IDEAS home Printed from https://ideas.repec.org/p/frd/wpaper/dp2009-02.html
   My bibliography  Save this paper

Improved Baseline Sales

Author

Listed:
  • Kurt A. Jetta

    (TABS Group)

  • Erick W. Rengifo

    (Fordham University, Department of Economics)

Abstract

This paper develops a more accurate and robust baseline sales (sales in the absence of price promotion) using Dynamic Linear Models and a Multiple Structural Change Model (DLM/MSCM). We first discuss the value of utilizing aggregated (chain-level) vs. disaggregated (store-level) point-of-sale (POS) data to estimate baseline sales and measure promotional effectiveness. We then discuss the practical advantage of the DLM/MSCM modeling approach using aggregated data, and we propose two tests to determine the superiority of a particular baseline estimate: the minimization of weekly sales volatility and the existence of no correlation with promotional activities in these estimates. Finally, we test this baseline against the industry standard ones on the two measures of performance. Our tests find the DLM/MSCM baseline sales to be superior to the existing log-linear models by reducing the weekly baseline sales volatility by over 80% and by being uncorrelated to promotional activities.

Suggested Citation

  • Kurt A. Jetta & Erick W. Rengifo, 2009. "Improved Baseline Sales," Fordham Economics Discussion Paper Series dp2009-02, Fordham University, Department of Economics.
    • Handle: RePEc:frd:wpaper:dp2009-02
    as

    Download full text from publisher

    File URL: https://archive.fordham.edu/ECONOMICS_RESEARCH/PAPERS/dp2009_02_jetta_rengifo.pdf
    Download Restriction: no
    ---><---

    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. 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.
    3. Vincent R. Nijs & Marnik G. Dekimpe & Jan-Benedict E.M. Steenkamps & Dominique M. Hanssens, 2001. "The Category-Demand Effects of Price Promotions," Marketing Science, INFORMS, vol. 20(1), pages 1-22, September.
    4. Magid M. Abraham & Leonard M. Lodish, 1993. "An Implemented System for Improving Promotion Productivity Using Store Scanner Data," Marketing Science, INFORMS, vol. 12(3), pages 248-269.
    5. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    6. 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.
    7. Randolph E. Bucklin & Sunil Gupta, 1999. "Commercial Use of UPC Scanner Data: Industry and Academic Perspectives," Marketing Science, INFORMS, vol. 18(3), pages 247-273.
    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. 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.
    2. 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.
    3. Leeflang, Peter, 2011. "Paving the way for “distinguished marketing”," International Journal of Research in Marketing, Elsevier, vol. 28(2), pages 76-88.
    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. Lu, Huidi & van der Lans, Ralf & Helsen, Kristiaan & Gauri, Dinesh K., 2023. "DEPART: Decomposing prices using atheoretical regression trees," International Journal of Research in Marketing, Elsevier, vol. 40(4), pages 781-800.
    7. Ma, Shaohui & Fildes, Robert, 2017. "A retail store SKU promotions optimization model for category multi-period profit maximization," European Journal of Operational Research, Elsevier, vol. 260(2), pages 680-692.
    8. 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.
    9. 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.
    10. Guyt, Jonne & Gijsbrechts, Els, 2018. "On consumer choice patterns and the net impact of feature promotions," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 490-508.
    11. van Heerde, Harald J. & Dekimpe, Marnik G., 2024. "Household and retail panel data in retailing research: Time for a renaissance?," Journal of Retailing, Elsevier, vol. 100(1), pages 104-113.
    12. Kumar, V. & Pereira, Arun, 1997. "Assessing the Competitive Impact of Type, Timing, Frequency, and Magnitude of Retail Promotions," Journal of Business Research, Elsevier, vol. 40(1), pages 1-13, September.
    13. Epstein, Leonardo D. & Inostroza-Quezada, Ignacio E. & Goodstein, Ronald C. & Choi, S. Chan, 2021. "Dynamic effects of store promotions on purchase conversion: Expanding technology applications with innovative analytics," Journal of Business Research, Elsevier, vol. 128(C), pages 279-289.
    14. Koen Pauwels & Shuba Srinivasan & Philip Hans Franses, 2007. "When Do Price Thresholds Matter in Retail Categories?," Marketing Science, INFORMS, vol. 26(1), pages 83-100, 01-02.
    15. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    16. Bogomolova, Svetlana & Szabo, Marietta & Kennedy, Rachel, 2017. "Retailers' and manufacturers' price-promotion decisions: Intuitive or evidence-based?," Journal of Business Research, Elsevier, vol. 76(C), pages 189-200.
    17. Evgeny A. Antipov & Elena B. Pokryshevskaya, 2020. "Interpretable machine learning for demand modeling with high-dimensional data using Gradient Boosting Machines and Shapley values," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(5), pages 355-364, October.
    18. Parreño-Selva, Josefa & Mas-Ruiz, Francisco J. & Ruiz-Conde, Enar, 2017. "The effects of price promotion on relative virtue and vice food products," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 20(5).
    19. Pauwels, Koen & Neslin, Scott A., 2015. "Building With Bricks and Mortar: The Revenue Impact of Opening Physical Stores in a Multichannel Environment," Journal of Retailing, Elsevier, vol. 91(2), pages 182-197.
    20. Jorge Silva-Risso & Irina Ionova, 2008. "—A Nested Logit Model of Product and Transaction-Type Choice for Planning Automakers' Pricing and Promotions," Marketing Science, INFORMS, vol. 27(4), pages 545-566, 07-08.

    More about this item

    Keywords

    Dynamic linear Models; Multiple Structural Change Model; Consumer Packaged Goods; Marketing; Sales; Promotions. Baseline Sales.;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:frd:wpaper:dp2009-02. 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: Fordham Economics (email available below). General contact details of provider: https://edirc.repec.org/data/edforus.html .

    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.