In the original Bayesian network model, the hierarchical structure of the variables is not assumed. When modeling the relation between the sales of products in a retail industry, it is better to consider a hierarchical structure of items (e.g., first, second, and third classifications). To apply a Bayesian network to such data, we have to focus on one hierarchy only in order to acquire a Bayesian network model. However, focusing only on the first or second classification provides a high-level view, but makes it difficult to understand customers' purchasing behavior in detail. On the other hand, focusing on the third classification results in a considerable number of nodes and a complicated network structure. Thus, capturing the overall relationship between products is not straightforward. Therefore, we propose a hierarchical Bayesian network model and a new learning method based on max-min hill-climbing learning algorithm. In the proposed method, we focus on the hierarchical structure of products, which enables us to construct a lower layer that considers the causal relationships in the upper layer. Furthermore, we investigate a case study using actual hierarchical data on consumer purchases, and demonstrate the proposed model using a simulation analysis.