IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v239y2014i2p556-564.html
   My bibliography  Save this article

Likelihood estimation of consumer preferences in choice-based conjoint analysis

Author

Listed:
  • Halme, Merja
  • Kallio, Markku

Abstract

In marketing research the measurement of individual preferences and assessment of utility functions have long traditions. Conjoint analysis, and particularly choice-based conjoint analysis (CBC), is frequently employed for such measurement. The world today appears increasingly customer or user oriented wherefore research intensity in conjoint analysis is rapidly increasing in various fields, OR/MS being no exception. Although several optimization based approaches have been suggested since the introduction of the Hierarchical Bayes (HB) method for estimating CBC utility functions, recent comparisons indicate that challenging HB is hard. Based on likelihood maximization we propose a method called LM and compare its performance with HB using twelve field data sets. Performance comparisons are based on holdout validation, i.e. predictive performance. Average performance of LM indicates an improvement over HB and the difference is statistically significant. We also use simulation based data sets to compare the performance for parameter recovery. In terms of both predictive performance and RMSE a smaller number of questions in CBC appears to favor LM over HB.

Suggested Citation

  • Halme, Merja & Kallio, Markku, 2014. "Likelihood estimation of consumer preferences in choice-based conjoint analysis," European Journal of Operational Research, Elsevier, vol. 239(2), pages 556-564.
    • Handle: RePEc:eee:ejores:v:239:y:2014:i:2:p:556-564
    DOI: 10.1016/j.ejor.2014.05.044
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221714004834
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2014.05.044?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. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    2. Asmussen, Søren & Glynn, Peter W., 2011. "A new proof of convergence of MCMC via the ergodic theorem," Statistics & Probability Letters, Elsevier, vol. 81(10), pages 1482-1485, October.
    3. 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.
    4. Natter, Martin & Feurstein, Markus, 2002. "Real world performance of choice-based conjoint models," European Journal of Operational Research, Elsevier, vol. 137(2), pages 448-458, March.
    5. Halme, Merja & Kallio, Markku, 2011. "Estimation methods for choice-based conjoint analysis of consumer preferences," European Journal of Operational Research, Elsevier, vol. 214(1), pages 160-167, October.
    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. Meeran, Sheik & Jahanbin, Semco & Goodwin, Paul & Quariguasi Frota Neto, Joao, 2017. "When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable?," European Journal of Operational Research, Elsevier, vol. 258(2), pages 512-524.
    2. 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.
    3. Jing Xiao & Pallavi Chitturi, 2018. "Some Results on Pareto Optimal Choice Sets for Estimating Main Effects and Interactions in 2 n and 3 n Factorial Plans," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 37-59, May.
    4. Christodoulakis, George, 2020. "Estimating the term structure of commodity market preferences," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1146-1163.
    5. Kallio, Markku & Halme, Merja & Dehghan Hardoroudi, Nasim & Aspara, Jaakko, 2022. "Transparent structured products for retail investors," European Journal of Operational Research, Elsevier, vol. 302(2), pages 752-767.
    6. 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.

    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. 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.
    2. 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.
    3. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    4. Meixner, Oliver & Haas, Rainer, 2017. "The Difficulties in Measuring Individual Utilities of Product Attributes: A Choice Based Experiment," 2018 International European Forum (163rd EAAE Seminar), February 5-9, 2018, Innsbruck-Igls, Austria 276887, International European Forum on System Dynamics and Innovation in Food Networks.
    5. Díaz, Verónica & Montoya, Ricardo & Maldonado, Sebastián, 2023. "Preference estimation under bounded rationality: Identification of attribute non-attendance in stated-choice data using a support vector machines approach," European Journal of Operational Research, Elsevier, vol. 304(2), pages 797-812.
    6. Schlereth, Christian & Skiera, Bernd & Schulz, Fabian, 2018. "Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1165-1179.
    7. Meixner, Oliver & Haas, Rainer, 2017. "The Difficulties in Measuring Individual Utilities of Product Attributes: A Choice Based Experiment," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 2017(1), June.
    8. Schlereth, Christian & Eckert, Christine & Schaaf, René & Skiera, Bernd, 2014. "Measurement of preferences with self-explicated approaches: A classification and merge of trade-off- and non-trade-off-based evaluation types," European Journal of Operational Research, Elsevier, vol. 238(1), pages 185-198.
    9. Meixner, Oliver & Kubinger, Magdalena & Haghirian, Parissa & Haas, Rainer, 2018. "Empirical Research in Foreign Cultures: The Case of Japanese Rice," 2018 International European Forum (163rd EAAE Seminar), February 5-9, 2018, Innsbruck-Igls, Austria 276881, International European Forum on System Dynamics and Innovation in Food Networks.
    10. Lee, Ungki & Kang, Namwoo & Lee, Ikjin, 2020. "Choice data generation using usage scenarios and discounted cash flow analysis," Journal of choice modelling, Elsevier, vol. 37(C).
    11. Jonas Schmidt & Tammo H. A. Bijmolt, 2020. "Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 499-518, May.
    12. Schlereth, Christian & Stepanchuk, Tanja & Skiera, Bernd, 2010. "Optimization and analysis of the profitability of tariff structures with two-part tariffs," European Journal of Operational Research, Elsevier, vol. 206(3), pages 691-701, November.
    13. Dongling Huang & Lan Luo, 2016. "Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning," Marketing Science, INFORMS, vol. 35(3), pages 445-464, May.
    14. Friederike Paetz & Winfried J. Steiner, 2017. "The benefits of incorporating utility dependencies in finite mixture probit models," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 793-819, July.
    15. Frank Ebbers & Jan Zibuschka & Christian Zimmermann & Oliver Hinz, 2021. "User preferences for privacy features in digital assistants," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 411-426, June.
    16. 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.
    17. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    18. Andreas Falke & Harald Hruschka, 2017. "Setting prices in mixed logit model designs," Marketing Letters, Springer, vol. 28(1), pages 139-154, March.
    19. 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.
    20. Franke, Melanie & Nadler, Claudia, 2019. "Energy efficiency in the German residential housing market: Its influence on tenants and owners," Energy Policy, Elsevier, vol. 128(C), pages 879-890.

    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:ejores:v:239:y:2014:i:2:p:556-564. 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: http://www.elsevier.com/locate/eor .

    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.