Abstract
Kalyanam and Shively (1998) and van Heerde et al. (2001) have proposed semiparametric models to estimate the influence of price promotions on brand sales, and both obtained superior performance for their models compared to strictly parametric modeling. Following these researchers, we suggest another semiparametric framework which is based on penalized B-splines to analyze sales promotion effects flexibly. Unlike these researchers, we introduce a stepwise procedure with simultaneous smoothing parameter choice for variable selection. Applying this stepwise routine enables us to deal with product categories with many competitive items without imposing restrictions on the competitive market structure in advance. We illustrate the new methodology in an empirical application using weekly store-level scanner data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
BLATTBERG, R.C. and GEORGE, E.I. (1991): Shrinkage Estimation of Price and Promotional Elasticities. Journal of the American Statistical Association, 86(414), 304–315.
DE BOOR, C. (1978): A Practical Guide to Splines. Springer, New York.
EILERS, P.H.C. and MARX, B.D. (1996): Flexible smoothing using B-splines and penalized likelihood (with comments and rejoinder). Statistical Science, 11(2), 89–121.
FOEKENS, E.W. (1995): Scanner Data Based Marketing Modelling: Empirical Applications. Labyrinth Publications, The Netherlands.
HANSSENS, D.M., PARSONS L.J. and SCHULTZ, R.L. (2001): Market Response Models: Econometric and Time Series Analysis. Chapman & Hall, London.
HASTIE, T. and TIBSHIRANI, R. (1990): Generalized Additive Models. Chapman & Hall, London.
HRUSCHKA, H. (2004): Relevance of Functional Flexibility for Heterogeneous Sales Response Models. A Comparison of Parametric and Seminonparametric Models. Discussion Paper 394, Faculty of Economics, University of Regensburg.
KALYANAM, K., SHIVELY, T.S. (1998): Estimating Irregular Pricing Effects: A Stochastic Spline Regression Approach. Journal of Marketing Research, 35(1), 16–29.
LANG, S. and BREZGER, A. (2004): Bayesian P-splines. Journal of Computational and Graphical Statistics, 13, 183–212.
MONTGOMERY, A.L. (1997): Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data. Marketing Science, 16(4), 315–337.
VAN HEERDE, H.J., LEEFLANG, P.S.H. and WITTINK, D.R. (2001): Semiparametric Analysis to Estimate the Deal Effect Curve. Journal of Marketing Research, 38(2), 197–215.
VAN HEERDE, H.J., LEEFLANG, P.S.H. and WITTINK, D.R. (2002): How Promotions Work: SCAN*PRO-Based Evolutionary Model Building. Schmalenbach Business Review, 54(3), 198–220.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer Berlin · Heidelberg
About this paper
Cite this paper
Steiner, W.J., Belitz, C., Lang, S. (2006). Semiparametric Stepwise Regression to Estimate Sales Promotion Effects. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_72
Download citation
DOI: https://doi.org/10.1007/3-540-31314-1_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31313-7
Online ISBN: 978-3-540-31314-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)