Call mapping systems and methods using variance algorithm (VA) and/or distribution compensation

• US 9,277,055 B2
• Filed: 10/31/2014
• Issued: 03/01/2016
• Est. Priority Date: 03/26/2012
• Status: Active Grant

• Current Assignee: Afiniti Ltd
• Original Assignee: Satmap International Holdings, Ltd.
• Inventors: Spottiswoode, S. James P., Chishti, Zia
• Primary Examiner(s): NGUYEN, QUYNH H
• Application Number:
  US14/530,058
• Publication Number:
  US 20150124955A1
• Time in Patent Office: 487 Days
• Field of Search: 379/265.06, 379/265.01, 379/265.05, 379/265.1, 379/265.11, 379/265.12, 379/266.08
• US Class Current: 1/1
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06Q 10/06398 Performance of employee with respect to a job function
• CPC Class Codes: H04M 2203/2061 Language aspects
• CPC Class Codes: H04M 2203/401 Performance feedback
• CPC Class Codes: H04M 2203/556 Statistical analysis and interpretation
• CPC Class Codes: H04M 3/5175 Call or contact centers supervision arrangements
• CPC Class Codes: H04M 3/5191 interacting with the Internet
• CPC Class Codes: H04M 3/523 with call distribution or queueing
• CPC Class Codes: H04M 3/5232 Call distribution algorithms
• CPC Class Codes: H04M 3/5233 Operator skill based call distribution
• CPC Class Codes: H04M 3/5234 Uniform load distribution
• CPC Class Codes: H04M 3/5235 Dependent on call type or called number [DNIS]
• CPC Class Codes: H04M 3/5238 with waiting time or load prediction arrangements
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

Method, system and program product, comprising obtaining agent performance data; ranking, agents based the agent performance data; dividing agents into agent performance ranges; partitioning callers based on criteria into a set of partitions; determining for each partition an outcome value for a first agent performance range and a second agent performance range; calculating for the partitions a respective outcome value difference indicator based on the outcome value for the first agent performance range and the outcome value for the second agent performance range for the partition; matching a respective agent to a respective caller in one of the partitions, based on the outcome value difference indicators for the partitions.

Claims

1. A method, comprising:

obtaining, by one or more computers, agent performance data for a set of agents;

ranking or percentiling, by the one or more computers, the agents based at least in part on the agent performance data;

dividing, by the one or more computers, the agents into agent performance ranges based at least in part on the ranking or percentiling step;

partitioning, by the one or more computers, callers in a set of callers based on one or more criteria into a set of partitions;

determining for each of the partitions, by the one or more computers, an outcome value comprising a statistical chance or likelihood for a particular outcome at least for a first one of the agent performance ranges and for a second one of the agent performance ranges based at least in part on caller partition data;

calculating, by the one or more computers, for each of the partitions an outcome value difference indicator based at least in part on the outcome value for the first agent performance range and the outcome value for the second agent performance range;

performing distribution compensation, by the one or more computers, using an edge compensation algorithm applied to at least one selected from the group of a distribution of the agent rankings or percentiling and a distribution of caller rankings or percentiling, near at least one edge of the respective distribution, to obtain edge compensated rankings or percentiles, wherein the ranking or percentile of the callers is based at least in part on the outcome values for the partitions of the respective callers; and

matching, by the one or more computers, a respective agent with respective performance data to a respective caller in one of the partitions, based at least in part on the outcome value difference indicators for the partitions, where at least one of the agent rankings or percentiles or caller rankings or percentiles has been distribution compensated.

2. The method as defined in claim 1, wherein the performing the distribution compensation step uses the edge compensation algorithm to provide edge compensation only to the agent rankings or percentiles.

3. The method as defined in claim 1, wherein the performing the distribution compensation step uses the edge compensation algorithm to provide edge compensation only to the caller rankings or percentiles.

4. The method as defined in claim 1, wherein the performing the distribution compensation step uses the edge compensation algorithm to provide edge compensation to both the caller rankings or percentiles and the agent rankings or percentiles.

5. The method as defined in claim 1, wherein the distribution compensation step uses the edge compensation algorithm to take the agents that are free at runtime, and to rescale the respective agent rankings or percentiles for these runtime available agents to provide more space/margin at the edges of the agent distribution.

6. The method as defined in claim 5, wherein the amount of the margin is based at least in part on a number of the agents that are free at runtime.

7. The method as defined in claim 1, wherein the distribution compensation step uses the edge compensation algorithm to weight multiple of the agents logged in at runtime near at least one edge of the agent distribution and to weight multiple of the callers near at least one edge of the caller distribution to increase utilization of the agents at the at least one edge of the agent distribution.

8. The method as defined in claim 1, wherein the distribution compensation step uses the edge compensation algorithm to weight multiple of the agents free at runtime near both edges of the agent distribution or to weight multiple of the callers near both edges of the caller distribution.

