35.1 Discrete Choice Models and Continuous Heterogeneity

  • Discrete and Continuous Heterogeneity

  • Price Customization

  • Targeting

Models:

  • Random Coefficients Logit

  • Purchase Incidence

  • Brand Choice Models

(P. K. Chintagunta and Nair 2011)

  • Goals of demand analysis (which affect model-form)

    • Forecast: (not much causal inference)

    • Measurement

      • usually used under experiments and causal inference

      • Structural models

      • Reduced-form, causal effects models

    • Testing

      • Reduced-form, causal effects models
  • Demand, supply, and marketing mix are endogenously determined.

    • Best case: find exogenous shocks to the system to estimate

    • Impose supply model into the demand estimation step (p. 980)

  • Counterintuitive to assume utility maximization for estimating consumer-level models, instead of firms. But we observe evidence of well-fitting model for the demand-side, but not yet in the supply side. But lack thereof evidence still does not mean that it’s wrong, it’s just mean we need more development.

  • Building blocks of individual-level demand models

    • Direct utility specification of demand system

    • Indirect utility specification of demand systems.

(Lehmann, McAlister, and Staelin 2011)

  • Tradeoff between rigor (sophistication) and relevance

  • Basic discipline migrated and viewed as more sophisticated, which lead to arms race. (cascade more and more sophisticated)

  • Execution rigor > idea quality. We should view analytical rigor and substantive content equally.

  • Impact:

    • Citation

      • Breadth and reach (to other disciplines)

      • Game the system: cite reviewers.

  • A good research paper should be (p.162)

    • reasonably realistic/general

    • relatively simple and robust

    • insightful

    • reasonably communicable

  • More complex methods are only appropriate when (p. 163)

(P. Chintagunta, Dubé, and Goh 2005)

  • Try to understand the role of potential weekly brand-specific characteristics that influence consumer choices, but they are unobserved

  • Endogeneity

  • Inclusion of the UBC

UBC: they are the first guys to do it in the dis aggregate model.

(J. Zhang and Wedel 2009)

(Dong, Manchanda, and Chintagunta 2009)

  • What is the benefit of individual-level targeting in the presence of strategic behavior by other firms?

  • Setting

    • Pharmaceutical industry

    • Individual-level targeting to physicians

    • Targeted ad (i.e., detailing)

  • Model

    • Physician response: capture the responsiveness each physician to targeting

    • Firm detailing choices: firms strategically target and how much ad

(Nair et al. 2017)

References

Bass, Frank M. 1969. “A New Product Growth for Model Consumer Durables.” Management Science 15 (5): 215–27. https://doi.org/10.1287/mnsc.15.5.215.
Chintagunta, Pradeep K., and Harikesh S. Nair. 2011. “Structural Workshop PaperDiscrete-Choice Models of Consumer Demand in Marketing.” Marketing Science 30 (6): 977–96. https://doi.org/10.1287/mksc.1110.0674.
Chintagunta, Pradeep, Jean-Pierre Dubé, and Khim Yong Goh. 2005. “Beyond the Endogeneity Bias: The Effect of Unmeasured Brand Characteristics on Household-Level Brand Choice Models.” Management Science 51 (5): 832–49. https://doi.org/10.1287/mnsc.1040.0323.
Dekimpe, Marnik G., and Dominique M. Hanssens. 1995. “The Persistence of Marketing Effects on Sales.” Marketing Science 14 (1): 1–21. https://doi.org/10.1287/mksc.14.1.1.
Dong, Xiaojing, Puneet Manchanda, and Pradeep K. Chintagunta. 2009. “Quantifying the Benefits of Individual-Level Targeting in the Presence of Firm Strategic Behavior.” Journal of Marketing Research 46 (2): 207–21. https://doi.org/10.1509/jmkr.46.2.207.
Guadagni, Peter M., and John D. C. Little. 1983. “A Logit Model of Brand Choice Calibrated on Scanner Data.” Marketing Science 2 (3): 203–38. https://doi.org/10.1287/mksc.2.3.203.
Lehmann, Donald R., Leigh McAlister, and Richard Staelin. 2011. “Sophistication in Research in Marketing.” Journal of Marketing 75 (4): 155–65. https://doi.org/10.1509/jmkg.75.4.155.
Nair, Harikesh S., Sanjog Misra, William J. Hornbuckle, Ranjan Mishra, and Anand Acharya. 2017. “Big Data and Marketing Analytics in Gaming: Combining Empirical Models and Field Experimentation.” Marketing Science 36 (5): 699–725. https://doi.org/10.1287/mksc.2017.1039.
Zhang, Jie, and Michel Wedel. 2009. “The Effectiveness of Customized Promotions in Online and Offline Stores.” Journal of Marketing Research 46 (2): 190–206. https://doi.org/10.1509/jmkr.46.2.190.