Agent satisfaction data for call routing based on pattern matching algorithm

• US 8,781,106 B2
• Filed: 08/29/2008
• Issued: 07/15/2014
• Est. Priority Date: 08/29/2008
• Status: Active Grant

• Current Assignee: Afiniti Ltd
• Original Assignee: Satmap International Holdings, Ltd.
• Inventors: Afzal, Hassan
• Primary Examiner(s): DEANE JR, WILLIAM J
• Application Number:
  US12/202,097
• Publication Number:
  US 20100054452A1
• Time in Patent Office: 2,146 Days
• Field of Search: 379/219, 37926501-26514, 370/352
• US Class Current: 379/265.11
• CPC Class Codes: H04M 2201/18 Comparators
• CPC Class Codes: H04M 2201/36 Memories
• CPC Class Codes: H04M 2203/408 Customer-specific call routing plans
• CPC Class Codes: H04M 3/5232 Call distribution algorithms

Abstract

Methods and systems are disclosed for routing callers to agents in a contact center with an intelligent routing system. An exemplary method includes routing callers to agents based on a pattern matching algorithm utilizing caller data and agent data, where the agent data includes agent satisfaction data from past agent-caller pairings. The agent satisfaction data may be obtained via surveys of the agents regarding their satisfaction with past agent-caller contacts. The agent satisfaction data may be used by the pattern matching algorithm in an attempt to increase agent satisfaction for future calls, thereby potentially reducing attrition of agents and cost to the call center, increasing morale of the agents, and so on. The agent satisfaction data and output from past agent-caller pairings may be weighted by the contact center against other agent data and caller data for a desired mixing of output variables.

Claims

1. A method for routing callers to agents in a call-center routing, environment, the method comprising:

obtaining, by one or more computers, multiple items of agent data associated agents in a set of agents;

obtaining, by the one or more computers, multiple items of caller data associated with callers in a set of callers;

matching, by the one or more computers, using a multi-element pattern matching algorithm, callers of a first portion of the set of callers with the agents in the set of agents, based at least in part on the data associated with the callers in the first portion of callers and the agents in the set of agents, to increase the chance of an optimal interaction;

connecting, by the one or more computers, the callers in the first portion of callers to agents in the set of agents based at least in part on results from the multi-element pattern matching algorithm;

matching, by the one or more computers, callers in a second portion of the set of callers to agents in the set of agents randomly by cycling on and off the matching operation of the multi-element pattern matching algorithm;

connecting, by the one or more computers, the respective callers in the second portion of the callers to respective of the agents in the set of agents based on results from the random matching step;

generating, by the one or more computers, a report or display data for the optimal interaction comparing outcome data from using the multi-element pattern matching algorithm against outcome data from using the random matching; and

generating data, by the one or more computers, for a visual computer interface for setting a degree of randomness for the method for routing.

2. The method of claim 1, wherein the step of setting a degree of randomness comprises changing percentages of the callers in the set of callers in the first portion and in the second portion.

3. The method of claim 1, wherein the pattern matching algorithm operates to increase the chance of a desired optimal interaction.

4. The method of claim 3, wherein the optimal interaction comprises a predetermined weighting of agent satisfaction and at least one of revenue generation, cost, or customer satisfaction.

5. The method of claim 3, wherein the optimal interaction comprises an increase in agent satisfaction.

6. The method of claim 5, wherein the agent satisfaction data comprises data from agent surveys from past agent-caller pairings.

7. The method of claim 1, wherein the agent data further comprises one of demographic data or psychographic data.

8. The method of claim 1, wherein the caller data comprises one of demographic data or psychographic data.

9. The method of claim 1, further comprising determining a Z-score for two or more output variables of a pattern matching algorithm for matching callers and agents from past agent-caller interaction data, wherein one of the output variables includes agent satisfaction;

determining a linear combination of the two or more output variables; and

routing a caller to an agent based on the linear combination and the pattern matching algorithm.

10. The method of claim 9, further comprising determining Z-scores for multiple agent-caller pairs and wherein routing is based on the highest linear combination score of the agent-caller pairs.

11. The method of claim 9, wherein routing based on the linear combination and pattern matching algorithm includes routing the caller via the pattern matching algorithm to approximate the linear combination of the two or more output variables.

