Abstract A person experiences different stages throughout the life, causing dramatically varying behavior patterns. In applications such as online-shopping, it has been observed that customer behaviors are largely affected by their stages and are evolving over time. Although this phenomena has been recognized previously, very few studies tried to model the life-stage and make use of it. In this paper, we propose to discover a latent space, called customermanifold, on which a position corresponds to a customer stage. The customer-manifold allows us to train a static prediction model that captures dynamic customer behavior patterns. We further embed the learned customer-manifold into a neural network model as a hidden layer output, resulting in an efficient and accurate customer behavior prediction system. We apply this system to onlineshopping recommendation. Experiments in real world data show that taking customer-manifold into account can improve the performance of the recommender system. Moreover, visualization of the customer-manifold space may also be helpful to understand the evolutionary customer behaviors.