Fully automated customer targeting algorithm optimized to maximize commercial value using machine learning methods

A computer-implemented system for targeted advertising to a customer comprising:

• a memory storing instructions;
• and
• at least one processor configured to execute the instructions to:
• receive, at a first server, customer action information associated with the customer;
• receive a plurality of advertising campaigns;
• generate, using a first algorithm, a list of products derived from the customer action information that the customer may have interest in over a first time period;
• generate, using a second algorithm, a ranked list of product categories derived from the customer action information that, if purchased by the customer, would generate a highest amount of revenue over a second time period;
• and
• send a first communication associated with a first advertising campaign of the plurality of advertising campaigns to a customer device, the first advertising campaign chosen by a third algorithm that incorporates, as input, the list of products and the ranked list of product categories;
• generate a ranked list of at least two advertising campaigns of the plurality of advertising campaigns chosen by the third algorithm that incorporates, as input, the list of products and the ranked list of product categories;
• wherein the first algorithm and the second algorithm are based on artificial intelligence or machine learning models;
• wherein the second algorithm outputs a solution based on either random forest machine learning or deep machine learning depending on model accuracy measurements.

A computer-implemented system for targeted advertising to a customer comprising:

• a memory storing instructions;
• and
• at least one processor configured to execute the instructions to:
• receive, at a first server, customer action information associated with the customer;
• receive, a plurality of advertising campaigns;
• generate, using a first algorithm based on artificial intelligence and/or machine learning models, a list of products derived from the customer action information that the customer may have interest in over a first time period;
• generate, using a second algorithm, a ranked list of product categories derived from the customer action information that, if purchased by the customer, would generate a highest amount of revenue over a second time period, the second algorithm based on artificial intelligence and/or machine learning models;
• generate a ranked list of at least two advertising campaigns of the plurality of advertising campaigns chosen by a third algorithm that incorporates, as input, the list of products and the ranked list of product categories, the ranked list of at least two advertising campaigns including the first advertising campaign as being ranked first and a second advertising campaign as being ranked second;
• send a first communication associated with a first advertising campaign of the plurality of advertising campaigns to a customer device, the first advertising campaign chosen by the third algorithm that incorporates, as input, the list of products and the ranked list of product categories;
• and
• send a second communication associated with the second advertising campaign to the customer device at a time later than the sending of the first communication associated with the first advertising campaign;
• wherein the list of products is generated by the first algorithm from a first subset of the customer action information, the first subset being information that specifically corresponds to the customer;
• wherein the ranked list of product categories is generated by the second algorithm from a second subset of the customer action information, the second subset being information that specifically corresponds to the customer and a group of customers to which the customer belongs.