Corporations spend billions of dollars annually caring for customers across multiple contact channels. A customer journey is the complete sequence of contacts that a given customer has with a company across multiple channels of communication. While each contact is important and contains rich information, studying customer journeys provides a better context to understand customers’ behavior in order to improve customer satisfaction and loyalty, and to reduce care costs. However, journey sequences have a complex format due to the heterogeneity of user behavior: they are variable-length, multi-attribute, and exhibit a large cardinality in categories (e.g. contact reasons). The question of how to characterize and learn representations of customer journeys has not been studied in the literature. We propose to learn journey embeddings using a sequence-to-sequence framework that converts each customer journey into a fixed-length latent embedding. In order to improve the disentanglement and distributional properties of embeddings, the model is further modified by incorporating a Wasserstein autoencoder inspired regularization on the distribution of embeddings. Experiments conducted on an enterprise-scale dataset demonstrate the effectiveness of the proposed model and reveal significant improvements due to the regularization in both distinguishing journey pattern characteristics and predicting future customer engagement.