U.S. patent number RE48,412 [Application Number 14/788,469] was granted by the patent office on 2021-01-26 for balancing multiple computer models in a call center routing system.
This patent grant is currently assigned to Afiniti, Ltd.. The grantee listed for this patent is Afiniti International Holdings, Ltd.. Invention is credited to Zia Chishti.
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United States Patent |
RE48,412 |
Chishti |
January 26, 2021 |
Balancing multiple computer models in a call center routing
system
Abstract
Systems and methods are disclosed for routing callers to agents
in a contact center utilizing a multi-layer processing approach to
matching a caller to an agent. A first layer of processing may
include two or more different computer models or methods for
scoring or determining caller-agent pairs in a routing center. The
output of the first layer may be received by a second layer of
processing for balancing or weighting the outputs and selecting a
final caller-agent match. The two or more methods may include
conventional queue based routing, performance based routing,
pattern matching algorithms, affinity matching, and the like. The
output or scores of the two or more methods may be processed be the
second layer of processing to select a caller-agent pair and cause
the caller to be routed to a particular agent.
Inventors: |
Chishti; Zia (Washington,
DC) |
Applicant: |
Name |
City |
State |
Country |
Type |
Afiniti International Holdings, Ltd. |
Hamilton |
N/A |
BM |
|
|
Assignee: |
Afiniti, Ltd. (Hamilton,
BM)
|
Family
ID: |
74185534 |
Appl.
No.: |
14/788,469 |
Filed: |
June 30, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
|
|
14750965 |
Jun 25, 2015 |
|
|
|
Reissue of: |
12266461 |
Nov 6, 2008 |
8472611 |
Jun 25, 2013 |
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Reissue of: |
12266461 |
Nov 6, 2008 |
8472611 |
Jun 25, 2013 |
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04M
3/5238 (20130101); H04M 3/5232 (20130101); H04M
3/5233 (20130101) |
Current International
Class: |
H04M
3/00 (20060101); H04M 3/523 (20060101) |
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WO-2011/081514 |
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Jul 2011 |
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WO |
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Primary Examiner: Wassum; Luke S
Attorney, Agent or Firm: Wilmer Cutler Pickering Hale and
Dorr LLP
Parent Case Text
.Iadd.Note: More than one reissue patent application has been filed
for the reissue of U.S. Pat. No. 8,472,611. The reissue patent
applications are U.S. Reissue patent application Ser. No.
14/750,965, filed Jun. 25, 2015, and the present U.S. Reissue
patent application Ser. No. 14/788,469, filed Jun. 30, 2015, which
is a continuation reissue application of U.S. Reissue patent
application Ser. No. 14/750,965, which is an application for
reissue of U.S. Pat. No. 8,472,611. .Iaddend.
Claims
I claim:
.[.1. A method for routing callers to agents in a call-center
routing environment, the method comprising the acts of: receiving,
by one or more computers, input data associated with callers in a
set of callers and agents in a set of agents at a first layer of
processing; processing, by the one or more computers, the input
data associated with the callers in the set of callers and the
agents in the set of agents using a first measurement algorithm in
a pair-wise fashion in the first layer of processing, to output
respective measurement data for each of a first set of caller-agent
pairs; processing, by the one or more computers, the input data
associated with the callers in the set of callers and the agents in
the set of agents using a second measurement algorithm in a
pair-wise fashion in the first layer of processing, to output
respective measurement data for each of the first set of
caller-agent pairs; receiving, by the one or more computers, the
output measurement data from each of the first measurement
algorithm and the second measurement algorithm at a second layer of
processing; processing, by the one or more computers, the
caller-agent pair output measurement data from each of the first
measurement algorithm and the second measurement algorithm by an
algorithm to weight the caller-agent pair output measurement data
of one of the measurement algorithms relative to the other of the
measurement algorithms in the second layer of processing;
selecting, by the one or more computers, a caller-agent pair based
at least in part on weighted caller-agent pair output data; and
mapping, by the one or more computers, the caller in the
caller-agent pair selected to the agent in the caller-agent pair
selected..].
.[.2. The method of claim 1, wherein the processing the input data
steps comprises scoring each of the caller-agent pairs using the
respective algorithm for the respective processing step, wherein
the output measurement data from the first measurement algorithm
comprises a respective score for each of the first set of
caller-agent pairs and is based on at least a first data associated
with one or both of the set of the callers and the set of the
agents; and wherein the output measurement data from the second
measurement algorithm comprises a respective score, wherein the
scores from the first and second measurement algorithms for each of
the caller-agent pairs are output to the second layer of
processing..].
.[.3. The method of claim 2, wherein scoring the caller-agent pairs
according to the second measurement algorithm is based on at least
a second data associated with one or both of the set of callers and
the set of agents..].
.[.4. The method of claim 2, wherein scoring the caller-agent pairs
comprises ranking agents based on performance..].
.[.5. The method of claim 2, wherein scoring the caller-agent pairs
comprises determining a suitability score for a desired output
performance..].
.[.6. The method of claim 2, wherein one of the measurement
algorithms comprises a multi-data element pattern matching
algorithm utilizing caller data associated with multiple callers in
the set of callers and agent data associated with multiple agents
in the set of agents..].
.[.7. The method of claim 1, wherein one of the measurement
algorithms comprises a performance based matching algorithm based
on performance data of at least two of the agents..].
.[.8. The method of claim 1, wherein one of the measurement
algorithms comprises a multi-data element pattern matching
algorithm utilizing caller data associated with multiple callers
from the set of callers and agent data associated with multiple
agents from the set of agents..].
.[.9. The method of claim 1, wherein one of the measurement
algorithms utilizes affinity data associated with one or both of
the agents and callers..].
.[.10. The method of claim 1, wherein at least one of the at least
two measurement algorithms comprises a neural network
algorithm..].
.[.11. The method of claim 1, wherein the second layer of
processing comprises a neural network algorithm..].
.[.12. The method of claim 1, further comprising: providing an
electronic interface, by the one or more computers, to change the
weighting of the output measurement data from one of the
measurement algorithms relative to the other of the measurement
algorithms in the second layer of processing; and generating, by
the one or more computers, display data for a display interface,
with the display data comprising an estimated effect of the change
of the weighting on one or more selected from the group of cost,
revenue generation, and customer satisfaction..].
.[.13. The method of claim 1, wherein the caller-agent pair output
measurement data from the first measurement algorithm and from the
second measurement algorithm are weighted equally..].
.[.14. The method of claim 1, wherein the caller-agent pair output
measurement data from the first measurement algorithm and from the
second measurement algorithm are weighted unequally..].
15. A system for routing callers to agents in a call-center routing
environment, comprising: one or more computers configured with
computer-readable program code to perform, when executed, the steps
.Iadd.of.Iaddend.: .[.receiving, by the one or more computers,
input data associated with callers in a set of callers and agents
in a set of agents at a first layer of processing;.]. processing,
by .[.the.]. one or more computers .Iadd.communicatively coupled to
and configured to operate in the call-center routing
environment.Iaddend., .[.the.]. input data associated with
.[.the.]. callers in .[.the.]. .Iadd.a .Iaddend.set of callers and
.[.the.]. agents in .[.the.]. .Iadd.a .Iaddend.set of agents using
a first measurement algorithm in a pair-wise fashion in .[.the.].
.Iadd.a .Iaddend.first layer of processing, to output
.[.respective.]. .Iadd.first .Iaddend.measurement data for each
.Iadd.caller-agent pair .Iaddend.of a .[.first.]. set of
caller-agent pairs; processing, by the one or more computers, the
input data associated with the callers in the set of callers and
the agents in the set of agents using a second measurement
algorithm in a pair-wise fashion in the first layer of processing,
to output .[.respective.]. .Iadd.second .Iaddend.measurement data
for each .Iadd.caller-agent pair .Iaddend.of the .[.first.]. set of
caller-agent pairs; receiving, by the one or more computers, .[.the
output measurement data from each of the first measurement
algorithm and the second measurement algorithm.]. at a second layer
of processing.Iadd., the first measurement data and the second
measurement data for each caller-agent pair of the set of
caller-agent pairs.Iaddend.; processing, by the one or more
computers, the .[.caller-agent pair output measurement data from
each of the first measurement algorithm and the second measurement
algorithm by an.]. .Iadd.first measurement data and the second
measurement data for each caller-agent pair of the set of
caller-agent pairs by a weighting .Iaddend.algorithm to weight the
.[.caller-agent pair output.]. .Iadd.first .Iaddend.measurement
data .[.of one of the measurement algorithms.]. .Iadd.for each
caller-agent pair of the set of caller-agent pairs
.Iaddend.relative to the .[.other of the measurement algorithms.].
