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Abstract
This paper develops more accurate and robust baseline sales estimates (sales in the absence of price promotion) using a dynamic linear model (DLM) enhanced with a multiple structural change model (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 to measure promotional effectiveness. We then present 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 new 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.
Keywords: dynamic linear models, multiple structural change model, consumer packaged goods, marketing, sales, promotions, baseline sales
JEL Classification codes: M30, M31, C01, C11
(ProQuest: ... denotes formulae omitted.)
In the United States, the consumer packaged goods industry (CPG) accounts for over $500 billion in annual retail sales according to ACNielsen and at least twice that worldwide. It is well documented that retailer price promotions (defined as a temporary reduction in retailer price for a specific set of products for a specific period of time) account for the largest share of CPG firms' marketing budget (Cannondale, 2007), and that percentage has grown consistently over time. Industry estimates peg the amount of annual spending on retailer price promotions at about $50-75billion annually in the United States (about 15-20% of factory sales according to Accenture) and over $100billion worldwide1.
The CPG industry has one of the most extensive information infrastructures of any industry. Most U.S. retail outlets are able to track the sales of virtually every product that is sold in the store with the use of scanners. These scanners can read the Universal Product Code (UPC) on each product. The UPC is matched to information that describes dozens of characteristics about the product: manufacturer, brand, product type, flavor, weight, count size, and so on. The in-store scanner data are augmented by household-level scanning data...