U.S. patent application number 16/422075 was filed with the patent office on 2019-09-12 for method and apparatus for facilitating staffing of resources.
The applicant listed for this patent is [24]7.ai, Inc.. Invention is credited to Kranthi Mitra ADUSUMILLI, Pallipuram V. KANNAN.
Application Number | 20190279141 16/422075 |
Document ID | / |
Family ID | 56108200 |
Filed Date | 2019-09-12 |
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United States Patent
Application |
20190279141 |
Kind Code |
A1 |
KANNAN; Pallipuram V. ; et
al. |
September 12, 2019 |
METHOD AND APPARATUS FOR FACILITATING STAFFING OF RESOURCES
Abstract
A computer-implemented method and an apparatus for facilitating
staffing of resources receives customer data corresponding to a
plurality of customers of an enterprise. At least one intention is
predicted for each customer to configure a plurality of intentions.
An expected volume of interactions is estimated for at least one
time period based on the plurality of intentions. Each interaction
in the expected volume of interactions is associated with
interaction attributes. Resource data corresponding to a plurality
of resources of the enterprise is received. Each resource is
associated with a plurality of resource attributes. At least one
resource is mapped to each interaction based on a match between
resource attributes associated with the at least one resource and
the interaction attributes associated with the each interaction. A
staffing of the plurality of resources is facilitated based on the
mapping of the at least one resource to the each interaction.
Inventors: |
KANNAN; Pallipuram V.;
(Saratoga, CA) ; ADUSUMILLI; Kranthi Mitra;
(Hyderabad, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
[24]7.ai, Inc. |
San Jose |
CA |
US |
|
|
Family ID: |
56108200 |
Appl. No.: |
16/422075 |
Filed: |
May 24, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
14964540 |
Dec 9, 2015 |
10339477 |
|
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16422075 |
|
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62090298 |
Dec 10, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0202 20130101;
G06Q 10/06313 20130101; G06Q 10/063118 20130101 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06; G06Q 30/02 20060101 G06Q030/02 |
Claims
1. A computer-implemented method comprising: receiving, by a
processor, customer data of a plurality of customers of an
enterprise; predicting, by the processor, at least one intention
for each customer from among the plurality of customers by using
the customer data thereby configuring a plurality of intentions;
estimating, by the processor, an expected volume of interactions
for at least one time period based on the plurality of intentions,
each interaction in the expected volume of interactions being
associated with one or more interaction attributes; receiving
resource data corresponding to a plurality of resources of the
enterprise, each resource from among the plurality of resources
being associated with a plurality of resource attributes; mapping,
by the processor, at least one resource from among the plurality of
resources to each interaction in the expected volume of
interactions based on a match between respective resource
attributes associated with the at least one resource and the one or
more interaction attributes associated with each interaction;
facilitating, by the processor, an allocation of the plurality of
resources of the enterprise based, at least in part, on the mapping
of the at least one resource to each interaction; and dynamically
adjusting an allocation of one or more resources from among the
plurality of resources for at least one forthcoming time period
upon detecting an occurrence of at least one event indicating a
change in respective expected volume of interactions.
2. The method of claim 1, wherein the customer data comprises
information related to at least one of a profile of a customer, one
or more interactions of the customer with the enterprise on one or
more interaction channels, one or more locations associated with
the customer, a presence of the customer on the one or more
interaction channels, a current attention of the customer in an
interaction channel from among the one or more interaction
channels, or social interactions of the customer and events of
significance to the customer.
3. The method of claim 1, wherein the resource data comprises data
corresponding to each resource from among the plurality of
resources, the data corresponding to each resource comprising
information related to at least one of a type of a resource, an
identification of the resource, a service level agreement
associated with the resource or a performance history of the
resource.
4. The method of claim 1, wherein at least one resource attribute
from among the plurality of resource attributes relates to a
capability of the resource and an availability status of the
resource.
5. The method of claim 1, wherein the one or more interaction
attributes associated with each interaction comprises information
related to at least one of an intention of a customer associated
with an interaction, or a priority level associated with the
interaction and a customer lifetime value (CLU) of the
customer.
6. The method of claim 1 further comprising: tracking, by the
processor, an activity of a customer on an interaction channel from
among one or more interaction channels; determining, by the
processor, if the customer requires an interaction assistance based
on the tracked activity; and determining, by the processor, if a
request for an interaction is to be proactively offered to the
customer on the interaction channel upon determining that the
customer requires the interaction assistance.
7. The method of claim 6 further comprising: determining, by the
processor, an appropriate time for proactively offering the request
for the interaction to the customer on the interaction channel, the
appropriate time being determined based on a likelihood of a
desired outcome being greater than a pre-determined value.
8. The method of claim 1, wherein the plurality of customers of the
enterprise comprises existing and prospective customers of the
enterprise.
9. An apparatus, comprising: at least one processor; and a memory
having stored therein machine executable instructions, that when
executed by the at least one processor, cause the apparatus to:
receive customer data corresponding to a plurality of customers of
an enterprise; predict at least one intention for each customer
from among the plurality of customers by using the customer data
thereby configuring a plurality of intentions; estimate an expected
volume of interactions for at least one time period based on the
plurality of intentions, each interaction in the expected volume of
interactions being associated with one or more interaction
attributes receive resource data corresponding to a plurality of
resources of the enterprise, each resource from among the plurality
of resources being associated with a plurality of resource
attributes; map at least one resource from among the plurality of
resources to each interaction in the expected volume of
interactions based on a match between respective resource
attributes associated with the at least one resource and the one or
more interaction attributes associated with each interaction;
facilitate an allocation of the plurality of resources of the
enterprise based, at least in part, on the map of the at least one
resource to each interaction; and dynamically adjust an allocation
of one or more resources from among the plurality of resources for
at least one forthcoming time period upon detecting an occurrence
of at least one event indicating a change in respective expected
volume of interactions.
10. The apparatus of claim 9, wherein the customer data comprises
information related to at least one of a profile of a customer, one
or more interactions of the customer with the enterprise on one or
more interaction channels, one or more locations associated with
the customer, a presence of the customer in the one or more
interaction channels, a current attention of the customer in an
interaction channel from among the one or more interaction
channels, or social interactions of the customer and events of
significance to the customer.
11. The apparatus of claim 9, wherein the resource data comprises
data corresponding to each resource from among the plurality of
resources, the data corresponding to each resource comprising
information related to at least one of a type of resource, an
identification of the resource, a service level agreement
associated with the resource and a performance history of the
resource.
12. The apparatus of claim 9, wherein at least one resource
attribute from among the plurality of resource attributes relates
to a capability of the resource and an availability status of the
resource.
13. The apparatus of claim 9, wherein the one or more interaction
attributes associated with each interaction comprises information
related to at least one of an intention of a customer associated
with an interaction, or a priority level associated with the
interaction and a customer lifetime value (CLU) of the
customer.
14. The apparatus of claim 9, wherein the apparatus is further
caused to: track an activity of a customer on an interaction
channel from among one or more interaction channels; determine if
the customer requires an interaction assistance based on the
tracked activity; and determine if a request for an interaction is
to be proactively offered to the customer on the interaction
channel upon determining that the customer requires the interaction
assistance.
15. The apparatus of claim 14, wherein the apparatus is further
caused to: determine an appropriate time for proactively offering
the request for the interaction to the customer on the interaction
channel, the appropriate time being determined based on a
likelihood of a desired outcome being greater than a pre-determined
value.
16. The apparatus of claim 9, wherein the plurality of customers of
the enterprise comprises existing and prospective customers of the
enterprise.
17. A non-transitory computer-readable medium storing a set of
instructions that when executed cause a computer to perform a
method comprising: receiving customer data corresponding to a
plurality of customers of an enterprise; predicting at least one
intention for each customer from among the plurality of customers
by using the customer data thereby configuring a plurality of
intentions; estimating an expected volume of interactions for at
least one time period based on the plurality of intentions, each
interaction in the expected volume of interactions being associated
with one or more interaction attributes receiving resource data
corresponding to a plurality of resources of the enterprise, each
resource from among the plurality of resources being associated
with a plurality of resource attributes; mapping at least one
resource from among the plurality of resources to each interaction
in the expected volume of interactions based on a match between
respective resource attributes associated with the at least one
resource and the one or more interaction attributes associated with
each interaction; facilitating an allocation of the plurality of
resources of the enterprise based, at least in part, on the map of
the at least one resource to each interaction; and dynamically
adjusting an allocation of one or more resources from among the
plurality of resources for at least one forthcoming time period
upon detecting an occurrence of at least one event indicating a
change in respective expected volume of interactions.
18. The non-transitory computer-readable medium of claim 17,
wherein at least one resource attribute from among the plurality of
resource attributes relates to a capability of the resource and an
availability status of the resource.
19. The non-transitory computer-readable medium of claim 17,
wherein the one or more interaction attributes associated with each
interaction comprises information related to at least one of an
intention of a customer associated with an interaction, or a
priority level associated with the interaction and a customer
lifetime value (CLU) of the customer.
