Thirty Years of Conjoint Analysis: Reflections and Prospects

  • Paul E. Green
  • Abba M. Krieger
  • Yoram Wind
Part of the International Series in Quantitative Marketing book series (ISQM, volume 14)


Conjoint analysis is marketers’ favorite methodology for finding out how buyers make tradeoffs among competing products and suppliers. Conjoint analysts develop and present descriptions of alternative products or services that are prepared from fractional factorial, experimental designs. They use various models to infer buyers’ partworths for attribute levels, and enter the partworths into buyer choice simulators to predict how buyers will choose among products and services. Easy-to-use software has been important for applying these models. Thousands of applications of conjoint analysis have been carried out over the past three decades.


Market Research Attribute Level Conjoint Analysis Conjoint Measurement Electronic tolI Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Addelman, S. (1962), “Orthogonal main-effect plans for asymmetrical factorial experiments,”Technometrics,4 (1), 21–46.Google Scholar
  2. Allenby, G. M., Arora, N., and Ginter, J. L. (1995), “Incorporating prior knowledge into the analysis of conjoint studies,” Journal of Marketing Research, 32 (May), 152–162.CrossRefGoogle Scholar
  3. Allenby, G. M., and Ginter, J. L. (1995), “Using extremes to design products and segment markets,” Journal of Marketing Research, 32 (May), 152–162.CrossRefGoogle Scholar
  4. Anderson, D. A., and Wiley, J. B. (1992), “Efficient choice set designs for estimating cross-effects models,” Marketing Letters, 3 (October), 357–70.CrossRefGoogle Scholar
  5. Anderson, N. H. (1970), “Functional measurement and psychophysical judgment,” Psychological Review, 77 (3) 153–70.CrossRefGoogle Scholar
  6. Ansari, A., Essegaier, S., and Kohli, R. (2000), “Internet recommendation systems,” Journal of Marketing Research, 37 (August), 363–375.CrossRefGoogle Scholar
  7. Bateson, J. E. G., Reibstein, D. J., and Boulding, W. (1987), “onjoint analysis reliability and validity: A framework for future research,” in M. J. Houston (ed.), Review of Marketing, Chicago, IL: American Marketing Association, pp. 451–481.Google Scholar
  8. Batsell, R. R., and Lodish, L. M. (1981), “A model and measurement methodology for predicting individual consumer choice,” Journal of Marketing Research, 18 (February), 1–12.CrossRefGoogle Scholar
  9. Batsell, R. R., and Louviere, J. J. (1991), “Experimental analysis of choice,”Marketing Letters, 2 (August), 199–214.Google Scholar
  10. Bell, D. F., Raiffa, H., and Tversky, A. (eds.) (1988), Decision Making: Descriptive Normative and Prescriptive Interactions, New York, NY: Cambridge University Press.Google Scholar
  11. Ben-Akiva, M., and Gershenfeld, S. (1998), “Multi-featured products and services: Analyzing pricing and bundling strategies,” Journal of Forecasting, 17, 175–196.CrossRefGoogle Scholar
  12. Carmone, F. J., Green, P. E., and Jain, A. K. (1978), “The robustness of conjoint analysis: Some Monte Carlo results,”Journal of Marketing Research, 15, 300–303.Google Scholar
  13. Carroll, J. D. (1969), “Categorical conjoint measurement,” Meeting of Mathematical Psychology, Ann Arbor, MI.Google Scholar
  14. Carroll, J. D., and Green, P. E. (1997), “Psychometric methods in marketing research: Part II, multidimensional scaling,” Journal of Marketing Research, 34 (May), 193–204.CrossRefGoogle Scholar
  15. Cattin, P., and Wittink, D. R. (1976), “A Monte Carlo study of metric and nonmetric estimation techniques,” Paper 341, Graduate School of Business, Stanford University.Google Scholar
  16. Choi, S. C., DeSarbo, W. S., and Harker, P. T. (1990), “Product positioning under price competition,” Management Science, 30, 175–199.CrossRefGoogle Scholar
