System and method for integrating and automatedly executing customer service representative resource tools

A method for completing a real-time interaction using machine learning to predict analytics rules for the real-time interaction, comprising:

• receiving a real-time interaction and associated interaction metadata in real-time;
• populating a real-time interaction data structure with the real-time interaction and associated interaction metadata, where the real-time interaction data structure includes a plurality of fields, including an interaction data field, an internal contact data field, and customer identification data fields;
• performing text analytics or voice analytics on the real-time interaction data structure;
• generating a customer sentiment based on at least one of the plurality of fields in the real-time interaction data structure and the text analytics or voice analytics;
• inputting the real-time interaction data structure and generated customer sentiment into a trained model to predict at least one analytics rule to be applied to the real-time interaction, wherein:
• the at least one analytics rule identifies one or more computerized resource modules of a plurality of computerized resource modules to automatedly execute in real-time while completing the real-time interaction, further wherein the plurality of computerized resource modules are computerized applications,
• the at least one analytics rule is a complex analytics rule corresponding to at least two of the following:
• a customer tier value, a customer sentiment, a customer feedback score, a type of interaction, a computerized resource module rating, or a set of keywords, and
• a category of the complex analytics rule indicates a mobile phone issue for a given customer tier value, a given customer sentiment, and a negative customer feedback score, and in response, the complex analytics rule indicates to provide a customer service representative with an electronic resource to identify and fix the mobile phone issue;
• applying the at least one analytics rule predicted by the trained model to the real-time interaction data structure and the generated customer sentiment;
• executing, by a central analytics engine, in real-time, the one or more computerized resource modules identified by the at least one analytics rule to complete the real-time interaction;
• generating a success indicator for each computerized resource module used to complete the real-time interaction, wherein the success indicator includes a degree of success associated with using a computerized resource module for the corresponding real-time interaction data structure;
• monitoring for the real-time interaction and automatically continually updating at least one model when the real-time interaction data structure is received based on analysis of the real-time interaction data structure such that the at least one model includes the real-time interaction data structure to create analytics rules;
• and
• using the updated at least one model to create at least one new analytics rule.

A system for machine learning to generate analytics rules for predicting one or more computerized resource modules for use in completing an interaction, comprising:

• a memory comprising computer readable instructions;
• a processor configured to read the computer readable instructions that when executed causes the system to:
• receive a plurality of historic interactions and interaction metadata as interaction data structures, each interaction data structure including a plurality of fields, including an interaction data field, an internal contact data field, customer identification data fields, and a representative electronic resource data field listing computerized resource modules used by a customer service representative associated with the internal contact data field for a historic interaction;
• perform agent desktop use analytics and text analytics or voice analytics on each interaction data structure, wherein the agent desktop use analytics include monitoring use of agent desktops, and receiving a set of data pertaining to use of agent desktops associated with each interaction data structure of the interaction data structures;
• generate a customer sentiment based on the agent desktop use analytics, at least one of the plurality of fields in an interaction data structure, and the text analytics or voice analytics;
• generate a success indicator for each computerized resource module included in the representative electronic resource data field based on the customer sentiment, the agent desktop use analytics, at least one of the plurality of fields in the interaction data structure, and voice analytics or text analytics, wherein the success indicator includes a degree of success associated with using a computerized resource module for the corresponding interaction data structure;
• generate at least one model configured to create at least one analytics rule using machine learning techniques based on the interaction data structures, generated customer sentiment, and generated success indicator, wherein the at least one analytics rule causes at least one of a plurality of computerized resource modules to be selected to be accessed by a central analytics engine in connection with receipt of a new interaction, further wherein the plurality of computerized resource modules are computerized applications;
• use the at least one model to create at least one analytics rule,
• wherein the at least one analytics rule is a complex analytics rule corresponding to at least two of the following:
• a customer tier value, a customer sentiment, a customer feedback score, a type of interaction, a computerized resource module rating, or a set of keywords, and
• wherein a category of the complex analytics rule indicates a mobile phone issue for a high-tier customer tier value and an unfavorable customer sentiment, and in response, the complex analytics rule indicates to provide an open scheduling electronic resource for a nearest shop for scheduling of an appointment;
• monitor the system for new interaction data structures and automatedly continually updating the at least one model when a new interaction data structure is received based on analysis of interaction data structures such that the at least one model includes the new interaction data structure when creating analytics rules;
• and
• use the updated at least one model to create at least one new analytics rule.