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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). The author first discusses the value of utilizing aggregated (chain-level) vs. disaggregated (store-level) point-of-sale (POS) data to estimate baseline sales and measure promotional effectiveness. The author then discusses the practical advantage of the DLM/MSCM modeling approach using aggregated data, and proposes 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, the author tests this baseline against the industry standard ones on the two measures of performance.
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