.Iadd.second measurement data for each caller-agent pair of the set
of caller-agent pairs .Iaddend.in the second layer of
processing.Iadd., to output a weighted caller-agent pair output
measurement data.Iaddend.; selecting, by the one or more computers,
a caller-agent pair based at least in part on weighted caller-agent
pair output .Iadd.measurement .Iaddend.data; and .[.mapping, by the
one or more computers, the caller in the caller-agent pair selected
to the agent in the caller-agent pair selected.].
.Iadd.establishing, by the one or more computers, in a switch
module of the call-center routing environment, a communication
channel between the caller and the agent in the selected agent-pair
to optimize performance of the call-center routing environment
attributable to the weighted caller-agent pair output measurement
data.Iaddend..
.[.16. The system of claim 15, wherein the program code for
processing the input data steps comprises program code for scoring
each of the caller-agent pairs using the respective algorithm for
the respective processing step, wherein the output measurement data
from the first measurement algorithm comprises a respective score
for each of the first set of caller-agent pairs and is based on at
least a first data associated with one or both of the set of the
callers and the set of the agents; and wherein the output
measurement data from the second measurement algorithm comprises a
respective score for each of the first set of caller-agent pairs,
wherein the scores from the first and second measurement algorithms
are output to the second layer of processing..].
.[.17. The system of claim 16, wherein scoring the caller-agent
pairs according to the second measurement algorithm is based on at
least a second data associated with one or both of the set of
callers and the set of agents..].
.[.18. The system of claim 16, wherein scoring the caller-agent
pairs comprises ranking agents based on performance..].
.[.19. The system of claim 16, wherein scoring the caller-agent
pairs comprises determining a suitability score for a desired
output performance..].
.[.20. The system of claim 16, wherein one of the measurement
algorithms comprises a multi-data element pattern matching
algorithm utilizing caller data associated with multiple callers in
the set of callers and agent data associated with multiple agents
in the set of agents..].
.[.21. The system of claim 15, wherein one of the measurement
algorithms comprises a performance based matching algorithm based
on performance data of at least two of the agents..].
.[.22. The system of claim 15, wherein one of the measurement
algorithms comprises a pattern matching algorithm utilizing caller
data associated with multiple callers from the set of callers and
agent data associated with multiple agents from the set of
agents..].
.[.23. The system of claim 15, wherein one of the measurement
algorithms utilizes affinity data associated with one or both of
the agents and callers..].
.[.24. The system of claim 15, wherein at least one of the at least
two measurement algorithms comprises a neural network
algorithm..].
.[.25. The system of claim 15, wherein the second layer of
processing comprises a neural network algorithm..].
.[.26. The system of claim 15, further comprising the one or more
computers configured with program code to perform the steps:
providing an electronic interface, by the one or more computers, to
change the weighting of the output measurement data from one of the
measurement algorithms relative to the other of the measurement
algorithms in the second layer of processing; and generating, by
the one or more computers, display data for a display interface,
with the display data comprising an estimated effect of the change
of the weighting on one or more selected from the group of cost,
revenue generation, and customer satisfaction..].
.[.27. The system of claim 15, wherein the caller-agent pair output
measurement data from the first measurement algorithm and from the
second measurement algorithm are weighted equally..].
.[.28. The system of claim 15, wherein the caller-agent pair output
measurement data from the first measurement algorithm and from the
second measurement algorithm are weighted unequally..].
.[.29. A non-transitory computer readable storage medium comprising
computer readable program code for carrying out, when executed by
one or more computers, the steps: receiving, by the one or more
computers, input data associated with callers in a set of callers
and agents in a set of agents at a first layer of processing;
processing, by the one or more computers, the input data associated
with the callers in the set of callers and the agents in the set of
agents using a first measurement algorithm in a pair-wise fashion
in the first layer of processing, to output respective measurement
data for each of a first set of caller-agent pairs; processing, by
the one or more computers, the input data associated with the
callers in the set of callers and the agents in the set of agents
using a second measurement algorithm in a pair-wise fashion in the
first layer of processing, to output respective measurement data
for each of the first set of caller-agent pairs; receiving, by the
one or more computers, the output measurement data from each of the
first measurement algorithm and the second measurement algorithm at
a second layer of processing; processing, by the one or more
computers, the caller-agent pair output measurement data from each
of the first measurement algorithm and the second measurement
algorithm by an algorithm to weight the caller-agent pair output
measurement data of one of the measurement algorithms relative to
the other of the measurement algorithms in the second layer of
processing; selecting, by the one or more computers, a caller-agent
pair based at least in part on weighted caller-agent pair output
data; and mapping, by the one or more computers, the caller in the
caller-agent pair selected to the agent in the caller-agent pair
selected..].
.[.30. The computer readable storage medium of claim 29, wherein
the program code for processing the input data steps comprises
program code for scoring each of the caller-agent pairs using the
respective algorithm for the respective processing step, wherein
the output measurement data from the first measurement algorithm
comprises a respective score for each of the first set of
caller-agent pairs and is based on at least a first data associated
with one or both of the set of the callers and the set of the
agents; and wherein the output measurement data from the second
measurement algorithm comprises a respective score for each of the
first set of caller-agent pairs, wherein the scores from the first
and second measurement algorithms are output to the second layer of
processing..].
.[.31. The computer readable storage medium of claim 30, wherein
scoring the caller-agent pairs according to the second measurement
algorithm is based on at least a second data associated with one or
both of the set of callers and the set of agents..].
.[.32. The computer readable storage medium of claim 30, wherein
scoring the caller-agent pairs comprises ranking agents based on
performance..].
.[.33. The computer readable storage medium of claim 30, wherein
scoring the caller-agent pairs comprises determining a suitability
score for a desired output performance..].
.[.34. The computer readable storage medium of claim 30, wherein
one of the measurement algorithms comprises a multi-data element
pattern matching algorithm utilizing caller data associated with
multiple callers in the set of callers and agent data associated
with multiple agents in the set of agents..].
.[.35. The computer readable storage medium of claim 29, wherein
one of the measurement algorithms comprises a performance based
matching algorithm based on performance data of at least two of the
agents..].
.[.36. The computer readable storage medium of claim 29, wherein
one of the measurement algorithms comprises a pattern matching
algorithm utilizing caller data associated with multiple callers
from the set of callers and agent data associated with multiple
agents from the set of agents..].
.[.37. The computer readable storage medium of claim 29, wherein
one of the measurement algorithms utilizes affinity data associated
with one or both of the agents and callers..].
.[.38. The computer readable storage medium of claim 29, wherein at
least one of the at least two measurement algorithms comprises a
neural network algorithm..].
.[.39. The computer readable storage medium of claim 29, wherein
the second layer of processing comprises a neural network
algorithm..].
.[.40. The computer readable storage medium of claim 29, further
comprising program code to perform the steps: providing an
electronic interface, by the one or more computers, to change the
weighting of the output measurement data from one of the
measurement algorithms relative to the other of the measurement
algorithms in the second layer of processing; and generating, by
the one or more computers, display data for a display interface,
with the display data comprising an estimated effect of the change
of the weighting on one or more selected from the group of cost,
revenue generation, and customer satisfaction..].
.[.41. The computer readable storage medium of claim 29, wherein
the caller-agent pair output measurement data from the first
measurement algorithm and from the second measurement algorithm are
weighted equally..].
.[.42. The computer readable storage medium of claim 29, wherein
the caller-agent pair output measurement data from the first
measurement algorithm and from the second measurement algorithm are
weighted unequally..].
