Note: Descriptions are shown in the official language in which they were submitted.
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PAYMENT PORTFOLIO OPTIMIZATION
RELATION TO US APPLICATIONS
[0001] This application claims priority to U.S. Application No. 12/108,354
entitled,
"Payment Portfolio Optimization" filed on April 23, 2008 and to U.S.
Application No.
12/108,342 entitled "Payment Portfolio Optimization" filed on April 23, 2008.
Both
applications are hereby incorporated in their entirety for all purposes.
BACKGROUND
[0002] Traditionally, an issuer, e.g. a bank, examines its own consumers'
spending
behaviors to find potential opportunities for increasing revenue. The issuer
may
compare the performance of its consumer portfolio to the performance of the
portfolios of other issuers to identify a general opportunity for growth. The
issuer
defines opportunities for a marketing analyst and the marketing analyst
recommends
marketing treatments. For example, a bank issuing credit cards may have
evaluated
their business accounts and discovered that they have low activation rates on
their
business credit cards. The bank might present this problem to a marketing
analyst.
The analyst could recommend sending out a mass mailing to remind these
consumers to activate their cards. In another example, a bank may have
evaluated
its business accounts and discovered that most consumers with active business
credit cards rarely use their cards. In this example, the analyst may
recommend that
the bank create a rewards plan for their business card accounts.
[0003] The issuer typically evaluates its own consumers' spending behaviors
using
information available over a "closed network" which is not generally open for
use by
other independently operated issuers. Because the closed network receives a
limited amount of data and cannot perform an optimum analysis of potential
revenue
growth opportunities, the issuer using the closed network may miss
opportunities
and potentially lose revenue.
[0004] Sometimes, propensity models are used to predict the likelihood that
consumers will respond to marketing treatments. Typically, multiple propensity
models are developed with each model predicting the likelihood of improving
performance in a single area such as penetration, activation, usage,
attrition, etc..
Since each model addresses only a single area, multiple combinations of
marketing
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treatments result. If each combination of marketing treatments is pursued,
marketing
funds may be wasted that could be used to take advantage of other potential
opportunities.
[0005] Embodiments of the present disclosure address these and other problems,
individually and collectively.
SUMMARY OF THE INVENTION
[0006] Embodiments of the invention are directed to methods and systems for
payment portfolio optimization.
[0007] One embodiment of the invention is a method of payment portfolio
optimization that retrieves information associated with at least one issuer
over a
payment processing network. The information is associated with a plurality of
consumers having accounts with the at least one issuer. The method separates
the
plurality of consumers into a plurality of consumer segments based on segment
definitions. The method analyzes the plurality of consumer segments using the
retrieved information based on a plurality of performance metrics and
identifies
opportunities associated with the plurality of consumer segments based on the
results of the analysis.
[0008] Another embodiment of the invention is a system of payment portfolio
optimization that comprises a database for storing information and a
diagnostics
module coupled to the database. The diagnostics module retrieves information
from
the database that is associated with at least one issuer on a payment
processing
network. The information is associated with a plurality of consumers having
accounts with the at least one issuer. The diagnostics module also separates
the
plurality of consumers into a plurality of consumer segments based on segment
definitions, analyzes the plurality of consumer segments using the retrieved
information based on a plurality of performance metrics, and identifies
opportunities
associated with the plurality of consumer segments based on the analysis of
the
consumer segments.
[0009] In some embodiments, information is collected from an issuer about its
consumer portfolio. The consumer portfolio is segmented based on shared
characteristics. The collected information is used to identify potential
opportunities
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for increasing revenue in particular consumer segments. The opportunities are
evaluated based on predicted net revenue that could be generated if the
opportunities are realized. The consumer segments with the most profitable
opportunities are selected. A likelihood that each consumer will act on
marketing
treatments is assessed. Each consumer is ranked based on this likelihood and
the
most promising consumers are selected as targets of a marketing plan. The
marketing plan is designed and tested based on multiple factors simultaneously
to
determine whether the marketing treatments in the marketing plan will
successfully
target the most promising consumers. The marketing plan is modified to include
only
the successful marketing treatments. An improved successful marketing plan is
delivered to the issuer that targets the most promising consumers and
optimizes
return on investment (ROI) to the issuer.
[0010] One embodiment of the invention is a method of payment portfolio
optimization that retrieves a plurality of consumer segments of a consumer
portfolio
from a diagnostics module. The plurality of consumer segments having
potentially
profitable opportunities. The method also develops a propensity model on a
computer based on at least one performance metric, determines, using the
propensity model, a likelihood that consumers in each of the plurality of
consumer
segments will perform favorably. The method also selects a set of consumer
segments from the plurality of consumer segments based on the determined
likelihood and designs a plurality of marketing treatments for the selected
set of
consumer segments.