  17. DeSarbo, W. S., Carroll, J. D., Lehmann, D. R., and O’Shaugnhessy, J. (1982). “Three-way multivariate conjoint analysis,” Marketing Science, 1 (Fall), 323–350.CrossRefGoogle Scholar
  18. DeSarbo, W. S., Wedel, M., Vriens, C., and Ramaswamy, V. (1992), “Latent class metric conjoint analysis,”Marketing Letters,3 (July), 273–288.CrossRefGoogle Scholar
  19. Gensch, D. H., and Recker, W. W. (1979), “The multinominal multiattribute logit choice model,” Journal of Marketing Research, 16 (February), 124–132.CrossRefGoogle Scholar
  20. Green, P. E. (1984), “Hybrid models for conjoint analysis: An expository review,” Journal of Marketing Research, 21 (May), 155–159.CrossRefGoogle Scholar
  21. Green, P. E., Frank, R. E., and Robinson, P. J. (1967), “Cluster analysis in test market selection,”Management Science, 13, B387–400.Google Scholar
  22. Green, P. E., Goldberg, S. M., and Montemayor, M. (1981), “A hybrid utility estimation model for conjoint analysis,” Journal of Marketing, 45 (Winter), 33–41.CrossRefGoogle Scholar
  23. Green, P. E., and Krieger, A. M. (1991), “Segmenting markets with conjoint analysis,” Journal of Marketing, 55 (October), 20–31.CrossRefGoogle Scholar
  24. Green, P. E., and Krieger, A. M. (1996), “Individualized hybrid models for conjoint analysis,” Management Science, 42 (June), 850–867.CrossRefGoogle Scholar
  25. Green, P. E., and Krieger, A. M. (1997), “Using conjoint analysis to view competitive interaction through the customer’s eyes,” in G. S. Day and D. J. Reibstein (eds.), Wharton on Dynamic Competitive Strategy, New York, NY: John Wiley amp; Sons, pp. 343–367.Google Scholar
  26. Green, P. E., Krieger, A. M., and Carroll, J. D. (1987), “Conjoint analysis and multidimensional scaling: A complementary approach,” Journal of Advertising Research, 27 (October/November), 21–27.Google Scholar
  27. Green, P. E., Krieger, A. M., and Zelnio, R. N. (1989), “A componential segmentation model with optimal design features,” Decision Sciences, 20 (Spring), 221–238.CrossRefGoogle Scholar
  28. Green, P. E., and Rao, V. R. (1971), “Conjoint measurement for quantifying judgmental data,” Journal of Marketing Research, 8 (August), 355–363.CrossRefGoogle Scholar
  29. Green, P. E., and Srinivasan, V. (1978), “Conjoint analysis in consumer research: Issues and outlook,” Journal of Consumer Research, 5 (September), 103–123.CrossRefGoogle Scholar
  30. Green, P. E., and Srinivasan, V. (1990), “Conjoint analysis in marketing: New developments with implications for research and practice,” Journal of Marketing, 54 (October), 3–19.CrossRefGoogle Scholar
  31. Green, P. E., and Wind, Y. (1973), Multiattribute Decisions in Marketing: A Measurement Approach, Hinsdale, IL: The Dryden Press…Google Scholar
  32. Green, P. E., and Wind, Y. (1975), “New way to measure consumers’ judgments,” Harvard Business Review, 53 (July-August), 107–117.Google Scholar
  33. Gustaffsson, A., Hermann, A., and Huber, F. (eds.) (2000), Conjoint Measurement: Methods and Applications, Berlin, Germany: Springer-Verlag.Google Scholar
  34. Haaijer, R., Kamakura, W., and Wedel, M. (2000), “Response latencies in the analysis of conjoint choice experiments,” Journal of Marketing Research,37 (August), 376–382.CrossRefGoogle Scholar
  35. Hagerty, M. R. (1985), ”Improving the predictive power of conjoint analysis: The use of factor analysis and cluster analysis,” Journal of Marketing Research,22 (May), 168–184.Google Scholar
  36. Herman,S.(1988), “Software for full-profile conjoint analysis,”in M. Metegrano (ed.), Proceedings of the Sawtooth Conference on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing,Ketchum, ID: Sawtooth Software, pp. 117–130.Google Scholar
  37. Hoffman, P. J., Slovic, P., and Rorer, L. G. (1968), “An analysis of variance model for the assessment of configurai cue utilization in clinical judgment,” Psychological Bulletin, 69, 338–349.CrossRefGoogle Scholar