.Iadd.43. A method for handling contacts and agents in a contact
center system comprising: determining, by at least one computer
processor communicatively coupled to and configured to operate in
the contact center system, if a first contact has previously
interacted with a first agent, wherein determining the previous
interaction is based at least in part upon: processing, by the at
least one computer processor, input data associated with contacts
in a set of contacts and agents in a set of agents using a first
measurement algorithm in a pair-wise fashion in a first layer of
processing, to output first measurement data for each caller-agent
pair of a set of contact-agent pairs; processing, by the at least
one computer processor, the input data associated with the contacts
in the set of contacts and the agents in the set of agents using a
second measurement algorithm in a pair-wise fashion in the first
layer of processing, to output second measurement data for each
caller-agent pair of the set of contact-agent pairs; receiving, by
the at least one computer processor, at a second layer of
processing, the first measurement data and the second measurement
data for each caller-agent pair of the set of caller-agent pairs;
processing, by the at least one computer processor, the first
measurement data and the second measurement data for each
contact-agent pair of the set of contact-agent pairs by a weighting
algorithm to weight the first measurement data for each
contact-agent pair of the set of contact-agent pairs relative to
the second measurement data for each contact-agent pair of the set
of contact-agent pairs in the second layer of processing, to output
a weighted contact-agent pair output measurement data; and
selecting, by the at least one computer processor, a contact-agent
pair based at least in part on the weighted contact-agent pair
output measurement data; matching, by the at least one computer
processor, the first contact with the first agent based at least in
part upon a previous interaction between the first contact and the
first agent; and establishing, by the at least one computer
processor, in a switch module of the contact center system, a
communication channel between the first agent and the first contact
to optimize performance of the contact center system attributable
to the previous interaction between the first contact and the first
agent. .Iaddend.
.Iadd.44. The method of claim 43, wherein contact outcome
information about a previous interaction between the first contact
and the first agent is stored in an affinity database.
.Iaddend.
.Iadd.45. The method of claim 43, wherein a round-robin contact
routing algorithm matches the first agent with a second contact
waiting in a queue of contacts. .Iaddend.
.Iadd.46. The method of claim 43, wherein a pattern-matching
algorithm matches the first agent with a second contact waiting in
a queue of contacts. .Iaddend.
.Iadd.47. The method of claim 43, wherein a round-robin contact
routing algorithm matches the first contact with a second agent
available for connection with a contact. .Iaddend.
.Iadd.48. The method of claim 43, wherein a pattern-matching
algorithm matches the first contact with a second agent available
for connection with a contact. .Iaddend.
.Iadd.49. The method of claim 43, further comprising: determining,
by the at least one computer processor, a first expected outcome
based on matching the first contact with the first agent;
determining, by the at least one computer processor, a second
expected outcome based on matching the first contact with a second
agent selected by a pattern-matching algorithm; and comparing, by
the at least one computer processor, the first expected outcome
with the second expected outcome. .Iaddend.
.Iadd.50. A system for handling contacts and agents in a contact
center system comprising: at least one computer processor
communicatively coupled to and configured to operate in the contact
center system, wherein the at least one computer processor is
configured to: determine if a first contact has previously
interacted with a first agent, wherein determining the previous
interaction is based at least in part upon: processing, by the at
least one computer processor, input data associated with contacts
in a set of contacts and agents in a set of agents using a first
measurement algorithm in a pair-wise fashion in a first layer of
processing, to output first measurement data for each contact-agent
pair of a set of contact-agent pairs; processing, by the at least
one computer processor, the input data associated with the contacts
in the set of contacts and the agents in the set of agents using a
second measurement algorithm in a pair-wise fashion in the first
layer of processing, to output second measurement data for each
contact-agent pair of the set of contact-agent pairs; receiving, by
the at least one computer processor, at a second layer of
processing, the first measurement data and the second measurement
data for each contact-agent pair of the set of contact-agent pairs;
processing, by the at least one computer processor, the first
measurement data and the second measurement data for each
contact-agent pair of the set of contact-agent pairs by a weighting
algorithm to weight the first measurement data for each
contact-agent pair of the set of contact-agent pairs relative to
the second measurement data for each contact-agent pair of the set
of contact-agent pairs in the second layer of processing, to output
a weighted contact-agent pair output measurement data; and
selecting, by the at least one computer processor, a contact-agent
pair based at least in part on the weighted contact-agent pair
output measurement data; match the first contact with the first
agent based at least in part upon a previous interaction between
the first contact and the first agent; and establish, in a switch
module of the contact center system, a communication channel
between the first agent and the first contact to optimize
performance of the contact center system attributable to the
previous interaction between the first contact and the first agent.
.Iaddend.
.Iadd.51. The system of claim 50, wherein contact outcome
information about a previous interaction between the first contact
and the first agent is stored in an affinity database.
.Iaddend.
.Iadd.52. The system of claim 50, wherein a round-robin contact
routing algorithm matches the first agent with a second contact
waiting in a queue of contacts. .Iaddend.
.Iadd.53. The system of claim 50, wherein a pattern-matching
algorithm matches the first agent with a second contact waiting in
a queue of contacts. .Iaddend.
.Iadd.54. The system of claim 50, wherein a round-robin contact
routing algorithm matches the first contact with a second agent
available for connection with a contact. .Iaddend.
.Iadd.55. The system of claim 50, wherein a pattern-matching
algorithm matches the first contact with a second agent available
for connection with a contact. .Iaddend.
.Iadd.56. The system of claim 50, wherein the at least one computer
processor is further configured to: determine a first expected
outcome based on matching the first contact with the first agent;
determine a second expected outcome based on matching the first
contact with a second agent selected by a pattern-matching
algorithm; and compare the first expected outcome with the second
expected outcome. .Iaddend.
.Iadd.57. An article of manufacture for handling contacts and
agents in a contact center system comprising: a non-transitory
processor readable medium; and instructions stored on the medium;
wherein the instructions are configured to be readable from the
medium by at least one computer processor communicatively coupled
to and configured to operate in the contact center system and
thereby cause the at least one computer processor to operate so as
to: determine if a first contact has previously interacted with a
first agent, wherein determining the previous interaction is based
at least in part upon: processing, by the at least one computer
processor, input data associated with contacts in a set of contacts
and agents in a set of agents using a first measurement algorithm
in a pair-wise fashion in a first layer of processing, to output
first measurement data for each contact-agent pair of a set of
contact-agent pairs; processing, by the at least one computer
processor, the input data associated with the contacts in the set
of contacts and the agents in the set of agents using a second
measurement algorithm in a pair-wise fashion in the first layer of
processing, to output second measurement data for each
contact-agent pair of the set of contact-agent pairs; receiving, by
the at least one computer processor, at a second layer of
processing, the first measurement data and the second measurement
data for each contact-agent pair of the set of contact-agent pairs;
processing, by the at least one computer processor, the first
measurement data and the second measurement data for each
contact-agent pair of the set of contact-agent pairs by a weighting
algorithm to weight the first measurement data for each
contact-agent pair of the set of contact-agent pairs relative to
the second measurement data for each contact-agent pair of the set
of contact-agent pairs in the second layer of processing, to output
a weighted contact-agent pair output measurement data; and
selecting, by the at least one computer processor, a contact-agent
pair based at least in part on the weighted contact-agent pair
output measurement data; match the first contact with the first
agent based at least in part upon a previous interaction between
the first contact and the first agent; and establish, in a switch
module of the contact center system, a communication channel
between the first agent and the first contact to optimize
performance of the contact center system attributable to the
previous interaction between the first contact and the first agent.
.Iaddend.
.Iadd.58. The article of manufacture of claim 57, wherein contact
outcome information about a previous interaction between the first
contact and the first agent is stored in an affinity database.
.Iaddend.
.Iadd.59. The article of manufacture of claim 57, wherein a
round-robin contact routing algorithm matches the first agent with
a second contact waiting in a queue of contacts. .Iaddend.
.Iadd.60. The article of manufacture of claim 57, wherein a
pattern-matching algorithm matches the first agent with a second
contact waiting in a queue of contacts. .Iaddend.
