A Model to Improve the Estimation of Baseline Retail Sales

Posted: 19 Jan 2011

See all articles by Erick W. Rengifo

Erick W. Rengifo

Fordham University - Department of Economics - Center for International Policy Studies (CIPS)

Kurt Jetta

TABS Group

Multiple version iconThere are 2 versions of this paper

Date Written: January 18, 2011

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: M30, M31, C01, C11

Suggested Citation

Rengifo, Erick W. and Jetta, Kurt, A Model to Improve the Estimation of Baseline Retail Sales (January 18, 2011). Available at SSRN: https://ssrn.com/abstract=1743168

Erick W. Rengifo (Contact Author)

Fordham University - Department of Economics - Center for International Policy Studies (CIPS) ( email )

United States
0017188174061 (Phone)
0017188173518 (Fax)

Kurt Jetta

TABS Group ( email )

2 Corporate Drive, Suite 254
Shelton, CT 06484
United States

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