Using POS Data for Price Promotions Evaluation: An Empirical Example from a Slovenian Grocery Chain

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Author(s): Danijel Bratina (University of Primorska, Slovenia)and Armand Faganel (University of Primorska, Slovenia)
Copyright: 2011
Pages: 19
Source title: Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection
Source Author(s)/Editor(s): Ali Serhan Koyuncugil (Capital Markets Board of Turkey, Turkey, and Baskent University, Turkey)and Nermin Ozgulbas (Baskent University, Turkey)
DOI: 10.4018/978-1-61692-865-0.ch014

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

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Abstract

Price promotions have been largely dealt with in the literature. Yet there are just a few generalizations made so far about this powerful marketing communication tool. The obvious effect, that all authors who have studied price promotions emphasize, is quantity increase during price promotions. Inference studies about the decomposition of the sales promotion bump do not converge to a generalization or a law, but end in radically different results. Most of these studies use consumer panel data, rich of demographical characteristics and consumers’ purchasing history. Companies that use such data, available from marketing research industry, usually complain that data is old and expensive. The authors start with literature review on price promotions in which they present existing models based on consumer panel data (Bell, et al., 1999; Mela, et al., 1998; Moriarty, 1985; Walters, 1991; Yeshin, 2006). Next they present existing POS analysis models and compare their findings to show the high level of heterogeneity among results. All existing models are based on powerful databases provided by professional research institutions (i.e. Nielsen or IRI) that usually cover the whole market for the analysed brand category geographically. The authors next apply existing models to find which best suits data available for Slovenian FMCG market. They show two models analysis – quantity (SCAN*PRO) and market share (MCI) and their power for explanatory and forecasting research using POS data. Having dealt with more than 30 brand categories within a wider research, they conclude that the models developed are usable for a fast decision making process within a company, but their exploratory power is still poor compared to panel data.

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Using POS Data for Price Promotions Evaluation: An Empirical Example from a Slovenian Grocery Chain

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