.Iadd.61. The article of manufacture of claim 57, wherein a
round-robin contact routing algorithm matches the first contact
with a second agent available for connection with a contact.
.Iaddend.
.Iadd.62. The article of manufacture of claim 57, wherein a
pattern-matching algorithm matches the first contact with a second
agent available for connection with a contact. .Iaddend.
Description
CROSS REFERENCE TO RELATED APPLICATION
This application is related to U.S. patent application Ser. No.
12/021,251, filed Jan. 28, 2008, which is hereby incorporated by
reference in its entirety.
BACKGROUND
1. Field
The present invention relates generally to the field of routing
phone calls and other telecommunications in a contact center
system.
2. Related Art
The typical contact center consists of a number of human agents,
with each assigned to a telecommunication device, such as a phone
or a computer for conducting email or Internet chat sessions, that
is connected to a central switch. Using these devices, the agents
are generally used to provide sales, customer service, or technical
support to the customers or prospective customers of a contact
center or a contact center's clients.
Typically, a contact center or client will advertise to its
customers, prospective customers, or other third parties a number
of different contact numbers or addresses for a particular service,
such as for billing questions or for technical support. The
customers, prospective customers, or third parties seeking a
particular service will then use this contact information, and the
incoming caller will be routed at one or more routing points to a
human agent at a contact center who can provide the appropriate
service. Contact centers that respond to such incoming contacts are
typically referred to as "inbound contact centers."
Similarly, a contact center can make outgoing contacts to current
or prospective customers or third parties. Such contacts may be
made to encourage sales of a product, provide technical support or
billing information, survey consumer preferences, or to assist in
collecting debts. Contact centers that make such outgoing contacts
are referred to as "outbound contact centers."
In both inbound contact centers and outbound contact centers, the
individuals (such as customers, prospective customers, survey
participants, or other third parties) that interact with contact
center agents using a telecommunication device are referred to in
this application as a "caller." The individuals acquired by the
contact center to interact with callers are referred to in this
application as an "agent."
Conventionally, a contact center operation includes a switch system
that connects callers to agents. In an inbound contact center,
these switches route incoming callers to a particular agent in a
contact center, or, if multiple contact centers are deployed, to a
particular contact center for further routing. In an outbound
contact center employing telephone devices, dialers are typically
employed in addition to a switch system. The dialer is used to
automatically dial a phone number from a list of phone numbers, and
to determine whether a live caller has been reached from the phone
number called (as opposed to obtaining no answer, a busy signal, an
error message, or an answering machine). When the dialer obtains a
live caller, the switch system routes the caller to a particular
agent in the contact center.
Routing technologies have accordingly been developed to optimize
the caller experience. For example, U.S. Pat. No. 7,236,584
describes a telephone system for equalizing caller waiting times
across multiple telephone switches, regardless of the general
variations in performance that may exist among those switches.
Contact routing in an inbound contact center, however, is a process
that is generally structured to connect callers to agents that have
been idle for the longest period of time. In the case of an inbound
caller where only one agent may be available, that agent is
generally selected for the caller without further analysis. In
another example, if there are eight agents at a contact center, and
seven are occupied with contacts, the switch will generally route
the inbound caller to the one agent that is available. If all eight
agents are occupied with contacts, the switch will typically put
the contact on hold and then route it to the next agent that
becomes available. More generally, the contact center will set up a
queue of incoming callers and preferentially route the
longest-waiting callers to the agents that become available over
time. Such a pattern of routing contacts to either the first
available agent or the longest-waiting agent is referred to as
"round-robin" contact routing. In round robin contact routing,
eventual matches and connections between a caller and an agent are
essentially random.
Some attempts have been made to improve upon these standard yet
essentially random processes for connecting a caller to an agent.
For example, U.S. Pat. No. 7,209,549 describes a telephone routing
system wherein an incoming caller's language preference is
collected and used to route their telephone call to a particular
contact center or agent that can provide service in that language.
In this manner, language preference is the primary driver of
matching and connecting a caller to an agent, although once such a
preference has been made, callers are almost always routed in
"round-robin" fashion.
BRIEF SUMMARY
Systems and methods of the present invention can be used to improve
or optimize the routing of callers to agents in a contact center.
According to one aspect of the present invention, a method for
routing callers to agents in a call-center routing system includes
using a multi-layer processing approach to matching a caller to an
agent, where a first layer of processing includes two or more
different computer models or methods for matching callers to
agents. The output of the first layer, e.g., the output of the
different methods for matching the callers to agents, is received
by a second layer of processing for balancing or weighting the
outputs and selecting a final caller-agent match for routing.
In one example, the two or more models or methods may include
conventional queue based routing, performance based matching (e.g.,
ranking a set of agents based on performance and preferentially
matching callers to the agents based on a performance ranking or
score), pattern matching algorithms (e.g., comparing .[.agent.].
.Iadd.caller .Iaddend.data associated with a set of callers to
agent data associated .Iadd.with .Iaddend.a set of agents and
determining a suitability score of different caller-agent pairs),
affinity data matching, and other models for matching callers to
agents. The methods may therefore operate to output scores or
rankings of the callers, agents, and/or caller-agent pairs for a
desired optimization (e.g., for optimizing cost, revenue, customer
satisfaction, and so on).
The output or scores of the two or more methods may be processed to
select a caller-agent pair and cause the caller to be routed to a
particular agent. For instance, the output of the two or more
methods may be balanced or weighted against each other to determine
a matching agent-caller pair. In one example, the output of the
different methods may be balanced equally to determine routing
instructions (e.g., the scores can be standardized and weighted
evenly to determine a "best" matching agent-caller pair from the
different methods). In other examples, the methods may be
unbalanced, e.g., weighting a pattern matching algorithm output
greater than a performance based routing output and so on.
Additionally, an interface may be presented to a user allowing for
adjustment of the balancing of the methods, e.g., a slider or
selector for adjusting the balance in real-time or a predetermined
time. The interface may allow a user to turn certain methods on and
off, change desired optimizations, and may display an estimated
effect of the balancing or a change in balancing of the different
routing methods.
In some examples, an adaptive algorithm (such as a neural network
or genetic algorithm) may be used to receive, as input, the outputs
of the two or more models to output a caller-agent pair. The
adaptive algorithm may compare performance over time and adapt to
pick the best model for a desired outcome variable.
According to another aspect, apparatus is provided comprising logic
for mapping and routing callers to agents. The apparatus may
include logic for receiving input data associated with callers and
agents at a first layer of processing, the first layer of
processing including at least two models for matching callers to
agents, each model outputting output data for at least one
caller-agent pair. The apparatus may further include logic for
receiving the output data from each processing model at a second
layer of processing, the second layer of processing operable to
balance the output data of the at least two models and map a caller
to an agent based on the received outputs.
Many of the techniques described here may be implemented in
hardware, firmware, software, or combinations thereof. In one
example, the techniques are implemented in computer programs
executing on programmable computers that each includes a processor,
a storage medium readable by the processor (including volatile and
nonvolatile memory and/or storage elements), and suitable input and
output devices. Program code is applied to data entered using an
input device to perform the functions described and to generate
output information. The output information is applied to one or
more output devices. Moreover, each program is preferably
implemented in a high level procedural or object-oriented
programming language to communicate with a computer system.
However, the programs can be implemented in assembly or machine
language, if desired. In any case, the language may be a compiled
or interpreted language.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a diagram reflecting the general setup of a contact
center operation.
FIG. 2 illustrates an exemplary routing system having a routing
engine for routing callers based on performance and/or pattern
matching algorithms.
FIG. 3 illustrates an exemplary routing system having a mapping
engine for routing callers based on performance and/or pattern
matching algorithms.
FIG. 4 illustrates an exemplary multi-layer approach to selecting a
caller-agent pair based on multiple matching methods.
FIG. 5 illustrates an exemplary method for scoring or ranking
agents, callers, and/or agent-caller pairs according to at least
two different methods and matching a caller to an agent based on a
balancing of the at least two different methods.
FIG. 6 illustrates another exemplary method for scoring or ranking
agents, callers, and/or agent-caller pairs according to at least
two different methods and matching a caller to an agent based on a
balancing of the at least two different methods.
