Retailers such as grocery stores or e-marketplaces often have vast selections of items for users to choose from. Predicting a customer’s next purchases has gained attention recently, in the form of Next Basket Recommendation (NBR), as it facilitates navigating extensive assortments for customers. Neural network-based models that focus on learning basket representations are the dominant approach in the recent literature. However, these methods do not consider the specific characteristics of the grocery shopping scenario, where customers shop for grocery items on a regular basis, and grocery items are repurchased frequently by the same customer. In this paper, we first provide a thorough data-driven understanding of the users’ repeat consumption behavior through an empirical study on six public and proprietary grocery shopping transaction datasets. We discover that, averaged over all datasets, over 54% of NBR performance in terms of recall comes from repeat items: items that users have already purchased in their history, which are only 1% of total collection of items on average. A NBR model that is focused on previously purchased items can potentially outperform existing state-of-the-art models by a large margin. We introduce ReCANet, a Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping that explicitly models the repeat consumption behavior of users in order to predict their next basket. ReCANet significantly outperforms state-of-the-art models for the NBR task, in terms of Recall and nDCG. We perform an ablation study and show that all of the components of ReCANet contribute to its performance, and demonstrate that a user’s repetition ratio has a direct influence on the treatment effect of ReCANet.