20. The non-transitory computer-readable medium of claim 17,
wherein the method further comprises: tracking an activity of a
customer on an interaction channel from among one or more
interaction channels; determining if the customer requires an
interaction assistance based on the tracked activity; and
determining if a request for an interaction is to be proactively
offered to the customer on the interaction channel upon determining
that the customer requires the interaction assistance.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 14/964,540 filed Dec. 9, 2015, now pending, which claims the
benefit of U.S. provisional patent application Ser. No. 62/090,298,
filed Dec. 10, 2014, which are incorporated herein in their
entirety by this reference thereto.
TECHNICAL FIELD
[0002] The invention generally relates to staffing of resources and
more particularly, to a method and apparatus for facilitating
staffing of resources associated with customer support operations
of enterprises.
BACKGROUND
[0003] Customer sales and service are vital components for success
of any enterprise and often a differentiating factor between
competing enterprises. Accordingly, many enterprises deploy human
and/or machine-based resources for interacting with the customers
for selling to them, for answering their queries and/or for
resolving their concerns.
[0004] The primary objective of deployment of resources is to
promptly handle the customer interactions. However, staffing of
adequate resources is often a challenge. Conventional mechanisms
staff resources based on historically observed averages of customer
interactions over a similar time period. However, such mechanisms
typically lead to over staffing or under-staffing of resources,
resulting in poor customer experience and/or operating losses.
Therefore, it is desirable to reduce instances of under staffing
and over staffing of resources, and, moreover to appropriately
staff resources for handling fluctuations in volume of customer
interactions.
SUMMARY
[0005] In an embodiment of the invention, a computer-implemented
method for facilitating staffing of resources is disclosed. The
method receives customer data corresponding to a plurality of
customers of an enterprise. The method predicts, by a processor, at
least one intention for each customer from among the plurality of
customers using data corresponding to the each customer in the
customer data. The prediction of the at least one intention for the
each customer configures a plurality of intentions. Further, the
method estimates, by the processor, an expected volume of
interactions for at least one time period based on the plurality of
intentions. Each interaction in the expected volume of interactions
is associated with one or more interaction attributes. The method
receives resource data corresponding to a plurality of resources of
the enterprise. Each resource from among the plurality of resources
is associated with a plurality of resource attributes. Further, the
method maps, by the processor, at least one resource from among the
plurality of resources to the each interaction in the expected
volume of interactions based on a match between respective resource
attributes associated with the at least one resource and the one or
more interaction attributes associated with the each interaction.
Further, the method facilitates, by the processor, a staffing of
the plurality of resources of the enterprise based, at least in
part, on the mapping of the at least one resource to the each
interaction.
[0006] In another embodiment of the invention, an apparatus for
facilitating staffing of resources includes at least one processor
and a memory. The memory stores machine executable instructions
therein, that when executed by the at least one processor, cause
the apparatus to receive customer data corresponding to a plurality
of customers of an enterprise. The apparatus predicts at least one
intention for each customer from among the plurality of customers
using data corresponding to the each customer in the customer data.
The prediction of the at least one intention for the each customer
configures a plurality of intentions. The apparatus estimates an
expected volume of interactions for at least one time period based
on the plurality of intentions. Each interaction in the expected
volume of interactions is associated with one or more interaction
attributes. The apparatus receives resource data corresponding to a
plurality of resources of the enterprise. Each resource from among
the plurality of resources is associated with a plurality of
resource attributes. Further, the apparatus maps at least one
resource from among the plurality of resources to the each
interaction in the expected volume of interactions based on a match
between respective resource attributes associated with the at least
one resource and the one or more interaction attributes associated
with the each interaction. Further, the apparatus facilitates a
staffing of the plurality of resources of the enterprise based, at
least in part, on the mapping of the at least one resource to the
each interaction.
[0007] In another embodiment of the invention, a non-transitory
computer-readable medium storing a set of instructions that when
executed cause a computer to perform a method for facilitating
staffing of resources is disclosed. The method executed by the
computer receives customer data corresponding to a plurality of
customers of an enterprise. The method predicts at least one
intention for each customer from among the plurality of customers
using data corresponding to the each customer in the customer data.
The prediction of the at least one intention for the each customer
configures a plurality of intentions. The method estimates an
expected volume of interactions for at least one time period based
on the plurality of intentions. Each interaction in the expected
volume of interactions is associated with one or more interaction
attributes. The method receives resource data corresponding to a
plurality of resources of the enterprise. Each resource from among
the plurality of resources is associated with a plurality of
resource attributes. Further, the method maps at least one resource
from among the plurality of resources to the each interaction in
the expected volume of interactions based on a match between
respective resource attributes associated with the at least one
resource and the one or more interaction attributes associated with
the each interaction. Further, the method facilitates a staffing of
the plurality of resources of the enterprise based, at least in
part, on the mapping of the at least one resource to the each
interaction.
BRIEF DESCRIPTION OF THE FIGURES
[0008] FIG. 1 shows an example representation of resources deployed
by an enterprise for addressing customer support requirements, in
accordance with an example scenario;
[0009] FIG. 2 is a block diagram showing an example apparatus
configured to facilitate staffing of resources, in accordance with
an embodiment of the invention;
[0010] FIG. 3 shows an example representation for illustrating
various types of data corresponding to a customer that may be
received by the apparatus of FIG. 2, in accordance with an
embodiment of the invention;
[0011] FIG. 4 shows an example plot illustrating fluctuations in
estimated expected volume of interactions for days of a week for a
chosen customer intention, in accordance with an embodiment of the
invention;
[0012] FIG. 5 is a block diagram showing a mapping of resources to
expected interactions, in accordance with an embodiment of the
invention;
[0013] FIG. 6 shows an example representation for illustrating a
provisioning of a request for interaction to a customer during an
on-going journey on an enterprise interaction channel, in
accordance with an embodiment of the invention; and
[0014] FIG. 7 is a flow diagram of an example method for
facilitating staffing of resources, in accordance with an
embodiment of the invention.
DETAILED DESCRIPTION
[0015] The detailed description provided below in connection with
the appended drawings is intended as a description of the present
examples and is not intended to represent the only forms in which
the present example may be constructed or utilized. However, the
same or equivalent functions and sequences may be accomplished by
different examples.
[0016] FIG. 1 shows an example representation 100 of resources
deployed by an enterprise 102 for addressing customer support
requirements, in accordance with an example scenario. It is
understood that the enterprise 102 may be any firm or organization
(for example, a corporation, a small business or even a brick and
mortar entity) offering products, services and/or information to
existing and prospective users (hereinafter collectively referred
to as customers). It is also understood that the enterprise 102 may
employ physical means (for example, stores or retail outlets, etc.)
and/or virtual means (for example, websites, social media, native
mobile applications etc.) to conduct business with its
customers.
[0017] Enterprises, such as the enterprise 102, may typically
deploy resources for customer support operations. In some example
scenarios, enterprises may extend a dedicated customer support
facility for serving their customers. The customer support facility
may include human resources and/or machine-based resources, such as
customer service representatives or agents, automated chat agents
or chat bots, interactive voice response (IVR) systems and/or self
assist systems such as either web or mobile digital self-service
systems for interacting with the customers, providing them with
information, selling to them, answering their queries and
addressing their concerns. The enterprise 102 is depicted to be
associated with such an example customer support facility 104. The
customer support facility 104 is exemplarily depicted to include
two human agents 106 and 108 (who provide customers with
voice-based assistance and chat-based/online assistance,
respectively), an automated voice response system, such as an IVR
system 110 and a direct sales/service personnel 112. It is
understood that the customer support facility 104 may also include
automated chat agents such as chat bots, and, web or digital
self-assist mechanisms such as web pages including frequently asked
questions (FAQ) content or self-help forms and questionnaires.
Moreover, it is noted that customer support facility 104 is
depicted to include only two human agents, one IVR system and one
direct sales/service personnel for illustration purposes and it is
understood that the customer support facility 104 may include fewer
or more number of resources than those depicted in FIG. 1.
[0018] The environment 100 further depicts a plurality of
customers, such as a customer 114, a customer 116 and a customer
118. It is understood that three customers are depicted herein for
example purposes and that the enterprise 102 may be associated with
many such customers. In some example scenarios, the customers 114,
116 and 118 may interact with resources deployed at the customer
support facility 104 over a network 120 using their respective
electronic devices. Examples of such electronic devices may include
mobile phones, Smartphones, laptops, personal computers, tablet
computers, personal digital assistants, Smart watches, wearable
devices and the like. Examples of the network 120 may include wired
networks, wireless networks or a combination thereof. Examples of
the wired networks may include Ethernet, local area network (LAN),
fiber-optic cable network and the like. Examples of the wireless
networks may include cellular networks like GSM/3G/4G/CDMA based
networks, wireless LAN, Bluetooth or Zigbee networks and the like.
An example of a combination of wired networks and wireless networks
may include the Internet.
[0019] a primary objective of deployment of resources at the
customer support facility 104 is to promptly handle customer
interactions, which may be facilitated over a plurality of
interaction channels, such as a voice channel, a chat channel, a
native mobile application channel, a web/online channel, a social
media channel and the like. However, staffing of adequate resources
is often a challenge. Conventional mechanisms staff resources based
on historically observed averages of customer interactions over a
similar time period. However, such mechanisms typically lead to
over staffing or under-staffing of resources. For example, an
understaffing of resources may occur when the customer support
facility 104 is unable to handle increased customer interactions
during peak business hours, special promotional events and the
like. In such a scenario, the customers having called the customer
service facility 104 for resolution of concerns have to wait for a
long time to interact with customer service representatives.
