Message routing in a contact center

1. A method of managing contacts in a contact center, the method comprising:

receiving a communication from a customer of the contact center;

analyzing the communication to determine a relevancy associated with the communication;

tagging the communication with a relevancy tag that identifies the determined relevancy;

assigning the communication to an agent of the contact center;

storing the communication with the relevancy tag in a database of communications, wherein the communication is also stored with at least one agent note associated therewith;

enabling a machine learning process to analyze the database of communications; and

updating a data model used to automatically tag communications with relevancy tags based on the analysis performed by the machine learning process.

2. The method of claim 1 , wherein the communication comprises a text-based communication from the customer to the contact center.

3. The method of claim 2 , wherein the text-based communication from the customer to the contact center comprises an email.

4. The method of claim 1 , wherein the at least one agent note comprises an affirmation of the agent that the relevancy tag is accurate.

5. The method of claim 1 , wherein the at least one agent note comprises a change of the agent applied to the relevancy tag.

6. The method of claim 1 , wherein the machine learning process performs a semantic and/or syntactic analysis of text contained in the communication.

7. The method of claim 1 , further comprising:

updating a second data model used to automatically tag communications other than text-based communications based on the analysis performed by the machine learning process.

8. The method of claim 1 , further comprising:

receiving a second communication from another customer of the contact center; and

automatically tagging the second communication with a second relevancy tag that identifies a relevancy level determined by the data model.

9. A method, comprising:

receiving a communication associated with a customer of a contact center;

analyzing the communication to determine a relevancy associated with the communication;

tagging the communication with a tag that identifies the determined relevancy;

updating a priority associated with assigning the communication to an agent of the contact center based at least in part on the relevancy tag;

storing the communication with the tag in a database of communications;

enabling a machine learning process to analyze the database of communications; and

updating a data model used to automatically tag communications with tags based on the analysis performed by the machine learning process.

10. The method of claim 9 , wherein the communication is also stored with at least post-interaction data structure associated therewith.

11. The method of claim 9 , wherein the communication comprises a text-based communication.

12. The method of claim 9 , further comprising:

routing the communication based on the determined relevancy associated with the communication.

13. The method of claim 9 , wherein the communication comprises a voice communication.

14. The method of claim 9 , wherein the communication comprises a video communication.

15. A system, comprising:

a processor; and

a memory coupled to the processor and storing data thereon that, when executed by the processor, enable the processor to:

receive a communication associated with a customer of a contact center;

analyze the communication to determine a relevancy associated with the communication;

tag the communication with a tag that identifies the determined relevancy;

update a priority associated with assigning the communication to an agent of the contact center based at least in part on the relevancy tag;

store the communication with the tag in a database of communications;

enable a machine learning process to analyze the database of communications; and

update a data model used to automatically tag communications with tags based on the analysis performed by the machine learning process.

16. The system of claim 15 , wherein the communication is also stored with at least post-interaction data structure associated therewith.

17. The system of claim 15 , wherein the communication comprises a text-based communication.

18. The system of claim 15 , wherein the data, when executed by the processor, further enable the processor to:

route the communication based on the determined relevancy associated with the communication.

19. The system of claim 15 , wherein the communication comprises a voice communication.

20. The system of claim 15 , wherein the communication comprises a video communication.

21. A system, comprising:

a processor; and

a memory coupled to the processor and storing data thereon, wherein the data, when processed by the processor, enable the processor to:

receive a communication from a customer of a contact center;

analyze the communication to determine a relevancy associated with the communication;

tag the communication with a relevancy tag that identifies the determined relevancy;

assign the communication to an agent of the contact center;

store the communication with the relevancy tag in a database of communications, wherein the communication is also stored with at least one agent note associated therewith; and

update a data model used to automatically tag communications with relevancy tags based on an analysis of the database performed by a machine learning process.

22. The system of claim 21 , wherein the communication comprises a text-based communication from the customer to the contact center.

23. The system of claim 22 , wherein the text-based communication from the customer to the contact center comprises an email.

24. The system of claim 21 , wherein the at least one agent note comprises an affirmation of the agent that the relevancy tag is accurate.

25. The system of claim 21 , wherein the at least one agent note comprises a change of the agent applied to the relevancy tag.

26. The system of claim 21 , wherein the machine learning process performs a semantic and/or syntactic analysis of text contained in the communication.

27. The system of claim 21 , wherein the data, when processed by the processor, further enable the processor to:

update a second data model used to automatically tag communications other than text-based communications based on the analysis performed by the machine learning process.

28. The system of claim 21 , wherein the data, when processed by the processor, further enable the processor to:

receive a second communication from another customer of the contact center; and

automatically tag the second communication with a second relevancy tag that identifies a relevancy level determined by the data model.

29. A contact center, comprising:

a server including a processor and a message routing engine that is executable by the processor and that enables the processor to:

receive a communication associated with a customer of the contact center;

analyze the communication to determine a relevancy associated with the communication;

tag the communication with a tag that identifies the determined relevancy;

update a priority associated with assigning the communication to an agent of the contact center based at least in part on the relevancy tag;

store the communication with the tag in a database of communications;

enable a machine learning process to analyze the database of communications; and

update a data model used to automatically tag communications with tags based on the analysis performed by the machine learning process.

30. The contact center of claim 29 , wherein the communication is also stored with at least post-interaction data structure associated therewith.

31. The contact center of claim 29 , wherein the communication comprises a text-based communication.

32. The contact center of claim 29 , wherein the processor is further enabled to route the communication based on the determined relevancy associated with the communication.

33. The contact center of claim 29 , wherein the communication comprises a voice communication.