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. The system can also infer and respond to changes in customer call center usage.
1 . A system for an optimized, self-learning and self-organizing contact center comprising:
a topic based routing module stored in a memory of and operating on a processor of a computing device;
an interaction information optimization module stored in a memory of and operating on a processor of a computing device; and
wherein the topic based routing module:
(a) receives requests for information or assistance by a plurality of means;
(b) infers the topic distribution of the request based upon information theory based algorithms;
(c) routes the request to the contact center resource best suited to respond to the request; and
wherein the interaction information optimization module:
(d) monitors all communication in to and out of the contact center;
(e) analyzes communication streams for all topics and topic related identifiers;
(f) creates optimized relationships between identifiers present in analyzed communications and business value related topics attached to contact center resources using information theory algorithms and machine learning.
2 . The system of claim 1 , wherein the interaction information is optimized using information distance calculations.
3 . The system of claim 1 , wherein an information theory algorithm used may be based upon the latent Dirichlet allocation to infer hidden generative data in support of interactive information optimization.
4 . The system of claim 1 , wherein the topic related identifiers may be categorical or numerical.
5 . A method for an optimized, self-learning and self-organizing contact center, the method comprising the steps of:
(a) receiving requests from customer interactive devices of an enterprise to that enterprise's contact center's interactive devices;
(b) analyzing those requests for topic information and topic related identifiers;
(c) routing each request to the contact center resource best suited to respond to it;
(d) monitoring all communications into and out of the contact center continuously;
(e) applying information theory algorithms and machine learning to optimize business value based upon information exchange, information distance and identifier to topic matching between incoming requests and outgoing responses.
6 . The method of claim 5 , wherein the business value information is optimized using information distance calculations.
7 . The method in claim 5 , wherein at least one information theory algorithm is based upon the latent Dirichlet allocation to infer hidden generative data in support of business value information optimization.
8 . The method of claim 5 , wherein the topic related identifiers may be categorical or numerical.