IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

A comparison of semiparametric and heterogeneous store sales models for optimal category pricing

Listed author(s):
  • Anett Weber


    (Institute of Management and Economics, Clausthal University of Technology)

  • Winfried J. Steiner


    (Institute of Management and Economics, Clausthal University of Technology)

  • Stefan Lang


    (Faculty of Economics and Statistics, University of Innsbruck)

Registered author(s):

    Abstract Category management requires sales response models helping to simultaneously estimate marketing mix effects for all brands of a product category. We, therefore, develop a general heterogeneity seemingly unrelated regression (SUR) model accommodating correlations between sales across brands. This model contains a latent class SUR model, the well-known hierarchical Bayesian SUR model and the homogeneous SUR model as special cases. We further propose a hierarchical Bayesian semiparametric SUR model based on Bayesian P-splines which comprises a homogeneous semiparametric SUR model as nested version. The results of an empirical application with store-level scanner data indicate that the flexible SUR approaches of modeling price response clearly outperform the various parametric (homogeneous and heterogeneous) SUR approaches with respect to not only predictive validity but also total expected category profits. In particular, functional flexibility turns out to be the primary driver for improving the predictive performance of a store sales model as heterogeneity pays off only once functional flexibility has been accounted for. Furthermore, since both flexible SUR models perform nearly equally well with respect to expected category profits, a uniform pricing strategy which is much less complex to implement than micromarketing can be recommended for our data.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Article provided by Springer & Gesellschaft für Operations Research e.V. in its journal OR Spectrum.

    Volume (Year): 39 (2017)
    Issue (Month): 2 (March)
    Pages: 403-445

    in new window

    Handle: RePEc:spr:orspec:v:39:y:2017:i:2:d:10.1007_s00291-016-0459-6
    DOI: 10.1007/s00291-016-0459-6
    Contact details of provider: Web page:

    Web page:

    Order Information: Web:

