Analytics feedback and routing

• US 8,718,267 B2
• Filed: 09/30/2011
• Issued: 05/06/2014
• Est. Priority Date: 09/30/2011
• Status: Active Grant

• Current Assignee: Avaya Incorporated
• Original Assignee: Avaya Incorporated
• Inventors: Flockhart, Andrew D., Kohler, Joylee, Steiner, Robert C.
• Primary Examiner(s): Hong, Harry S
• Application Number:
  US13/250,718
• Publication Number:
  US 20130083916A1
• Time in Patent Office: 949 Days
• Field of Search: 37926501-26514
• US Class Current: 379/265.06
• CPC Class Codes: H04M 2203/402 Agent or workforce management
• CPC Class Codes: H04M 3/5232 Call distribution algorithms
• CPC Class Codes: H04M 3/5238 with waiting time or load prediction arrangements

Abstract

A contact center is described along with various methods and mechanisms for administering the same. The contact center proposed herein provides the ability to, among other things, dynamically and in real-time utilize contact center analytics feedback mechanisms to adjust parameters that are used in making work assignment decisions. The adjusted parameters may correspond to Key Performance Indicators (KPIs) for agents of the contact center rather than skill values for the agents.

Claims

1. A method of assisting routing behavior in a contact center, the method comprising:

receiving, at an analytics engine in the contact center, a set of performance indicator inputs that describe different aspects of resource performance in at least one of real-time and near-real-time;

receiving, at the analytics engine, one or more business rules that define a desired performance of the contact center;

based on the one or more business rules, selecting a subset of performance indicators from the set of performance indicator inputs;

utilizing the subset of performance indicators as matching parameters; and

assigning at least one work item in the contact center to at least one resource in the contact center based on both the at least one work item in the contact center and at least one resource satisfying the matching parameters.

2. The method of claim 1, wherein at least one matching parameter corresponds to a normalized performance indicator in the subset of performance indicators.

3. The method of claim 1, wherein the subset of performance indicators are used to generate a bitmap that defines criteria for assigning work items from a work item pool to resources in a resource pool.

4. The method of claim 3, wherein the work item pool comprises a data instance for each work item in the contact center, wherein the resource pool comprises a data instance for each resource in the contact center, and wherein the assigning step comprises an analysis of every work item in the work item pool as well as an analysis of every resource in the resource pool.

5. The method of claim 1, wherein the set of performance indicators correspond to real-time performance indicators that describe at least one of an efficiency and effectiveness of a human agent in the contact center.

6. The method of claim 5, wherein the set of performance indicators comprise at least one of the following:

First Call Resolution, $/contact, $/min, Profit/contact, Profit/min, Average Handle Time, Abandon Rate, agent preference, closure rate, quality score, commit to pay rate, revenue/target, and customer satisfaction.

7. The method of claim 1, wherein the one or more business rules comprise at least one of a maximization, minimization, and near-to function.

8. The method of claim 1, wherein a first subset of performance indicators is selected for a first business rule and wherein a second subset of performance indicators is selected for a second business rule.

9. The method of claim 1, wherein the set of performance indicator inputs comprise customer feedback information, real-time agent performance information, and routing information.

10. A non-transitory computer readable medium having stored thereon instructions that cause a computing system to execute a method, the instructions comprising:

instructions configured to receive a set of performance indicator inputs that describe different aspects of contact center resource performance in at least one of real-time and near-real-time;

instructions configured to receive one or more business rules that define a desired performance of the contact center;

instructions configured to select, based on the one or more business rules, a subset of performance indicators from the set of performance indicator inputs;

instructions configured to generate one or more bitmaps based on the subset of performance indicators; and

instructions configured to assign at least one work item in the contact center to at least one resource in the contact center by referencing the one or more bitmaps.

11. The computer readable medium of claim 10, wherein the subset of performance indicators are normalized prior to generating the one or more bitmaps.

12. The computer readable medium of claim 10, wherein the one or more bitmaps define criteria for assigning work items from a work item pool to resources in a resource pool.

13. The computer readable medium of claim 12, wherein the work item pool comprises a data instance for each work item in the contact center, wherein the resource pool comprises a data instance for each resource in the contact center, and wherein the assigning step comprises an analysis of every work item in the work item pool as well as an analysis of every resource in the resource pool.

14. The computer readable medium of claim 10, wherein the set of performance indicators correspond to real-time performance indicators that describe at least one of an efficiency and effectiveness of a human agent in the contact center.

15. The computer readable medium of claim 13, wherein the set of performance indicators comprise at least one of the following:

First Call Resolution, $/contact, $/min, Profit/contact, Profit/min, Average Handle Time, Abandon Rate, agent preference, and customer satisfaction.

16. The computer readable medium of claim 10, wherein the one or more business rules comprise at least one of a maximization, minimization, and near-to function.

17. A contact center, comprising:

an analytics engine configured to receive a set of performance indicator inputs that describe different aspects of contact center resource performance in at least one of real-time and near-real-time, receive one or more business rules that define a desired performance of the contact center, and select, based on the one or more business rules, a subset of performance indicators from the set of performance indicator inputs; and

a work assignment engine configured to receive the subset of performance indicators and then assign work items received at the contact center to resources of the contact center by generating one or more bitmaps based on the matching parameters the subset of performance indicators.

18. The contact center of claim 17, wherein the subset of performance indicators are normalized prior to being provided to the work assignment engine.

19. The contact center of claim 17, wherein the one or more bitmaps define criteria for assigning work items from a work item pool to resources in a resource pool, wherein the work item pool comprises a data instance for each work item in the contact center, wherein the resource pool comprises a data instance for each resource in the contact center, and wherein the assigning step comprises an analysis of every work item in the work item pool as well as an analysis of every resource in the resource pool.

20. The contact center of claim 17, wherein the set of performance indicators comprise at least one of the following:

First Call Resolution, $/contact, $/min, Profit/contact, Profit/min, Average Handle Time, Abandon Rate, closure rate, quality score, commit to pay rate, revenue/target, agent preference, and customer satisfaction, wherein the one or more business rules comprise at least one of a maximization, minimization, and near-to function, and wherein different subsets of performance indicators are selected for the subset of performance indicators depending upon whether the one or more business rules comprises a maximization, minimization, or near-to function.