IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v206y2010i1p260-264.html
   My bibliography  Save this article

On the product line selection problem under attraction choice models of consumer behavior

Author

Listed:
  • Schön, Cornelia

Abstract

Product line design (PLD) involves important decisions at the interface of operations and marketing that are very costly to implement and change, and, simultaneously, determinant for market success. To evaluate the financial performance of a product line, a number of mathematical programming approaches have been proposed. Problem formulations are typically mixed or pure integer non-linear optimization models that are intractable for exact solution - in particular when empirically supported consumer choice models are incorporated. In this note, we present an exact approach for determining a profit-maximizing product line with continuous prices when consumers choose among available products according to a general and widely applied attraction choice model including the MNL, the BTL, the MCI, and approximately the first choice model. In particular, we show how to efficiently exploit the structural properties resulting from attraction models when consumer behavior is (a) modelled at the aggregate level or (b) disaggregated into customer segments in such a way that each segment can be offered a customized price - a strategy that firms more and more engage in, recognizing it to be not only very profitable but also implementable in the era of e-business. Under these assumptions, we can transform the standard MINLP formulation of the PLD problem into a more convenient convex MIP that can be solved globally with current solvers even for large instances with ten-thousands of products in reasonable time. Therefore our work contributes by accommodating a new trend increasingly encountered in practice and by providing an efficient exact approach to profit-driven PLD for real-world applications.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:206:y:2010:i:1:p:260-264
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(10)00020-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. 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.
    2. Neeraj Arora & Xavier Dreze & Anindya Ghose & James Hess & Raghuram Iyengar & Bing Jing & Yogesh Joshi & V. Kumar & Nicholas Lurie & Scott Neslin & S. Sajeesh & Meng Su & Niladri Syam & Jacquelyn Thom, 2008. "Putting one-to-one marketing to work: Personalization, customization, and choice," Marketing Letters, Springer, vol. 19(3), pages 305-321, December.
    3. SCHMALENSEE, Richard & THISSE, Jacques-François, 1988. "Perceptual maps and the optimal location of new products: an integrative essay," LIDAM Reprints CORE 840, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. V. Krishnan & Karl T. Ulrich, 2001. "Product Development Decisions: A Review of the Literature," Management Science, INFORMS, vol. 47(1), pages 1-21, January.
    5. 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.
    6. G. E. Fruchter & A. Fligler & R. S. Winer, 2006. "Optimal Product Line Design: Genetic Algorithm Approach to Mitigate Cannibalization," Journal of Optimization Theory and Applications, Springer, vol. 131(2), pages 227-244, November.
    7. Alexandre Belloni & Robert Freund & Matthew Selove & Duncan Simester, 2008. "Optimizing Product Line Designs: Efficient Methods and Comparisons," Management Science, INFORMS, vol. 54(9), pages 1544-1552, September.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    9. Green, Paul E & Srinivasan, V, 1978. "Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 5(2), pages 103-123, Se.
    10. Joel Huber & Bryan Orme & Richard Miller, 2007. "Dealing with Product Similarity in Conjoint Simulations," Springer Books, in: Anders Gustafsson & Andreas Herrmann & Frank Huber (ed.), Conjoint Measurement, edition 0, chapter 17, pages 347-362, Springer.
    11. Gregory Dobson & Shlomo Kalish, 1988. "Positioning and Pricing a Product Line," Marketing Science, INFORMS, vol. 7(2), pages 107-125.
    12. 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.
    13. Vidyanand Choudhary & Anindya Ghose & Tridas Mukhopadhyay & Uday Rajan, 2005. "Personalized Pricing and Quality Differentiation," Management Science, INFORMS, vol. 51(7), pages 1120-1130, July.
    14. 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.
    15. 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.
    16. Kohli, Rajeev & Krishnamurti, Ramesh, 1989. "Optimal product design using conjoint analysis: Computational complexity and algorithms," European Journal of Operational Research, Elsevier, vol. 40(2), pages 186-195, May.
    17. Sunder Kekre & Kannan Srinivasan, 1990. "Broader Product Line: A Necessity to Achieve Success?," Management Science, INFORMS, vol. 36(10), pages 1216-1232, October.
    18. Paul E. Green & Abba M. Krieger, 1985. "Models and Heuristics for Product Line Selection," Marketing Science, INFORMS, vol. 4(1), pages 1-19.
    19. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    20. Jeffrey D. Camm & James J. Cochran & David J. Curry & Sriram Kannan, 2006. "Conjoint Optimization: An Exact Branch-and-Bound Algorithm for the Share-of-Choice Problem," Management Science, INFORMS, vol. 52(3), pages 435-447, March.
    21. Varian, Hal R., 1989. "Price discrimination," Handbook of Industrial Organization, in: R. Schmalensee & R. Willig (ed.), Handbook of Industrial Organization, edition 1, volume 1, chapter 10, pages 597-654, Elsevier.
    22. Ward Hanson & Kipp Martin, 1996. "Optimizing Multinomial Logit Profit Functions," Management Science, INFORMS, vol. 42(7), pages 992-1003, July.
    23. Richard D. McBride & Fred S. Zufryden, 1988. "An Integer Programming Approach to the Optimal Product Line Selection Problem," Marketing Science, INFORMS, vol. 7(2), pages 126-140.
    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. Domínguez, Concepción & Labbé, Martine & Marín, Alfredo, 2021. "The rank pricing problem with ties," European Journal of Operational Research, Elsevier, vol. 294(2), pages 492-506.

