System and method for an optimized, self-learning and self-organizing contact center
A system and method for an optimized, self-learning and self-organizing contact center has been developed. This system and method uses principles and tools of information theory, including the latent Dirichlet allocation which reduces information to specific predetermined topics and a distribution of topic related words to infer its hidden, generative underpinnings so to self-organize a contact center, infer its desired electronic versus human make up, and optimally route all customer requests to an electronic resource or a specific human agent best suited to respond to the request for maximal business value per interaction.
1. A self-learning and self-organizing contact center routing system comprising:
a topic-based routing module stored in a memory of and operating on a processor of a computing device; and
an interaction information optimization module stored in a memory of and operating on a processor of a computing device;
wherein the interaction information optimization module:
continuously monitors all communications into and out of the contact center;
for each incoming communication, analyzes the incoming communication using probabilistic models to identify any topics and hidden variables within the incoming communication that led a contact center customer to initiate the incoming communication;
for each outgoing communication, identifies the outgoing communication in response to a particular incoming communication;
determines an effectiveness of a response to each incoming communication by comparing the topics and hidden variables identified from the analysis of the incoming communication with a plurality of topics and hidden variables identified by similar analyses of corresponding outgoing communications for each respective incoming communication; and
ranks each human agent in the contact center based on the agent's knowledge of, experience with, and determined effectiveness for each identified topic and hidden variable; and
wherein the topic-based routing module:
receives a text request for assistance from a user via a network;
automatically identifies a specific human agent best suited to service the request based on the rankings from the interaction information optimization module pertaining to a topic and a hidden variable derived from the text request; and
automatically routes the request directly to the specific human agent.
2. A method for self-learning and self-optimizing contact center routing, comprising the steps of:
(a) continuously monitoring, at an interaction information optimization module stored in a memory of and operating on a processor of a computing device, all communications into and out of a contact center;
(b) analyzing each incoming communication to the contact center using probabilistic models to identify any topics and hidden variables within the communication that may have led a contact center customer to initiate the incoming communication on those topics;
(c) determining an effectiveness of a response to each incoming communication by comparing information obtained from the analysis of the incoming communication with a plurality of topics and hidden variables identified by similar analyses of corresponding outgoing communications for each respective incoming communication;
(d) ranking each human agent in the contact center based on the agent's knowledge of, experience with, and determined effectiveness for each identified topic and hidden variable;
(e) receiving, at a topic-based routing module stored in a memory of and operating on a processor of a computing device, a text request for assistance from a user via a network;
(f) automatically identifying a specific human agent best suited to service the request based on the rankings pertaining to a topic and a hidden variable derived from the text request; and
(g) automatically routing the request directly to the specific human agent best suited to service the request.