Similarly, an overstaffing of resources may occur, when a large
number of customer service representatives may be available at any
given point in time to handle interactions for only a few
customers.
[0020] Various embodiments of the invention provide methods and
apparatuses that are capable of overcoming these and other
obstacles and providing additional benefits. More specifically,
methods and apparatuses disclosed herein suggest techniques for
appropriately staffing of resources associated with customer
support operations for handling fluctuations in volume of the
customer interactions and thereby reducing instances of under
staffing and over staffing of resources. An apparatus configured to
facilitate staffing of resources is explained with reference to
FIG. 2.
[0021] FIG. 2 is a block diagram showing an example apparatus 200
configured to facilitate staffing of resources of enterprises, in
accordance with an embodiment of the invention. The term
`resources` as used herein refers to human and machine-based
resources associated with customer support operations of
enterprises. Some non-limiting examples of human resources may
include human agents for handling voice interactions and/or
text-based interactions (for example, chat interactions or social
media interactions) with customers, direct sales/service personnel
and the like. Examples of the machine-based resources may include,
but are not limited to, chat bots (for example, virtual chat
agents), interactive voice response (IVR) systems, digital web or
mobile self-assist mechanisms (such as for example, digital kiosks
etc.), physical resources including web servers which host
websites, social media sites etc., and/or communication equipment
which facilitate interaction with customers, and the like. It is
understood that in some example scenarios, the human resources and
machine-based resources may collectively handle interactions with
customers. In an illustrative example, a customer interaction with
an IVR system may be transferred to a human agent to more
effectively address the customer's concern. In another illustrative
example, a human agent may proactively provide a customer on a
website with a web link to a self-help web page (for example, a web
page including FAQ content) to assist the customer with a
customer's specific requirement. Furthermore, the term
`facilitating a staffing of resources` as used herein implies
enabling appropriate staffing of resources of an enterprise for
different time periods (for example, days, weeks, months etc.) such
that a skill/capability of the resource is effectively utilized for
adequately assisting the customers, while simultaneously precluding
instances of overstaffing and understaffing of resources. It is
noted that the facilitation of staffing of resources is explained
hereinafter with respect to one enterprise. However, it is
understood that the apparatus 200 is configured to facilitate
staffing of resources for multiple enterprises. Moreover, as
explained with reference to FIG. 1, the term `enterprise` as used
herein refers to any firm or organization (for example, a
corporation, a small business or even a brick and mortar entity)
offering products, services and/or information to existing and
prospective customers.
[0022] The apparatus 200 includes at least one processor, such as a
processor 202 and a memory 204. It is noted that although the
apparatus 200 is depicted to include only one processor, the
apparatus 200 may include more number of processors therein. In an
embodiment, the memory 204 is capable of storing machine executable
instructions. Further, the processor 202 is capable of executing
the stored machine executable instructions. In an embodiment, the
processor 202 may be embodied as a multi-core processor, a single
core processor, or a combination of one or more multi-core
processors and one or more single core processors. For example, the
processor 202 may be embodied as one or more of various processing
devices, such as a coprocessor, a microprocessor, a controller, a
digital signal processor (DSP), a processing circuitry with or
without an accompanying DSP, or various other processing devices
including integrated circuits such as, for example, an application
specific integrated circuit (ASIC), a field programmable gate array
(FPGA), a microcontroller unit (MCU), a hardware accelerator, a
special-purpose computer chip, or the like. In an embodiment, the
processor 202 may be configured to execute hard-coded
functionality. In an embodiment, the processor 202 is embodied as
an executor of software instructions, wherein the instructions may
specifically configure the processor 202 to perform the algorithms
and/or operations described herein when the instructions are
executed.
[0023] The memory 204 may be embodied as one or more volatile
memory devices, one or more non-volatile memory devices, and/or a
combination of one or more volatile memory devices and non-volatile
memory devices. For example, the memory 204 may be embodied as
magnetic storage devices (such as hard disk drives, floppy disks,
magnetic tapes, etc.), optical magnetic storage devices (e.g.
magneto-optical disks), CD-ROM (compact disc read only memory),
CD-R (compact disc recordable), CD-R/W (compact disc rewritable),
DVD (Digital Versatile Disc), BD (Blu-ray.RTM. Disc), and
semiconductor memories (such as mask ROM, PROM (programmable ROM),
EPROM (erasable PROM), flash ROM, RAM (random access memory),
etc.).
[0024] The apparatus 200 also includes an input/output module 206
(hereinafter referred to as `I/O module 206`) for providing an
output and/or receiving an input. The I/O module 206 is configured
to be in communication with the processor 202 and the memory 204.
Examples of the I/O module 206 include, but are not limited to, an
input interface and/or an output interface. Examples of the input
interface may include, but are not limited to, a keyboard, a mouse,
a joystick, a keypad, a touch screen, soft keys, a microphone, and
the like. Examples of the output interface may include, but are not
limited to, a display such as a light emitting diode display, a
thin-film transistor (TFT) display, a liquid crystal display, an
active-matrix organic light-emitting diode (AMOLED) display, a
microphone, a speaker, a ringer, a vibrator, and the like. In an
example embodiment, the processor 202 may include I/O circuitry
configured to control at least some functions of one or more
elements of the I/O module 206, such as, for example, a speaker, a
microphone, a display, and/or the like. The processor 202 and/or
the I/O circuitry may be configured to control one or more
functions of the one or more elements of the I/O module 206 through
computer program instructions, for example, software and/or
firmware, stored on a memory, for example, the memory 204, and/or
the like, accessible to the processor 202.
[0025] In an embodiment, the I/O module 206 may be configured to
provide a user interface (UI) configured to enable enterprise users
to utilize the apparatus 200 for dynamically managing a staffing of
resources so as to preclude overstaffing and/or understaffing of
resources. Furthermore, the I/O module 206 may be integrated with a
monitoring mechanism configured to provide the enterprise users
with real-time updates/alerts (for example, email notifications,
SMS alerts, etc.) of changes to be made to the staffing of
resources for efficiently addressing customer support
requirements.
[0026] In an embodiment, various components of the apparatus 200,
such as the processor 202, the memory 204 and the I/O module 206
are configured to communicate with each other via or through a
centralized circuit system 208. The centralized circuit system 208
may be various devices configured to, among other things, provide
or enable communication between the components (202-206) of the
apparatus 200. In certain embodiments, the centralized circuit
system 208 may be a central printed circuit board (PCB) such as a
motherboard, a main board, a system board, or a logic board. The
centralized circuit system 208 may also, or alternatively, include
other printed circuit assemblies (PCAs) or communication channel
media.
[0027] It is understood that the apparatus 200 as illustrated and
hereinafter described is merely illustrative of an apparatus that
could benefit from embodiments of the invention and, therefore,
should not be taken to limit the scope of the invention. It is
noted that the apparatus 200 may include fewer or more components
than those depicted in FIG. 2. Moreover, the apparatus 200 may be
implemented as a centralized apparatus, or, alternatively, the
various components of the apparatus 200 may be deployed in a
distributed manner while being operatively coupled to each other.
In another embodiment, the apparatus 200 may be embodied as a mix
of existing open systems, proprietary systems and third party
systems. In another embodiment, the apparatus 200 may be
implemented completely as a set of software layers on top of
existing hardware systems. In an exemplary scenario, the apparatus
200 may be any machine capable of executing a set of instructions
(sequential and/or otherwise) so as to facilitate staffing of
resources.
[0028] In an embodiment, the processor 202 is configured to, with
the content of the memory 204, cause the apparatus 200 to receive
customer data corresponding to a plurality of customers of an
enterprise. More specifically, the I/O module 206 of the apparatus
200 may be configured to receive the customer data, i.e. aggregate
data corresponding to the customers of the enterprise. It is noted
that the term `customers` as used herein refers to both existing
and prospective customers of the enterprise. The customer data may
be received from customer support facilities, such as the customer
support facility 104 explained with reference to FIG. 1, and/or,
remote data gathering servers, which are configured to track
activity of the customers of the enterprise on various enterprise
interaction channels, such as websites, native mobile applications,
social media etc. In an illustrative example, an existing customer
of the enterprise may contact a human agent to make a bill payment.
In such a scenario, information such as a concern category of the
customer, a timing of the interaction, details of the agent
handling the interaction, an outcome of the interaction and the
like, may be gathered and provided by the customer support facility
to the I/O module 206. In an embodiment, the customer support
facilities and/or remote data gathering servers may provision the
customer data to the I/O module 206 in real-time or on periodic
basis in an online or an offline manner.
[0029] In an embodiment, the customer data includes data
corresponding to the each customer of the enterprise. In an
embodiment, the data corresponding to the each customer may include
information related to at least one of (1) a profile of a customer;
(2) interactions of the customer with the enterprise using one or
more interaction channels; (3) locations associated with the
customer; (4) a presence of the customer in the one or more
enterprise interaction channels; (5) a current attention of the
customer in an enterprise interaction channel; (6) social
interactions of the customer; and (7) events of significance to the
customer. The various types of data that may be received
corresponding to each customer are explained in further detail with
reference to an example representation in FIG. 3.
