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Subject category: Marketing
Published by: INSEAD
Originally published in: 2006
Version: 06.2014
Format: .xls
Data source: Field research

Abstract

This Excel data file is for use with the simulation exercise. The abstract for the exercise is as follows: One of the key problems in marketing decision-making is how to measure the effectiveness of marketing actions, for example, the effect of temporary price promotions on sales. Many outcomes of marketing decisions are multiply determined and involve both short- and long-term effects that are hard to pin down. So marketing managers are often unable to specify the precise effects of what they are doing and instead rely on intuitive estimates of the effects of their decision-making, or worse, on commonsensical arguments to complement ill-conceived cost-plus pricing approaches. Scanner data-based records of customer purchase decisions provide a wealth of data that managers could use, but how can they get useful input for their decision making out of thousands and thousands of purchase transactions? This exercise provides students with a hands-on tool to measure the effect of price promotions. It allows students to work with real store-level scanner data of the sort that retailers are collecting store by store on a weekly basis. Students estimate regular and promotional price elasticities of demand for two disguised soft-drink brands.
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Abstract

This Excel data file is for use with the simulation exercise. The abstract for the exercise is as follows: One of the key problems in marketing decision-making is how to measure the effectiveness of marketing actions, for example, the effect of temporary price promotions on sales. Many outcomes of marketing decisions are multiply determined and involve both short- and long-term effects that are hard to pin down. So marketing managers are often unable to specify the precise effects of what they are doing and instead rely on intuitive estimates of the effects of their decision-making, or worse, on commonsensical arguments to complement ill-conceived cost-plus pricing approaches. Scanner data-based records of customer purchase decisions provide a wealth of data that managers could use, but how can they get useful input for their decision making out of thousands and thousands of purchase transactions? This exercise provides students with a hands-on tool to measure the effect of price promotions. It allows students to work with real store-level scanner data of the sort that retailers are collecting store by store on a weekly basis. Students estimate regular and promotional price elasticities of demand for two disguised soft-drink brands.

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