Abstract
We focus on cross effects of marketing variables and cross category dependences for multi-category decisions which households take during a shopping trip to a retail store. A cross effect is defined as the effect which a marketing variable used for a certain product category exerts on purchases of another category. Using Dirichlet process mixture models with multivariate probit components we ...
Abstract
We focus on cross effects of marketing variables and cross category dependences for multi-category decisions which households take during a shopping trip to a retail store. A cross effect is defined as the effect which a marketing variable used for a certain product category exerts on purchases of another category. Using Dirichlet process mixture models with multivariate probit components we analyze purchase incidences of 24,047 shopping visits of a random sample of 1500 households. Independent variables of these models encompass marketing variables for 25 product categories and household attributes. We discuss differences between the two best performing models, a full model which includes both cross effects and cross category dependences, and a related restricted model which ignores cross effects. We obtain several high and significant differences with respect to category constants and cross category dependences between these two models. We also present explanations for the larger (in absolute terms) cross effects of features or displays. We demonstrate that by ignoring cross effects management runs the risk to obtain in many product categories too optimistic forecasts of sales revenue changes due to promotions. In contrast to previous related work suggesting not to use promotions which are not tailored to individual households in any of the investigated categories, we obtain support for such promotions in at least 48 % of the 25 product categories. In addition, based on the full model we demonstrate that often different categories are appropriate for promotions which are targeted at household clusters.
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