Using daily store-level data to understand price promotion effects in a semiparametric regression model

Though it has been widely reported in the marketing literature that temporary price discounts generate substantial short-term sales increase, the shape of the deal effect curve constitutes a key research topic, for which there are still limited empirical results. To address this issue, a semiparamet...

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Bibliographic Details
Published in:Journal of retailing and consumer services 2006-05, Vol.13 (3), p.193-204
Main Authors: Martínez-Ruiz, M.P., Mollá-Descals, A., Gómez-Borja, M.A., Rojo-Álvarez, J.L.
Format: Article
Language:English
Subjects:
Daily store-level scanner-data
Deal effect curve
Semiparametric regression
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Summary:Though it has been widely reported in the marketing literature that temporary price discounts generate substantial short-term sales increase, the shape of the deal effect curve constitutes a key research topic, for which there are still limited empirical results. To address this issue, a semiparametric regression approach is used to model the complex nature of this phenomenon. Our model is developed at the brand level using daily store-level scanner-data, which allows the study of several nonreported promotional effects, such as the influence of the day of the week both in promotional and nonpromotional periods. The results show that the weekend is the most effective in increasing promotional sales and that asymmetric and neighborhood effects hold. However, 9-ending promotional prices are not impactful.
ISSN:0969-6989
1873-1384
DOI:10.1016/j.jretconser.2005.08.017