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Technical Note: Mathematical Properties of the Optimal Product Line Selection Problem Using Choice-Based Conjoint Analysis

Author

Listed:
  • Kyle D. Chen

    () (Computer Science Department, IBM Almaden Research Center, San Jose, California 95120-6099)

  • Warren H. Hausman

    () (Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305-4024)

Abstract

Selecting and pricing product lines is an essential activity in many businesses. In recent years, quantitative approaches for such tasks have been gaining in popularity. One often-employed method is to use data from traditional rankings/ratings-based conjoint analysis and attack the product line selection problem with enumeration or heuristics. In this note, we employ a relatively new methodology known as choice-based conjoint analysis (to model customer preferences) and investigate its mathematical properties when used to model the product line selection problem. Despite some inherent limitations resulting from its aggregated formulation, we show that this more parsimonious conjoint approach has some special mathematical properties that lead to an efficient optimal algorithm to tackle the product line/price selection problem. As a result, problems of realistic size can be solved efficiently using standard, commercially available mathematical programming codes.

Suggested Citation

  • Kyle D. Chen & Warren H. Hausman, 2000. "Technical Note: Mathematical Properties of the Optimal Product Line Selection Problem Using Choice-Based Conjoint Analysis," Management Science, INFORMS, vol. 46(2), pages 327-332, February.
  • Handle: RePEc:inm:ormnsc:v:46:y:2000:i:2:p:327-332
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    File URL: http://dx.doi.org/10.1287/mnsc.46.2.327.11931
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    References listed on IDEAS

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    1. Hausman, Jerry & McFadden, Daniel, 1984. "Specification Tests for the Multinomial Logit Model," Econometrica, Econometric Society, vol. 52(5), pages 1219-1240, September.
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    Citations

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    Cited by:

    1. Winfried Steiner & Harald Hruschka, 2002. "A Probabilistic One-Step Approach to the Optimal Product Line Design Problem Using Conjoint and Cost Data," Review of Marketing Science Working Papers 1-4-1003, Berkeley Electronic Press.
    2. Mayer, Stefan & Steinhardt, Claudius, 2016. "Optimal product line pricing in the presence of budget-constrained consumers," European Journal of Operational Research, Elsevier, vol. 248(1), pages 219-233.
    3. Kraus, Ursula G. & Yano, Candace Arai, 2003. "Product line selection and pricing under a share-of-surplus choice model," European Journal of Operational Research, Elsevier, vol. 150(3), pages 653-671, November.
    4. Schön, Cornelia, 2010. "On the product line selection problem under attraction choice models of consumer behavior," European Journal of Operational Research, Elsevier, vol. 206(1), pages 260-264, October.
    5. 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.
    6. Day, Jamison M. & Venkataramanan, M.A., 2006. "Profitability in product line pricing and composition with manufacturing commonalities," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1782-1797, December.
    7. Wallace J. Hopp & Xiaowei Xu, 2005. "Product Line Selection and Pricing with Modularity in Design," Manufacturing & Service Operations Management, INFORMS, vol. 7(3), pages 172-187, August.
    8. Talebian, Masoud & Boland, Natashia & Savelsbergh, Martin, 2014. "Pricing to accelerate demand learning in dynamic assortment planning for perishable products," European Journal of Operational Research, Elsevier, vol. 237(2), pages 555-565.
    9. Slavomir Bednar & Jan Modrak, 2015. "Product Variety Management As A Tool For Successful Mass Customized Product Structure," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 12(1), pages 16-25, DEcember.
    10. Hongmin Li & Woonghee Tim Huh, 2011. "Pricing Multiple Products with the Multinomial Logit and Nested Logit Models: Concavity and Implications," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 549-563, October.
    11. Cornelia Schön, 2010. "On the Optimal Product Line Selection Problem with Price Discrimination," Management Science, INFORMS, vol. 56(5), pages 896-902, May.
    12. Ahmed Ghoniem & Bacel Maddah & Ameera Ibrahim, 2016. "Optimizing assortment and pricing of multiple retail categories with cross-selling," Journal of Global Optimization, Springer, vol. 66(2), pages 291-309, October.
    13. Burkart, Wolfgang R. & Klein, Robert & Mayer, Stefan, 2012. "Product line pricing for services with capacity constraints and dynamic substitution," European Journal of Operational Research, Elsevier, vol. 219(2), pages 347-359.
    14. repec:spr:annopr:v:254:y:2017:i:1:d:10.1007_s10479-017-2478-3 is not listed on IDEAS
    15. Alexandre Belloni & Mitchell J. Lovett & William Boulding & Richard Staelin, 2012. "Optimal Admission and Scholarship Decisions: Choosing Customized Marketing Offers to Attract a Desirable Mix of Customers," Marketing Science, INFORMS, vol. 31(4), pages 621-636, July.

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