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Determining the attractiveness of product attributes in consumer goods markets using POS scanner data

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This paper suggests a simple Poisson regression framework that facilitates the estimation of parameters which, similar to established conjoint analysis, allow inferences on the relative effect a certain product attribute has on the overall willingness to purchase the respective consumer good. Furthermore, we discuss options for considering demand heterogeneity and show how the suggested approach to preference analysis can be used to predict the presumptive sales of new products intended to be introduced into the product category of interest. Both the usability and the benefits of the suggested framework are demonstrated using POS scanner data on two typical consumer goods categories provided by a German supermarket chain. In both cases, the available results provide significant information for new product development and retail assortment planning.
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Keywords: CONSUMER GOODS; CUSTOMER PREFERENCES; POINT OF SALE SCANNER DATA; POISSON REGRESSION; PRODUCT SALES PREDICTION

Document Type: Research Article

Publication date: August 1, 2010

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