[0030] Referring now to FIG. 3, an example representation 300 is
shown for illustrating various types of data corresponding to a
customer 302 that may be received by the I/O module 206 of the
apparatus 200, in accordance with an embodiment of the invention.
In an example scenario, the customer 302 may wish to interact with
an enterprise for a variety of purposes, such as for example,
payment of a monthly bill, resolution of disputed transaction,
cancellation of a reservation, enquiry of a newly launched product
or a service and the like. Accordingly, the customer 302 may
initiate one or more interactions with the enterprise. The
interactions may be conducted using one or more electronic devices
and over a variety of interaction channels, such as for example a
speech or a voice channel (for example, phone call conversation
with the customer service representative or the IVR system), a
native mobile application, a web channel (a video call or through
emails or online forms), a chat channel (for example, a chat with
an customer service representative or an automated chat agent), and
the like. Accordingly, a variety of information related to the
customer 302 may be gathered and subsequently stored in the memory
204. Some of the information types related to the customer 302 that
are stored in the memory 204 are depicted in example representation
300 as profile data 304, interaction data 306, location and sensor
data 308, presence and attention data 310, social interactions data
312 and events data 314.
[0031] In at least one example embodiment, the profile data 304
includes information related to a profile of the customer 302, such
as for example, a customer's name, contact details, personal and
family information, banking account information, information
related to products and services associated with the customer 302,
messaging or sharing platforms used by the customer 302 and the
like. The profile information may further include information
related to customer interests and preferences, such as for example,
the customer's device preferences and/or interaction channel
preferences. In some example embodiments, the profile information
may also include calendar information associated with the customer
302. For example, the calendar information may include information
related to an availability of the customer 302 during the duration
of a day/week/month.
[0032] In at least one example embodiment, the interaction data 306
includes information related to past interactions of the customer
302 with the resources of the enterprise, the types of channel used
for interactions, types of customer issues involved, whether the
issues were resolved or not, the frequency of interactions and the
like. In an illustrative example, the data collated corresponding
to an interaction of the customer 302 with an IVR resource of the
enterprise may include information such as details of the
customer's concern, IVR options selected, call transfer to an agent
if any, time duration of the IVR interaction, etc. In another
illustrative example, the data collated corresponding to a chat
interaction of the customer 302 with a human agent of the
enterprise may include information such as chat stages involved
during the interaction, emotion scores associated with chat lines,
concern resolution status, feedback received from the customer 302
and the agent, and the like. In another illustrative example, the
data collated corresponding to the customer's visit to a website of
the enterprise may include details of the customer's browsing
pattern, frequency of visits, time spent on the web pages, images
viewed, hyperlinks accessed, mouse roll over events and the like.
Further, in some embodiments, the interaction data 306 collated
corresponding to the customer 302 may also include the search terms
entered by the customer 302 on an enterprise interaction channel or
even on a search engine, advertisements presented to the customer
302 during a web journey, advertisements clicked or viewed by the
customer 302 and the like. It is noted that the interaction data
306 may include information related to one or more interactions of
the customer 302 with the enterprise conducted using multiple
devices and/or multiple interaction channels.
[0033] In at least one example embodiment, the location and sensor
data 308 may include information gleaned from geo-location tracking
satellites and/or Wi-Fi sensors (in cars, stores and the like).
Such information may include current and past geo-location
information of the customer 302, for example, the places frequented
by the customer 302, a current location the customer 302, and the
like. In some embodiments, the location and sensor data 308 may
also include data collated from sensors, such as sensors aiding
authentication (for example, fingerprint/biometric sensors etc.)
and/or from sensors capable of detecting movement (for example,
accelerometer, gyroscope etc.), and the like.
[0034] The presence and attention data 310 corresponds to
information related to customer's presence/attention in an
interaction channel. In some embodiments, a presence of the
customer 302 in one or more interaction channels may be actively
tracked to determine the presence information related to the
customer 302. In an illustrative example, the customer 302 may have
logged in to one or more social networking media accounts, such as
for example, in any of Facebook.RTM., Twitter.RTM., LinkedIn.RTM.
and the like. Accordingly, a presence of the customer 302 in a
social channel may be recorded as presence information. In another
illustrative example, the customer 302 may be chatting with a
friend using a chat application. In such a scenario, a presence of
the customer 302 in the messaging channel may be detected and
recorded as the presence information. In another illustrative
example, if an instance of a customer browsing a website
corresponding to the selected product/service is detected, then the
presence of the customer in the web channel may be determined. In
an embodiment, one or more tracking cookies may be configured to be
included in a device browser/native device application associated
with the customer device, which may enable detection of presence of
the customer in an interaction channel.
[0035] Further, the I/O module 206 may also receive attention
information indicative of customer's current attention. For
example, even though the customer 302 has logged in one or more
social media accounts, the customer 302 may be currently browsing
some other website, then in such a scenario the attention
information may be determined as the web channel (as opposed to a
social channel as described above). In an embodiment, the presence
and the attention data 310 may be utilized to determine the
interaction mode in which the user is most active or most likely to
be active. The presence and attention data 310 determined in such a
manner may be utilized in a variety of ways. For example, if the
presence and attention data 310 indicates that the customer 302 is
currently present in a customer facility (or near to a customer
facility), then this information of proximity of the customer 302
to a service location may be utilized in providing an enhanced
customer service experience to the customer 302. In another
illustrative example, if it is determined that the customer 302 is
currently online (implying that the customer 302 is currently
present/attentive in the web channel), then the customer 302 may be
offered an interactive chat or a web-based call for providing
assistance to the customer 302.
[0036] The social interactions data 312 may include information
corresponding to customer's online networking accounts, recent
posts or tweets and other such information related to customer's
online conversations in the public domain. The events data 314 may
include information related to events of significance to the
customer 302. Some examples of the events of significance to the
customer 302 may include personal events such as for example
birthdays, festivals, anniversaries etc., and non-personal events
such as for example billing cycle dates, weather related events,
current and projected socio-political events, data outage events
and the like.
[0037] As explained above, the received customer data may include
various types of data corresponding to the customer 302 of the
enterprise. It is understood that similar types of data may be
received corresponding to other new and prospective customers of
the enterprise, by the I/O module 206.
[0038] Referring now to FIG. 2, the processor 202 is configured to,
with the content of the memory 204, cause the apparatus 200 to
transform data corresponding to the each customer to generate
suitable variables for enabling a prediction of one or more
intentions of the customer. In an illustrative example, the
location information of a customer (for example, the location and
sensor data 308 as explained with reference to FIG. 3) may be
transformed to derive variables related to how close the customer
is to an enterprise location, does the customer travel a lot
(derived from all location data elements), is the customer driving
or is the customer indoors or outdoors, and the like. The presence
and attention information may be similarly transformed to derive
variables related to a frequency of visiting interaction channels,
attention span of the customer for a particular visit, etc. In
another illustrative example, social interactions data may be
transformed to derive variables related to emerging topics
regarding the industry or the enterprise in general, and these
topics may thereafter be classified with similar topics from the
past. In at least one example embodiment, the processor 202 may be
configured to assign weights to the variables such that possibility
of error during prediction is minimized and/or likelihood of
prediction being right is maximized.
[0039] In an embodiment, the processor 202 is configured to, with
the content of the memory 204, cause the apparatus 200 to predict
at least one intention for each customer of the enterprise. More
specifically, the processor 202 may predict at least one intention
for each customer by using respective data of the customer from
among the customer data stored in the memory 204. More
specifically, the processor 202 may be configured to subject the
variables derived from the data corresponding to the each customer
to a set of structured and un-structured data analytical models
including text mining & predictive models for customer
intention prediction purposes. Examples of the prediction models
may include, but are not limited to Logistic regression, Naive
Bayesian, Rule Engines, Neural Networks, Decision Trees, Support
Vector Machines, k-nearest neighbor, K-means and the like. In an
example embodiment, all the variables derived from the profile
data, the interaction data, the location and sensor data, the
attention and presence data, the events data, the social
interactions data and the like, corresponding to the customer may
be classified into a several columns and fed to one or more
prediction models to predict intention(s) of a customer. In an
illustrative example, the prediction models may predict that the
customer may make a purchase on a subsequent visit to the website
or not. In another illustrative example, the prediction models may
predict that a customer may return a product to the retail outlet
of the enterprise within a week's time frame. In yet another
illustrative example, the prediction models may predict that the
customer plans a leisure trip and may make travel arrangements as
well as purchase travel insurance. In at least one embodiment, the
prediction of one or more intentions for each customer of an
enterprise may result in a finite set of overall intentions for
customers of the enterprise. The finite set of intentions is
referred to hereinafter as a plurality of intentions.
