Abstract: According to at least one embodiment, a method for providing secondary content, related to primary content, for targeted transmission, includes: receiving a set of user metadata for a plurality of users, the user metadata comprising one or more of user browsing history, purchase history, term usage history, social media posts and actions, or location information; based on the set of user metadata, identifying, via a machine learning model, a subset of the users having an affinity for purchasing digital home-entertainment content, wherein the affinity is above a threshold level; and providing an indication of the subset of users having the affinity above the threshold level.

Abstract: According to at least one embodiment, a method for providing secondary content, related to primary content, for targeted transmission, includes: receiving a set of user metadata for a plurality of users, the user metadata comprising one or more of user browsing history, purchase history, term usage history, social media posts and actions, or location information; based on the set of user metadata, identifying, via a machine learning model, a subset of the users having an affinity for purchasing digital home-entertainment content, wherein the affinity is above a threshold level; and providing an indication of the subset of users having the affinity above the threshold level.

Abstract: Machine readable instructions for providing targeted advertisements for a piece of digital content receive a set of user metadata for a group of users. The machine readable instructions select at least one user from the group of users. In addition, the machine readable instructions select a term from a category of terms related to the piece of digital content. Moreover, the machine readable instructions determine a first value corresponding to the at least one user. Also, the machine readable instructions determine a second for the group of users. In addition, the machine readable instructions determine a user score based at least in part on the first value and the second value. When the user score is within a particular range, the machine readable instructions provide an advertisement for the piece of digital content to an electronic device associated with the at least one user.

Abstract: To determine pricing recommendations for goods and service products in a business-to-business environment, a set of transaction data corresponding to a set of products are processed to generate a set of pricing recommendations optimized according to an objective. Furthermore, a set of product segments may be determined, transaction data may be associated with one or more of the product segments and a demand model and associated price elasticity may be formulated for one or more of the product segments based upon the transaction data associated with the segment. Using these formulated price elasticities, pricing recommendations for each product may be determined for each of a set of customers. Using an optimization process, price elasticities are used to determine price dependent entity goals for any combination of products, customers and sets of prices, using a mathematical objective function.