9. The method as defined in claim 1, wherein the determining an outcome value step comprises determining a mean outcome value for a particular outcome for the agent performance range.

10. The method as defined in claim 1, wherein the partition is based at least in part on one or more selected from the group of demographic data, area code, zip code, NPANXX, VTN, geographic area, 800 number, and transfer number.

11. The method as defined in claim 1, wherein the outcome value comprises one or more selected from the group of sale, number of items sold per call, and revenue per call coupled with handle time.

12. The method as defined in claim 1, wherein the matching step is based at least in part on a rule to assign a higher performing agent to a caller from one of the partitions when the outcome value difference indicator for the partition of the caller is higher relative to the outcome value difference indicators of other of the partitions, and to assign a lower performing agent to a caller from one of the partitions when the outcome value difference indicator for the partition of the caller is lower relative to the outcome value difference indicators of other of the partitions, wherein the higher performing agent and the lower performing agent are determined based at least in part on the agent performance data for the respective agent relative to the agent performance data for other of the agents.

13. The method as defined in claim 1, wherein the matching step further comprises the steps:

calculating, by the one or more computers, a ranking or percentile for the partitions by outcome value difference indicator from a high outcome value difference indicator to a low outcome value difference indicator based at least in part on a first number of callers;

calculating a ranking or percentile of the agent performances, by the one or more computers, for the respective agents in the set of agents based at least in part on the respective agent performance data and a second number of callers; and

matching, by the one or more computers, the respective agent to the respective caller based at least in part on a rule to minimize a difference between the partition ranking or percentile of the outcome value difference indicator for the partition of the respective caller and the performance ranking or percentile of the respective agent.

14. The method as defined in claim 1, wherein the matching step is performed based at least in part using a multi-data element pattern matching algorithm in a pair-wise fashion to obtain a score or other indicator for each of multiple caller-agent pairs from a set of agents and a set of callers.

15. The method as defined in claim 1, further comprising:

switching, by the one or more computers, between the outcome value difference indicator matching algorithm and a second matching algorithm that is different from the outcome value difference indicator matching algorithm, based on one or more criteria; and

when there has been switching to the second matching algorithm, then performing, by the one or more computers, the second matching algorithm to match a respective one of the agents with a respective one of the callers.

16. The method as defined in claim 15, further comprising:

obtaining results data, by the one or more computers, using each of the outcome value difference indicator matching algorithm and the second matching algorithm in the matching step;

determining a switch-over point in a distribution for agent performance and/or a distribution for caller propensity for the particular outcome where better results are obtained from one of the algorithms relative to the other of the algorithms; and

using, by the one or more computers, this switch-over point to switch between the algorithms.

17. The method as defined in claim 1, further comprising:

switching, by the one or more computers, between the outcome value difference indicator matching algorithm and a second matching algorithm configured to match a respective agent with a respective ranking or percentile to a caller in one of the partitions with a closest ranking or percentile, based on one or more criteria; and

when there has been switching to the second matching algorithm, then performing, by the one or more computers, the second matching algorithm to match a respective one of the agents with a respective one of the callers.

18. A non-transitory computer-readable medium comprising computer-readable codes to be executed by one or more computers, the computer-readable code comprising:

computer-readable code to obtain, by one or more computers, agent performance data for a set of agents;

computer-readable code to rank or percentile, by the one or more computers, the agents based at least in part on the agent performance data;

computer-readable code to divide, by the one or more computers, the agents into agent performance ranges based at least in part on the ranking step;

computer-readable code to partition, by the one or more computers, callers in a set of callers based on one or more criteria into a set of partitions;

computer-readable code to determine for each of the partitions, by the one or more computers, an outcome value comprising a statistical chance or likelihood for a particular outcome at least for a first one of the agent performance ranges and for a second one of the agent performance ranges based at least in part on caller partition data;

computer-readable code to calculate, by the one or more computers, for each of the partitions an outcome value difference indicator based at least in part on the outcome value for the first agent performance range and the outcome value for the second agent performance range;

computer-readable code to perform distribution compensation, by the one or more computers, using an edge compensation algorithm applied to at least one selected from the group of a distribution of the agent rankings or percentiles and a distribution of caller rankings or percentiles, near at least one edge of the respective distribution, to obtain edge compensated rankings, wherein the ranking or percentiles of the callers are based at least in part on the outcome values for the partitions of the respective callers; and

computer-readable code to match, by the one or more computers, a respective agent with respective performance data to a respective caller in one of the partitions, based at least in part on the outcome value difference indicators for the partitions, where at least one of the agent rankings or percentiles or caller rankings or percentiles has been distribution compensated.

