Computer-based systems involving machine learning associated with generation of recommended content and methods of use thereof

A computer-implemented method comprising:

• training, by at least one computer, a natural language generation (NLG) machine learning model on customer data derived from a plurality of customers, the customer data comprising content types, audiences, message types, and conversion rates associated with messages previously transmitted to the plurality of customers;
• receiving, by the at least one computer, first data comprising content preferences associated with an audience that comprises a subset of the plurality of customers, the content preferences comprising a likelihood of engagement with content types and a likelihood of engagement with message types;
• receiving, by the at least one computer, initial message data comprising an initial digital message being proposed for transmission to the audience;
• generating, by the at least one computer, a recommendation data set based at least in part on the content preferences and the initial message data, wherein the recommendation data set identifies at least one recommended content type and at least one recommended message type;
• identifying, by the at least one computer, a repository of content messages that comprises a plurality of content messages, wherein the messages in the repository of content messages are categorized into a plurality of message sentiment categories based on scored conversion events associated with the plurality of sentiments;
• analyzing, by the at least one computer, via the NLG machine learning model, the repository of content messages based at least in part on the recommendation data set to map the content preferences of the audience to content messages based on one or more correlations between the recommendation data set and one or more content messages in the repository of content messages;
• generating, by the at least one computer, via the NLG machine learning model and using content messages identified in the analysis that correlate with the recommendation data set as inputs to the NLG machine learning model, a tailored digital message that comprises one or more suggested content messages having one or both of a suggested message type and a suggested message language;
• providing, by the at least one computer, via an application program interface (API), the tailored digital message to at least one computing device for transmission to the audience;
• and repeating, by the at least one computer, training of the NLG machine learning model based on feedback data relating to customer engagement with the tailored digital message.