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Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis

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  • Toubia, Olivier
  • Hauser, John
  • Simester, Duncan

Abstract

Choice-based conjoint analysis (CBC) is used widely in marketing for product design, segmentation, and marketing strategy. We propose and test a new "polyhedral" question-design method that adapts each respondent's choice sets based on previous answers by that respondent. Individual adaptation appears promising because, as demonstrated in the aggregate customization literature, question design can be improved based on prior estimates of the respondent's partworths Â€Ó information that is revealed by respondents' answers to prior questions. The otherwise impractical computational problems of individual CBC adaptation become feasible based on recent polyhedral "interior-point" algorithms, which provide the rapid solutions necessary for real-time computation. To identify domains where individual adaptation is promising (and domains where it is not), we evaluate the performance of polyhedral CBC methods with Monte Carlo experiments. We vary magnitude (response accuracy), respondent heterogeneity, estimation method, and question-design method in a 4x23 experiment. The estimation methods are Hierarchical-Bayes estimation (HB) and Analytic-Center estimation (AC). The latter is a new individual-level estimation procedure that is a by-product of polyhedral question design. The benchmarks for individual adaptation are random designs, orthogonal designs, and aggregate customization. The simulations suggest that polyhedral question design does well in many domains, particularly those in which heterogeneity and partworth magnitudes are relatively large. In the comparison of estimation methods, HB is strong across all domains, but AC estimation shows promise when heterogeneity is high. We close by describing an empirical application to the design of executive education programs in which 354 web-based respondents answered stated-choice tasks with four service profiles each. The profiles varied on eight multi-level features. With the help of this study a major university is revising its executive education programs with new formats and a new focus.

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Bibliographic Info

Paper provided by Massachusetts Institute of Technology (MIT), Sloan School of Management in its series Working papers with number 4285-03.

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Date of creation: 26 Feb 2003
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Handle: RePEc:mit:sloanp:1832

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Postal: MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT), SLOAN SCHOOL OF MANAGEMENT, 50 MEMORIAL DRIVE CAMBRIDGE MASSACHUSETTS 02142 USA

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Cited by:
  1. David Hensher & Sean Puckett & John Rose, 2007. "Extending stated choice analysis to recognise agent-specific attribute endogeneity in bilateral group negotiation and choice: a think piece," Transportation, Springer, vol. 34(6), pages 667-679, November.
  2. Srinivasan, V. "Seenu" & Netzer, Oded, 2007. "Adaptive Self-Explication of Multi-attribute Preferences," Research Papers 1979, Stanford University, Graduate School of Business.
  3. Vishva Danthurebandara & Jie Yu & Martina Vandebroek, 2011. "Sequential choice designs to estimate the heterogeneity distribution of willingness-to-pay," Quantitative Marketing and Economics, Springer, vol. 9(4), pages 429-448, December.
  4. Greg Allenby & Geraldine Fennell & Joel Huber & Thomas Eagle & Tim Gilbride & Dan Horsky & Jaehwan Kim & Peter Lenk & Rich Johnson & Elie Ofek & Bryan Orme & Thomas Otter & Joan Walker, 2005. "Adjusting Choice Models to Better Predict Market Behavior," Marketing Letters, Springer, vol. 16(3), pages 197-208, December.
  5. Oded Netzer & Olivier Toubia & Eric Bradlow & Ely Dahan & Theodoros Evgeniou & Fred Feinberg & Eleanor Feit & Sam Hui & Joseph Johnson & John Liechty & James Orlin & Vithala Rao, 2008. "Beyond conjoint analysis: Advances in preference measurement," Marketing Letters, Springer, vol. 19(3), pages 337-354, December.

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