FIG. 7 illustrates an exemplary method or computer model for
matching callers to agents based on performance.
FIG. 8 illustrates an exemplary method or computer model for
matching callers to agents based on caller data and agent data.
FIG. 9 illustrates a typical computing system that may be employed
to implement some or all processing functionality in certain
embodiments of the invention.
DETAILED DESCRIPTION OF THE INVENTION
The following description is presented to enable a person of
ordinary skill in the art to make and use the invention, and is
provided in the context of particular applications and their
requirements. Various modifications to the embodiments will be
readily apparent to those skilled in the art, and the generic
principles defined herein may be applied to other embodiments and
applications without departing from the spirit and scope of the
invention. Moreover, in the following description, numerous details
are set forth for the purpose of explanation. However, one of
ordinary skill in the art will realize that the invention might be
practiced without the use of these specific details. In other
instances, well-known structures and devices are shown in block
diagram form in order not to obscure the description of the
invention with unnecessary detail. Thus, the present invention is
not intended to be limited to the embodiments shown, but is to be
accorded the widest scope consistent with the principles and
features disclosed herein.
While the invention is described in terms of particular examples
and illustrative figures, those of ordinary skill in the art will
recognize that the invention is not limited to the examples or
figures described. Those skilled in the art will recognize that the
operations of the various embodiments may be implemented using
hardware, software, firmware, or combinations thereof, as
appropriate. For example, some processes can be carried out using
processors or other digital circuitry under the control of
software, firmware, or hardwired logic. (The term "logic" herein
refers to fixed hardware, programmable logic and/or an appropriate
combination thereof, as would be recognized by one skilled in the
art to carry out the recited functions.) Software and firmware can
be stored on computer-readable storage media. Some other processes
can be implemented using analog circuitry, as is well known to one
of ordinary skill in the art. Additionally, memory or other
storage, as well as communication components, may be employed in
embodiments of the invention.
According to one aspect of the present invention systems, methods,
and displayed computer interfaces are provided for routing callers
to agents within a call center. In one example, a method includes
using a first layer of processing, the first layer including two or
more methods or models for determining caller-agent pairs. For
example, the two or more methods may include conventional queue
based routing, performance based matching (e.g., ranking a set of
agents based on performance and preferentially matching callers to
the agents based on a performance ranking or score), pattern
matching algorithms (e.g., comparing agent data associated with a
set of callers to agent data associated a set of agents and
determine a suitability score of different caller-agent pairs),
affinity data matching, and other models for matching callers to
agents. The methods may therefore operate to output scores or
rankings of the callers, agents, and/or caller-agent pairs for a
desired optimization (e.g., for optimizing cost, revenue, customer
satisfaction, and so on) to a second layer of processing. The
second layer of processing may receive the output of the first
layer and determine an agent-caller pair based on the output of
different methods of the first layer of processing. In one example,
the second layer of processing includes a computer model to balance
or weight the different outputs, which may be altered by a
user.
Initially, exemplary call routing systems and methods utilizing
performance and/or pattern matching algorithms (either of which may
be used within generated computer models for predicting the chances
of desired outcomes) are described for routing callers to available
agents. This description is followed by exemplary systems and
methods for multi-layer processing of input data to select a
caller-agent pairing.
FIG. 1 is a diagram reflecting the general setup of a contact
center operation 100. The network cloud 101 reflects a specific or
regional telecommunications network designed to receive incoming
callers or to support contacts made to outgoing callers. The
network cloud 101 can comprise a single contact address, such as a
telephone number or email address, or multiple contract addresses.
The central router 102 reflects contact routing hardware and
software designed to help route contacts among call centers 103.
The central router 102 may not be needed where there is only a
single contact center deployed. Where multiple contact centers are
deployed, more routers may be needed to route contacts to another
router for a specific contact center 103. At the contact center
level 103, a contact center router 104 will route a contact to an
agent 105 with an individual telephone or other telecommunications
equipment 105. Typically, there are multiple agents 105 at a
contact center 103, though there are certainly embodiments where
only one agent 105 is at the contact center 103, in which case a
contact center router 104 may prove to be unnecessary.
FIG. 2 illustrates an exemplary contact center routing system 200
(which may be included with contact center router 104 of FIG. 1).
Broadly speaking, routing system 200 is operable to match callers
and agents based, at least in part, on agent performance or pattern
matching algorithms using caller data and/or agent data. Routing
system 200 may include a communication server 202 and a routing
engine 204 (referred to at times as "SatMap" or "Satisfaction
Mapping") for receiving and matching callers to agents (referred to
at times as "mapping" callers to agents).
Routing engine 204 may operate in various manners to match callers
to agents based on performance data of agents, pattern matching
algorithms, and computer models, which may adapt over time based on
the performance or outcomes of previous caller-agent matches. In
one example, the routing engine 204 includes a neural network based
adaptive pattern matching engine. Various other exemplary pattern
matching and computer model systems and methods which may be
included with content routing system and/or routing engine 204 are
described, for example, in U.S. Ser. No. 12/021,251, filed Jan. 28,
2008, and U.S. Ser. No. U.S. patent application Ser. No.
12/202,091, filed Aug. 29, 2008, both of which are hereby
incorporated by reference in their entirety. Of course, it will be
recognized that other performance based or pattern matching
algorithms and methods may be used alone or in combination with
those described here.
Routing system 200 may further include other components such as
collector 206 for collecting caller data of incoming callers, data
regarding caller-agent pairs, outcomes of caller-agent pairs, agent
data of agents, and the like. Further, routing system 200 may
include a reporting engine 208 for generating reports of
performance and operation of routing system 200. Various other
servers, components, and functionality are possible for inclusion
with routing system 200. Further, although shown as a single
hardware device, it will be appreciated that various components may
be located remotely from each other (e.g., communication server 202
and routing engine 204 need not be included with a common
hardware/server system or included at a common location).
Additionally, various other components and functionality may be
included with routing system 200, but have been omitted here for
clarity.
FIG. 3 illustrates detail of exemplary routing engine 204. Routing
engine 204 includes a main mapping engine 304, which receives
caller data and agent data from databases 310 and 312. In some
examples, routing engine 204 may route callers based solely or in
part on performance data associated with agents. In other examples,
routing engine 204 may make routing decisions based solely or in
part on comparing various caller data and agent data, which may
include, e.g., performance based data, demographic data,
psychographic data, and other business-relevant data. Additionally,
affinity databases (not shown) may be used and such information
received by routing engine 204 for making routing decisions.
In one example, routing engine 204 includes or is in communication
with one or more neural network engines 306. Neural network engines
306 may receive caller and agent data directly or via routing
engine 204 and operate to match and route callers based on pattern
matching algorithms and computer models generated to increase the
changes of desired outcomes. Further, as indicated in FIG. 3, call
history data (including, e.g., caller-agent pair outcomes with
respect to cost, revenue, customer satisfaction, etc.) may be used
to retrain or modify the neural network engine 306.
Routing engine 204 further includes or is in communication with
hold queue 308, which may store or access hold or idle times of
callers and agents, and operates to map callers to agents based on
queue order of the callers (and/or agents). Mapping engine 304 may
operate, for example, to map callers based on a pattern matching
algorithm, e.g., as included with neural network engine 306, or
based on queue order, e.g., as retrieved from hold queue 308.
FIG. 4 illustrates an exemplary mapping system 406. Mapping system
406 includes two layers of processing - a first layer includes at
least two processing engines or computer models as indicated by
420-1, 420-2, and 420-3. The processing engines 420-1, 420-2, and
420-3 may each operate on different data and/or according to a
different model or method for matching callers to agents. In this
particular example, processing engine 420-1 may receive agent grade
data, e.g., data associated with agent performance for a particular
desired performance. As will be described in further detail with
respect to FIG. 7 below, performance based routing may include
ranking or scoring a set of agents based on performance for a
particular outcome (such as revenue generation, cost, customer
satisfaction, combinations thereof, and the like) and
preferentially routing callers to agents based on a performance
ranking or score. Accordingly, processing engine 420-1 may receive
agent grades or agent history data and output one or more rankings
of agents based on one or more desired outcome variables.
