Embodiments of the present invention provide systems, methods, and computer storage media for digital user identification across different devices, channels, and venues. Generally, digital interactions of a user can reveal a pattern of digital behavior that can be detected and assigned to the user, and a classifier can be learned to identify the user. Various types of digital interaction data may be utilized to identify a user, including device data, geolocation data associated with a user device, clickstream data or other attributes of web traffic, and the like. Anonymity can be provided by only utilizing behavioral-based user data. Digital interaction data can be encoded and fed into a multi-class classifier (e.g., deep neural network, support vector machine, random forest, k-nearest neighbors, etc.), with each user corresponding to a different class. New users can be detected and used to automatically grow a deep neural network to identify additional classes for the new users.