[0040] In an embodiment, the processor 202 is configured to, with
the content of the memory 204, cause the apparatus 200 to
determine, for each customer, a likelihood of a customer initiating
an interaction with the enterprise for the respective predicted
intention. More specifically, upon determining one or more
intentions of the customers, the processor 202 is caused to
determine, if the customers would need assistance for their
respective predicted intentions. If it is determined that a
customer may need assistance for the respective predicted
intention, then the apparatus 200 is caused to predict a likelihood
of the customer initiating an interaction with the enterprise. In
an illustrative scenario, upon determining that the customer
intends to purchase a product or avail a service, the processor 202
may be configured to determine a likelihood of the customer
requiring assistance for such an intention. The likelihood may be
measured on a scale, such as for example--a scale with `very high`,
`high`, `average`, `low` and `very low` gradations, or,
alternatively, likelihood may be quantified by a numerical value on
a scale ranging from 0 to 1. For example, the processor 202 may
predict that a likelihood of the customer purchasing the product or
availing the service within next two months is 0.95, indicating a
95% probability of occurrence of such an event. In some example
scenarios, the processor 202 may predict that the customer may not
be interested in purchasing the product or availing the service, in
which case, the likelihood of the customer initiating an
interaction with the enterprise may be low or zero.
[0041] In at least one example embodiment, the processor 202 is
configured to determine if the predicted likelihood is greater than
a predefined threshold value. In an illustrative example, the
predefined threshold value may be an empirically chosen value or a
value chosen based on manual observations of effects of choosing
various predefined threshold values on predicted and actual
customer behaviour. In an illustrative example, the predefined
threshold value may be a number chosen on a numerical scale ranging
from 0 to 1. For example, the predefined threshold value may be
0.75 (indicating a 75% probability of the customer initiating an
interaction with the customer for the respective intention).
Accordingly, if it is determined that a customer needs assistance
for respective predicted intention and a likelihood of the customer
initiating an interaction with the enterprise is 0.2 (i.e. 20%
probability), then the processor 202 may be configured to determine
that the likelihood of the customer initiating an interaction with
the enterprise for the respective predicted intention does not
exceed a pre-defined threshold value. It is noted different
threshold values may be chosen (i.e. predefined) for different time
periods. For example, a predefined threshold value chosen for a
weekday in the month of October may be different than the
predefined threshold value chosen for a weekday in the last week of
December. Moreover, it is understood that the predefined threshold
values, in some cases, may be dynamically updated based on observed
feedback results or even on perceived changes in expected volumes
of interactions.
[0042] In an embodiment, the processor 202 is configured to, with
the content of the memory 204, cause the apparatus 200 to predict a
time of interaction for one or more customers for whom it is
determined that the likelihood of initiating the interaction with
the enterprise exceeds the pre-defined threshold value. More
specifically, the processor 202 is caused to determine when the
customer would need assistance (or when the customer interaction is
expected). For example, if it is known that the customer is
scheduled to take flight services in a week's time (for example,
such information may be obtained from a customer's calendar
information), then the processor 202 may predict an intention of
the customer to know an on-time status of the flight. For such
predicted intention, the processor 202 may determine a likelihood
of the customer initiating an interaction with the enterprise. In
an example scenario, using the various prediction models, the
processor 202 may determine that the customer may require
assistance in this regard. In such a scenario, the processor 202
may determine if a likelihood of the customer to initiate an
interaction with the enterprise is greater than a predefined
threshold value. If the determined likelihood is greater than the
predefined threshold value, then the processor 202 may determine a
probable time the customer may seek assistance. For example, the
processor 202 may determine that the customer may call (i.e. use a
voice channel) to inquire the flight's on-time status on the day of
departure. In some embodiments, the processor 202 may predict
intentions of the customers on a larger time scale, such as for
example a month or a week's duration. However, if it is determined
that the customer may seek interaction within a shorter time
duration, such as in a few days time, then the processor 202 may be
configured to predict the expected time of interaction at a
granular level, such as for example, the probable day or the
probable hour, at which the customer may seek assistance.
[0043] In at least one example embodiment, the processor 202 may
further be configured to predict what interaction channel may be
preferred by the customer for the predicted interaction. In an
example embodiment, the prediction of an interaction channel may be
made based on the historical data such as a customer's channel
affinity (i.e. historical customer channel preferences), location
and sensor data, presence and attention data associated with the
customer, customer's profile information and the like. Some
non-limiting examples of interaction channels may include a chat
interaction channel, a voice interaction channel, a video
interaction channel, a web channel, a physical visit and the
like.
[0044] In an embodiment, the processor 202 is configured to, with
the content of the memory 204, cause the apparatus 200 to estimate
an expected volume of interactions for at least one time period
based on the plurality of intentions. For example, the processor
202 may be configured to estimate an expected volume of
interactions for one or more time periods (for example, time
periods such as forthcoming week, a specific month or even next
work shift). To that effect, the processor 202 may be configured to
compute an expected volume of interactions in a chosen time period
for existing customers and for new customers and for several
customer intentions.
[0045] In an embodiment, the expected volume of interactions from
existing customers within a given time period may be computed by
adding probabilities corresponding to predicted customer
interactions for each of the several customer intentions. For
example, those customers for whom, a likelihood of interaction for
a specific customer intention is greater than a predefined
threshold value (for example, a value of 0.5 implying that there is
50% probability that the customer may call for the specific
intention in that time period), then such customers may together
constitute the interaction volume of existing customers for that
specific intention and time period. Accordingly, the processor 202
may be configured to aggregate number of interactions that are
predicted to take place within the chosen time period for the
existing customers for the several customer intentions to configure
expected volume of interactions from the existing customers.
[0046] In an embodiment, an expected volume of interactions for new
or prospective customers for various time periods may be estimated
by the processor 202 using traditional techniques enhanced with
interaction volumes observed across clients and channels. Some
non-exhaustive examples of the traditional techniques may include
predictions based on a general pattern of the customer interactions
at a customer support facility as well as on specific patterns of
the customer interactions on specific time slots such as, on Monday
or on an evening prior to a major holiday etc. Further, the
processor 202 may estimate an expected volume of interactions for
new or prospective customers for various time periods by using data
from social feeds and other sources, which provide indications of
possible interaction from the new customers. Some examples of the
social feeds and other sources may include online information
through social networking websites, online forums, chat groups,
blogs and the like. Furthermore, the processor 202 may estimate an
expected volume of interactions for new or prospective customers
for various time periods by using estimates from existing customers
to predict fluctuations in volumes of new customers. For example,
historical data associated with the existing customers may be used
to predict the interaction volume for the new customers.
[0047] In an embodiment, the processor 202 may be configured to
aggregate number of interactions from existing customers and new
customers for one or more time periods for several customer
intentions to estimate an expected volume of interactions for those
respective time periods.
[0048] In some example embodiments, the apparatus 200 may be caused
to define a hierarchical framework whereby a plurality of customer
intentions for contacting the customer support facility may first
be identified and classified into broad categories, such as for
example, information request, concern resolution, bill payment,
change of profile information and the like. The customer intentions
for existing and new customers may then be predicted and classified
into one or more intention categories. For a given intention, when
an existing/new customer would seek assistance may then be
predicted and accordingly an expected interaction volume for
various time periods may be determined. In an example embodiment,
the time scales may be gradually made granular from monthly basis
to weekly basis, daily basis and then hourly basis. An example plot
illustrating fluctuations in expected volume of interactions over a
plurality of days in a week for chosen customer intention is
depicted in FIG. 4.
[0049] Referring now to FIG. 4, an example plot 400 illustrating
fluctuations in estimated expected volume of interactions for days
of a week for a chosen customer intention is shown in accordance
with an embodiment of the invention. The chosen customer intention
may be one from among several customer intentions that customers
typically seek assistance for, from the resources in the customer
support facility. For example, existing and new customers may
contact the customer support facility for payment of a bill for
monthly cellular services availed by them. For such a `bill
payment` customer intention, interaction volumes may be predicted
for a chosen time scale, such as a quarter, a month, a week, a day
or even on hourly basis. The expected volume of interactions may
then be estimated as explained above, i.e. by aggregating number of
interactions for existing customers and new customers for several
customer intentions and for a given time period.
[0050] Accordingly, FIG. 4 depicts the plot 400 with the X-axis 402
corresponding to the days of a week and the Y-axis 404
corresponding to the predicted interaction volumes for a chosen
intention. As can be seen from the plot 400, for the chosen
intention, 500 customers would seek interaction on day 1 of the
week, 1000 customers on day 2, 750 customers on day 3, 500
customers on day 4, 1000 customers on day 5, 1500 customers on day
6 and 2500 customers on day 7. It is noted that the plot 400 is
included herein for illustration purposes and various example
representations of estimated volume of interactions, may be
generated to visualize fluctuation in number of customer
interactions over various time frames.
[0051] In at least one example embodiment, each interaction in the
expected volume of interactions may be associated with one or more
interaction attributes. For example, an attribute associated with
an expected interaction may refer to an intention of a customer
seeking the interaction. For example, a customer may seek
assistance with a resource of an enterprise to seek a refund on a
prior product purchase. Information related to such a predicted
intention of the customer may configure an attribute of the
expected interaction.
[0052] In another illustrative example, an attribute associated
with an expected interaction may be priority level associated with
the interaction. For instance, it may be predicted that the
customer will call in next hour upon detecting a fraudulent
transaction on his card. Such an expected interaction may be
associated with higher priority than other expected interactions.
Information related to a priority level (for example, high, low or
medium priority) of the customer may configure another attribute of
the expected interaction.
