Abstract: An analytics platform configured to create clusters of media content consumers and to automatically generate human-readable, text labels that describe each of the created clusters is disclosed. An analytics platform may use any of a number of clustering algorithms to group a population of media content consumers into a set of user clusters based on viewing history data, user profile data, and/or any other data associated with the consumers. Furthermore, the analytics platform may be configured to create, based on the viewing history data and/or other data associated with a selected sample of users from each cluster, a text label for each cluster. For example, if viewing history data for a population of users includes channel and genre identifiers associated with media content items accessed by users of the population, a generated text label may provide an indication of channels and/or genres popular with users of each particular cluster.

Abstract: A system and methods are provided for analyzing user interactions with a media content discovery system using statistical hypothesis testing techniques. As used herein, a media content discovery system generally represents any service that enables users to browse, view, record, purchase, and/or otherwise interact with media content. Examples of media content that may be made available to users via a media content discovery system include, without limitation, movies, television shows, music, etc. Users may interact with a media content discovery system using any of a number of different types of computing devices, including set-top boxes, desktop computers, laptops, handheld devices, game consoles, etc., and over one or more networks, such as the Internet.