  38. Huber, J., and Zwerina, K. (1996), “The importance of utility balance in efficient choice designs,” Journal of Marketing Research, 33 (August), 307–317.CrossRefGoogle Scholar
  39. Johnson, E., Meyer, R. J., and Ghose, S. (1989), “When choice models fail: Compensatory models in negatively correlated environments,” Journal of Marketing Research, 26 (August), 255–270.CrossRefGoogle Scholar
  40. Johnson, R. M. (1974), “Trade-off analysis of consumer values,” Journal of Marketing Research, 11 (May), 121–127.CrossRefGoogle Scholar
  41. Johnson, R. M. (1987), “Adaptive conjoint analysis,” in Sawtooth Software Conference on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing, Ketchum, ID: Sawtooth Software, pp. 253–265.Google Scholar
  42. Kamakura, W. (1988), “A least squares procedure for benefit segmentation with conjoint experiments,” Journal of Marketing Research, 25 (May), 157–167.CrossRefGoogle Scholar
  43. Kamakura, W., Wedel, M., and Agrawal, J. (1994), “Concomitant variable latent class models for conjoint analysis,” International Journal of Research in Marketing, 11, 451–464.CrossRefGoogle Scholar
  44. Kaul, A., and Rao, V. R. (1994), “Research for product position and design decisions: An integrative review,” International Journal of Research on Marketing, 12, 293–320.CrossRefGoogle Scholar
  45. Keeney, R. L., and Raiffa, H. (1976), Decisions with Multiple Objectives: Preferences and Value Trade-offs, New York, NY: Wiley.Google Scholar
  46. Kohli, R., and Mahajan, V. (1991), “A reservation price model for optimal pricing of multiattribute products in conjoint analysis,” Journal of Marketing Research, 28 (August), 347–354.CrossRefGoogle Scholar
  47. Krieger, A., Green, P., Lodish, L., D’Arcangelo, J., Rothey, C., Thirty, P. (2002), Consumer Evaluations of “Really New” Services: The TRAFFICPULSE System,“ Working Paper, The Wharton School Marketing Department.Google Scholar
  48. Kruskal, J. B. (1965), “Analysis of factorial experiments by estimating monotone transformations of the data,” Journal of the Royal Statistical Society, Series B, 27, 251–263.Google Scholar
  49. Kuhfeld, W. F., Tobias, R. D., and Garratt, M. (1994), “Efficient experimental designs with marketing research applications,” Journal of Marketing Research, 31 (November), 545–557.CrossRefGoogle Scholar
  50. Lazari, A. G., and Anderson, D. A. (1994), “Designs of discrete choice set experiments for estimating both attribute and availability cross effects,” Journal of Marketing Research, 31 (August), 375–383.CrossRefGoogle Scholar
  51. 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, No. 2, 173–191.CrossRefGoogle Scholar
  52. Louviere, J., and Woodworth, G. (1983), “Design and analysis of simulated consumer choice or allocation experiments,” Journal of Marketing Research,20 (November), 350–367.CrossRefGoogle Scholar
  53. Luce, R. D., and Tukey, J. W. (1964), “Simultaneous conjoint measurement: A new type of fundamental measurement,” Journal of Mathematical Psychology, 1, 1–27.CrossRefGoogle Scholar
  54. Mahajan, V., Green, P. E., and Goldberg, S. M. (1982), “A conjoint model for measuring self-and cross-price demand relationships,” Journal of Marketing Research, 19 (August), 334–342.CrossRefGoogle Scholar
  55. McFadden, D. (1974), “Conditional logit analysis of qualitative choice behavior,” in P. Zarembka (ed.), Frontiers on Econometrics, New York, NY: Academic Press, pp. 105–421.Google Scholar
  56. Myers, J. G., Massy, W. F., and Greyser, S. A. (1980), Marketing Research and Knowledge Development, Englewood Cliffs, NJ: Prentice-Hall…Google Scholar
  57. Oppewal, H., Louviere, J., and Timmermans, H. (1994), “Modeling hierarchical conjoint processes with integrated choice experiments,”Journal of Marketing Research,31 (February), 92–105.Google Scholar
