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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

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    File URL: https://archive.fordham.edu/ECONOMICS_RESEARCH/PAPERS/dp2009_02_jetta_rengifo.pdf
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    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)

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    More about this item

    Keywords

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

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