Routing of contacts based on predicted escalation time

• US 8,644,487 B2
• Filed: 07/20/2010
• Issued: 02/04/2014
• Est. Priority Date: 07/20/2010
• Status: Active Grant

• Current Assignee: Avaya Incorporated
• Original Assignee: Avaya Incorporated
• Inventors: Erhart, George, Matula, Valentine, Skiba, David
• Primary Examiner(s): Nguyen, Joseph J
• Application Number:
  US12/840,151
• Publication Number:
  US 20120020471A1
• Time in Patent Office: 1,295 Days
• Field of Search: 37926501-26604
• US Class Current: 379/265.01
• CPC Class Codes: H04M 2201/18 Comparators
• CPC Class Codes: H04M 2203/551 Call history recording a computer conference H04L12/1831
• CPC Class Codes: H04M 3/42068 where the identifier is used to access a profile
• CPC Class Codes: H04M 3/5232 Call distribution algorithms

Abstract

Systems and methods that employ contact escalation periods as criterion for managing routing procedures of a contact center. A prediction component can predict when a customer is likely to escalate a contact that is forwarded to a contact center, and hence facilitate resource matching based on such prediction. Accordingly, proactive and anticipatory contact interaction is enabled, wherein routing of contacts occur in-part based on predicted likelihood of escalations.

Claims

1. A system, comprising:

a storage device, coupled to a processing device, that stores computer-executable components, comprising;

a prediction component that predicts a time period associated with a likelihood of an escalation for a contact, the time period is based on a defined criterion changeable from one user to another user, wherein the escalation is an abandonment of an initial request in favor of another request requiring a first resource of the contact center that is determined to be more expensive than a second resource required by the initial request;

a routing component that routes the contact based on the likelihood of the escalation; and

an artificial intelligence component that determines the likelihood of the escalation for the contact based on an artificial intelligence model.

2. The system of claim 1, wherein the computer-executable components further comprise an analysis component that performs a cost-benefit analysis for contacts and resources of the system.

3. The system of claim 1, wherein the computer-executable components further comprise a queuing model generated based at least in part on the likelihood of the escalation for the contact.

4. The system of claim 1, wherein the escalation is a follow-up of the initial request, via a channel that is determined to be more expensive than an original channel via which the initial request is sent to the system.

5. The system of claim 1, wherein the prediction component is associated with a monitor component that monitors social networking data associated with a social network.

6. The system of claim 5, wherein the monitor component further comprises a search engine that searches queries submitted to the social network.

7. The system of claim 1, wherein the computer-executable components further comprise an evaluation component that evaluates historical data representing a history of a customer with the system, to facilitate prediction of the escalation of the contact.

8. The system of claim 7, wherein the computer-executable components further comprise an instigation component that initiates operation for the routing component.

9. The system of claim 1, wherein the artificial intelligence component determines the likelihood of the escalation of the contact based on a Markov model.

10. A method of routing a contact, comprising:

predicting, by a contact center device comprising a processor, a time for an escalation likelihood of a contact received by the contact center device via a prediction component, wherein the time period is based on a first criterion changeable from one user identity to another user identity;

employing the time for the escalation likelihood as a second criterion for resource matching by the contact center;

routing the contact based on the escalation likelihood; and

determining a percentage chance of the likelihood of the escalation of the contact based on an artificial intelligence model.

11. The method of claim 10, further comprising prioritizing the contact based on the escalation likelihood.

12. The method of claim 10, wherein the predicting is based on at least one of a subject matter of the contact, or a character trait of a customer identity generating the contact, or an environment of the contact.

13. The method of claim 10, wherein the contact is submitted to a social network data store before being received by the contact center device.

14. The method of claim 10, further comprising:

engaging in a remedial measure to appease a customer identity determined to have submitted the contact; and

prolonging an escalation period.

15. The method of claim 10, wherein the predicting is based on employing a queuing model with Markov chains.

16. The method of claim 15, further comprising updating the queuing model responsive to model update data received or determined by the contact center device.

17. The method of claim 10, further comprising matching the contact to a resource of the contact center device based on the predicting.

18. The method of claim 10, further comprising facilitating the predicting via an inference supplied by an inference component.

19. A contact center system, comprising:

means for predicting a time for an escalation likelihood of a contact, wherein the time for the escalation likelihood is employed as at least part of a model for resource matching in the contact center system and the time period is based on a criterion changeable from one user identity to another user identity;

means for routing the contact based on the escalation likelihood; and

means for determining a percentage chance of the likelihood of the escalation of the contact based on an artificial intelligence model.

20. The contact center system of claim 19, further comprising means for analyzing defined costs relative to defined benefits of contacts and resources associated with the contact center system.