IDEAS home Printed from https://ideas.repec.org/a/eee/jouret/v97y2021i4p697-714.html
   My bibliography  Save this article

Predicting Under- and Overperforming SKUs within the Distribution–Market Share Relationship

Author

Listed:
  • Hirche, Martin
  • Farris, Paul W.
  • Greenacre, Luke
  • Quan, Yiran
  • Wei, Susan

Abstract

This research presents a retail analytics application which uses machine learning (ML) to identify and predict under- and overperforming consumer packaged goods (CPGs) using retail scanner data. Essential to measuring market performance at the SKU level is the relationship between distribution and market share (the velocity curve). We validate that ML can reproduce the velocity curve, and ML is further used to predict underperforming, in-line performing, and overperforming SKUs relative to the velocity curve, based on a range of variables (SKU features) at a point in time. Our ML approach can correctly predict 83% of SKUs as under-, in-line-, or overperforming based on their characteristics. The research analyzes 9,321 SKUs of 2,565 brands across seven product categories of CPGs which were sold in 8,117 stores from 49 different retail chains of five different retail channels located in the US states of California, New York, Texas, and Wisconsin. The retail stores comprise convenience stores, drug stores, food stores, liquor stores, and mass merchandise retail stores. The data is Nielsen retail store scanner data for the calendar year 2014. The relationship between distribution and market share is a market-wide proxy for the ratio of relative sales in a category to, for example, aggregate shelf space, a key retail productivity metric. We further find indications that the distribution of SKUs across different store sizes, the stores’ category specialization, the line length of the brands, the overall performance of the parent brand, and sales consistency are the most important characteristics for the prediction of market share performance beyond the velocity curve. The methods and results presented will help CPG marketers (suppliers and retailers) understand which SKUs are under-, in-line-, or overperforming and the potential factors contributing to that performance. Optimizing assortments and portfolios is essential to decrease failure rates of individual SKUs. ML approaches can evolve to complementary support tools for such management problems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jouret:v:97:y:2021:i:4:p:697-714
    DOI: 10.1016/j.jretai.2021.04.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0022435921000324
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jretai.2021.04.002?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. Kumar, V. & Anand, Ankit & Song, Hyunseok, 2017. "Future of Retailer Profitability: An Organizing Framework," Journal of Retailing, Elsevier, vol. 93(1), pages 96-119.
    2. Grewal, Dhruv & Roggeveen, Anne L. & Nordfält, Jens, 2017. "The Future of Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 1-6.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. 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.
    5. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    6. Kumar, V., 1994. "Forecasting performance of market share models: an assessment, additional insights, and guidelines," International Journal of Forecasting, Elsevier, vol. 10(2), pages 295-312, September.
    7. Venkatesan, Rajkumar & Farris, Paul & Guissoni, Leandro A. & Neves, Marcos Fava, 2015. "Consumer Brand Marketing through Full- and Self-Service Channels in an Emerging Economy," Journal of Retailing, Elsevier, vol. 91(4), pages 644-659.
    8. Cadeaux, Jack M., 1994. "Flexibility and performance of branch store stock plans for a manufacturer's product line," Journal of Business Research, Elsevier, vol. 29(3), pages 207-218, March.
    9. Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
    10. Anita Elberse & Jehoshua Eliashberg, 2003. "Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures," Marketing Science, INFORMS, vol. 22(3), pages 329-354.
    11. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    12. Rajan Varadarajan, 2019. "Theoretical underpinnings of research in strategic marketing: a commentary," Journal of the Academy of Marketing Science, Springer, vol. 47(1), pages 30-36, January.
    13. 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.
    14. Paul Farris & James Olver & Cornelis De Kluyver, 1989. "The Relationship Between Distribution and Market Share," Marketing Science, INFORMS, vol. 8(2), pages 107-128.
    15. Marshall Fisher & Ramnath Vaidyanathan, 2014. "A Demand Estimation Procedure for Retail Assortment Optimization with Results from Implementations," Management Science, INFORMS, vol. 60(10), pages 2401-2415, October.
    16. Eisend, Martin, 2014. "Shelf space elasticity: A meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 168-181.
    17. Baty, Florent & Ritz, Christian & Charles, Sandrine & Brutsche, Martin & Flandrois, Jean-Pierre & Delignette-Muller, Marie-Laure, 2015. "A Toolbox for Nonlinear Regression in R: The Package nlstools," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i05).
    18. Wilbur, Kenneth C. & Farris, Paul W., 2014. "Distribution and Market Share," Journal of Retailing, Elsevier, vol. 90(2), pages 154-167.
    19. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    20. Preyas S. Desai, 2000. "Multiple Messages to Retain Retailers: Signaling New Product Demand," Marketing Science, INFORMS, vol. 19(4), pages 381-389, August.
    21. 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.
    22. Nash, John C. & Varadhan, Ravi, 2011. "Unifying Optimization Algorithms to Aid Software System Users: optimx for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i09).
    23. 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).
    24. Bart J. Bronnenberg & Jean-Pierre Dubé, 2017. "The Formation of Consumer Brand Preferences," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 353-382, September.
    25. Frank M. Bass, 1995. "Empirical Generalizations and Marketing Science: A Personal View," Marketing Science, INFORMS, vol. 14(3_supplem), pages 6-19.
    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. Dawes, John, 2022. "Factors that influence manufacturer and store brand behavioral loyalty," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    2. Kęstutis Peleckis, 2021. "Application of the DEMATEL Model for Assessing IT Sector’s Sustainability," Sustainability, MDPI, vol. 13(24), pages 1-14, December.
    3. 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.