[0053] In yet another illustrative example, an attribute associated
with an expected interaction may be a customer lifetime value (CLV)
of the customer. For example, a customer who frequently uses
enterprise products or avails services of the customer may be
associated with a high CLV. Information related to a priority of
the customer (for example, high, low or medium CLV) may configure
another attribute of the expected interaction.
[0054] Accordingly, each interaction in the expected volume of
interactions may be associated with one or more interaction
attributes.
[0055] Referring now to FIG. 2, in at least one embodiment, the
processor 202 is configured to, with the content of the memory 204,
cause the apparatus 200 to receive resource data corresponding to a
plurality of resources of the enterprise. More specifically, the
I/O module 206 of the apparatus 200 may be configured to receive
the resource data, i.e. aggregated data corresponding to the
resources of the enterprise. As explained, the term `resources`
implies both human resources as well as machine-based resources of
the enterprise. The resource data may be received from customer
support facilities, such as the customer support facility 104
explained with reference to FIG. 1, and/or, remote data gathering
servers, which are configured to track activity of the resources,
such as human agents of the enterprise.
[0056] In an embodiment, the resource data includes data
corresponding to the each resource of the enterprise. In an
embodiment, the data corresponding to each resource may include
information related to at least one of a type of a resource (for
example, a human resource or machine-based resource, a voice agent
or a chat agent, and the like) an identification of the resource
(for example, an agent identification number or a machine
identification number), a service level agreement associated with
the resource (for example, an agreement including information
related to working hours, weekly day offs, shift information etc.)
and a performance history of the resource (for example, a number of
interactions historically handled by the resource, call transfers
if any, concern resolution status of the handled interactions and
the like).
[0057] In an embodiment, each resource is associated with a
plurality of resource attributes. For example, an attribute
associated with a resource may relate to a skill of the resource.
In an illustrative example, a voice agent may be skilled in
assisting customers with opening a new account for financial
transactions. In another illustrative example, a chat agent may be
skilled in assisting customers with completing a purchase on the
website. In yet another example, a human agent may be an expert in
handling high priority interactions or dealing with customers with
high CLU. The information related to skill and/or area of expertise
of a resource may configure an attribute of the resource.
[0058] In another example, an attribute associated with a resource
may relate to a capability of the resource. For example, one human
agent may handle five different interactions in an hour, whereas
another human agent may handle eight different interactions in an
hour's time. In another example, a human agent may handle both
online and offline interactions, simultaneously. For example, the
human agent may engage in live chat with two or more customers and
also send email reminders to other customers in an offline manner.
The resource attribute may capture information related to such a
capability of the resource. In case of machine-based resource, such
as a web server, a server capacity or a bandwidth associated with a
communication channel may define capabilities of respective
machine-based resources. In another illustrative example, an
attribute associated with a resource may relate to an availability
status of the resource. For example, in case of human resources,
the attribute capturing an availability of the resource may include
information such as whether a resource is currently free to handle
an interaction or can a resource handle interactions during morning
sessions next week, etc. In case of machine-based resources, the
attribute capturing an availability of the resource may include
information such as available bandwidth, available storage space
and the like.
[0059] In an embodiment, the processor 202 is configured to, with
the content of the memory 204, cause the apparatus 200 to map at
least one resource to each interaction in the expected volume of
interactions. The at least one resource may be mapped to each
interaction based on a match between respective resource attributes
associated with the at least one resource and the one or more
interaction attributes associated with the each interaction. More
specifically, the processor 202 may be configured to compare
resource attributes of various resources with interaction
attributes of expected interactions for identifying matches between
the resource attributes and the interaction attributes. In an
illustrative example, the processor 202 may be configured to
compare skills of human resources with requirements of interactions
(for example, priority of interaction or intention associated with
the interaction) to identify possible matches there between. Upon
identification of a match, the processor 202 may be configured to
match a resource to the corresponding interaction. The term `map`
or `mapping` as used herein refers to loose association of a
resource to an interaction such that if the interaction is
initiated by the customer as expected, then the resource may be
entrusted with handling the interaction so as to assist the
customer with the customer's concern. In at least one example
embodiment, skills of agents are matched with requirements of an
interaction (i.e. interaction attributes related to intention of
the customer as well as priority of the interaction) so as to map
agents to interactions. In an illustrative example, an interaction
attribute may indicate that the interaction is associated with high
priority and relates to a resolution of complaint over unreliable
data services by a mobile provider. In such a case, an agent who is
an expert in resolution of complaints and who has experience in
dealing with high priority interactions, or in other words, whose
attributes match the requirement of the interaction, may be mapped
to the interaction. The mapping of resources to expected
interactions is further explained with reference to FIG. 5.
[0060] FIG. 5 is a block diagram 500 showing a mapping of resources
to expected interactions, in accordance with an embodiment of the
invention. As explained with reference to FIGS. 2 to 4, the I/O
module 206 (not shown in FIG. 5) of the apparatus 200 is configured
to receive resource data comprising data corresponding to a
plurality of resources. The plurality of resources includes human
and machine-based resources of an enterprise. Each resource is
further associated with a plurality of resource attributes. The
received resource data is further stored in the memory 204 of the
apparatus 200 as depicted in FIG. 5. Accordingly, in the block
diagram 500 in FIG. 5, the memory 204 of the apparatus 200 is
depicted to store resource data 502 including data corresponding to
a plurality of resources such as resources 504, 506 and 508
(exemplarily depicted to be resources `R.sub.1`, `R.sub.2` and
`R.sub.n` in FIG. 5). Each resource is further associated with one
or more resource attributes. For example resource 504 (i.e.
resource R.sub.1) is associated with resource attributes R.sub.a,
R.sub.b and R.sub.c; resource 506 (i.e. resource R.sub.2) is
associated with resource attributes R.sub.a and R.sub.d; and
resource 508 (i.e. resource R.sub.n) is associated with resource
attribute R.sub.e.
[0061] Further, as explained with reference to FIGS. 2 to 4, the
I/O module 206 of the apparatus 200 is configured to receive
customer data corresponding to a plurality of customers of the
enterprise and store the customer data in the memory 204.
Accordingly, the block diagram 500 depicts customer data 510 stored
in the memory 204 of the apparatus 200. Further, as explained with
reference to FIGS. 2 to 4, the processor 202 is configured to
predict at least one intention for each customer and thereafter
predict an expected time of interaction for each customer. The
processor 202 is further configured to estimate an expected volume
of interactions for one or more time periods based on the predicted
time of interactions. An expected volume of interactions for a
chosen time period is exemplarily depicted using block 512 in FIG.
5. More specifically, the expected volume of interactions includes
a plurality of interactions, such as interactions 514, 516 and 518
(exemplarily depicted to be interaction `I.sub.1`, `I.sub.2` and
`I.sub.3` in FIG. 5). Each interaction is further associated with
one or more interaction attributes. For example interaction 514
(i.e. interaction I.sub.1) is associated with interaction
attributes I.sub.a, I.sub.b and I.sub.c; interaction 516 (i.e.
interaction I.sub.2) is associated with interaction attributes
I.sub.a and I.sub.d; and interaction 518 (i.e. interaction I.sub.n)
is associated with interaction attribute I.sub.e.
[0062] As explained with reference to FIG. 2, the processor 202 is
configured to compare resource attributes of various resources with
interaction attributes of expected interactions to identify matches
and map one or more resources to an interaction based on the
identified matches. More specifically, the processor 202 may match
resource attributes R.sub.a, R.sub.b, R.sub.e, R.sub.d and R.sub.e
with interaction attributes I.sub.a, I.sub.b, I.sub.c, I.sub.d and
I.sub.e. In an illustrative example, the processor 202 may deem
resource attribute R.sub.e (for example, a particular skill of a
resource) to be a good match with an interaction attribute I.sub.d
(for example, a specific intention associated with the
interaction). Accordingly, the processor 202 may map resource 508
(i.e. resource R.sub.n) to the interaction I.sub.2. Similarly, the
processor 202 may map resources 504 and 506 to interactions I.sub.n
and I.sub.1, respectively, as exemplarily depicted in a block 520
depicting resource to interaction mapping.
[0063] Referring now to FIG. 2, in at least one embodiment, the
processor 202 is configured to, with the content of the memory 204,
cause the apparatus 200 to facilitate a staffing of the plurality
of resources of the enterprise based, at least in part, on the
mapping of the at least one resource to the each interaction. More
specifically, the apparatus 200 is caused to facilitate a
management of deployment of resources for a given time period and
for a given customer intention based on the resource to interaction
mapping provided by the apparatus 200 as explained with reference
to FIG. 5. In some embodiments, the apparatus 200 is further caused
to determine which customer intention or which set of customers to
give priority to and which resource to be assigned to which
intention based on resource skill level and interaction priority.
In an embodiment, the apparatus 200 is caused to perform resource
to interaction mapping for facilitating staffing of resources at
regular intervals (for example, every week) and dynamically adjust
staffing requirements for changing interaction/intention queue
traffics.
[0064] In some example scenarios, the apparatus 200 includes the
flexibility to facilitate dynamic changing of staffing requirements
for next instance, where the term `instance` may refer to a
particular work shift or even a time period, such as on an hourly
basis, whereas, in cases of non-human resources, the term
`instance` may refer to near real-time instances.
