Research papers

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Economic theory provides a great deal of information about demand models. Specifically, theory can dictate many relationships that expenditure and price elasticities should fulfill. Unfortunately, analysts cannot be certain whether these relationships will hold exactly. Many analysts perform hypothesis tests to determine if the theory is correct. If the theory is accepted then the relationships are assumed to hold exactly, but if the theory is rejected they are ignored.

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Micro-marketing refers to the customization of marketing mix variables to the store level. We show how prices could be customized to the store level, rather than adopting a uniform marketing policy across all stores. A basis for these customized pricing strategies is a result of differences in interbrand competition across stores. These changes in inter-brand competition are related to demographic and competitive characteristics of the store’s trading area. This study finds that profitable micromarketing pricing strategies can be implemented.

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We show how price elasticity estimates can be improved in demand systems involving multiple brands and stores. We treat these demand models in a hierarchical Bayesian framework. Unlike more standard Bayesian hierarchical treatments, we utilize prior information based on the restrictions imposed by additive utility models. In an additive utility model approach, price elasticities are driven by a general substitution parameter as well as brand specific expenditure elasticities.

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Using weekly scanner data representing 18 product categories, we estimate store-specific price elasticities for a chain of 83 supermarkets. The elasticities prove to be robust to model specification and display remarkable commonality across the diverse set of product categories. We then relate these price sensitivities to a comprehensive set of demographic and competitor variables that describe the trading areas of each of the stores.

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Micro-marketing refers to the customization of marketing mix variables to the store-level. This paper shows how prices can be profitably customized at the store-level, rather than adopting a uniform pricing policy across all stores. Historically, there has been a trend by retailers to consolidate independent stores into large national and regional chains. This move towards consolidation has been driven by the economies of scale associated with these larger operations. However, some of these large chains have lost the adaptability of independent neighborhood stores.

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Generally, profit predictions are made conditional upon a particular functional form. The typical caveat offered is that this is not the ‘‘true’’ demand model, but is instead some reasonable approximation. We show how the notion of an approximation can be explicitly represented using a random coefficient model. Our model nests the usual situation of complete model certainty as a special case. We go on to show how ignoring the uncertainty in functional form induced by approximation will lead to erroneous pricing decisions that may frequently lead to overpricing. 

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There has been an explosion in the availability of data and computing ability in retail management that has led to a new desire on the part of managers to implement demand based management. Demand based management uses statistical models to predict consumer price response using historical information. These models can be used to construct pricing decision support systems for retail managers. Currently, many firms have begun offering software to perform price optimization.

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