Abstract: Described herein is a system and method for processing analytics data in a windowed manner. In some embodiments, each user of a number of users is assigned to a group (e.g., a treatment group or a control group). The system may monitor each user to obtain metric data associated with each of the users over the course of an experiment. In some embodiments, the system may identify an indication of a trigger event with respect to each user. For example, the indication of the trigger event may comprise a time at which an event occurred for that user. The system may identify and compile data from the metric data for each of the users with respect to the trigger event. In some embodiments the metric data may be aggregated for each of the users with respect to a window or timespan positioned relative to the identified trigger event.

Abstract: Systems are provided to analyze user behavior with respect to different versions of content, and to identify the behavioral effects that can be attributed to the differences between the versions of content. Systems can determine whether an observed difference in user interactions with different versions of content is caused by the specific changes that have been made to content (e.g., an actual improvement or other substantive change), or whether the observed difference in user interactions is caused by the mere fact that the versions of content are different (e.g., due to the novelty of seeing a different version of content for the first time, regardless of whether the difference is a substantive improvement). In some cases, the observed effect may be caused both by the novelty of the content being changed and by the substance of the changes.