Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.

Abstract: Techniques described herein relate to calculating the effectiveness or marketing “lift” of online social media promotions (e.g., Tweets® made on Twitter® or postings made on Facebook®), based on the impact that any such promotion is measured to have, after the promotion is made. Key performance indicators (KPI) for online social media marketing efforts may be established or updated based on such calculations. The techniques disclosed herein may also provide a direct way of measuring impact of an online social media promotion on brand awareness among an online social media audience. This may be accomplished while taking into account any effects of other promotions that have been made or are being made contemporaneously on the online social media platform or other non-social media marketing efforts.