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Setting prices in mixed logit model designs

Author

Listed:
  • Andreas Falke

    (University of Regensburg)

  • Harald Hruschka

    (University of Regensburg)

Abstract

We investigate different procedures to set prices in designs for choice-based conjoint analysis using the mixed logit model which captures latent consumer heterogeneity. Besides discrete attributes, we include a linear price term in the deterministic utility function thereby treating price as continuous variable. We consider two different price intervals and several price sets which contain either two or three prices. We compare these alternatives to set prices by simulating choices for different constellations on the basis of the mixed logit model. Furthermore, we generate ten designs simultaneously instead of just one. Using these simulated choices, we estimate the parameters of the mixed logit model in the next step. To reduce the needed sample size and computation time caused by accounting for latent consumer heterogeneity, we apply Halton draws and set a minimum potential design for prior draws. ANOVA with root mean squared error between estimated and true price coefficient values of individual consumers as dependent variable shows that using more extreme prices as interval bounds and one intermediate price positioned to the right of the interval performs best.

Suggested Citation

  • Andreas Falke & Harald Hruschka, 2017. "Setting prices in mixed logit model designs," Marketing Letters, Springer, vol. 28(1), pages 139-154, March.
  • Handle: RePEc:kap:mktlet:v:28:y:2017:i:1:d:10.1007_s11002-015-9396-4
    DOI: 10.1007/s11002-015-9396-4
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    References listed on IDEAS

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    1. Silvio S. Zocchi & Anthony C. Atkinson, 1999. "Optimum Experimental Designs for Multinomial Logistic Models," Biometrics, The International Biometric Society, vol. 55(2), pages 437-444, June.
    2. Thomas Baier & Erich Neuwirth, 2007. "Excel :: COM :: $$\mathsf{R}$$," Computational Statistics, Springer, vol. 22(1), pages 91-108, April.
    3. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    4. Kenneth Train ., 2000. "Halton Sequences for Mixed Logit," Economics Working Papers E00-278, University of California at Berkeley.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    6. Jie Yu & Peter Goos & Martina Vandebroek, 2009. "Efficient Conjoint Choice Designs in the Presence of Respondent Heterogeneity," Marketing Science, INFORMS, vol. 28(1), pages 122-135, 01-02.
    7. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
    8. Kessels, Roselinde & Jones, Bradley & Goos, Peter & Vandebroek, Martina, 2009. "An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 279-291.
    9. Dominique Rouzies & Renée Darmon, 1989. "Assessing Conjoint Analysis Internal Validity: the Effect of Various Continuous Attribute Level Spacings," Post-Print hal-00537869, HAL.
    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.
    Full references (including those not matched with items on IDEAS)

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