19. A call center system, comprising:

one or more computers configured with program code to perform the following method;

obtaining, by the one or more computers, agent performance data for a set of agents;

ranking or percentiling, by the one or more computers, the agents based at least in part on the agent performance data;

dividing, by the one or more computers, the agents into agent performance ranges based at least in part on the ranking or percentiling step;

partitioning, by the one or more computers, callers in a set of callers based on one or more criteria into a set of partitions;

determining for each of the partitions, by the one or more computers, an outcome value comprising a statistical chance or likelihood for a particular outcome at least for a first one of the agent performance ranges and for a second one of the agent performance ranges based at least in part on caller partition data;

calculating, by the one or more computers, for each of the partitions an outcome value difference indicator based at least in part on the outcome value for the first agent performance range and the outcome value for the second agent performance range;

performing distribution compensation, by the one or more computers, using an edge compensation algorithm applied to at least one selected from the group of a distribution of the agent rankings or percentiling and a distribution of caller rankings or percentiling, near at least one edge of the respective distribution, to obtain edge compensated rankings or percentiles, wherein the ranking or percentile of the callers is based at least in part on the outcome value for the partitions of the respective callers; and

matching, by the one or more computers, a respective agent with respective performance data to a respective caller in one of the partitions, based at least in part on the outcome value difference indicators for the partitions, where at least one of the agent rankings or percentiles or caller rankings or percentiles has been distribution compensated.

20. The system as defined in claim 19, wherein the performing the distribution compensation step uses the edge compensation algorithm to provide edge compensation only to the agent rankings or percentiles.

21. The system as defined in claim 19, wherein the performing the distribution compensation step uses the edge compensation algorithm to provide edge compensation only to the caller rankings or percentiles.

22. The system as defined in claim 19, wherein the distribution compensation step uses the edge compensation algorithm to take the agents that are free at runtime, and to rescale the respective agent rankings or percentiles for these runtime available agents to provide more space/margin at the edges of the agent distribution.

23. The system as defined in claim 22, wherein the amount of the margin is based at least in part on a number of the agents that are free at runtime.

24. The system as defined in claim 19, wherein the distribution compensation step uses the edge compensation algorithm to weight multiple of the agents logged in at runtime near at least one edge of the agent distribution and to weight multiple of the callers near at least one edge of the caller distribution to increase utilization of the agents at the at least one edge of the agent distribution.

25. The system as defined in claim 19, wherein the distribution compensation step uses the edge compensation algorithm to weight multiple of the agents free at runtime near both edges of the agent distribution or to weight multiple of the callers near both edges of the caller distribution.

26. The system as defined in claim 19, wherein the determining an outcome value step comprises determining a mean outcome value for a particular outcome for the agent performance range.

27. The system as defined in claim 19, wherein the partition is based at least in part on one or more selected from the group of demographic data, area code, zip code, NPANXX, VTN, geographic area, 800 number, and transfer number.

28. The system as defined in claim 19, wherein the matching step is based at least in part on a rule to assign a higher performing agent to a caller from one of the partitions when the outcome value difference indicator for the partition of the caller is higher relative to the outcome value difference indicators of other of the partitions, and to assign a lower performing agent to a caller from one of the partitions when the outcome value difference indicator for the partition of the caller is lower relative to the outcome value difference indicators of other of the partitions, wherein the higher performing agent and the lower performing agent are determined based at least in part on the agent performance data for the respective agent relative to the agent performance data for other of the agents.

29. The system as defined in claim 19, wherein the matching step further comprises the steps:

calculating, by the one or more computers, a ranking or percentile for the partitions by outcome value difference indicator from a high outcome value difference indicator to a low outcome value difference indicator based at least in part on a first number of callers;

calculating a ranking or percentile of the agent performances, by the one or more computers, for the respective agents in the set of agents based at least in part on the respective agent performance data and a second number of callers; and

matching, by the one or more computers, the respective agent to the respective caller based at least in part on a rule to minimize a difference between the partition ranking or percentile of the outcome value difference indicator for the partition of the respective caller and the performance ranking or percentile of the respective agent.

30. The system as defined in claim 19, further comprising:

switching, by the one or more computers, between the outcome value difference indicator matching algorithm and a second matching algorithm that is different from the outcome value difference indicator matching algorithm, based on one or more criteria; and

when there has been switching to the second matching algorithm, then performing, by the one or more computers, the second matching algorithm to match a respective one of the agents with a respective one of the callers.

31. The system as defined in claim 30, further comprising:

obtaining results data, by the one or more computers, using each of the outcome value difference indicator matching algorithm and the second matching algorithm in the matching step;

determining a switch-over point in a distribution for agent performance and/or a distribution for caller propensity for the particular outcome where better results are obtained from one of the algorithms relative to the other of the algorithms; and

using, by the one or more computers, this switch-over point to switch between the algorithms.

32. The system as defined in claim 19, further comprising:

switching, by the one or more computers, between the outcome value difference indicator matching algorithm and a second matching algorithm configured to match a respective agent with a respective ranking or percentile to a caller in one of the partitions with a closest ranking or percentile, based on one or more criteria; and

when there has been switching to the second matching algorithm, then performing, by the one or more computers, the second matching algorithm to match a respective one of the agents with a respective one of the callers.