Based on market basket data, using multicategory purchase incidence models, we analyze demand interdependencies between product categories. We propose a finite mixture multivariate logit model to derive segment-specific intercategory effects of market basket purchase. Under the assumption that only a fraction of intercategory effects are significant, we exclude irrelevant effects by variable selection. This leads to a detailed description of consumers’ shopping behavior that varies over segments not only with respect to (w.r.t.) parameters’ values but also w.r.t. included interaction effects. As the high number of product categories in the model prohibits exact maximum likelihood estimation, we adopt pseudo-likelihood estimation. We apply our model to a data set with 31 product categories and 1,794 households purchasing 17,280 baskets in one store. The best fitting model is determined by predictive model selection. We find that a homogeneous model would overestimate the intensity of interaction between product categories.