We investigate the application of two topic models, latent Dirichlet allocation (LDA) and the correlated topic model (CTM), to market basket analysis. Topic models measure the
association between observed purchases and underlying latent activities of shoppers by conceiving each basket as random mixture of latent activities. We explain the structure of the two topic models used. We discuss estimation of LDA models by blocked Gibbs sampling. In addition we show how to evaluate the performance of topic models on estimation and holdout data. In the empirical study we analyse a total of 18,000 purchases made at a medium-sized supermarket which refer to 60 product categories. The LDA model performs better than the CTM in terms of log likelihood values. Latent activities inferred by this models are intuitive and interpretable, e.g., related to shopping of beverages or personal care, to baking or to an inclination towards luxury food. To illustrate the managerial relevance of estimated topic models we sketch the core of a recommender system which ranks purchase probabilities of other product categories conditional on the basket of a shopper.