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Abstract

A customized, stepwise, log-linear, distributed lag, restricted market response model is proposed to estimate the effects of various elements of promotion expenditures on sales in the presence of potentially significant effects due to trend and/or seasonality when using time-series data. As distinct from standardized software packages, the customization offers management several benefits: (a) an (optional) imposition of prior restrictions on the directions of the coefficient variables; (b) an empirical determination of the lag structure for selected variables; (c) the detrending of the data to allow for the assessment of incremental marketing mix effects above trend; and (d) a simplified sensitivity analysis. The model is empirically tested and validated using sales data for a brand where the impact of several marketing mix variables is estimated and investigated via policy simulations. A comparison of these results with those obtained from a corresponding unrestricted model illustrates the advantages of this approach. Finally, the limitations of this procedure and directions for future research are discussed.

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A Customized Market Response Model: Development,Estimation,and Empirical Testing

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