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Market Oriented Product Design and Pricing: Effects of Intra-Individual Varying Partworths

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Analysis of Large and Complex Data

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Abstract

Conjoint analysis is a widespread method for modeling and measuring preferences for multi-attributed products in marketing: A sample of customers are asked to evaluate (fictive) offers (attribute-level-combinations). From these individual responses partworths are estimated and used to design and price offers that maximize, e.g., market share, sales, or profit. However, it can be theoretically and empirically argued that partworths not only vary across individuals but also within them. In this paper, we discuss an approach that respects these variations. Partworths are situation-specific modeled at the individual level. The empirical partworth distributions are estimated using Bayesian procedures. The approach is applied to waterpark design and pricing using simulated and real data. It is shown that taking these variations into account influences the maximization.

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References

  • Baier, D. (2014). Bayesian methods for conjoint analysis-based predictions: Do we still need Latent Classes? In German-Japanese interchange of data analysis results. Studies in classification, data analysis, and knowledge organization (Vol. 47, pp. 103–113). Berlin: Springer.

    Google Scholar 

  • Baier, D., Pelka, M., Rybicka, A., & Schreiber, S. (2015). TCA/HB compared to CBC/HB for predicting choices among multi-attributed products. Archives of Data Science, 1(1), 1–11.

    Google Scholar 

  • Baier, D., & Polasek, W. (2003). Market simulation using Bayesian procedures in conjoint analysis. In Exploratory data analysis in empirical research. Studies in classification, data analysis, and knowledge organization (Vol. 23, pp. 413–421). Berlin: Springer.

    Google Scholar 

  • Ben-Akiva, M., & Lerman, S. R. (1985). Discrete choice analysis: Theory and application to travel demand. Cambridge: MIT Press.

    Google Scholar 

  • Desarbo, W. S., Ramaswamy, V., & Cohen, S. H. (1995). Market segmentation with choice-based conjoint analysis. Marketing Letters, 6(2), 137–147.

    Article  Google Scholar 

  • Green, P. E., Krieger, A. M., & Wind, Y. (2001). Thirty years of conjoint analysis: Reflections and prospects. Interfaces, 31(3), 56–73.

    Article  Google Scholar 

  • Louviere, J. J., & Woodworth, G. (1983). Design and analysis of simulated consumer choice or allocation experiments: An approach based on aggregate data. Journal of Marketing Research, 20(4), 350–367.

    Article  Google Scholar 

  • Otter, T., Tüchler, R., & Frühwirth-Schnatter, S. (2004). Capturing consumer heterogeneity in metric conjoint analysis using Bayesian mixture models. International Journal of Research in Marketing, 21(3), 285–297.

    Article  Google Scholar 

  • Sawtooth Software. (2014a). The CBC system for choice-based conjoint analysis version 8. Orem, UT: Sawtooth Software, Inc.

    Google Scholar 

  • Sawtooth Software. (2014b). The CBC/HB system for hierarchical Bayes estimation version 5. Sequim, WA: Sawtooth Software, Inc.

    Google Scholar 

  • Selka, S., & Baier, D. (2014). Kommerzielle Anwendung auswahlbasierter Verfahren der Conjointanalyse: Eine empirische Untersuchung zur Validitätsentwicklung. Marketing ZFP – Journal of Research and Management, 36(1), 54–64.

    Article  Google Scholar 

  • Selka, S., Baier, D., & Kurz, P. (2014). The validity of conjoint analysis: An investigation of commercial studies over time. In Data analysis, machine learning and knowledge discovery. Studies in classification, data analysis, and knowledge organization (Vol. 48, pp. 227–234). Berlin: Springer

    Google Scholar 

  • Wind, J., Green, P. E., Shifflet, D., & Scarbrough, M. (1989). Courtyard by Marriott: Designing a hotel facility with consumer-based marketing models. Interfaces, 19(1),25–47.

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Business Administration and Economics, BTU Cottbus-Senftenberg, Senftenberg, Germany

    Stephanie Löffler & Daniel Baier

Authors
  1. Stephanie Löffler
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  2. Daniel Baier
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Correspondence to Stephanie Löffler .

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Editors and Affiliations

  1. Jacobs University Bremen , Bremen, Germany

    Adalbert F.X. Wilhelm

  2. Universität Ulm, Institute of Medical Systems Biology Universität Ulm, Ulm, Baden-Württemberg, Germany

    Hans A. Kestler

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Löffler, S., Baier, D. (2016). Market Oriented Product Design and Pricing: Effects of Intra-Individual Varying Partworths. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_26

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