Machine learning systems and methods to corroborate and forecast user experience issues on web or mobile applications utilizing user verbatim, machine logs and user interface interaction analytics

A method, comprising:

• generating structured customer feedback data from unstructured customer feedback data received over a predefined period of time;
• retrieve usage analytics and system logs associated with the structured customer feedback data;
• comparing, using machine learning usage analytics data modeling, the usage analytics and the system logs for the predefined period of time with the structured customer feedback data for the predefined period of time to predict a probable customer issue, wherein the probable customer issue is identified based on both the structured customer feedback data and the usage analytics and the system logs for the predefined period of time;
• automatically generating one or more action items based on a prediction of the probable customer issue;
• and
• determining an appropriate recipient each action items of the one or more action items.

One or more non-transitory computer-readable media storing instructions thereon, wherein the instructions cause one or more processors to perform operations comprising:

• generating structured customer feedback data from unstructured customer feedback data received over a predefined period of time;
• retrieve usage analytics and system logs associated with the structured customer feedback data;
• comparing, using machine learning usage analytics data modeling, the usage analytics and the system logs for the predefined period of time with the structured customer feedback data for the predefined period of time to predict a probable customer issue, wherein the probable customer issue is identified based on both the structured customer feedback data and the usage analytics and the system logs for the predefined period of time;
• automatically generating one or more action items based on a prediction of the probable customer issue;
• and
• determining an appropriate recipient each action items of the one or more action items.