[0065] Moreover as explained above, the apparatus 200 is caused to
facilitate assignment of the right resource/agent to each customer
based on skill and priority of the interaction. Further, staffing
of resources may also take into account the priority levels of the
customers, for example, appropriate staffing may be predicted for
preferred customers. In some scenarios, deploying of resources may
be delayed because higher priority customers may be expected to
initiate interaction in an hour's time.
[0066] In some example embodiments, the processor 202 may be
configured to only partially utilize the resource to interaction
mapping and instead facilitate proactive initiation of interactions
with some customers (for whom it is determined that the likelihood
of initiating the interaction with the enterprise exceeds the
pre-defined threshold value) using most suitable among available
agents. For example, if a likelihood of a customer initiating an
interaction is greater than a pre-defined threshold value of 0.9
(implying 90% probability that the customer will initiate an
interaction with the customer during a given time frame, for
example, a week's time), then the apparatus 200 may be caused to
facilitate proactive reaching out to the customer. For example, if
a fraudulent transaction is detected, then the processor 202 may be
configured to facilitate proactive outbound call to the customer as
there is a high likelihood that the customer may contact the
enterprise. In such a case, the resource to interaction mapping for
the customer may serve as a guide to select the next best suitable
agent if the mapped agent is not available for the interaction.
[0067] In some example embodiments, if one or more customers with
high likelihood of initiating interactions with the enterprise are
predicted to contact the enterprise in a time period, which is
associated with a volume of expected interactions greater than a
prescribed limit, then such customers may be proactively contacted
prior to their predicted time of interaction. For example, if the
expected volume of interactions for a chosen time slot is greater
than 500 interactions, which is the prescribed limit for a number
of interactions that can be adequately handled in a given time
slot, then one or more customers with high likelihood of contacting
the enterprise may be contacted prior in a time slot prior to the
predicted time slot. In some example scenarios, the proactive
reaching out to customers may be initiated in time periods
associated with lesser expected volume of interactions.
[0068] In some embodiments, the processor 202 is caused to factor
in customer lifetime value of the customer prior to initiating
outbound contact. Alternatively or additionally, self serving
solutions, such as widgets, dynamic uniform resource locators,
dynamic IVR messages, and the like may be utilized to interact with
customers. Further, emails or SMS may be sent to the customers to
provide them with information and avoid their seeking out help from
the customer support facility. In some scenarios, the nature of
interaction assistance may also be personalized for a customer
segment as well as for a specific customer need.
[0069] In at least one example embodiment, the I/O module 206 of
the apparatus 200 may be caused to receive tracked activity of a
customer on an interaction channel. The tracked activity may
correspond to an on-going customer journey on an interaction
channel. In such a scenario, the processor 202 may be configured to
determine, in real-time, if the customer requires interaction
assistance based on the tracked activity. More specifically, the
processor 202 may predict an intention of the customer based on
information related to customer's journey on the interaction
channel so far, and, past interaction history of the customer.
Further, the processor 202 may determine if the customer needs
assistance based on the predicted intention. The processor 202 may
also determine if a request for interaction is to be proactively
offered to the customer on the interaction channel upon determining
that the customer requires interaction assistance. Such a
determination for proactively offering the request for interaction
may be performed based, at least in part, on a current staffing of
the plurality of resources and a customer lifetime value (CLU) of
the customer. The proactive offering of interaction assistance to a
customer is further explained with reference to an illustrative
example in FIG. 6.
[0070] Referring now to FIG. 6, an example representation 600 is
shown for illustrating a provisioning of a request for interaction
to a customer 602 during an on-going journey on an enterprise
interaction channel, in accordance with an embodiment of the
invention. More specifically, the customer 602 may access an
enterprise website (i.e. visit a web interaction channel
corresponding to the enterprise) by using a web browser application
604 included in a customer's device 606. It is understood that the
website may be hosted on a remote server and the web browser
application 604 may be configured to retrieve one or more web pages
of the enterprise website over a network, such as the network 120
explained with reference to FIG. 1.
[0071] One or more remote data gathering servers (not shown in FIG.
6) may be configured to track the customer's activity on the
enterprise website. For example, tracking customer's activity on
the enterprise website may include tracking a sequence of web pages
visited, time spent on each web page, images clicked, hyper links
accessed etc., and such information may be provided to the I/O
module 206 of the apparatus 200 explained with reference to FIG. 2,
in substantially real-time. Further, the processor 202 of the
apparatus 200 may predict customer's intention and may determine if
the customer 602 needs interaction assistance for the predicted
intention or not. For example, in an illustrative scenario, the
customer 602 may have visited the FAQ web page associated with the
enterprise website and/or searched for links related to booking
reservations. In such a scenario, based on the tracked activity of
the customer 602 on the enterprise website, the processor 202 may
predict that the customer 602 intends to cancel a recently booked
travel reservation. The processor 202 may further determine that
the customer 602 needs assistance for the predicted intention as
the customer 602 seems to have continued his search for a
satisfactory resolution to his concern. For example, the processor
202 may also be configured to predict, at regular intervals (for
example, five second intervals) how likely the customer 602 is to
interact.
[0072] As explained above, the apparatus 200 may further be caused
to determine if a request for interaction is to be proactively
offered to the customer 602 on the enterprise website upon
determining that the customer 602 requires interaction assistance.
For example, the processor 202 may be configured to predict a
benefit of offering chat assistance to the customer 602 (whether
the assistance may result in sale or enhance a customer experience)
as opposed to not offering chat assistance to the customer 602. The
processor 202 may also check a current staffing of the resources to
determine how many resources may be able to chat with the said
customer 602. Given the availability of resources, the processor
202 may determine a score based on predictions related to how
likely the customer 602 is to seek assistance and the benefit of
offering assistance to a customer likely to interact. Only when the
score is greater than a pre-determined value (for example a
numerical value chosen empirically, such as 0.5 for instance), the
chat assistance may be proactively offered to the customer 602. In
some embodiments, a customer lifetime value (CLU) of the customer
602 may also be accounted for, during determination of offering of
interaction assistance to the customer 602.
[0073] In some embodiments, the processor 202 may be configured to
determine an appropriate time for proactively offering the request
for interaction to the customer 602 on the interaction channel. In
an embodiment, the appropriate time may be determined based on
determining a likelihood of a desired outcome to be greater than a
pre-determined value. For example, upon n.sup.th web page load, if
it is determined that the likelihood of the customer 602 purchasing
a product is greater than a pre-determined value (for example, if
there is more than 50% probability of the customer making a
purchase for the current visit to the website), then the processor
202 may be configured to proactively offer the request for
interaction as soon as the likelihood exceeds the pre-determined
value, i.e. on the n.sup.th web page visited on the enterprise
website.
[0074] Accordingly, a pop-up window 608 may be displayed on the web
page. The pop-up window 608 may include text, such as `Would you
like to chat with our agent to discuss your requirements?` to
facilitate provisioning of the request for invitation (for example,
a chat interaction) to the customer 602. The customer 602 may
choose to provide his/her acceptance by clicking on the pop-up
window 608. Alternatively, the customer 602 may decline the
interaction invitation may clicking on `close` icon 610 on the
pop-up window 608. The customer 602 may also choose to ignore the
interaction invitation.
[0075] In some embodiments, the processor 202 is configured to
determine whether a customer needs assistance and once it is
determined that the customer needs assistance, then the processor
202 is further configured to determine if it is optimal to offer
assistance to the customer now or a later time period. In another
illustrative example, chat assistance may be offered to a customer
when it is determined that the customer is currently
present/attentive on the web channel (or via native app) at the
right time by anticipating that a customer needs specific
assistance.
[0076] Such proactive assistance offered to the customer provides
several advantages as the assistance may be provided while taking
into account a number of factors, such as (1) whether an agent is
available; (2) Whether the customer needs help; (3) whether there
are more customers who need help and are they more important than
this customer; (4) whether the customer can wait for few more
minutes till another agent is free, and the like.
[0077] Referring now to FIG. 2, in an embodiment, the apparatus 200
is also associated with a feedback loop, where predicted
interaction volumes for various time periods are matched with
corresponding actual observed interaction volumes (including both
inbound and outbound/proactive interactions) and appropriate
adjustments to prediction models and/or resource-to-interaction
mapping algorithms are performed to further reduce instances of
inappropriate staffing.
[0078] In at least one example embodiment, the processor 202 is
further configured to predict customer intentions at regular
intervals (for example, every week), and adjust the estimates of
expected volume of interactions dynamically with changing
interaction traffic and as per the manual observational data.
[0079] Furthermore, the processor 202 may be configured to make
various assumptions for facilitating predictions. For instance, the
processor 202 may be configured to assume that a customer may call
after a failed transition, or after a flight cancellation to change
hotel and transport reservations, or in scenarios when a customer's
discount period is about to expire, or when the customer is delayed
in traffic on way to airport, or to ask for an upgrade upon
receiving news of a new product launch and the like. Similarly, it
may be assumed that a customer may call regarding insurance
immediately, when the customers' vehicle has been involved in an
accident. In some embodiments, it may be assumed that the customer
may call within a day upon expressing interest for a specific
product and/or browsing for related information.