    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. 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).
    2. 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.
    3. 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.
    4. 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.
    5. Dekimpe, Marnik G., 2020. "Retailing and retailing research in the age of big data analytics," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 3-14.
    6. Ionut Anica-Popa & Liana Anica-Popa & Cristina Radulescu & Marinela Vrincianu, 2021. "The Integration of Artificial Intelligence in Retail: Benefits, Challenges and a Dedicated Conceptual Framework," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 120-120, February.
    7. 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.
    8. Gupta, Shaphali & Ramachandran, Divya, 2021. "Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach," Journal of Retailing, Elsevier, vol. 97(4), pages 597-620.
    9. Patil, Ashutosh & Borle, Sharad & Singh, Siddharth, 2023. "An empirical investigation of unique traits of retailing industry in emerging economies: The perspective of consumer-packaged goods manufacturers," Journal of Business Research, Elsevier, vol. 154(C).
    10. Michel Clement & Anke Hille & Bernd Lucke & Christina Schmidt-Stölting & Frank Sambeth, 2008. "Der Einfluss von Rankings auf den Absatz — Eine empirische Analyse der Wirkung von Bestsellerlisten und Rangpositionen auf den Erfolg von Büchern," Schmalenbach Journal of Business Research, Springer, vol. 60(8), pages 746-777, December.
    11. Derek W. Bunn & Stefania Pantelidaki, 2005. "Development of a multifunctional sales response model with the diagnostic aid of artificial neural networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(7), pages 505-521.
    12. Youssef Boulaksil & M. Jaafar Belkora, 2017. "Distribution Strategies Toward Nanostores in Emerging Markets: The Valencia Case," Interfaces, INFORMS, vol. 47(6), pages 505-517, December.
    13. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
    14. Ngui Min Fui Tom, 2020. "Crashed! Why Drone Delivery Is Another Tech Idea not Ready to Take Off," International Business Research, Canadian Center of Science and Education, vol. 13(7), pages 251-251, July.
    15. 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.
    16. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    17. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    18. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
    19. Christiane Lehrer & Manuel Trenz, 2022. "Omnichannel Business," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 687-699, June.
    20. Dawes, John, 2022. "Factors that influence manufacturer and store brand behavioral loyalty," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).

    More about this item

    Keywords

    Predictive analytics; Machine learning; SKU performance; Category management; Portfolio management;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    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:jouret:v:97:y:2021:i:4:p:697-714. 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: https://www.journals.elsevier.com/journal-of-retailing .

    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.