Software that utilizes cognitive services to analyze proposed communications and determine their predicted acceptance by a target audience. The software performs the following operations: (i) receiving a communication from a sender; (ii) determining a demography of a target audience for the communication using natural language processing; (iii) analyzing a set of data sources to determine a predicted amount of acceptance of the communication by the target audience based, at least in part, on the target audience's determined demography; and (iv) identifying a set of adjustments to the communication based, at least in part, on a predicted amount of improvement to the predicted amount of acceptance of the communication by the target audience, wherein the set of adjustments utilizes one or more synonyms to replace one or more words in the communication.

Software that utilizes cognitive services to analyze proposed communications and determine their predicted acceptance by a target audience. The software performs the following operations: (i) receiving a communication from a sender; (ii) determining a demography of a target audience for the communication using natural language processing; (iii) analyzing a set of data sources to determine a predicted amount of acceptance of the communication by the target audience based, at least in part, on the target audience's determined demography; and (iv) identifying a set of adjustments to the communication based, at least in part, on a predicted amount of improvement to the predicted amount of acceptance of the communication by the target audience, wherein the set of adjustments utilizes one or more synonyms to replace one or more words in the communication.

Systems and methods of dynamically varying the intensity of providing content items in a remarketing campaign based on tracking client device interactions are provided. The system can assign an account identifier to a first segment for a pre-conversion model, responsive to receiving a first interaction associated with a content provider from a client device. The system can assign the account identifier to a second segment for the pre-conversion model, responsive to receiving a second interaction. The system can assign the account identifier to a third segment, responsive to receiving a third interaction. The third interaction can include a conversion event. The system can generate a post-conversion model based on the third segment and the pre-conversion model. The system can determine an intent index for the account identifier based on the post-conversion model. The system can store the account identifier into an interest cluster based on the intent index.

A system that analyzes a user's communications to select a promotion that is presented to the user. The analysis may occur in two stages: a first stage analyzes a single communication from a user to determine whether the user is a potential target for a promotion; for potential targets, a second stage analyzes a history of communications from the user to generate a user profile. The system may then select a promotion based on the profile. The profile may include a set of profile tags that are considerably more detailed and granular than traditional demographic data; tags may for example indicate user affiliations with groups or ideas (such as religions or political parties), or user life cycle stages. Using these rich, detailed user profile tags, the system may achieve promotion response rates far above those from traditional advertising, which relies on cookies or simple demographic categories.

Systems and methods for determining whether and how to present digital animations in a user interface are disclosed. According to certain embodiments, the systems and methods may facilitate the identification of a set of products or services purchased by an individual, and the determination of a reward level associated with the set of products or services. The systems and methods may select a digital animation, from a set of digital animations that is predetermined based on a set of probabilities, corresponding to the reward level, and present the digital animation in a user interface.

A system includes a set of crawlers that find and retrieve documents from an information network, an information extraction system, a knowledge graph storing nodes and edges that connect them, wherein each node represents a respective entity of a corresponding entity type of a plurality of entity types, and wherein the knowledge graph further stores event data relating to events detected by the information extraction system, a machine learning system that trains models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance.

Systems and methods for automatically soliciting the purchase of a first or second machine-related resource in a forward market, wherein the first resource and the second resource are distinct instances of the same type of resource, are described. A sample system may include a fleet of machines, each having a resource requirement comprising at least two of: a compute resource, a spectrum resource, or a network bandwidth resource. The system may include an circuits to aggregate data corresponding to the machine-related resources from at least a behavioral data source, to determine a substitution cost of a second resource; to determine a machine-related resource acquisition value; and to automatically solicit a purchase, in a forward market, of one of the first resource or the second resource in response to the determined substitution cost of the second resource.

The present disclosure is directed to models for predicting customer behavior, including the use or adoption of products by current customers and prospective customers of a service platform offering multiple service products.

Embodiments of a method for automatically generating, scheduling, posting, and recycling social media posts are disclosed, the method comprising: receiving instructions to publish social media posts on a plurality of social media platforms; for each social media platform in the plurality, generating, using an artificial intelligence (AI) engine, a first rule for content in the social media posts and a second rule for a publishing schedule; automatically generating, using the AI engine, a social media post according to the first rule and a publishing schedule for the generated social media post according to the second rule; publishing the social media post on the respective social media platform according to the respective publishing schedule; monitoring post insights on the published social media post; and retraining the AI engine with the post insights.

Some implementations relate to a computer-implemented method that includes identifying candidate content items from a set of eligible content items. The computer-implemented method further includes assigning a corresponding rank to each of the candidate content items using an objective function that mitigates a popularity bias among the candidate content items. The computer-implemented method further includes determining an impression-distribution mix of the ranked candidate content items. The computer-implemented method further includes causing one or more of the ranked candidate content items to be displayed based on the impression-distribution mix.