IDEAS home Printed from https://ideas.repec.org/a/pal/jmarka/v11y2023i3d10.1057_s41270-022-00165-8.html
   My bibliography  Save this article

Retail distribution evaluation in brand-level sales response models

Author

Listed:
  • Antonis A. Michis

    (Central Bank of Cyprus)

Abstract

The effectiveness of a product’s distribution network in retail stores is an important consideration for marketing managers. An effective distribution network typically covers a large number of stores in the geographic area of a market and establishes a continuous presence in the top-selling outlets of a product category at the same time. This study proposes a semiparametric, brand-level version of the SCAN*PRO sales model, to evaluate the impact of retail distribution changes on sales. The model is estimated using the iteratively reweighted least squares method and provides the following outputs: (i) least squares coefficient estimates for the price and promotional drivers in the model specification and (ii) two-dimensional plots of the nonmonotonic relationship between the weighted distribution and sales. The proposed model can be estimated with commonly available retail scanning data and is demonstrated using three laundry detergent brands from The Netherlands.

Suggested Citation

  • Antonis A. Michis, 2023. "Retail distribution evaluation in brand-level sales response models," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 366-378, September.
    • Handle: RePEc:pal:jmarka:v:11:y:2023:i:3:d:10.1057_s41270-022-00165-8
    DOI: 10.1057/s41270-022-00165-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41270-022-00165-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41270-022-00165-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Foekens, Eijte W. & Leeflang, Peter S. H. & Wittink, Dick R., 1994. "A comparison and an exploration of the forecasting accuracy of a loglinear model at different levels of aggregation," International Journal of Forecasting, Elsevier, vol. 10(2), pages 245-261, September.
    2. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    3. 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.
    4. David J. Reibstein & Paul W. Farris, 1995. "Market Share and Distribution: A Generalization, a Speculation, and Some Implications," Marketing Science, INFORMS, vol. 14(3_supplem), pages 190-202.
    5. Robert E. Krider & Tieshan Li & Yong Liu & Charles B. Weinberg, 2005. "The Lead-Lag Puzzle of Demand and Distribution: A Graphical Method Applied to Movies," Marketing Science, INFORMS, vol. 24(4), pages 635-645, April.
    6. Paul Farris & James Olver & Cornelis De Kluyver, 1989. "The Relationship Between Distribution and Market Share," Marketing Science, INFORMS, vol. 8(2), pages 107-128.
    7. Antonis A Michis, 2015. "A wavelet smoothing method to improve conditional sales forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(5), pages 832-844, May.
    8. Guissoni, Leandro Angotti & Rodrigues, Jonny Mateus & Zambaldi, Felipe & Neves, Marcos Fava, 2021. "Distribution effectiveness through full- and self-service channels under economic fluctuations in an emerging market," Journal of Retailing, Elsevier, vol. 97(4), pages 545-560.
    9. Leeflang, P.S.H. & Wittink, Dick R., 2000. "Building models for marketing decisions: past, present and future," Research Report 00F20, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    10. Richard Friberg & Mark Sanctuary, 2017. "The Effect of Retail Distribution on Sales of Alcoholic Beverages," Marketing Science, INFORMS, vol. 36(4), pages 626-641, July.
    11. Michis Antonis A, 2009. "Regression Analysis of Marketing Time Series: A Wavelet Approach with Some Frequency Domain Insights," Review of Marketing Science, De Gruyter, vol. 7(1), pages 1-43, July.
    12. repec:dgr:rugsom:00f20 is not listed on IDEAS
    13. Tenn, Steven & Yun, John M., 2008. "Biases in demand analysis due to variation in retail distribution," International Journal of Industrial Organization, Elsevier, vol. 26(4), pages 984-997, July.
    14. Albers, Sönke, 2012. "Optimizable and implementable aggregate response modeling for marketing decision support," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 111-122.
    15. Wilbur, Kenneth C. & Farris, Paul W., 2014. "Distribution and Market Share," Journal of Retailing, Elsevier, vol. 90(2), pages 154-167.
    16. Makoto Abe, 1995. "A Nonparametric Density Estimation Method for Brand Choice Using Scanner Data," Marketing Science, INFORMS, vol. 14(3), pages 300-325.
    17. Brissimis, Sophocles N. & Kioulafas, K. E., 1987. "An analysis of advertising and distribution effectiveness," European Journal of Operational Research, Elsevier, vol. 28(2), pages 175-179, February.
    18. Hirche, Martin & Farris, Paul W. & Greenacre, Luke & Quan, Yiran & Wei, Susan, 2021. "Predicting Under- and Overperforming SKUs within the Distribution–Market Share Relationship," Journal of Retailing, Elsevier, vol. 97(4), pages 697-714.
    19. Park, Inyoung & Lee, Jieon & Lee, Daeho & Lee, Changjun & Chung, Won Young, 2022. "Changes in consumption patterns during the COVID-19 pandemic: Analyzing the revenge spending motivations of different emotional groups," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    20. Mitsukuni Nishida, 2017. "First-Mover Advantage Through Distribution: A Decomposition Approach," Marketing Science, INFORMS, vol. 36(4), pages 590-609, July.
    21. Hirche, Martin & Greenacre, Luke & Nenycz-Thiel, Magda & Loose, Simone & Lockshin, Larry, 2021. "SKU performance and distribution: A large-scale analysis of the role of product characteristics with store scanner data," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    22. Praveen K. Kopalle & Carl F. Mela & Lawrence Marsh, 1999. "The Dynamic Effect of Discounting on Sales: Empirical Analysis and Normative Pricing Implications," Marketing Science, INFORMS, vol. 18(3), pages 317-332.
    23. Pantano, Eleonora & Pizzi, Gabriele & Scarpi, Daniele & Dennis, Charles, 2020. "Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak," Journal of Business Research, Elsevier, vol. 116(C), pages 209-213.
    24. 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.
    25. Robert Krider & Tieshan Li & Yong Liu & Charles Weinberg, 2008. "Demand and distribution relationships in the ready-to-drink iced tea market: A graphical approach," Marketing Letters, Springer, vol. 19(1), pages 1-12, March.
    26. Erjavec, Jure & Manfreda, Anton, 2022. "Online shopping adoption during COVID-19 and social isolation: Extending the UTAUT model with herd behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    Full references (including those not matched with items on IDEAS)

