Article ID: | iaor20061962 |
Country: | United States |
Volume: | 22 |
Issue: | 1 |
Start Page Number: | 58 |
End Page Number: | 84 |
Publication Date: | Dec 2003 |
Journal: | Marketing Science |
Authors: | Srinivasan Kannan, Rajiv Surendra, Mehta Nitin |
Keywords: | pricing, Bayesian modelling, scanner data |
We offer an econometric framework that models consumer's consideration set formation as an outcome of her costly information search behaviour. Because frequently purchased products are characterized by frequent price promotions of varying depths of discounts, a consumer faces significant uncertainty about the prices of the brands. The consumers engage in a fixed-sample search strategy that results in their discovering the posted prices of a subset of the available brands. This subset is referred to as the consumers' ‘consideration set’. The proposed model is estimated using the scanner data set for liquid detergents. Our key empirical results are: (i) consumers incur significant search costs to discover the posted prices of the brands; (ii) whereas in-store displays and feature ads do not influence consumers' quality perceptions of the brands, they significantly reduce search costs for observing the prices of the brands; (iii) per capita income of consumer's household significantly increases her search costs; and (iv) the consumers' price sensitivity is seriously underestimated if we were to assume that consumers get to know all the posted prices at zero cost. The proposed model is also estimated for the ketchup category to enable us to do cross-category comparisons of consumers' price search behaviour.