Processing engine 420-2, in this example, includes one or more
pattern matching algorithms, which may operate to compare agent
data associated with a set of callers to agent data associated a
set of agents and determine a suitability score of each
caller-agent pair. Processing engine 420-2 may receive caller data
and agent data from various databases and output caller-agent pair
scores or a ranking of caller-agent pairs, for example. The pattern
matching algorithm may include a neural network algorithm, genetic
algorithm, or other adaptive algorithms. Further, in some examples,
different processing engines may be used with different pattern
matching algorithms operating on the same or different input data,
e.g., a first processing engine utilizing a neural network
algorithm and a second processing engine utilizing a different
algorithm such as a genetic algorithm or other pattern matching
algorithm. Additionally, first and second processing engines may
include similar pattern matching algorithms operable to maximize
different output variables; for example, a first neural network
algorithm operable to maximize revenue and a second neural network
algorithm operable to maximize customer satisfaction.
Processing engine 420-3, in this example, includes one or more
affinity matching algorithms, which operate to receive affinity
data associated with the callers and/or agents. Processing engine
420-3 may receive affinity data from various databases and output
caller-agent pairs or a ranking of caller-agent pairs based, at
least in part, on the affinity data. It should be noted that
various other methods or models may be used in the first layer of
processing, and further that the first layer of processing may
include multiple sub-layers of processing (e.g., processing engine
420-1 outputting to processing engine 420-2 and so on). Further, in
some examples a processing engine may include conventional queue
based routing, e.g., routing agents and callers based on queue
order.
As described, the processing engines 420-1, 420-2, and 420-3 each
output scores or rankings of the callers, agents, and/or
caller-agent pairs for a desired optimization (e.g., for optimizing
cost, revenue, customer satisfaction, and so on). The output or
scores of the two or more methods may then be processed by
balancing manager 410, e.g., at the second level of processing, to
select a caller-agent pair. For instance, the output of processing
engines 420-1, 420-2, and 420-3 is received by balancing manager
410 and may be weighted against each other to determine a matching
agent-caller pair. In one example, the outputs of processing
engines 420-1, 420-2, and 420-3 are balanced equally to determine
routing instructions (e.g., the scores can be standardized and
weighted evenly to determine a "best" matching agent-caller pair).
In other examples, the methods may be unbalanced, e.g., weighting a
pattern matching algorithm method output greater than a performance
based routing method, turning certain processing engines "off", and
so on.
Additionally, an interface may be presented to a user allowing for
adjustment of balancing manager 410, e.g., a slider or selector for
adjusting the balance of the processing engines in real-time or at
a predetermined time. Additionally, the interface may allow a user
to turn certain methods on and off, and may display an estimated
effect of the balancing or a change in the balancing. For instance,
an interface may display the probable change in one or more of
cost, revenue generation, or customer satisfaction by changing the
operation of balancing manager 410. Various estimation methods and
algorithms for estimating outcome variables are described, for
example, in copending U.S. provisional Patent application Ser. No.
61/084,201, filed on Jul. 28, 2008, and which is incorporated
herein by reference in its entirety. In one example, the estimate
includes evaluating a past time period of the same (or similar) set
of agents and constructing a distribution of agent/caller pairs.
Using each pair, an expected success rate can be computed via the
performance based matching, pattern matching algorithm, etc., and
applied to current information to estimate current performance
(e.g., with respect to one or more of sales, cost, customer
satisfaction, etc.). Accordingly, taking historical call data and
agent information the system can compute estimates of changing the
balance or weighting of the level one processing methods. It is
noted that a comparable time (e.g., time of day, day of the week
etc.) for the historical information may be important as
performance will likely vary with time.
In some examples, balancing manager 410 may include an adaptive
algorithm (such as a neural network or genetic algorithm) for
receiving, as input, the outputs of the two or more models to
output a caller-agent pair. Accordingly, balancing manger 410 via
an adaptive algorithm may compare performance over time and adapt
to pick or weight the level one processing engines to increase the
chances of a desired outcome.
FIG. 5 illustrates an exemplary method for scoring or ranking
agents, callers, and/or agent-caller pairs according to at least
two different computer models or methods and matching a caller to
an agent based on a balancing of the at least two different models.
In this example, a caller, agent, or caller-agent pair is scored
based on at least first input data at 502. The input data may
include agent performance grades, caller data and/or agent data,
queue order of the callers and agents, combinations thereof, and so
on. Further, the score may include a raw score, normalized score,
ranking relative to other callers, agents, and/or caller-agent
pairs, and so on.
The method further includes scoring callers, agents, or
caller-agent pairs at 504 according to a second model for mapping
callers to agents, the second model different than the first model.
Note, however, the second model may use some or all of the same
first input data as used in 502 or may rely on different input
data, e.g., at least a second input data. Similarly, the scoring
may include a raw score, normalized score, ranking relative to
other callers, agents, and/or caller-agent pairs, and so on.
The scores determined in 502 and 504 may be balanced at 506 to
determine routing instructions for a caller. The balancing may
include weighting scores from 502 and 504 equally or unequally, and
may be adjusted over time by a user or in response to adaptive
feedback of the system. It will also be recognized that the scores
output from 502 and 504 may be normalized in any suitable fashion,
e.g., computing a Z-score or the like as described in co-pending
U.S. patent application Ser. No. 12/202,091, filed on Aug. 29,
2008, which is incorporated herein by reference in its
entirety.
The final selection or mapping of a caller to an agent may then be
passed to a routing engine or router for causing the caller to be
routed to the agent at 508. It is noted that the described actions
do not need to occur in the order in which they are stated and some
acts may be performed in parallel (for example, the first layer
processing of 502 and 504 may be performed partially or wholly in
parallel). Further, additional models for scoring and mapping
callers to agents may be used and output to the balancing at 506
for determining a final selection of a caller-agent pair.
FIG. 6 illustrates another exemplary method for scoring or ranking
agents, callers, and/or agent-caller pairs according to at least
two different methods and matching a caller to an agent based on a
balancing of the at least two different methods. In this particular
example, a first model operates to score a set of agents based on
performance at 602, and may output a ranking or score associated
with the performance of the agents. Such a method for ranking
agents based on performance is described in greater detail with
respect to FIG. 7 below.
The method further includes scoring caller-agent pairs at 604
according to a second model for mapping callers to agents, in
particular, according to a pattern matching algorithm. The pattern
matching algorithm may include comparing caller data and agent data
for each caller-agent pair and computing a suitability score or
ranking of caller-agent pairs for a desired outcome variable (or
weighting of outcome variables). Such a pattern matching algorithm
is described in greater detail with respect to FIG. 8 below, and
may include a neural network algorithm.
The method further includes scoring caller-agent pairs at 606
according to a third model for mapping callers to agents based on
affinity data. The use of affinity data and affinity databases
alone or in combination with pattern matching algorithms is
described in greater detail below.
The scores (or rankings) determined in 602, 604, and 606 may be
balanced at 608 to determine the routing instructions for a caller.
The balancing may include weighting scores from 602, 604, and 606
equally or unequally, and may be adjusted by a user or in response
to adaptive feedback of the system. It will also be recognized that
the scores output from 602, 604, and 60 may be normalized in any
suitable fashion as described with respect to FIG. 5.
The final selection or mapping of a caller to an agent may then be
passed to a routing engine or router for causing the caller to be
routed to the agent. It is again noted that the described actions
do not need to occur in the order in which they are stated and some
acts may be performed in parallel (for example, the first layer
processing of 602, 604, and 606 may be performed partially or
wholly in parallel). Further, additional (or fewer) matching models
for scoring and mapping callers to agents may be used and output to
the balancing at 608 for determining a final selection of a
caller-agent pair.
