A Model to Improve the Estimation of Baseline Retail Sales

17 Pages Posted: 11 Apr 2011 Last revised: 20 Dec 2014

See all articles by Kurt Jetta

Kurt Jetta

TABS Group

Erick W. Rengifo

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

Multiple version iconThere are 2 versions of this paper

Date Written: April 11, 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

Jetta, Kurt and Rengifo, Erick W., A Model to Improve the Estimation of Baseline Retail Sales (April 11, 2011). Journal of CENTRUM Cathedra, Vol. 4, Issue 1, pp. 10-26, 2011. Available at SSRN: https://ssrn.com/abstract=1807162

Kurt Jetta

TABS Group ( email )

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

Erick W. Rengifo (Contact Author)

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

United States
0017188174061 (Phone)
0017188173518 (Fax)

Register to save articles to
your library

Register

Paper statistics

Downloads
555
Abstract Views
2,423
rank
47,991
PlumX Metrics