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    in new window

    1. Smith, Michael & Kohn, Robert, 2000. "Nonparametric seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 98(2), pages 257-281, October.
    2. Daniel Levy & Hainpeng (Allan) Chen & Sourav RayAuthor-Name: Mark Bergen, 2004. "Asymmetric Price Adjustment in the Small: An Implication of Rational Inattention," Emory Economics 0408, Department of Economics, Emory University (Atlanta).
    3. K. Sudhir & Vrinda Kadiyali & Vithala R. Rao, 2001. "Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer," Yale School of Management Working Papers ysm229, Yale School of Management.
    4. Dobson, Paul W. & Waterson, Michael, 2008. "Chain-Store Competition: Customized vs. Uniform Pricing," The Warwick Economics Research Paper Series (TWERPS) 840, University of Warwick, Department of Economics.
    5. Gerard J. Tellis & Fred S. Zufryden, 1995. "Tackling the Retailer Decision Maze: Which Brands to Discount, How Much, When and Why?," Marketing Science, INFORMS, vol. 14(3), pages 271-299.
    6. Thomas Otter & Timothy J. Gilbride & Greg M. Allenby, 2011. "Testing Models of Strategic Behavior Characterized by Conditional Likelihoods," Marketing Science, INFORMS, vol. 30(4), pages 686-701, July.
    7. Harald J. van Heerde & Peter S. H. Leeflang & Dick R. Wittink, 2002. "How Promotions Work: Scan Pro-Based Evolutionary Model Building," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(3), pages 198-220, July.
    8. Alan L. Montgomery & Eric T. Bradlow, 1999. "Why Analyst Overconfidence About the Functional Form of Demand Models Can Lead to Overpricing," Marketing Science, INFORMS, vol. 18(4), pages 569-583.
    9. K. Sudhir, 2001. "Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer," Marketing Science, INFORMS, vol. 20(3), pages 244-264, October.
    10. ., 2007. "The 1930s," Chapters,in: Pioneers of Industrial Organization, chapter 11 Edward Elgar Publishing.
    11. Kim, Byung-Do & Blattberg, Robert C & Rossi, Peter E, 1995. "Modeling the Distribution of Price Sensitivity and Implications for Optimal Retail Pricing," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 291-303, July.
    12. Mebane Jr., Walter R. & Sekhon, Jasjeet S., 2011. "Genetic Optimization Using Derivatives: The rgenoud Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i11).
    13. Brezger, Andreas & Steiner, Winfried J., 2008. "Monotonic Regression Based on Bayesian PSplines: An Application to Estimating Price Response Functions From Store-Level Scanner Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 90-104, January.
    14. Peter S.H. Leeflang & Harald J. van Heerde & Dick Wittink, 2002. "How Promotions Work: SCAN*PRO-Based Evolutionary Model Building," Yale School of Management Working Papers ysm292, Yale School of Management.
    15. Peter E. Rossi, 2014. "Invited Paper —Even the Rich Can Make Themselves Poor: A Critical Examination of IV Methods in Marketing Applications," Marketing Science, INFORMS, vol. 33(5), pages 655-672, September.
    16. Puneet Manchanda & Asim Ansari & Sunil Gupta, 1999. "The “Shopping Basket”: A Model for Multicategory Purchase Incidence Decisions," Marketing Science, INFORMS, vol. 18(2), pages 95-114.
    17. Jorge M. Silva-Risso & Randolph E. Bucklin & Donald G. Morrison, 1999. "A Decision Support System for Planning Manufacturers' Sales Promotion Calendars," Marketing Science, INFORMS, vol. 18(3), pages 274-300.
    18. Vrinda Kadiyali & Pradeep Chintagunta & Naufel Vilcassim, 2000. "Manufacturer-Retailer Channel Interactions and Implications for Channel Power: An Empirical Investigation of Pricing in a Local Market," Marketing Science, INFORMS, vol. 19(2), pages 127-148, September.
    19. Andrew Ainslie & Peter E. Rossi, 1998. "Similarities in Choice Behavior Across Product Categories," Marketing Science, INFORMS, vol. 17(2), pages 91-106.
    20. Yuxin Chen & James D. Hess & Ronald T. Wilcox & Z. John Zhang, 1999. "Accounting Profits Versus Marketing Profits: A Relevant Metric for Category Management," Marketing Science, INFORMS, vol. 18(3), pages 208-229.
    21. Lang, Stefan & Steiner, Winfried J. & Weber, Anett & Wechselberger, Peter, 2015. "Accommodating heterogeneity and nonlinearity in price effects for predicting brand sales and profits," European Journal of Operational Research, Elsevier, vol. 246(1), pages 232-241.
    22. Vincent R. Nijs & Shuba Srinivasan & Koen Pauwels, 2007. "Retail-Price Drivers and Retailer Profits," Marketing Science, INFORMS, vol. 26(4), pages 473-487, 07-08.
    23. Alan L. Montgomery, 1997. "Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data," Marketing Science, INFORMS, vol. 16(4), pages 315-337.
    24. Venkatesh Shankar & Ruth N. Bolton, 2004. "An Empirical Analysis of Determinants of Retailer Pricing Strategy," Marketing Science, INFORMS, vol. 23(1), pages 28-49, May.
    25. J. Miguel Villas-Boas & Russell S. Winer, 1999. "Endogeneity in Brand Choice Models," Management Science, INFORMS, vol. 45(10), pages 1324-1338, October.
    26. ., 2007. "To the 1930s," Chapters,in: Pioneers of Industrial Organization, chapter 10 Edward Elgar Publishing.
    27. Fruhwirth-Schnatter, Sylvia & Tuchler, Regina & Otter, Thomas, 2004. "Bayesian Analysis of the Heterogeneity Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 2-15, January.
    28. Peter Lenk & Wayne DeSarbo, 2000. "Bayesian inference for finite mixtures of generalized linear models with random effects," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 93-119, March.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:spr:orspec:v:39:y:2017:i:2:d:10.1007_s00291-016-0459-6. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla)

    or (Rebekah McClure)

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.