Abstract: Systems and methods associated with generation and/or provision of predictive content are disclosed. One exemplary method includes receiving communications associated with a plurality of customers; determining a message type for each message of the communications; splitting first messages of the first message type into a first set of subcomponent text sections; splitting second messages of the second message type into a second set of subcomponent text sections; analyzing the first set and the second set to generate a plurality of semantic numerical scores for each respective subcomponent text section; determining at least one impactful semantic category for a target audience by selecting at least one semantic category corresponding to at least one semantic numerical score of at least one subcomponent text section of the first set or the second set that is equal to or higher than a first pre-determined threshold value; generating personalized textual content targeting the audience.

Abstract: Systems and methods associated with providing content personalization are disclosed. In one embodiment, an exemplary method may comprise receiving first data including content preferences associated with an audience, receiving second data including an initial digital message being proposed for transmission to the audience, generating a recommendation data set based at least in part on the first data and the second data, wherein the recommendation data set identifies at least one recommended content type and at least one recommended message type, determining via a natural language generation machine learning model suggested content for the audience, and providing the suggested content for dissemination to the audience.

Abstract: Systems and methods associated with generation and/or provision of predictive content are disclosed. One exemplary method includes receiving communications associated with a plurality of customers; determining a message type for each message of the communications; splitting first messages of the first message type into a first set of subcomponent text sections; splitting second messages of the second message type into a second set of subcomponent text sections; analyzing the first set and the second set to generate a plurality of semantic numerical scores for each respective subcomponent text section; determining at least one impactful semantic category for a target audience by selecting at least one semantic category corresponding to at least one semantic numerical score of at least one subcomponent text section of the first set or the second set that is equal to or higher than a first pre-determined threshold value; generating personalized textual content targeting the audience.