FIG. 7 illustrates a flowchart of an exemplary method or model for
matching callers to agents based on performance. The method
includes grading two agents on an optimal interaction and matching
a caller with at least one of the two graded agents to increase the
chance of the optimal interaction. At the initial block 701, agents
are graded on an optimal interaction, such as increasing revenue,
decreasing costs, or increasing customer satisfaction. Grading can
be accomplished by collating the performance of a contact center
agent over a period of time on their ability to achieve an optimal
interaction, such as a period of at least 10 days. However, the
period of time can be as short as the immediately prior contact to
a period extending as long as the agent's first interaction with a
caller. Moreover, the method of grading agent can be as simple as
ranking each agent on a scale of 1 to N for a particular optimal
interaction, with N being the total number of agents. The method of
grading can also comprise determining the average contact handle
time of each agent to grade the agents on cost, determining the
total sales revenue or number of sales generated by each agent to
grade the agents on sales, or conducting customer surveys at the
end of contacts with callers to grade the agents on customer
satisfaction. The foregoing, however, are only examples of how
agents may be graded; many other methods may be used.
At block 702 a caller uses contact information, such as a telephone
number or email address, to initiate a contact with the contact
center. At block 703, the caller is matched with an agent or group
of agents such that the chance of an optimal interaction is
increased, as opposed to just using the round robin matching
methods of the prior art. The method may further include grading a
group of at least two agents on two optimal interactions, weighting
one optimal interaction against another optional interaction, and
matching the caller with one of the two graded agents to increase
the chance of a more heavily-weighted optimal interaction. In
particular, agents may be graded on two or more optimal
interactions, such as increasing revenue, decreasing costs, or
increasing customer satisfaction, which may then be weighted
against each other. The weighting can be as simple as assigning to
each optimal interaction a percentage weight factor, with all such
factors totaling to 100 percent. Any comparative weighting method
can be used, however. The weightings placed on the various optimal
interactions can take place in real-time in a manner controlled by
the contact center, its clients, or in line with pre-determined
rules. Optionally, the contact center or its clients may control
the weighting over the internet or some another data transfer
system. As an example, a client of the contact center could access
the weightings currently in use over an internet browser and modify
these remotely. Such a modification may be set to take immediate
effect and, immediately after such a modification, subsequent
caller routings occur in line with the newly establishing
weightings. An instance of such an example may arise in a case
where a contact center client decides that the most important
strategic priority in their business at present is the maximization
of revenues. In such a case, the client would remotely set the
weightings to favor the selection of agents that would generate the
greatest probability of a sale in a given contact. Subsequently the
client may take the view that maximization of customer satisfaction
is more important for their business. In this event, they can
remotely set the weightings of the present invention such that
callers are routed to agents most likely to maximize their level of
satisfaction. Alternatively the change in weighting may be set to
take effect at a subsequent time, for instance, commencing the
following morning
FIG. 8 illustrate another exemplary model or method for matching a
caller to an agent, and which may combine agent grades, agent
demographic data, agent psychographic data, and other
business-relevant data about the agent (individually or
collectively referred to in this application as "agent data"),
along with demographic, psychographic, and other business-relevant
data about callers (individually or collectively referred to in
this application as "caller data"). Agent and caller demographic
data can comprise any of: gender, race, age, education, accent,
income, nationality, ethnicity, area code, zip code, marital
status, job status, and credit score. Agent and caller
psychographic data can comprise any of introversion, sociability,
desire for financial success, and film and television preferences.
It will be appreciated that the acts outlined in the flowchart of
FIG. 8 need not occur in that exact order.
This exemplary model or method includes determining at least one
caller data for a caller, determining at least one agent data for
each of two agents, using the agent data and the caller data in a
pattern matching algorithm, and matching the caller to one of the
two agents to increase the chance of an optimal interaction. At
801, at least one caller data (such as a caller demographic or
psychographic data) is determined. One way of accomplishing this is
by retrieving this from available databases by using the caller's
contact information as an index. Available databases include, but
are not limited to, those that are publicly available, those that
are commercially available, or those created by a contact center or
a contact center client. In an outbound contact center environment,
the caller's contact information is known beforehand. In an inbound
contact center environment, the caller's contact information can be
retrieved by examining the caller's CallerID information or by
requesting this information of the caller at the outset of the
contact, such as through entry of a caller account number or other
caller-identifying information. Other business-relevant data such
as historic purchase behavior, current level of satisfaction as a
customer, or volunteered level of interest in a product may also be
retrieved from available databases.
At 802, at least one agent data for each of two agents is
determined. One method of determining agent demographic or
psychographic data can involve surveying agents at the time of
their employment or periodically throughout their employment. Such
a survey process can be manual, such as through a paper or oral
survey, or automated with the survey being conducted over a
computer system, such as by deployment over a web-browser.
Though this advanced embodiment preferably uses agent grades,
demographic, psychographic, and other business-relevant data, along
with caller demographic, psychographic, and other business-relevant
data, other embodiments of the present invention can eliminate one
or more types or categories of caller or agent data to minimize the
computing power or storage necessary to employ the present
invention.
Once agent data and caller data have been collected, this data is
passed to a computational system. The computational system then, in
turn, uses this data in a pattern matching algorithm at 803 to
create a computer model that matches each agent with the caller and
estimates the probable outcome of each matching along a number of
optimal interactions, such as the generation of a sale, the
duration of contact, or the likelihood of generating an interaction
that a customer finds satisfying.
The pattern matching algorithm to be used in the present invention
can comprise any correlation algorithm, such as a neural network
algorithm or a genetic algorithm. To generally train or otherwise
refine the algorithm, actual contact results (as measured for an
optimal interaction) are compared against the actual agent and
caller data for each contact that occurred. The pattern matching
algorithm can then learn, or improve its learning of, how matching
certain callers with certain agents will change the chance of an
optimal interaction. In this manner, the pattern matching algorithm
can then be used to predict the chance of an optimal interaction in
the context of matching a caller with a particular set of caller
data, with an agent of a particular set of agent data. Preferably,
the pattern matching algorithm is periodically refined as more
actual data on caller interactions becomes available to it, such as
periodically training the algorithm every night after a contact
center has finished operating for the day.
At 804, the pattern matching algorithm is used to create a computer
model reflecting the predicted chances of an optimal interaction
for each agent and caller matching. Preferably, the computer model
will comprise the predicted chances for a set of optimal
interactions for every agent that is logged in to the contact
center as matched against every available caller. Alternatively,
the computer model can comprise subsets of these, or sets
containing the aforementioned sets. For example, instead of
matching every agent logged into the contact center with every
available caller, the present invention can match every available
agent with every available caller, or even a narrower subset of
agents or callers. Likewise, the present invention can match every
agent that ever worked on a particular campaign--whether available
or logged in or not--with every available caller. Similarly, the
computer model can comprise predicted chances for one optimal
interaction or a number of optimal interactions.
The computer model can also be further refined to comprise a
suitability score for each matching of an agent and a caller. The
suitability score can be determined by taking the chances of a set
of optimal interactions as predicted by the pattern matching
algorithm, and weighting those chances to place more or less
emphasis on a particular optimal interaction as related to another
optimal interaction. The suitability score can then be used in the
present invention to determine which agents should be connected to
which callers.
In other examples, exemplary models or methods may utilize affinity
data associated with callers and/or agents. For example, affinity
data may relate to an individual caller's contact outcomes
(referred to in this application as "caller affinity data"),
independent of their demographic, psychographic, or other
business-relevant information. Such caller affinity data can
include the caller's purchase history, contact time history, or
customer satisfaction history. These histories can be general, such
as the caller's general history for purchasing products, average
contact time with an agent, or average customer satisfaction
ratings. These histories can also be agent specific, such as the
caller's purchase, contact time, or customer satisfaction history
when connected to a particular agent.
As an example, a certain caller may be identified by their caller
affinity data as one highly likely to make a purchase, because in
the last several instances in which the caller was contacted, the
caller elected to purchase a product or service. This purchase
history can then be used to appropriately refine matches such that
the caller is preferentially matched with an agent deemed suitable
for the caller to increase the chances of an optimal interaction.
Using this embodiment, a contact center could preferentially match
the caller with an agent who does not have a high grade for
generating revenue or who would not otherwise be an acceptable
match, because the chance of a sale is still likely given the
caller's past purchase behavior. This strategy for matching would
leave available other agents who could have otherwise been occupied
with a contact interaction with the caller. Alternatively, the
contact center may instead seek to guarantee that the caller is
matched with an agent with a high grade for generating revenue,
irrespective of what the matches generated using caller data and
agent demographic or psychographic data may indicate.
