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Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem

Author

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  • Jeffrey D. Camm

    (Department of Quantitative Analysis and Operations Management, University of Cincinnati, Cincinnati, Ohio 45221)

  • James J. Cochran

    (Department of Marketing and Analysis, Louisiana Tech University, Ruston, Louisiana 71272)

  • David J. Curry

    (Department of Marketing, University of Cincinnati, Cincinnati, Ohio 45221)

  • Sriram Kannan

    (Sabre Travel Technologies, Bangalore, India)

Abstract

Conjoint analysis is a statistical technique used to elicit partworth utilities for product attributes from consumers to aid in the evaluation of market potential for new products. The objective of the share-of-choice problem (a common approach to new product design) is to find the design that maximizes the number of respondents for whom the new product's utility exceeds a specific hurdle (reservation utility). We present an exact branch-and-bound algorithm to solve the share-of-choice problem. Our empirical results, based on several large commercial data sets and simulated data from a controlled experiment, suggest that the approach is useful for finding provably optimal solutions to realistically sized problems, including cases where partworths contain estimation error.

Suggested Citation

  • Jeffrey D. Camm & James J. Cochran & David J. Curry & Sriram Kannan, 2006. "Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem," Management Science, INFORMS, vol. 52(3), pages 435-447, March.
    • Handle: RePEc:inm:ormnsc:v:52:y:2006:i:3:p:435-447
    DOI: 10.1287/mnsc.1050.0461
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    1. Jerry Wind & Paul E. Green & Douglas Shifflet & Marsha Scarbrough, 1989. "Courtyard by Marriott : Designing a Hotel Facility with Consumer-Based Marketing Models," Interfaces, INFORMS, vol. 19(1), pages 25-47, February.
    2. Brian T. Downs & Jeffrey D. Camm, 1996. "An exact algorithm for the maximal covering problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 43(3), pages 435-461, April.
    3. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    4. Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
    5. P. V. (Sundar) Balakrishnan & Varghese S. Jacob, 1996. "Genetic Algorithms for Product Design," Management Science, INFORMS, vol. 42(8), pages 1105-1117, August.
    6. Kohli, Rajeev & Krishnamurti, Ramesh, 1989. "Optimal product design using conjoint analysis: Computational complexity and algorithms," European Journal of Operational Research, Elsevier, vol. 40(2), pages 186-195, May.
    7. Rajeev Kohli & Ramesh Krishnamurti, 1987. "A Heuristic Approach to Product Design," Management Science, INFORMS, vol. 33(12), pages 1523-1533, December.
    8. Richard D. McBride & Fred S. Zufryden, 1988. "An Integer Programming Approach to the Optimal Product Line Selection Problem," Marketing Science, INFORMS, vol. 7(2), pages 126-140.
    9. Timothy J. Gilbride & Greg M. Allenby, 2004. "A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules," Marketing Science, INFORMS, vol. 23(3), pages 391-406, October.
    10. Green, Paul E. & Krieger, Abba M., 1989. "Recent contributions to optimal product positioning and buyer segmentation," European Journal of Operational Research, Elsevier, vol. 41(2), pages 127-141, July.
    11. Leyuan Shi & Sigurdur Ólafsson & Qun Chen, 2001. "An Optimization Framework for Product Design," Management Science, INFORMS, vol. 47(12), pages 1681-1692, December.
    12. Paul E. Green & Abba M. Krieger & Yoram Wind, 2001. "Thirty Years of Conjoint Analysis: Reflections and Prospects," Interfaces, INFORMS, vol. 31(3_supplem), pages 56-73, June.
    13. Rajeev Kohli & R. Sukumar, 1990. "Heuristics for Product-Line Design Using Conjoint Analysis," Management Science, INFORMS, vol. 36(12), pages 1464-1478, December.
    14. Leyuan Shi & Sigurdur Ólafsson, 2000. "Nested Partitions Method for Global Optimization," Operations Research, INFORMS, vol. 48(3), pages 390-407, June.
    15. Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
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    Cited by:

    1. Alan T. Murray, 2016. "Maximal Coverage Location Problem," International Regional Science Review, , vol. 39(1), pages 5-27, January.
    2. Julio López & Sebastián Maldonado & Ricardo Montoya, 2017. "Simultaneous preference estimation and heterogeneity control for choice-based conjoint via support vector machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1323-1334, November.
    3. Tsafarakis, Stelios & Marinakis, Yannis & Matsatsinis, Nikolaos, 2011. "Particle swarm optimization for optimal product line design," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 13-22.
    4. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    5. Xinfang (Jocelyn) Wang & Jeffrey D. Camm & David J. Curry, 2009. "A Branch-and-Price Approach to the Share-of-Choice Product Line Design Problem," Management Science, INFORMS, vol. 55(10), pages 1718-1728, October.
    6. Choi, Hyunhong & Koo, Yoonmo, 2023. "New technology product introduction strategy with considerations for consumer-targeted policy intervention and new market entrant," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    7. Schön, Cornelia, 2010. "On the product line selection problem under attraction choice models of consumer behavior," European Journal of Operational Research, Elsevier, vol. 206(1), pages 260-264, October.
    8. Maldonado, Sebastián & Montoya, Ricardo & Weber, Richard, 2015. "Advanced conjoint analysis using feature selection via support vector machines," European Journal of Operational Research, Elsevier, vol. 241(2), pages 564-574.
    9. Emmanuel Fragnière & Roman Kanala & Francesco Moresino & Adriana Reveiu & Ion Smeureanu, 2017. "Coupling techno-economic energy models with behavioral approaches," Operational Research, Springer, vol. 17(2), pages 633-647, July.
    10. Dong, Songting & Ding, Min & Huber, Joel, 2010. "A simple mechanism to incentive-align conjoint experiments," International Journal of Research in Marketing, Elsevier, vol. 27(1), pages 25-32.
    11. Tan Wang & Genaro Gutierrez, 2022. "Robust Product Line Design by Protecting the Downside While Minding the Upside," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 194-217, January.
    12. YiChun Miriam Liu & Jeff D. Brazell & Greg M. Allenby, 2022. "Non-linear pricing effects in conjoint analysis," Quantitative Marketing and Economics (QME), Springer, vol. 20(4), pages 397-430, December.
    13. James Cochran & David Curry & Rajesh Radhakrishnan & Jon Pinnell, 2014. "Political engineering: optimizing a U.S. Presidential candidate’s platform," Annals of Operations Research, Springer, vol. 215(1), pages 63-87, April.
    14. Francesco Moresino, 2021. "A Robust Share-of-Choice Model," Mathematics, MDPI, vol. 9(3), pages 1-10, February.
    15. Maoqi Liu & Li Zheng & Changchun Liu & Zhi‐Hai Zhang, 2023. "From share of choice to buyers' welfare maximization: Bridging the gap through distributionally robust optimization," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1205-1222, April.
    16. Hein, Maren & Goeken, Nils & Kurz, Peter & Steiner, Winfried J., 2022. "Using Hierarchical Bayes draws for improving shares of choice predictions in conjoint simulations: A study based on conjoint choice data," European Journal of Operational Research, Elsevier, vol. 297(2), pages 630-651.
    17. Furrer, Olivier & Sudharshan, Devanathan & Tsiotsou, Rodoula H. & Liu, Ben S., 2016. "A framework for innovative service design," FSES Working Papers 476, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    18. Zhiqiao Wu & C.K. Kwong & C.K.M. Lee & Jiafu Tang, 2016. "Joint decision of product configuration and remanufacturing for product family design," International Journal of Production Research, Taylor & Francis Journals, vol. 54(15), pages 4689-4702, August.
    19. Alexandre Belloni & Robert Freund & Matthew Selove & Duncan Simester, 2008. "Optimizing Product Line Designs: Efficient Methods and Comparisons," Management Science, INFORMS, vol. 54(9), pages 1544-1552, September.
    20. Wang, Xinfang (Jocelyn) & Curry, David J., 2012. "A robust approach to the share-of-choice product design problem," Omega, Elsevier, vol. 40(6), pages 818-826.
    21. Tsafarakis, Stelios & Zervoudakis, Konstantinos & Andronikidis, Andreas & Altsitsiadis, Efthymios, 2020. "Fuzzy self-tuning differential evolution for optimal product line design," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1161-1169.
    22. James J. Cochran & Martin S. Levy & Jeffrey D. Camm, 2010. "Bayesian coverage optimization models," Journal of Combinatorial Optimization, Springer, vol. 19(2), pages 158-173, February.
    23. Ronny Baierl, 2018. "Understanding Entrepreneurial Team Decisions: Measuring Team Members’ Influences With The Metricized Limit Conjoint Analysis," SAGE Open, , vol. 8(2), pages 21582440187, May.

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