Abstract: Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to generate a first digital media identifier related to a first user account and store data associated with a provenance of the first digital media identifier. Such embodiments further receive data indicating an interaction on a remote software platform(s) by a second user account with a publication of the first digital media identifier on the remote software platform(s).

Abstract: In one example implementation, behavioral data describing past actions including products viewed and purchases made by users while visiting applications is compiled. The behavioral data is segmented into clusters of behavior factors according to statistically related actions of the users. An electronic address for the user is received from a computer of the user. The electronic address is then processed for user data profiling the user. A match between the user data profiling the user and one of the clusters of behavior factors is computed. Targeted information is generated based on the match. The targeted information is provided to the computer of the user for presentation.

Abstract: An example system is configured to compile coupled to a computer network behavioral data describing past actions including products viewed and purchases made by users while visiting applications; segment the behavioral data into clusters of behavior factors according to statistically related actions of the users; compile present user data describing a current action of a user while visiting a merchant website hosted by a server coupled to the computer network and accessible by the user on a merchant application; while the user is still visiting the website, compute a match between the present user data and one of the clusters of behavior factors; generate based on the match targeted information influenced by the present user data and the past delivery performance of a merchant and a shipping carrier; and provide the targeted information for presentation while the user is visiting the merchant application.

Abstract: An example system is configured to compile behavioral data describing past actions including products viewed and purchases made by users while visiting applications hosted by servers accessible by the users via a computer network; segment the behavioral data into clusters of behavior factors according to statistically related actions of the users; compile present user data describing a current action and a geo-location of a user while visiting a merchant application hosted by a server coupled to the computer network and accessible by the user; determine a geo-location of the mobile device of the user; while the user is still using the merchant application, compute a match; generate based on the match targeted information influenced by the geo-location of the user; and transmit the targeted information to the mobile device of the user for display prior to the user leaving the merchant application.

Abstract: In an example implementation, behavioral data describing past actions including products viewed and purchases made by users while using applications is compiled. The behavioral data is then segmented into clusters of behavior factors according to statistically related actions of the users. Present user data describing a current action of a user while using a merchant application is compiled. A comparative analysis that includes determining a match between the present user data and a cluster from the clusters of behavior factors is performed. A demand function is generated based on the match and the business rules associated with the merchant application. Targeted information is generated based on the comparative analysis. The targeted information includes a discount for the product in the virtual shopping cart. The targeted information including the discount for the product is provided to the user for presentation before the user leaves the merchant application.

Abstract: Embodiments for targeting information to a website visitor are disclosed. One method includes collecting behavioral data of a plurality of users from a plurality of websites. The collected behavioral data is analyzed. For this embodiment, analyzing the collected behavior data includes clustering the collected behavioral data according to behavioral factors wherein collected behavioral data within each cluster include at least one common statistic, and collected behavioral data of different clusters have at least one differentiating statistic. Further, a server collects present user data while a present user is visiting a target website. The present user data is matched with at least one of the clusters of behavior factors based on a comparative analysis of the present user data with the clustered behavior factors.