Escalation management and journey mining

A method for identifying remedial action for a shared timeline event within a contact center environment among one or more customers, the method comprising:

• accessing, by a computing device, a first customer timeline stored in a database, wherein the first customer timeline includes a first plurality of events, and wherein each event comprises a timestamp, a specified type, and at least one customer associated with it;
• accessing, by the computing device, a second customer timeline stored in the database, wherein the second customer timeline includes a second plurality of events, and wherein each event comprises a timestamp, a specified type, and at least one customer associated with it;
• displaying, by the computing device, the first customer timeline together with the second customer timeline on a graphical user interface;
• comparing, by the computing device, the first customer timeline with the second customer timeline to identify a shared event that is common to the first customer timeline and the second customer timeline;
• connecting, by the computing device, the shared event common to the first customer timeline and the second customer timeline by specifying shared customers and the relative position in time on the respective timelines via the timestamp associated with the event;
• analyzing, by the computing device, the shared event to determine whether a pattern in the first customer timeline and the second customer timeline led to the shared event, wherein the analyzing further comprises:
• investigating each customer history and transcripts, and
• applying natural language processing to the transcripts to identify contextual data;
• identifying, by an escalation management engine of the computing device, the shared event as a problematic shared event as a function of a machine learning model of a machine learning system, wherein the machine learning model continually learns statistical characteristics of events and event patterns denoted as problematic;
• identifying and outputting, by the escalation management engine of the computing device, a remedial action configured to address a cause of a remedial issue associated with the problematic shared event;
• applying, by the escalation management engine of the computing device, the remedial action configured to address the cause of the remedial issue associated with the problematic shared event, wherein the applying comprises identifying opportunities to guide an interaction towards a better outcome by creating deliverables from the shared timeline event;
• and
• improving an accuracy of the machine learning model by incorporating into the machine learning model interactions provided via the graphical user interface with an output of the machine learning model, including a determination of an impact of the problematic shared event, wherein the impact is representative of a quantity of the customers that experienced the problematic shared event and an effect of the problematic shared event on one or more key performance indicators, to reinforce accurate identification, by the machine learning model, of problematic events and suppress future inaccuracies by the machine learning model.