A High-Dimensional Choice Model for Online Retailing

Published Online:https://doi.org/10.1287/mnsc.2020.02715

References

  • Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2014) Tracking whole-brain connectivity dynamics in the resting state. Cerebral Cortex 24(3):663–676.CrossrefGoogle Scholar
  • Amano T, Rhodes A, Seiler S (2019) Large-scale demand estimation with search data. Harvard Business School Working Paper 19-022, Harvard Business School, Boston.Google Scholar
  • Banerjee O, Ghaoui LE, d’Aspremont A (2008) Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data. J. Machine Learning Res. 9:485–516.Google Scholar
  • Bray RL, Stamatopoulos I (2022) Menu costs and the bullwhip effect: Supply chain implications of dynamic pricing. Oper. Res. 70(2):748–765.Google Scholar
  • Cai TT, Li H, Liu W, Xie J (2013) Covariate-adjusted precision matrix estimation with an application in genetical genomics. Biometrika 100(1):139–156.CrossrefGoogle Scholar
  • Chen Y, Yao S (2017) Sequential search with refinement: Model and application with click-stream data. Management Sci. 63(12):4345–4365.LinkGoogle Scholar
  • Chiong KX, Shum M (2019) Random projection estimation of discrete-choice models with large choice sets. Management Sci. 65(1):256–271.LinkGoogle Scholar
  • Cohen MC, Zhang R, Jiao K (2022) Data aggregation and demand prediction. Oper. Res. 70(5):2597–2618.Google Scholar
  • Dotson JP, Howell JR, Brazell JD, Otter T, Lenk PJ, MacEachern S, Allenby GM (2018) A Probit model with structured covariance for similarity effects and source of volume calculations. J. Marketing Res. 55(1):35–47.CrossrefGoogle Scholar
  • Ertekin N, Agrawal A (2020) How does a return period policy change affect multichannel retailer profitability? Manufacturing Service Oper. Management 23(1):210–229.LinkGoogle Scholar
  • Feldman J, Zhang DJ, Liu X, Zhang N (2022) Customer choice models vs. machine learning: Finding optimal product displays on Alibaba. Oper. Res. 70(1):309–328.Google Scholar
  • Ferreira KJ, Lee BHA, Simchi-Levi D (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.LinkGoogle Scholar
  • Fisher M, Gallino S, Li J (2018) Competition-based dynamic pricing in online retailing: A methodology validated with field experiments. Management Sci. 64(6):2496–2514.LinkGoogle Scholar
  • Fox JT (2007) Semiparametric estimation of multinomial discrete-choice models using a subset of choices. RAND J. Econom. 38(4):1002–1019.CrossrefGoogle Scholar
  • Friedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3):432–441.CrossrefGoogle Scholar
  • Gallino S, Moreno A (2014) Integration of online and offline channels in retail: The impact of sharing reliable inventory availability information. Management Sci. 60(6):1434–1451.LinkGoogle Scholar
  • Gallino S, Karacaoglu N, Moreno A (2023) Need for speed: The impact of in-process delays on customer behavior in online retail. Oper. Res. 71(3):876–894.Google Scholar
  • Glaeser CK, Fisher M, Su X (2019) Optimal retail location: Empirical methodology and application to practice: Finalist-2017 M&SOM practice-based research competition. Manufacturing Service Oper. Management 21(1):86–102.Google Scholar
  • Gomez-Rodriguez M, Leskovec J, Krause A (2012) Inferring networks of diffusion and influence. ACM Trans. Knowledge Discovery Data 5(4):1–37.CrossrefGoogle Scholar
  • Hammersley JM, Clifford P (1971) Markov fields on finite graphs and lattices. Unpublished manuscript, University of Oxford, Oxford, UK.Google Scholar
  • Jagabathula S, Subramanian L, Venkataraman A (2018) A model-based embedding technique for segmenting customers. Oper. Res. 66(5):1247–1267.LinkGoogle Scholar
  • Kesavan S, Kushwaha T, Gaur V (2016) Do high and low inventory turnover retailers respond differently to demand shocks? Manufacturing Service Oper. Management 18(2):198–215.LinkGoogle Scholar
  • Kim JB, Albuquerque P, Bronnenberg BJ (2010) Online demand under limited consumer search. Marketing Sci. 29(6):1001–1023.LinkGoogle Scholar
  • Lee TY, Bradlow ET (2011) Automated marketing research using online customer reviews. J. Marketing Res. 48(5):881–894.CrossrefGoogle Scholar
  • Lee J, Gaur V, Muthulingam S, Swisher GF (2016) Stockout-based substitution and inventory planning in textbook retailing. Manufacturing Service Oper. Management 18(1):104–121.LinkGoogle Scholar
  • Mankad S, Shunko M, Yu Q (2019) How to find your most valuable service outlets: Measuring influence using network analysis. Preprint, submitted April 4, http://dx.doi.org/10.2139/ssrn.3366127.Google Scholar
  • McFadden D, Train K (2000) Mixed MNL models for discrete response. J. Appl. Econometrics 15(5):447–470.CrossrefGoogle Scholar
  • Netzer O, Feldman R, Goldenberg J, Fresko M (2012) Mine your own business: Market-structure surveillance through text mining. Marketing Sci. 31(3):521–543.LinkGoogle Scholar
  • Ngwe D, Ferreira KJ, Teixeira T (2019) The impact of increasing search frictions on online shopping behavior: Evidence from a field experiment. J. Marketing Res. 56(6):944–959.CrossrefGoogle Scholar
  • Ringel DM, Skiera B (2016) Visualizing asymmetric competition among more than 1,000 products using big search data. Marketing Sci. 35(3):511–534.LinkGoogle Scholar
  • Rothman AJ, Levina E, Zhu J (2010) A new approach to Cholesky-based covariance regularization in high dimensions. Biometrika 97(3):539–550.CrossrefGoogle Scholar
  • Rothman AJ, Bickel PJ, Levina E, Zhu J (2008) Sparse permutation invariant covariance estimation. Electr. J. Statist. 2:494–515.CrossrefGoogle Scholar
  • Ruiz FJ, Athey S, Blei DM (2020) Shopper: A probabilistic model of consumer choice with substitutes and complements. Ann. Appl. Statist. 14(1):1–27.CrossrefGoogle Scholar
  • Smith AN, Allenby GM (2019) Demand models with random partitions. J. Amer. Statist. Assoc. 115(529):47–65.CrossrefGoogle Scholar
  • Smith AN, Rossi PE, Allenby GM (2019) Inference for product competition and separable demand. Marketing Sci. 38(4):690–710.LinkGoogle Scholar
  • Train KE (2009) Discrete Choice Methods with Simulation (Cambridge University Press, New York).CrossrefGoogle Scholar
  • Wan M, Wang D, Goldman M, Taddy M, Rao J, Liu J, Lymberopoulos D, McAuley J (2017) Modeling consumer preferences and price sensitivities from large-scale grocery shopping transaction logs. Proc. 26th Internat. Conf. World Wide Web (International World Wide Web Conferences Steering Committee, Geneva), 1103–1112.Google Scholar
  • Yai T, Iwakura S, Morichi S (1997) Multinomial probit with structured covariance for route choice behavior. Transportation Res. Part B Methodological 31(3):195–207.CrossrefGoogle Scholar
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