modeling dynamic heterogeneity using Gaussian processes. J. Marketing Res. 57(1):55–77.Google Scholar
• Dzyabura D , Hauser JR (2011) Active machine learning for consideration heuristics. Marketing Sci. 30(5):801–819.Google Scholar
• Guadagni PM , Little JDC (1983) A logit model of brand choice calibrated on scanner data. Marketing Sci. 2(3):203–238.Google Scholar
• Gupta S (1988) Impact of sales promotions on when, what, and how much to buy. J. Marketing Res. 25(4):342–355.Google Scholar
• Hoffman MD , Blei DM , Wang C , Paisley J (2013) Stochastic variational inference. J. Machine Learning Res. 14:1303–1347.Google Scholar
• Jacobs BJD , Donkers B , Fok D (2016) Model-based purchase predictions for large assortments. Marketing Sci. 35(3):389–404.Google Scholar
• Jordan MI , Ghahramani Z , Jaakkola TS , Saul LK (1999) An introduction to variational methods for graphical models. Machine Learning 37(2):183–233.Google Scholar
• Karatzoglou A , Amatriain X , Baltrunas L , Oliver N (2010) Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. Amatriain X, Torrens M, eds. Proc. 4th ACM Conf. on Recommender Systems (Association for Computing Machinery, New York), 79–86.Google Scholar
• Koren Y , Bell R , Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37.Crossref, Google Scholar
• Kucukelbir A , Tran D , Ranganath R , Gelman A , Blei DM (2017) Automatic differentiation variational inference. J. Machine Learning Res. 18(14):1–45.Google Scholar
• Liu J , Toubia O (2018) A semantic approach for estimating consumer content preferences from online search queries. Marketing Sci. 37(6):930–952.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.Google Scholar
• Neal RM (2011) MCMC using Hamiltonian Dynamics. Brooks S, Gelman A, Jones GL, Meng X, eds. Handbook of Markov Chain Monte Carlo (CRC Press, Boca Raton, FL), 113–162.Google Scholar
• Ormerod JT , Wand MP (2010) Explaining variational approximations. Amer. Statist. 64(2):140–153.Google Scholar
• Pachali MJ , Kurz P , Otter T (2020) How to generalize from a hierarchical model? Quant. Marketing Econom. 18:343–380.Crossref, Google Scholar
• Pearl J (2009) Causality: Models, Reasoning and Inference (Cambridge University Press, Cambridge, UK). Crossref, Google Scholar
• Puranam D , Narayan V , Kadiyali V (2017) The effect of calorie posting regulation on consumer opinion: A flexible latent Dirichlet allocation model with informative priors. Marketing Sci. 36(5):726–746.Google Scholar
• Rossi PE , Allenby GM (2003) Bayesian statistics and marketing. Marketing Sci. 22(3):304–328.Google Scholar
• Rossi PE , McCulloch RE , Allenby GM (1996) The value of purchase history data in target marketing. Marketing Sci. 15(4):321–340.Google Scholar
• Ruiz FJR , Athey S , Blei DM (2020) Shopper: A probabilistic model of consumer choice with substitutes and complements. Ann. Appl. Statist. 14(1):1–27.Google Scholar
• Rutz OJ , Sonnier GP , Trusov M (2017) A new method to aid copy testing of paid search text advertisements. J. Marketing Res. 54(6):885–900.Google Scholar
• Tirunillai S , Tellis GJ (2014) Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent Dirichlet allocation. J. Marketing Res. 51(4):463–479.Google Scholar
• Toubia O (2021) A Poisson factorization topic model for the study of creative documents (and their summaries). J. Marketing Res. Forthcoming.Crossref, Google Scholar
• Train KE (2009) Discrete Choice Methods with Simulation (Cambridge University Press, Cambridge, UK). Crossref, Google Scholar
• Trusov M , Ma L , Jamal Z (2016) Crumbs of the cookie: User profiling in customer-base analysis and behavioral targeting. Marketing Sci. 35(3):405–426.Google Scholar
• Wallach HM, Mimno DM, McCallum A (2009) Rethinking LDA: Why priors matter. Bengio Y, Schuurmans D, Lafferty JD, Williams CKI, Culotta A, eds. Advances in Neural Information Processing Systems (Curran Associates), 22:1973–1981.Google Scholar
• Wedel M , Kannan P (2016) Marketing analytics for data-rich environments. J. Marketing 80(6):97–121,.Google Scholar
• Xia F , Chatterjee R , May JH (2019) Using conditional restricted Boltzmann machines to model complex consumer shopping patterns. Marketing Sci. 38(4):711–727.Google Scholar