Abstract
One of the reasons conjoint analysis has been so popular as a management decision tool has been the availability of a choice simulator. These simulators often arrive in the form of a software or spreadsheet program accompanying the output of a conjoint study. These simulators enable managers to perform ‘what if’ questions about their market - estimating market shares under various assumptions about competition and their own offerings. As examples, simulators can predict the market share of a new offering; they can estimate the direct and cross elasticity of price changes within a market, or they can form the logical guide to strategic simulations that anticipate short- and long-term competitive responses (Green and Krieger 1988).
Originally presented at the Sawtooth Software Conference, February 2, 1999 and updated for this volume in 2006.
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References
Arora, N., Allenby, G. and Ginter, J. L. (1998), A Hierarchical Bayes Model of Primary and Secondary Demand, Marketing Science, 17, 29–44.
Chintagunta, P., Jain, D. C. and Vilcassim, N. J. (1991), Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data, Journal of Marketing Research, 28, 417–428.
DeSarbo, W. S., Ramaswamy, V. and Cohen, S. H. (1995), Market Segmentation with Choice-Based Conjoint Analysis, Marketing Letters, 6, 137–148.
Elrod, T. and Kumar, S. K. (1989), Bias in the First Choice Rule for Predicting Share, Sawtooth Software Conference Proceedings.
Green, P. E and Krieger, A. M. (1988), Choice Rules and Sensitivity Analysis in Conjoint Simulators, Journal of the Academy of Marketing Science, 16, 114–127.
Hausman, J. and Wise, G. (1978), A Conditional Probit Model for Quantitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences, Econometrica, 43, 403–426.
Huber, J. and Zwerina, K. (1996), The Importance of Utility Balance in Efficient Choice Designs, Journal of Marketing Research, 23, 307–317.
Johnson, R. M. (1997), ICE: Individual Choice Estimation, Sawtooth Software Technical Paper.
Kamakura, W. A. and Russell, G. J. (1989), A Probabilistic Choice Model for Market Segmentation and Elasticity Structure, Journal of Marketing Research, 26, 339–390.
Lenk, P. J., DeSarbo, W. S., Green, P. E. and Young, M. R. (1996), Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs Marketing Science, 15, 173–191.
Orme, B. K. and Heft, M. (1999), Predicting Actual Sales with CBC: How Capturing Heterogeneity Improves Results, Sawtooth Software Conference Proceedings.
Revelt, D. and Train, D. (1998), Mixed Logit with Repeated Choices: Household’s Choices of Appliance Efficiency Level, Review of Economics and Statistics, forthcoming.
Rossi, P. and Allenby, G. (1993), A Bayesian Approach to Estimating Household Parameters, Journal of Marketing Research, 30, 171–182.
Additional References
Allenby, Greg, Jeff Brazell, Tim Gilbride, and Thomas Otter (2004), Avoiding IIA Meltdown: Choice Modeling with Many Alternatives, Sawtooth Software Conference Proceedings.
Orme, Bryan and Gary Baker (2000), Comparing Hierarchical Bayes Draws and Randomized First Choice for Conjoint Simulations, Sawtooth Software Conference Proceedings.
Orme, Bryan and Joel Huber (2000), Improving the Value of Conjoint Simulations, Marketing Research, winter 2000.
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Huber, J., Orme, B., Miller, R. (2007). Dealing with Product Similarity in Conjoint Simulations. In: Gustafsson, A., Herrmann, A., Huber, F. (eds) Conjoint Measurement. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71404-0_17
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