In one example, affinity data and an affinity database developed by
the described examples may be one in which a caller's contact
outcomes are tracked across the various agent data. Such an
analysis might indicate, for example, that the caller is most
likely to be satisfied with a contact if they are matched to an
agent of similar gender, race, age, or even with a specific agent.
Using this embodiment, the present invention could preferentially
match a caller with a specific agent or type of agent that is known
from the caller affinity data to have generated an acceptable
optimal interaction.
Affinity databases can provide particularly actionable information
about a caller when commercial, client, or publicly-available
database sources may lack information about the caller. This
database development can also be used to further enhance contact
routing and agent-to-caller matching even in the event that there
is available data on the caller, as it may drive the conclusion
that the individual caller's contact outcomes may vary from what
the commercial databases might imply. As an example, if the present
invention was to rely solely on commercial databases in order to
match a caller and agent, it may predict that the caller would be
best matched to an agent of the same gender to achieve optimal
customer satisfaction. However, by including affinity database
information developed from prior interactions with the caller, the
present invention might more accurately predict that the caller
would be best matched to an agent of the opposite gender to achieve
optimal customer satisfaction.
Another aspect of the present invention is that it may develop
affinity databases that comprise revenue generation, cost, and
customer satisfaction performance data of individual agents as
matched with specific caller demographic, psychographic, or other
business-relevant characteristics (referred to in this application
as "agent affinity data"). An affinity database such as this may,
for example, result in the present invention predicting that a
specific agent performs best in interactions with callers of a
similar age, and less well in interactions with a caller of a
significantly older or younger age. Similarly this type of affinity
database may result in the present invention predicting that an
agent with certain agent affinity data handles callers originating
from a particular geography much better than the agent handles
callers from other geographies. As another example, the present
invention may predict that a particular agent performs well in
circumstances in which that agent is connected to an irate
caller.
Though affinity databases are preferably used in combination with
agent data and caller data that pass through a pattern matching
algorithm to generate matches, information stored in affinity
databases can also be used independently of agent data and caller
data such that the affinity information is the only information
used to generate matches. For instance, in some examples, the first
level of processing may include a first computer model that relies
on both a pattern matching algorithm and affinity data, and a
second computer model that relies on affinity data alone.
Many of the techniques described here may be implemented in
hardware or software, or a combination of the two. Preferably, the
techniques are implemented in computer programs executing on
programmable computers that each includes a processor, a storage
medium readable by the processor (including volatile and
nonvolatile memory and/or storage elements), and suitable input and
output devices. Program code is applied to data entered using an
input device to perform the functions described and to generate
output information. The output information is applied to one or
more output devices. Moreover, each program is preferably
implemented in a high level procedural or object-oriented
programming language to communicate with a computer system.
However, the programs can be implemented in assembly or machine
language, if desired. In any case, the language may be a compiled
or interpreted language.
Each such computer program is preferably stored on a storage medium
or device (e.g., CD-ROM, hard disk or magnetic diskette) that is
readable by a general or special purpose programmable computer for
configuring and operating the computer when the storage medium or
device is read by the computer to perform the procedures described.
The system also may be implemented as a computer-readable storage
medium, configured with a computer program, where the storage
medium so configured causes a computer to operate in a specific and
predefined manner.
FIG. 9 illustrates a typical computing system 900 that may be
employed to implement processing functionality in embodiments of
the invention. Computing systems of this type may be used in
clients and servers, for example. Those skilled in the relevant art
will also recognize how to implement the invention using other
computer systems or architectures. Computing system 900 may
represent, for example, a desktop, laptop or notebook computer,
hand-held computing device (PDA, cell phone, palmtop, etc.),
mainframe, server, client, or any other type of special or general
purpose computing device as may be desirable or appropriate for a
given application or environment. Computing system 900 can include
one or more processors, such as a processor 904. Processor 904 can
be implemented using a general or special purpose processing engine
such as, for example, a microprocessor, microcontroller or other
control logic. In this example, processor 904 is connected to a bus
902 or other communication medium.
Computing system 900 can also include a main memory 908, such as
random access memory (RAM) or other dynamic memory, for storing
information and instructions to be executed by processor 904. Main
memory 908 also may be used for storing temporary variables or
other intermediate information during execution of instructions to
be executed by processor 904. Computing system 900 may likewise
include a read only memory ("ROM") or other static storage device
coupled to bus 902 for storing static information and instructions
for processor 904.
The computing system 900 may also include information storage
system 910, which may include, for example, a media drive 912 and a
removable storage interface 920. The media drive 912 may include a
drive or other mechanism to support fixed or removable storage
media, such as a hard disk drive, a floppy disk drive, a magnetic
tape drive, an optical disk drive, a CD or DVD drive (R or RW), or
other removable or fixed media drive. Storage media 918 may
include, for example, a hard disk, floppy disk, magnetic tape,
optical disk, CD or DVD, or other fixed or removable medium that is
read by and written to by media drive 912. As these examples
illustrate, the storage media 918 may include a computer-readable
storage medium having stored therein particular computer software
or data.
In alternative embodiments, information storage system 910 may
include other similar components for allowing computer programs or
other instructions or data to be loaded into computing system 900.
Such components may include, for example, a removable storage unit
922 and an interface 920, such as a program cartridge and cartridge
interface, a removable memory (for example, a flash memory or other
removable memory module) and memory slot, and other removable
storage units 922 and interfaces 920 that allow software and data
to be transferred from the removable storage unit 918 to computing
system 900.
Computing system 900 can also include a communications interface
924. Communications interface 924 can be used to allow software and
data to be transferred between computing system 900 and external
devices. Examples of communications interface 924 can include a
modem, a network interface (such as an Ethernet or other NIC card),
a communications port (such as for example, a USB port), a PCMCIA
slot and card, etc. Software and data transferred via
communications interface 924 are in the form of signals which can
be electronic, electromagnetic, optical or other signals capable of
being received by communications interface 924. These signals are
provided to communications interface 924 via a channel 928. This
channel 928 may carry signals and may be implemented using a
wireless medium, wire or cable, fiber optics, or other
communications medium. Some examples of a channel include a phone
line, a cellular phone link, an RF link, a network interface, a
local or wide area network, and other communications channels.
In this document, the terms "computer program product,"
"computer-readable medium" and the like may be used generally to
refer to physical, tangible media such as, for example, memory 908,
storage media 918, or storage unit 922. These and other forms of
computer-readable media may be involved in storing one or more
instructions for use by processor 904, to cause the processor to
perform specified operations. Such instructions, generally referred
to as "computer program code" (which may be grouped in the form of
computer programs or other groupings), when executed, enable the
computing system 900 to perform features or functions of
embodiments of the present invention. Note that the code may
directly cause the processor to perform specified operations, be
compiled to do so, and/or be combined with other software,
hardware, and/or firmware elements (e.g., libraries for performing
standard functions) to do so.
In an embodiment where the elements are implemented using software,
the software may be stored in a computer-readable medium and loaded
into computing system 900 using, for example, removable storage
media 918, drive 912 or communications interface 924. The control
logic (in this example, software instructions or computer program
code), when executed by the processor 904, causes the processor 904
to perform the functions of the invention as described herein,
It will be appreciated that, for clarity purposes, the above
description has described embodiments of the invention with
reference to different functional units and processors. However, it
will be apparent that any suitable distribution of functionality
between different functional units, processors or domains may be
used without detracting from the invention. For example,
functionality illustrated to be performed by separate processors or
controllers may be performed by the same processor or controller.
Hence, references to specific functional units are only to be seen
as references to suitable means for providing the described
functionality, rather than indicative of a strict logical or
physical structure or organization.
The above-described embodiments of the present invention are merely
meant to be illustrative and not limiting. Various changes and
modifications may be made without departing from the invention in
its broader aspects. The appended claims encompass such changes and
modifications within the spirit and scope of the invention.
* * * * *
References