IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v54y2008i1p83-99.html
   My bibliography  Save this article

On the Recoverability of Choice Behaviors with Random Coefficients Choice Models in the Context of Limited Data and Unobserved Effects

Author

Listed:
  • Rick L. Andrews

    (Department of Business Administration, Lerner College of Business and Economics, University of Delaware, Newark, Delaware 19716)

  • Andrew Ainslie

    (Anderson School of Management, University of California-Los Angeles, Los Angeles, California 90095)

  • Imran S. Currim

    (Graduate School of Management, University of California-Irvine, Irvine, California 92697)

Abstract

Random coefficients choice models are seeing widespread adoption in marketing research, partly because of their ability to generate household-level parameter estimates with limited data. However, the power of such models may tempt researchers to trust that they continue to produce reasonable estimates, when in fact either model misspecification or insufficient data limits the models' ability to recover household-level parameters successfully. If household-level choice behaviors are not recovered successfully, managerial decisions such as marketing-mix planning and targeting, direct marketing, segmentation, and forecasting may not produce the desired results. This study addresses the following questions. First, can random coefficients choice models correctly identify markets characterized by preference and response heterogeneity, state dependence, the use of alternative decision heuristics that result in reduced choice sets, and combinations of these effects? If so, how much data is required, and is this realistic given the size of data sets typically used in marketing analyses? Which model selection criteria should be used to identify these markets? When there is spurious market identification, which parameters contribute to the spurious result? An extensive simulation experiment is conducted wherein random coefficients logit models with varying specifications of parameter heterogeneity, state dependence effects, and choice set heterogeneity are applied to 128 experimental conditions. The results show which types of markets can be identified reliably and which cannot. Based on the results of the simulation, the authors develop a model selection heuristic that identifies the correct market in 81% of the experimental conditions. In contrast, strict application of the best model selection criterion alone results in correct market identification in at most 34% of experimental conditions. Interestingly, we find that the amount of data (number of households or number of purchases per household) does not affect our ability to identify the correct market type with this heuristic, so there is a good chance of identifying the correct market type even when little data is available.

Suggested Citation

  • Rick L. Andrews & Andrew Ainslie & Imran S. Currim, 2008. "On the Recoverability of Choice Behaviors with Random Coefficients Choice Models in the Context of Limited Data and Unobserved Effects," Management Science, INFORMS, vol. 54(1), pages 83-99, January.
    • Handle: RePEc:inm:ormnsc:v:54:y:2008:i:1:p:83-99
    DOI: 10.1287/mnsc.1070.0749
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.1070.0749
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.1070.0749?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
    ---><---

    References listed on IDEAS

    as
    1. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    2. Keane, Michael P, 1997. "Modeling Heterogeneity and State Dependence in Consumer Choice Behavior," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 310-327, July.
    3. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    4. Nitin Mehta & Surendra Rajiv & Kannan Srinivasan, 2003. "Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation," Marketing Science, INFORMS, vol. 22(1), pages 58-84, June.
    5. Timothy J. Gilbride & Greg M. Allenby, 2006. "Estimating Heterogeneous EBA and Economic Screening Rule Choice Models," Marketing Science, INFORMS, vol. 25(5), pages 494-509, September.
    6. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    7. Swait, Joffre, 2001. "A non-compensatory choice model incorporating attribute cutoffs," Transportation Research Part B: Methodological, Elsevier, vol. 35(10), pages 903-928, November.
    8. 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.
    9. P. B. Seetharaman, 2004. "Modeling Multiple Sources of State Dependence in Random Utility Models: A Distributed Lag Approach," Marketing Science, INFORMS, vol. 23(2), pages 263-271, April.
    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. Swait, Joffre, 2009. "Choice models based on mixed discrete/continuous PDFs," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 766-783, August.
    2. Linda Court Salisbury & Fred M. Feinberg, 2010. "Alleviating the Constant Stochastic Variance Assumption in Decision Research: Theory, Measurement, and Experimental Test," Marketing Science, INFORMS, vol. 29(1), pages 1-17, 01-02.
    3. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.
    4. Linda Court Salisbury & Fred M. Feinberg, 2010. "—Temporal Stochastic Inflation in Choice-Based Research," Marketing Science, INFORMS, vol. 29(1), pages 32-39, 01-02.
    5. Neuerburg, Christian & Koschate-Fischer, Nicole & Pescher, Christian, 2021. "Menu-Based Choice Models for Customization: On the Recoverability of Reservation Prices, Model Fit, and Predictive Validity," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 1-14.
    6. Andrews, Rick L. & Currim, Imran S. & Leeflang, Peter & Lim, Jooseop, 2008. "Estimating the SCAN⁎PRO model of store sales: HB, FM or just OLS?," International Journal of Research in Marketing, Elsevier, vol. 25(1), pages 22-33.

