IDEAS home Printed from https://ideas.repec.org/a/eee/ijrema/v34y2017i2p325-335.html
   My bibliography  Save this article

An approach to improve the predictive power of choice-based conjoint analysis

Author

Listed:
  • Voleti, Sudhir
  • Srinivasan, V.
  • Ghosh, Pulak

Abstract

Conjoint analysis continues to be popular with over 18,000 applications each year. Choice-based conjoint (CBC) analysis is currently the most often used method of conjoint analysis accounting for eight-tenths of all conjoint studies. The CBC employs a multinomial logit model with heterogeneous parameters across the population. The most commonly used models of heterogeneity are the Latent Class Model, the single multivariate normal distribution, or a mixture of multivariate normal distributions. A more recent approach to capture heterogeneity is the Dirichlet Process Mixture (DPM) model and its predecessor Dirichlet Process Prior (DPP) model. The alternative models are empirically tested over eleven CBC data sets with varying characteristics. The DPM model provides the best predictive validity (percent of choices correctly predicted) for each of the eleven datasets studied, and provides a significant improvement over extant models of heterogeneity.

Suggested Citation

  • Voleti, Sudhir & Srinivasan, V. & Ghosh, Pulak, 2017. "An approach to improve the predictive power of choice-based conjoint analysis," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 325-335.
    • Handle: RePEc:eee:ijrema:v:34:y:2017:i:2:p:325-335
    DOI: 10.1016/j.ijresmar.2016.08.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167811616301161
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijresmar.2016.08.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Elie Ofek & V. Srinivasan, 2002. "How Much Does the Market Value an Improvement in a Product Attribute?," Marketing Science, INFORMS, vol. 21(4), pages 398-411, June.
    2. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    3. Joel H. Steckel & Wilfried R. Vanhonacker, 1993. "Cross-Validating Regression Models in Marketing Research," Marketing Science, INFORMS, vol. 12(4), pages 415-427.
    4. Ghosh, Pulak & Basu, Sanjib & Tiwari, Ram C., 2009. "Bayesian Analysis of Cancer Rates From SEER Program Using Parametric and Semiparametric Joinpoint Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 439-452.
    5. Young‐Hoon Park & Chang Hee Park & Pulak Ghosh, 2011. "Modelling member behaviour in on‐line user‐generated content sites: a semiparametric Bayesian approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 1051-1069, October.
    6. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    7. Michael Braun & Peter S. Fader & Eric T. Bradlow & Howard Kunreuther, 2006. "Modeling the "Pseudodeductible" in Insurance Claims Decisions," Management Science, INFORMS, vol. 52(8), pages 1258-1272, August.
    8. Theodoros Evgeniou & Massimiliano Pontil & Olivier Toubia, 2007. "A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation," Marketing Science, INFORMS, vol. 26(6), pages 805-818, 11-12.
    9. Green, Paul E & Srinivasan, V, 1978. "Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 5(2), pages 103-123, Se.
    10. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Linde, 2014. "The deviance information criterion: 12 years on," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 485-493, June.
    11. Andrews, Rick L. & Currim, Imran S., 2009. "Multi-stage purchase decision models: Accommodating response heterogeneity, common demand shocks, and endogeneity using disaggregate data," International Journal of Research in Marketing, Elsevier, vol. 26(3), pages 197-206.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Barwitz, Niklas, 2020. "The relevance of interaction choice: Customer preferences and willingness to pay," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    2. Weber, Anett & Steiner, Winfried J., 2021. "Modeling price response from retail sales: An empirical comparison of models with different representations of heterogeneity," European Journal of Operational Research, Elsevier, vol. 294(3), pages 843-859.
    3. 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.
    4. Narine Yegoryan & Daniel Guhl & Friederike Paetz, 2023. "When Zeros Count: Confounding in Preference Heterogeneity and Attribute Non-attendance," Rationality and Competition Discussion Paper Series 482, CRC TRR 190 Rationality and Competition.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sudhir Voleti & Pulak Ghosh, 2013. "A robust approach to measure latent, time-varying equity in hierarchical branding structures," Quantitative Marketing and Economics (QME), Springer, vol. 11(3), pages 289-319, September.
    2. 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.
    3. 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.
    4. Yupeng Chen & Raghuram Iyengar & Garud Iyengar, 2017. "Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis—A Sparse Learning Approach," Marketing Science, INFORMS, vol. 36(1), pages 140-156, January.
    5. Kai Yang & Qingqing Zhang & Xinyang Yu & Xiaogang Dong, 2023. "Bayesian inference for a mixture double autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 188-207, May.
    6. Kelvyn Jones & David Manley & Ron Johnston & Dewi Owen, 2018. "Modelling residential segregation as unevenness and clustering: A multilevel modelling approach incorporating spatial dependence and tackling the MAUP," Environment and Planning B, , vol. 45(6), pages 1122-1141, November.
    7. Papastamoulis, Panagiotis, 2018. "Overfitting Bayesian mixtures of factor analyzers with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 220-234.
    8. John Liechty & Duncan Fong & Eelko Huizingh & Arnaud Bruyn, 2008. "Hierarchical Bayesian conjoint models incorporating measurement uncertainty," Marketing Letters, Springer, vol. 19(2), pages 141-155, June.
    9. Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
    10. 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.
    11. 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.
    12. Shuhui Guo & Lihua Xiong & Jie Chen & Shenglian Guo & Jun Xia & Ling Zeng & Chong-Yu Xu, 2023. "Nonstationary Regional Flood Frequency Analysis Based on the Bayesian Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 659-681, January.
    13. Adriane Hartmann & Henrik Sattler, 2004. "Wie robust sind Methoden zur Präferenzmessung?," Schmalenbach Journal of Business Research, Springer, vol. 56(1), pages 3-22, February.
    14. 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.
    15. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    16. Marc R. Dotson & Joachim Büschken & Greg M. Allenby, 2020. "Explaining Preference Heterogeneity with Mixed Membership Modeling," Marketing Science, INFORMS, vol. 39(2), pages 407-426, March.
    17. Butori, Raphaëlle & De Bruyn, Arnaud, 2013. "So you want to delight your customers: The perils of ignoring heterogeneity in customer evaluations of discretionary preferential treatments," International Journal of Research in Marketing, Elsevier, vol. 30(4), pages 358-367.
    18. Olivier Toubia & Eric Johnson & Theodoros Evgeniou & Philippe Delquié, 2013. "Dynamic Experiments for Estimating Preferences: An Adaptive Method of Eliciting Time and Risk Parameters," Management Science, INFORMS, vol. 59(3), pages 613-640, June.
    19. Hazelton, Martin L. & Parry, Katharina, 2016. "Statistical methods for comparison of day-to-day traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 22-34.
    20. Muhammed Semakula & Franco̧is Niragire & Christel Faes, 2020. "Bayesian spatio-temporal modeling of malaria risk in Rwanda," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ijrema:v:34:y:2017:i:2:p:325-335. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-research-in-marketing/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.