12. The method of claim 9, wherein the linear combination includes a relative weighting of the two or more output variables.

13. The method of claim 9, wherein the linear combination of the two or more output variables is adjustable by the call routing center.

14. A system for routing callers to agents in a call-center routing environment, comprising:

one or more computers configured to perform the steps

obtaining, by the one or more computers, multiple items of agent data associated agents in a set of agents;

obtaining, by the one or more computers, multiple items of caller data associated with callers in a set of callers;

matching, by the one or more computers, using a multi-element pattern matching algorithm, callers of a first portion of the set of callers with the agents in the set of agents, based at least in part on the data associated with the callers in the first portion of callers and the agents in the set of agents, to increase the chance of an optimal interaction;

connecting, by the one or more computers, the callers in the first portion of callers to agents in the set of agents based at least in part on results from the multi-element pattern matching algorithm;

matching, by the one or more computers, callers in a second portion of the set of callers to agents in the set of agents randomly by cycling on and off the matching operation of the multi-element pattern matching algorithm;

connecting, by the one or more computers, the respective callers in the second portion of the callers to respective of the agents in the set of agents based on results from the random matching step;

generating, by the one or more computers, a report or display data for the optimal interaction comparing outcome data from using the multi-element pattern matching algorithm against outcome data from using the random matching; and

generating data, by the one or more computers, for a visual computer interface for setting a degree of randomness for the method for routing.

15. The system of claim 14, wherein the operation of setting a degree of randomness comprises changing percentages of the callers in the set of callers in the first portion and in the second portion.

16. The system of claim 14, wherein the pattern matching algorithm operates to increase the chance of a desired optimal interaction.

17. The system of claim 16, wherein the optimal interaction comprises a predetermined weighting of agent satisfaction and at least one of revenue generation, cost, or customer satisfaction.

18. The system of claim 16, wherein the optimal interaction comprises an increase in agent satisfaction.

19. The system of claim 18, wherein the agent satisfaction data comprises data from agent surveys from past agent-caller pairings.

20. The system of claim 14, wherein the agent data further comprises one of demographic data or psychographic data.

21. The system of claim 14, wherein the caller data comprises one of demographic data or psychographic data.

22. A non-transitory computer readable storage medium comprising computer readable instructions for carrying out, when executed by one or more computers, steps for routing callers to agents in a call-center routing environment, comprising:

obtaining, by the one or more computers, multiple items of agent data associated agents in a set of agents;

obtaining, by the one or more computers, multiple items of caller data associated with callers in a set of callers;

matching, by the one or more computers, using a multi-element pattern matching algorithm, callers of a first portion of the set of callers with the agents in the set of agents, based at least in part on the data associated with the callers in the first portion of callers and the agents in the set of agents, to increase the chance of an optimal interaction;

connecting, by the one or more computers, the callers in the first portion of callers to agents in the set of agents based at least in part on results from the multi-element pattern matching algorithm;

matching, by the one or more computers, callers in a second portion of the set of callers to agents in the set of agents randomly by cycling on and off the matching operation of the multi-element pattern matching algorithm;

connecting, by the one or more computers, the respective callers in the second portion of the callers to respective of the agents in the set of agents based on results from the random matching step;

generating, by the one or more computers, a report or display data for the optimal interaction comparing outcome data from using the multi-element pattern matching algorithm against outcome data from using the random matching; and

generating data, by the one or more computers, for a visual computer interface for setting a degree of randomness for the method for routing.

23. The computer readable storage medium of claim 22, wherein the pattern matching algorithm operates to increase the chance of a desired optimal interaction.

24. The computer readable storage medium of claim 23, wherein the optimal interaction comprises a predetermined weighting of agent satisfaction and at least one of revenue generation, cost, or customer satisfaction.

25. The computer readable storage medium of claim 23, wherein the optimal interaction comprises an increase in agent satisfaction.

26. The computer readable storage medium of claim 25, wherein the agent satisfaction data comprises data from agent surveys from past agent-caller pairings.

27. The computer readable storage medium of claim 22, wherein the agent data further comprises one of demographic data or psychographic data.

28. The computer readable storage medium of claim 22, wherein the caller data comprises one of demographic data or psychographic data.