    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. Weber, Anett & Steiner, Winfried J., 2021. "Modeling price response from retail sales: An empirical comparison of models with different representations of heterogeneity," European Journal of Operational Research, Elsevier, vol. 294(3), pages 843-859.
    2. Hirche, Martin & Farris, Paul W. & Greenacre, Luke & Quan, Yiran & Wei, Susan, 2021. "Predicting Under- and Overperforming SKUs within the Distribution–Market Share Relationship," Journal of Retailing, Elsevier, vol. 97(4), pages 697-714.
    3. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    4. Philipp Aschersleben & Winfried J. Steiner, 2022. "A semiparametric approach to estimating reference price effects in sales response models," Journal of Business Economics, Springer, vol. 92(4), pages 591-643, May.
    5. Andrews, Rick L. & Currim, Imran S. & Leeflang, Peter & Lim, Jooseop, 2008. "Estimating the SCAN⁎PRO model of store sales: HB, FM or just OLS?," International Journal of Research in Marketing, Elsevier, vol. 25(1), pages 22-33.
    6. Hirche, Martin & Greenacre, Luke & Nenycz-Thiel, Magda & Loose, Simone & Lockshin, Larry, 2021. "SKU performance and distribution: A large-scale analysis of the role of product characteristics with store scanner data," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    7. Kurt A. Jetta & Erick W. Rengifo, 2009. "Improved Baseline Sales," Fordham Economics Discussion Paper Series dp2009-02, Fordham University, Department of Economics.
    8. Dawes, John, 2022. "Factors that influence manufacturer and store brand behavioral loyalty," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    9. Marusia Ivanova, 2007. "Genesis and Evolution of Market Share Predictive Models," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 117-148.
    10. Richard Friberg & Mark Sanctuary, 2017. "The Effect of Retail Distribution on Sales of Alcoholic Beverages," Marketing Science, INFORMS, vol. 36(4), pages 626-641, July.
    11. Suresh Divakar & Brian T. Ratchford & Venkatesh Shankar, 2005. "Practice Prize Article—: A Multichannel, Multiregion Sales Forecasting Model and Decision Support System for Consumer Packaged Goods," Marketing Science, INFORMS, vol. 24(3), pages 334-350, July.
    12. Pasirayi, Simbarashe & Fennell, Patrick B. & Sen, Argha, 2023. "The effect of third-party delivery partnerships on firm value," Journal of Business Research, Elsevier, vol. 167(C).
    13. Ailawadi, Kusum L. & Farris, Paul W., 2017. "Managing Multi- and Omni-Channel Distribution: Metrics and Research Directions," Journal of Retailing, Elsevier, vol. 93(1), pages 120-135.
    14. Gielens, Katrijn & Gijsbrechts, Els & Dekimpe, Marnik G., 2014. "Gains and losses of exclusivity in grocery retailing," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 239-252.
    15. Guissoni, Leandro Angotti & Rodrigues, Jonny Mateus & Zambaldi, Felipe & Neves, Marcos Fava, 2021. "Distribution effectiveness through full- and self-service channels under economic fluctuations in an emerging market," Journal of Retailing, Elsevier, vol. 97(4), pages 545-560.
    16. Leeflang, Peter, 2011. "Paving the way for “distinguished marketing”," International Journal of Research in Marketing, Elsevier, vol. 28(2), pages 76-88.
    17. Kumar, V. & Sunder, Sarang & Sharma, Amalesh, 2015. "Leveraging Distribution to Maximize Firm Performance in Emerging Markets," Journal of Retailing, Elsevier, vol. 91(4), pages 627-643.
    18. Bernhard Baumgartner & Daniel Guhl & Thomas Kneib & Winfried J. Steiner, 2018. "Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 837-873, October.
    19. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    20. Ataman, B.M., 2007. "Managing brands," Other publications TiSEM 462dcbba-2ac1-46d1-a61c-f, Tilburg University, School of Economics and Management.

    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:pal:jmarka:v:11:y:2023:i:3:d:10.1057_s41270-022-00165-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    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.