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...
| Authors: | , , , |
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| Format: | article |
| Publication Date: | 2009 |
| Country: | España |
| Institution: | Universidad Rey Juan Carlos |
| Repository: | BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos |
| OAI Identifier: | oai:burjcdigital.urjc.es:10115/2406 |
| Online Access: | http://hdl.handle.net/10115/2406 |
| Access Level: | Open access |
| Keyword: | Telecomunicaciones 3325 Tecnología de las Telecomunicaciones |
| 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. |
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