  58. Plackett, R. L., and Burman, J. P. (1946), “The design of optimum multifactorial experiments,” Biometrika, 33, 305–325.CrossRefGoogle Scholar
  59. Punj, G. N., and Staelin, R. (1978), “The choice process for graduate business schools,” Journal of Marketing Research, 15 (November), 588–598.CrossRefGoogle Scholar
  60. Ramaswamy, V., and Cohen, S. H. (2000), “Latent class models for conjoint analysis,” in A. Gustafsson, A. Hermann, and F. Huber (eds.), Conjoint Measurement: Methods and Applications, Berlin, Germany: Springer-Verlag.Google Scholar
  61. Saaty, T. L. (1980), The Analytical Hierarchy Process, New York, NY: McGraw-Hill. Sawtooth Software (1999), CBC for Windows, Sequim, WA.Google Scholar
  62. Shocker, A. D., and Srinivasan, V. (1977), “LINMAP (Version II): A FORTRAN IV computer program for analyzing ordinal preference (dominance) judgments via linear programming techniques and for conjoint measurement,” Journal of Marketing Research, 14, 101–103.Google Scholar
  63. Srinivasan, V. (1988), “A conjunctive-compensatory approach to the self-explication of multiattributed preferences,” Decision Sciences, 19 (Spring), 295–305.CrossRefGoogle Scholar
  64. Srinivasan, V., Jain, A. K., and Malhotra, N. K. (1983), “Improving the predictive power of conjoint analysis by constrained parameter estimation,” Journal of Marketing Research, 20 (November), 433–438.CrossRefGoogle Scholar
  65. Starr, M. K., and Zeleny, M. (1977), Multiple Criteria Decisions Making, Amsterdam, Holland: North-Holland.Google Scholar
  66. Steckel, J. H., DeSarbo, W. S., and Mahajan, V. (1991), “On the creation of feasible conjoint analysis experimental designs,” Decision Sciences, 22, 435–442.CrossRefGoogle Scholar
  67. van der Lans, I. A., and Heiser, W. H. (1992), “Constrained partworth estimation in conjoint analysis using the self-explicated utility model,” International Journal of Research in Marketing, 9, 325–344.Google Scholar
  68. Vavra, T. G., Green, P. E., and Krieger, A. M. (1999), “Evaluating E-Z Pass,” Marketing Research, 11 (Summer), 5–16.Google Scholar
  69. Vriens, M., Wedel, M., and Wilms, T. (1996), “Metric conjoint segmentation methods: A Monte Carlo comparison, ” Journal of Marketing Research, 33 (February), 73–85.CrossRefGoogle Scholar
  70. Wedel, M., and Kamakura, W. A. (1998), Market Segmentation: Conceptual and Methodological Foundations, Boston, MA: Kluwer…Google Scholar
  71. Westwood, D., Lunn, T., and Beazley, D. (1974), “The trade-off model and its extensions,” Journal of the Market Research Society, 16, 227–241.Google Scholar
  72. Wind, J., Green, P. E., Shifflet, D., and Scarbrough, M. (1989), “Courtyard by Marriott: Designing a hotel facility with consumer-based marketing models,” Interfaces 19 (January-February), 25–47.CrossRefGoogle Scholar
  73. Wittink, D., and Cattin, P. (1989), “Commercial use of conjoint analysis: An update,” Journal of Marketing, 53 (July), 91–96.CrossRefGoogle Scholar
  74. Wittink, D. R., Krishnamurthi, L., and Nutter, J. B. (1982), “Comparing derived importance weights across attributes,” Journal of Consumer Research, 8 (March), 471–474.CrossRefGoogle Scholar
  75. Wittink, D., Vriens, M., and Burhenne, W. (1994), “Commercial use of conjoint in Europe: Results and critical reflections,” International Journal of Research in Marketing, 11, 41–52.CrossRefGoogle Scholar
  76. Young, F. W. (1969), “Polynomial conjoint analysis of similarities: Definitions for a special algorithm,”Research paper No. 76, Psychometric Laboratory, University of North Carolina.Google Scholar

Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Paul E. Green
    • 1
  • Abba M. Krieger
    • 1
  • Yoram Wind
    • 1
  1. 1.The Wharton SchoolUniversity of PennsylvaniaUSA

Personalised recommendations