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The estimation of pre- and postpromotion dips with store-level scanner data

  • Heerde, Harald J. van
  • Leeflang, Peter S.H.
  • Wittink, Dick R.

    (Groningen University)

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    One of the mysteries of store-level scanner data modeling is the lack of a dip in sales in the week(s) following a promotion. Researchers expect to find a postpromotion dip because analyses of household scanner panel data indicate that consumers tend to accelerate their purchases in response to a promotion that is, they buy earlier and/or purchase larger quantities than they would in the absence of a promotion. Thus, one should also find a pronounced dip in store-level sales in the week(s) following a promotion. However, researchers find such dips usually neither at the category nor at the brand level. Several arguments have been proposed for the lack of a postpromotion dip in store-level sales data. These arguments explain why dips may be hidden. Given that dips are difficult to detect by traditional models (and by a visual inspection of the data), we propose models that can account for a multitude of factors which together cause complex pre- and postpromotion dips. We use three alternative distributed lead- and lag structures: an Almon model, an Unrestricted dynamic effects model, and an Exponential decay model. In each model, we include four types of price discounts: without any support, with display-only support, with feature-only support, and with feature and display support. The models are calibrated on store-level scanner data for two product categories: tuna and toilet tissue. We estimate the dip to be between 4 and 25 percent of the current sales effect, which is consistent with household-level studies.

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    Paper provided by University of Groningen, Research Institute SOM (Systems, Organisations and Management) in its series Research Report with number 99B36.

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    Date of creation: 1999
    Date of revision:
    Handle: RePEc:dgr:rugsom:99b36
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    1. Jeongwen Chiang, 1991. "A Simultaneous Approach to the Whether, What and How Much to Buy Questions," Marketing Science, INFORMS, vol. 10(4), pages 297-315.
    2. P. Leone, Robert, 1987. "Forecasting the effect of an environmental change on market performance: An intervention time-series approach," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 463-478.
    3. Foekens, Eijte W. & S.H. Leeflang, Peter & Wittink, Dick R., 1998. "Varying parameter models to accommodate dynamic promotion effects," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 249-268, November.
    4. Hsiao, C. & Appelbe, T.W. & Dineen, C.R., 1992. "A General Framework for Panel Data Models with an Application to Canadian Customer-Dialed Long Distance Telephone Service," Papers 90-92-15, California Irvine - School of Social Sciences.
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