    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. Peter Stüttgen & Peter Boatwright & Robert T. Monroe, 2012. "A Satisficing Choice Model," Marketing Science, INFORMS, vol. 31(6), pages 878-899, November.
    2. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    3. Pradeep Chintagunta & Jean-Pierre Dubé & Khim Yong Goh, 2005. "Beyond the Endogeneity Bias: The Effect of Unmeasured Brand Characteristics on Household-Level Brand Choice Models," Management Science, INFORMS, vol. 51(5), pages 832-849, May.
    4. Michel Wedel & Rik Pieters & Ralf Lans, 2023. "Modeling Eye Movements During Decision Making: A Review," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 697-729, June.
    5. Vardit Landsman & Moshe Givon, 2010. "The diffusion of a new service: Combining service consideration and brand choice," Quantitative Marketing and Economics (QME), Springer, vol. 8(1), pages 91-121, March.
    6. Jean‐Pierre Dubé & Günter J. Hitsch & Peter E. Rossi, 2010. "State dependence and alternative explanations for consumer inertia," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 417-445, September.
    7. Anocha Aribarg & Thomas Otter & Daniel Zantedeschi & Greg M. Allenby & Taylor Bentley & David J. Curry & Marc Dotson & Ty Henderson & Elisabeth Honka & Rajeev Kohli & Kamel Jedidi & Stephan Seiler & X, 2018. "Advancing Non-compensatory Choice Models in Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 82-92, March.
    8. Jourdain, Damien & Lairez, Juliette & Striffler, Bruno & Lundhede, Thomas, 2022. "A choice experiment approach to evaluate maize farmers’ decision-making processes in Lao PDR," Journal of choice modelling, Elsevier, vol. 44(C).
    9. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, September.
    10. 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.
    11. Marshall Freimer & Dan Horsky, 2008. "Try It, You Will Like It—Does Consumer Learning Lead to Competitive Price Promotions?," Marketing Science, INFORMS, vol. 27(5), pages 796-810, 09-10.
    12. Nobuhiko Terui & Wirawan Dony Dahana, 2006. "Research Note—Estimating Heterogeneous Price Thresholds," Marketing Science, INFORMS, vol. 25(4), pages 384-391, 07-08.
    13. Nobuhiko Terui & Masataka Ban & Greg M. Allenby, 2011. "The Effect of Media Advertising on Brand Consideration and Choice," Marketing Science, INFORMS, vol. 30(1), pages 74-91, 01-02.
    14. Roozbeh Irani-Kermani & Edward C. Jaenicke & Ardalan Mirshani, 2023. "Accommodating heterogeneity in brand loyalty estimation: application to the U.S. beer retail market," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 820-835, December.
    15. Hong, Seung-Hyun & Rezende, Leonardo, 2012. "Lock-in and unobserved preferences in server operating systems: A case of Linux vs. Windows," Journal of Econometrics, Elsevier, vol. 167(2), pages 494-503.
    16. Damien Jourdain & Juliette Lairez & Bruno Striffler & Thomas Lundhede, 2022. "A choice experiment approach to evaluate maize farmers’ decision-making processes in Lao PDR," Post-Print hal-03737618, HAL.
    17. José M. Labeaga & Mercedes Martos-Partal, 2007. "A Proposal to Distinguish State Dependence and Unobserved Heterogeneity in Binary Brand Choice Models," Working Papers 2007-02, FEDEA.
    18. Zhenghui Sha & Yun Huang & Jiawei Sophia Fu & Mingxian Wang & Yan Fu & Noshir Contractor & Wei Chen, 2018. "A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations," Complexity, Hindawi, vol. 2018, pages 1-14, January.
    19. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    20. Qing Liu & Neeraj Arora, 2011. "Efficient Choice Designs for a Consider-Then-Choose Model," Marketing Science, INFORMS, vol. 30(2), pages 321-338, 03-04.

    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:inm:ormnsc:v:54:y:2008:i:1:p:83-99. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.