    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. Cornelia Schön, 2010. "On the Optimal Product Line Selection Problem with Price Discrimination," Management Science, INFORMS, vol. 56(5), pages 896-902, May.
    2. Bechler, Georg & Steinhardt, Claudius & Mackert, Jochen & Klein, Robert, 2021. "Product line optimization in the presence of preferences for compromise alternatives," European Journal of Operational Research, Elsevier, vol. 288(3), pages 902-917.
    3. Hongmin Li & Scott Webster & Gwangjae Yu, 2020. "Product Design Under Multinomial Logit Choices: Optimization of Quality and Prices in an Evolving Product Line," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 1011-1025, September.
    4. Michalek, Jeremy J. & Ebbes, Peter & Adigüzel, Feray & Feinberg, Fred M. & Papalambros, Panos Y., 2011. "Enhancing marketing with engineering: Optimal product line design for heterogeneous markets," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 1-12.
    5. Andrade, Xavier & Guimarães, Luís & Figueira, Gonçalo, 2021. "Product line selection of fast-moving consumer goods," Omega, Elsevier, vol. 102(C).
    6. Dimitris Bertsimas & Velibor V. Mišić, 2017. "Robust Product Line Design," Operations Research, INFORMS, vol. 65(1), pages 19-37, February.
    7. Tan Wang & Genaro Gutierrez, 2022. "Robust Product Line Design by Protecting the Downside While Minding the Upside," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 194-217, January.
    8. Dimitris Bertsimas & Velibor V. Mišić, 2019. "Exact First-Choice Product Line Optimization," Operations Research, INFORMS, vol. 67(3), pages 651-670, May.
    9. Dimitris Bertsimas & Velibor V. Mišić, 2017. "Robust Product Line Design," Operations Research, INFORMS, vol. 65(1), pages 19-37, February.
    10. 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.
    11. 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.
    12. 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.
    13. Maoqi Liu & Li Zheng & Changchun Liu & Zhi‐Hai Zhang, 2023. "From share of choice to buyers' welfare maximization: Bridging the gap through distributionally robust optimization," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1205-1222, April.
    14. Lacourbe, Paul, 2012. "A model of product line design and introduction sequence with reservation utility," European Journal of Operational Research, Elsevier, vol. 220(2), pages 338-348.
    15. Domínguez, Concepción & Labbé, Martine & Marín, Alfredo, 2021. "The rank pricing problem with ties," European Journal of Operational Research, Elsevier, vol. 294(2), pages 492-506.
    16. 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.
    17. Daria Dzyabura & Srikanth Jagabathula, 2018. "Offline Assortment Optimization in the Presence of an Online Channel," Management Science, INFORMS, vol. 64(6), pages 2767-2786, June.
    18. Albritton, M. David & McMullen, Patrick R., 2007. "Optimal product design using a colony of virtual ants," European Journal of Operational Research, Elsevier, vol. 176(1), pages 498-520, January.
    19. 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.
    20. 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.

    More about this item

    Keywords

    Pricing Product line design;

    Statistics

    Access and download statistics

    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:206:y:2010:i:1:p:260-264. 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.