Fast Polyhedral Adaptive Conjoint Estimation
We propose and test a new adaptive conjoint analysis method that draws on recent polyhedral “interior-point” developments in mathematical programming. The method is designed to offer accurate estimates after relatively few questions in problems involving many parameters. Each respondent’s ques-tions...
| Main Authors: | , , |
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| Format: | Working Paper |
| Language: | en_US |
| Published: |
2003
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/3800 |
| _version_ | 1826190409198469120 |
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| author | Olivier, Toubia Duncan, Simester John, Hauser |
| author_facet | Olivier, Toubia Duncan, Simester John, Hauser |
| author_sort | Olivier, Toubia |
| collection | MIT |
| description | We propose and test a new adaptive conjoint analysis method that draws on recent polyhedral “interior-point” developments in mathematical programming. The method is designed to offer accurate estimates after relatively few questions in problems involving many parameters. Each respondent’s ques-tions are adapted based upon prior answers by that respondent. The method requires computer support but can operate in both Internet and off-line environments with no noticeable delay between questions. We use Monte Carlo simulations to compare the performance of the method against a broad array of relevant benchmarks. While no method dominates in all situations, polyhedral algorithms appear to hold significant potential when (a) metric profile comparisons are more accurate than the self-explicated importance measures used in benchmark methods, (b) when respondent wear out is a concern, and (c) when product development and/or marketing teams wish to screen many features quickly. We also test hybrid methods that combine polyhedral algorithms with existing conjoint analysis methods. We close with suggestions on how polyhedral methods can be used to address other marketing problems. |
| first_indexed | 2024-09-23T08:39:49Z |
| format | Working Paper |
| id | mit-1721.1/3800 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:39:49Z |
| publishDate | 2003 |
| record_format | dspace |
| spelling | mit-1721.1/38002019-04-09T19:03:39Z Fast Polyhedral Adaptive Conjoint Estimation Olivier, Toubia Duncan, Simester John, Hauser adaptive conjoint analysis polyhedral interior-point mathematical programming accurate estimates Monte Carlo simulations polyhedral algorithms We propose and test a new adaptive conjoint analysis method that draws on recent polyhedral “interior-point” developments in mathematical programming. The method is designed to offer accurate estimates after relatively few questions in problems involving many parameters. Each respondent’s ques-tions are adapted based upon prior answers by that respondent. The method requires computer support but can operate in both Internet and off-line environments with no noticeable delay between questions. We use Monte Carlo simulations to compare the performance of the method against a broad array of relevant benchmarks. While no method dominates in all situations, polyhedral algorithms appear to hold significant potential when (a) metric profile comparisons are more accurate than the self-explicated importance measures used in benchmark methods, (b) when respondent wear out is a concern, and (c) when product development and/or marketing teams wish to screen many features quickly. We also test hybrid methods that combine polyhedral algorithms with existing conjoint analysis methods. We close with suggestions on how polyhedral methods can be used to address other marketing problems. Sloan School of Management and the Center for Innovation in Product Development at MIT 2003-12-09T17:41:06Z 2003-12-09T17:41:06Z 2002-02 Working Paper http://hdl.handle.net/1721.1/3800 en_US 1287038 bytes application/pdf application/pdf |
| spellingShingle | adaptive conjoint analysis polyhedral interior-point mathematical programming accurate estimates Monte Carlo simulations polyhedral algorithms Olivier, Toubia Duncan, Simester John, Hauser Fast Polyhedral Adaptive Conjoint Estimation |
| title | Fast Polyhedral Adaptive Conjoint Estimation |
| title_full | Fast Polyhedral Adaptive Conjoint Estimation |
| title_fullStr | Fast Polyhedral Adaptive Conjoint Estimation |
| title_full_unstemmed | Fast Polyhedral Adaptive Conjoint Estimation |
| title_short | Fast Polyhedral Adaptive Conjoint Estimation |
| title_sort | fast polyhedral adaptive conjoint estimation |
| topic | adaptive conjoint analysis polyhedral interior-point mathematical programming accurate estimates Monte Carlo simulations polyhedral algorithms |
| url | http://hdl.handle.net/1721.1/3800 |
| work_keys_str_mv | AT oliviertoubia fastpolyhedraladaptiveconjointestimation AT duncansimester fastpolyhedraladaptiveconjointestimation AT johnhauser fastpolyhedraladaptiveconjointestimation |