Reality check: Combining choice experiments with market data to estimate the importance of product attributes

Management Science

Volumn 56, Issue 5, 2010, Pages 785-800

  Feit, Eleanor McDonnell ^a   Beltramo, Mark A ^b   Feinberg, Fred M ^b  

^a UNIVERSITY OF MICHIGAN   (United States)

^b GENERAL MOTORS CORPORATION   (United States)

Author keywords

Bayesian estimation; Choice experiments; Conjoint analysis; Data enrichment; Discrete choice modeling; Hierarchical models; Missing data methods

Indexed keywords

BAYESIAN ESTIMATIONS; CHOICE EXPERIMENTS; CONJOINT ANALYSIS; DISCRETE CHOICE; HIERARCHICAL MODEL;

BAYESIAN NETWORKS; COMMERCE; COMPETITION; DATA HANDLING; DECISION MAKING; DECISION THEORY; EXPERIMENTS; HIERARCHICAL SYSTEMS; PRODUCT DESIGN;

POPULATION STATISTICS;

EID: 77952485937     PISSN: 00251909     EISSN: 15265501     Source Type: Journal    
DOI: 10.1287/mnsc.1090.1136     Document Type: Article

References (35)

1. Allenby, G. M., J. L. Ginter. 1995. Using extremes to design products and segment markets. J. Marketing Res. 32(4) 392-1349
2. Andrews, R. L., A. Ainslie, I. S. Currim. 2002. An empirical comparison of logit choice models with discrete versus continuous representations of heterogeneity. J. Marketing Res. 39(11) 479-487.
3. Ben-Akiva, M., T. Morikawa. 1990. Estimation of switching models from revealed preferences and stated intentions. Transportation Res. A 24A(6) 485-495.
4. Ben-Akiva, M., M. Bradley, T. Morikawa, J. Benjamin, T. Novak, H. Oppewal, V. Rao. 1994. Combining revealed and stated preferences data. Marketing Lett. 5(4) 335-350.
5. Bhat, C. R., S. Castelar. 2002. A unified mixed logit framework for modeling revealed and stated preferences: Formulation and application to congestion pricing analysis in the San Francisco Bay area. Transportation Res. B 36(7) 593-616.
6. Blamey, R., J. Bennett. 2001. Yea-saying and validation of a choice model of green product choice. J. Bennett, R. Blamey, eds. The Choice Modeling Approach to Environmental Valuation. Edward Elgar, Northampton, MA, 178-201.
7. Bradley, M., A. Daly. 1994. Use of the logit scaling approach to test for rank-order and fatigue effects in stated preference data. Transportation 21(2) 167-184.
8. Brooks, S. P., A. Gelman. 1998. General methods for monitoring convergence of iterative simulations. J. Comput. Graphical Statist. 7(4) 434-455.
9. Brownstone, D., D. S. Bunch, K. Train. 2000. Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles. Transportation Res. B 34(5) 315-338.
10. Chib, S., E. Greenberg. 1998. Analysis of multivariate probit models. Biometrika 85(2) 347-361.
11. Ding, M., R. Grewal, J. Liechty. 2005. Incentive-aligned conjoint analysis. J. Marketing Res. 42(1) 67-82.
12. De Bruyn, A., J. C. Liechty, E. K. R. E. Huizingh, G. L. Lilien. 2008. Offering online recommendations with minimum customer input through conjoint-based decision aids. Marketing Sci. 27(3) 443-460.
13. Dominici, F., G. Parmigiani, K. H. Reckhow, R. L. Wolpert. 1997. Combining information from related regressions. J. Agricultural, Biol. Environ. Statist. 2(3) 313-332.
14. Fennell, G., G. M. Allenby, S. Yang, Y. Edwards. 2003. The effectiveness of demographic and psychographic variables for explaining brand and product category use. Quant. Marketing Econom. 1(2) 223-244.
15. Gilbride, T. J., P. J. Lenk, J. D. Brazell. 2008. Market share constraints and the loss function in choice-based conjoint analysis. Marketing Sci. 27(6) 995-1011.
16. Green, P. E., V. R. Rao. 1971. Conjoint measurement for quantifying judgmental data. J. Marketing Res. 8(3) 355-363.
17. Green, P. E., A. M. Krieger, Y. J. Wind. 2001. Thirty years of conjoint analysis: Reflections and prospects. Interfaces 31(3) S56-S73.
18. Haario, H., E. Saksman, J. Tamminen. 2001. An adaptive metropolis algorithm. Bernoulli 7(2) 223-242.
19. Heckman, J. J. 1979. Sample bias as a specification error. Econometrica 47(1) 153-162.
20. Ibrahim, J. G., S. R. Lipsitz, M.-H. Chen. 1999. Missing covariates in generalized linear models when the missing data mechanism is non-ignorable. J. Roy. Statist. Soc., Ser. B 61(1) 173-190.
21. Krishnan, V., K. T. Ulrich. 2001. Product development decisions: A review of the literature. Management Sci. 47(1) 1-21.
22. Little, R. J. A., D. B. Rubin. 2002. Statistical Analysis with Missing Data, 2nd ed. Wiley Interscience, Hoboken, NJ.
23. Louviere, J. J., D. A. Hensher, J. D. Swait. 2000. Stated Choice Methods: Analysis and Application. Cambridge University Press, Cambridge, UK.
24. Louviere, J. J., R. J. Meyer, D. S. Bunch, R. Carson, B. Dellaert, W. M. Hanemann, D. Hensher, J. Irwin. 1999. Combining sources of preference data for modeling complex decision processes. Marketing Lett. 10(3) 205-217.
25. Manski, C. F., S. R. Lerman. 1977. The estimation of choice probabilities from choice based samples. Econometrica 45(8) 1977-1988.
26. McCulloch, R., P. E. Rossi. 1994. An exact likelihood analysis of the multinomial probit model. J. Econometrics 64(1-2) 207-240.
27. Newton, M. A., A. E. Raftery. 1994. Approximate Bayesian inference with the weighted likelihood bootstrap. J. Roy. Statist. Soc. Ser. B Methodological 56(1) 3-48.
28. Orme, B., R. Johnson. 2006. External effect adjustments in conjoint analysis. Research paper, Sawtooth Software, Sequim, WA.
29. Rossi, P. E., G. M. Allenby, R. McCulloch. 2005. Bayesian Statistics and Marketing. John Wiley & Sons, West Sussex, UK.
30. Sándor, Z., M. Wedel. 2002. Profile construction in experimental choice designs for mixed logit models. Marketing Sci. 21(4) 455-475.
31. Swait, J., R. L. Andrews. 2003. Enriching scanner panel models with choice experiments. Marketing Sci. 22(4) 442-460.
32. Swait, J., J. Louviere. 1993. The role of the scale parameter in the estimation and comparison of multinomial logit models. J. Marketing Res. 30(3) 305-314.
33. Tanner, M. A., W. H. Wong. 1987. The calculation of posterior distributions by data augmentation. J. Amer. Statist. Assoc. 82(398) 528-540.
34. Urban, G. L., J. R. Hauser, J. H. Roberts. 1990. Prelaunch forecasting of new automobiles. Management Sci. 36(4) 401-421.
35. Zeithammer, R., P. Lenk. 2006. Bayesian estimation of multivariatenormal models when dimensions are absent. Quant. Marketing Econom. 4(3) 241-265.