Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server for electronic requests routing and distribution. The server receives a plurality of requests from a plurality of electronic user devices. Aiming to routing the plurality of requests to appropriate agents, the server trains an artificial intelligence model for each agent based on historical data. For each request, the server executes the artificial intelligence model to determine a score indicating the probability of the agent converting the request to a successful sale. The server determines an entropy value for each request based on the scores and order the requests into a queue based on the entropy values. The server also calculates a capacity for each agent based on historical agent data. For each request in the queue, the server routes the request to an agent based on at least one of the score and capacity of the agent.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server for electronic requests routing and distribution. The server receives a plurality of requests from a plurality of electronic user devices. Aiming to routing the plurality of requests to appropriate agents, the server trains an artificial intelligence model for each agent based on historical data. For each request, the server executes the artificial intelligence model to determine a score indicating the probability of the agent converting the request to a successful sale. The server determines an entropy value for each request based on the scores and order the requests into a queue based on the entropy values. The server also calculates a capacity for each agent based on historical agent data. For each request in the queue, the server routes the request to an agent based on at least one of the score and capacity of the agent.

Abstract: A processor-based system and method retrieve customer purchase history information from an internal customer purchase history database for a plurality of customer records representing customers that previously purchased products of an enterprise, and retrieve customer profile information for each customer record. The processor executes a predictive machine learning model to determine a set of product purchase scores for each of the customers by applying a logistic regression model utilizing gradient boosting to the customer purchase history information and the customer profile information. The processor classifies the customers into a target customer group and a non-target customer group by applying a classification criterion to the set of product purchase scores, and generates a report of customers in the target customer group including highest product purchase scores and products recommended for cross-sale.

Abstract: A processor-based system and method retrieve customer purchase history information from an internal customer purchase history database for a plurality of customer records representing customers that previously purchased products of an enterprise, and retrieve customer profile information for each customer record. The processor executes a predictive machine learning model to determine a set of product purchase scores for each of the customers by applying a logistic regression model utilizing gradient boosting to the customer purchase history information and the customer profile information. The processor classifies the customers into a target customer group and a non-target customer group by applying a classification criterion to the set of product purchase scores, and generates a report of customers in the target customer group including highest product purchase scores and products recommended for cross-sale.

Abstract: A processor-based system and method retrieve customer purchase history information from an internal customer purchase history database for a plurality of customer records representing customers that previously purchased products of an enterprise, and retrieve customer profile information for each customer record. The processor executes a predictive machine learning model to determine a set of product purchase scores for each of the customers by applying a logistic regression model utilizing gradient boosting to the customer purchase history information and the customer profile information. The processor classifies the customers into a target customer group and a non-target customer group by applying a classification criterion to the set of product purchase scores, and generates a report of customers in the target customer group including highest product purchase scores and products recommended for cross-sale.