[0080] In an embodiment, the processor 202 may also detect
occurrence of at least one event indicating a change in expected
volume of interactions and may cause the apparatus 200 to
facilitate dynamic adjustment (almost in real-time) to staffing of
one or more resources from among the plurality of resources of the
enterprise for at least one forthcoming time period (such as for
example, a next hour or a next work shift). Some non-exhaustive
examples of such events may include (1) enterprise triggered events
such as for example, change in policies, campaigns, new product
launches, and the like; (2) customer lifecycle events such as for
example, bill generation, fraudulent transaction, delay in
shipment, and the like; and (3) external events such as for
example, extreme weather, government policies, rare events such as
outages, data breach, major competitor/affiliate activities,
activities of others within network, such as for example
influential posts on social networking websites and the like. In
some embodiments, the dynamic adjustment to staffing of resources
may also be facilitated using location triggers, such as for
example location of customers obtained from geo-location tracking
satellites and/or one or more sensors installed in stores,
vehicles, and the like. A method for facilitating staffing of
resources is explained with reference to FIG. 7.
[0081] FIG. 7 is a flow diagram of an example method 700 for
facilitating staffing of resources, in accordance with an
embodiment of the invention. The method 700 depicted in the flow
diagram may be executed by, for example, the apparatus 200
explained with reference to FIGS. 2 to 6. Operations of the
flowchart, and combinations of operation in the flowchart, may be
implemented by, for example, hardware, firmware, a processor,
circuitry and/or a different device associated with the execution
of software that includes one or more computer program
instructions. The operations of the method 700 are described herein
with help of the apparatus 200. For example, one or more operations
corresponding to the method 700 may be executed by a processor,
such as the processor 202 of the apparatus 200. It is noted that
although the one or more operations are explained herein to be
executed by the processor alone, it is understood that the
processor is associated with a memory, such as the memory 204 of
the apparatus 200, which is configured to store machine executable
instructions for facilitating the execution of the one or more
operations. It is also noted that, the operations of the method 700
can be described and/or practiced by using an apparatus other than
the apparatus 200. The method 700 starts at operation 702.
[0082] At operation 704 of the method 700, customer data
corresponding to a plurality of customers of an enterprise is
received. The data received corresponding to each customer may
include customer profile data, interaction data, location and
sensor data, presence and attention data, social interactions data
and events data as explained with reference to customer 302 in FIG.
3.
[0083] At operation 706 of the method 700, at least one intention
is predicted for each customer from among the plurality of
customers using data corresponding to the each customer in the
customer data. More specifically, the data corresponding to each
customer is transformed into variables, assigned weights, and is
subjected to a set of structured and un-structured data analytical
models including text mining & predictive models for customer
intention prediction purposes. Examples of the prediction models
may include, but are not limited to Logistic regression, Naive
Bayesian, Rule Engines, Neural Networks, Decision Trees, Support
Vector Machines, k-nearest neighbor, K-means and the like. Further,
the weights of variables and the prediction models may be
learnt/adjusted using a feedback loop, wherein outcomes of the
predictions are received at a later stage and the
weights/prediction models dynamically adjusted to account for
observed errors. The prediction of the at least one intention for
the each customer configures a plurality of intentions.
[0084] At operation 708 of the method 700, an expected volume of
interactions is estimated for at least one time period based on the
plurality of intentions. The estimation of the expected volume of
interactions for one or more time periods may be performed as
explained with reference to FIG. 2 and is not explained again
herein. Further, each interaction in the expected volume of
interactions may be associated with interaction attributes relating
to at least one of an intention of a customer seeking the
interaction, a priority level of the customer and a CLU of the
customer.
[0085] At operation 710 of the method 700, resource data
corresponding to a plurality of resources of the enterprise is
received. The plurality of resources may include human resources
and machine based resources. Each resource is associated with a
plurality of resource attributes, such as those related to a skill
of a resource, a capability of the resource and an availability
status of the resource and the like.
[0086] At operation 712 of the method 700, at least one resource
from among the plurality of resources is mapped to each interaction
in the expected volume of interactions based on a match between
respective resource attributes associated with the at least one
resource and the one or more interaction attributes associated with
the each interaction. The mapping of resources to interactions may
be performed as explained with reference to FIG. 5 and is not
explained herein.
[0087] At operation 714 of the method 700, a staffing of the
plurality of resources of the enterprise is facilitated based, at
least in part, on the mapping of the at least one resource to the
each interaction. More specifically, a management of deployment of
resources for a given time period and for a given customer
intention is facilitated based on the resource to interaction
mapping. Further, an assignment of the right resource/agent to each
customer based on skill and priority of the interaction is also
facilitated. Further, staffing of resources may also take into
account the priority levels of the customers, for example,
appropriate staffing may be predicted for preferred customers. In
some embodiments, dynamic changing of staffing requirements for
next instance is also facilitated as explained with reference to
FIG. 2.
[0088] The method 700 ends at operation 716. At operation 716, an
enterprise user may manage staffing of the plurality of resources
based on the resource to interaction mapping as well as suggested
dynamic adjustments to staffing of the plurality of resources.
[0089] Without in any way limiting the scope, interpretation, or
application of the claims appearing below, advantages of one or
more of the exemplary embodiments disclosed herein include enabling
staffing of resources associated with customer support operations
based upon estimation of expected interaction volumes for various
time periods while taking multiple customer intentions and
plurality of interaction channels into account. Staffing of
resources based on such predicted interaction volumes as opposed to
staffing based on historical staffing patterns enables reducing
instances of under staffing and over staffing of resources, thereby
precluding bad customer experiences and/or operating losses.
Moreover, techniques suggested herein enable dynamic (almost
real-time) adjustments to staffing thereby enabling appropriate
allocation of resources for handling fluctuations in volume of the
customer interactions.
[0090] Additionally, the method and apparatus disclosed herein also
suggest techniques for proactive contacting of customers who are
likely to interact, thereby aiding to preclude situations, where
customer interaction traffic exceeds resource deployment at the
customer support facility. Moreover, techniques disclosed herein
also enable (1) admission control, where it is determined whether
to offer assistance to a particular customer now or later, while
taking into account all possible channels and other customers, and
(2) skill and/or priority based matching of resources to customer
interaction requirements and techniques.
[0091] Although the present technology has been described with
reference to specific exemplary embodiments, it is noted that
various modifications and changes may be made to these embodiments
without departing from the broad spirit and scope of the present
technology. For example, the various operations, blocks, etc.,
described herein may be enabled and operated using hardware
circuitry (for example, complementary metal oxide semiconductor
(CMOS) based logic circuitry), firmware, software and/or any
combination of hardware, firmware, and/or software (for example,
embodied in a machine-readable medium). For example, the
apparatuses and methods may be embodied using transistors, logic
gates, and electrical circuits (for example, application specific
integrated circuit (ASIC) circuitry and/or in Digital Signal
Processor (DSP) circuitry).
[0092] Particularly, the apparatus 200, the processor 202, the
memory 204 and the I/O module 206 may be enabled using software
and/or using transistors, logic gates, and electrical circuits (for
example, integrated circuit circuitry such as ASIC circuitry).
Various embodiments of the present technology may include one or
more computer programs stored or otherwise embodied on a
computer-readable medium, wherein the computer programs are
configured to cause a processor or computer to perform one or more
operations (for example, operations explained herein with reference
to FIG. 7). A computer-readable medium storing, embodying, or
encoded with a computer program, or similar language, may be
embodied as a tangible data storage device storing one or more
software programs that are configured to cause a processor or
computer to perform one or more operations. Such operations may be,
for example, any of the steps or operations described herein. In
some embodiments, the computer programs may be stored and provided
to a computer using any type of non-transitory computer readable
media. Non-transitory computer readable media include any type of
tangible storage media. Examples of non-transitory computer
readable media include magnetic storage media (such as floppy
disks, magnetic tapes, hard disk drives, etc.), optical magnetic
storage media (e.g. magneto-optical disks), CD-ROM (compact disc
read only memory), CD-R (compact disc recordable), CD-R/W (compact
disc rewritable), DVD (Digital Versatile Disc), BD (Blu-ray
(registered trademark) Disc), and semiconductor memories (such as
mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flash
ROM, RAM (random access memory), etc.). Additionally, a tangible
data storage device may be embodied as one or more volatile memory
devices, one or more non-volatile memory devices, and/or a
combination of one or more volatile memory devices and non-volatile
memory devices. In some embodiments, the computer programs may be
provided to a computer using any type of transitory computer
readable media. Examples of transitory computer readable media
include electric signals, optical signals, and electromagnetic
waves. Transitory computer readable media can provide the program
to a computer via a wired communication line (e.g. electric wires,
and optical fibers) or a wireless communication line.
[0093] Various embodiments of the present disclosure, as discussed
above, may be practiced with steps and/or operations in a different
order, and/or with hardware elements in configurations, which are
different than those which, are disclosed. Therefore, although the
technology has been described based upon these exemplary
embodiments, it is noted that certain modifications, variations,
and alternative constructions may be apparent and well within the
spirit and scope of the technology.
[0094] Although various exemplary embodiments of the present
technology are described herein in a language specific to
structural features and/or methodological acts, the subject matter
defined in the appended claims is not necessarily limited to the
specific features or acts described above. Rather, the specific
features and acts described above are disclosed as exemplary forms
of implementing the claims.
* * * * *