[0011] Another embodiment of the invention is a system of payment portfolio
optimization that comprises a database for storing information and a financial
modeling module coupled to the database. The financial modeling module on a
computer retrieves a plurality of consumer segments of a consumer portfolio
from a
diagnostics module. The plurality of consumer segments have profitable
opportunities. The system also develops a propensity model based on at least
one
performance metric and determines, using the propensity model, a likelihood
that
consumers in each of the plurality of consumer segments will perform
favorably. The
system also selects a set of consumer segments from the plurality of consumer
segments based on the determined likelihood and designs a plurality of
marketing
treatments for the selected set of consumer segments.
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[0012] These and other embodiments of the invention are described in further
detail
below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 is a block diagram illustrating a payment portfolio optimization
system, in accordance with an embodiment of the invention.
[0014] FIG. 2 is a flowchart illustrating a method of payment portfolio
optimization
that includes diagnosing opportunities in the consumer portfolio, developing
targeting
tools, designing and launching a pilot marketing plan, and rolling out a
successful
marketing plan, in accordance with an embodiment of the invention.
[0015] FIG. 3 is a flowchart illustrating a method of payment portfolio
optimization,
in accordance with an embodiment of the invention.
[0016] FIG. 4 is a flowchart illustrating a method of payment portfolio
optimization,
in accordance with an embodiment of the invention.
DETAILED DESCRIPTION
[0017] Embodiments of the invention address the above-noted problems by
providing a method and system of payment portfolio optimization that uses
information about issuer's consumers to identify and evaluate potential
opportunities
for increased net revenue to the issuer. This information is used to develop
optimal
marketing treatments for the issuer that target only those consumers with the
greatest likelihood of responding to the marketing treatments.
[0018] In some embodiments, information is collected from an issuer about its
consumer portfolio. The consumer portfolio is segmented based on shared
characteristics. The collected information is used to identify potential
opportunities
for increasing revenue in particular consumer segments. The opportunities are
evaluated based on predicted net revenue that could be generated if the
opportunities are realized. The consumer segments with the most profitable
opportunities are selected. A likelihood that each consumer will act on
marketing
treatments is assessed. Each consumer is ranked based on this likelihood and
the
most promising consumers are selected as targets of a marketing plan. The
marketing plan is designed and tested based on multiple factors simultaneously
to
determine whether the marketing treatments in the marketing plan will
successfully
target the most promising consumers. The marketing plan is modified to include
only
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the successful marketing treatments. An improved successful marketing plan is
delivered to the issuer that targets the most promising consumers and
optimizes
return on investment (ROI) to the issuer.
[0019] One embodiment of the invention is a method of payment portfolio
optimization comprising retrieving information associated with at least one
issuer
over a payment processing network. The information is associated with a
plurality of
consumers having accounts with the at least one issuer. The method also
includes
separating by a diagnostics module on a computer the plurality of consumers
into a
plurality of consumer segments based on segment definitions and analyzing by a
diagnostics module on a computer the plurality of consumer segments using the
retrieved information based on a plurality of performance metrics, and
identifying
opportunities associated with the plurality of consumer segments based on the
analysis of the consumer segments.
[0020]Another embodiment of the invention is a system of payment portfolio
optimization comprising a database for storing information and a diagnostics
module
on a computer, the diagnostics module coupled to the database. The diagnostics
module is configured to retrieve information, from the database, associated
with at
least one issuer on a payment processing network. The information is
associated
with a plurality of consumers having accounts with the at least one issuer.
The
diagnostics module is further configured to separate the plurality of
consumers into a
plurality of consumer segments based on segment definitions, analyze the
plurality
of consumer segments using the retrieved information based on a plurality of
performance metrics, and identify opportunities associated with the plurality
of
consumer segments based on the analysis of the consumer segments.
[0021] Certain embodiments of the invention may provide one or more technical
advantages to issuers and consumers. One technical advantage to an issuer may
be that using this method and system may provide better customized marketing
plans that optimize the return on investment to the issuer. Another technical
advantage to the issuer may be reducing marketing expenditures since a single
marketing plan can be developed that improves performance in multiple areas.
Also,
a technical advantage to an issuer may be that using this method and system
may
more accurately define opportunities for revenue growth since they can be
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information available from one or more sources. Another technical advantage to
an
issuer may be that an issuer can reduce their marketing expenditures by
benchmarking performance of consumer segments to determine potential
improvement to better understand where to focus marketing funds. A technical
advantage to a consumer may be that the consumer may be more likely to learn
of
products or services that will benefit them or their businesses.
[0022] Certain embodiments of the invention may include none, some, or all of
the
above technical advantages. One or more other technical advantages may be
readily apparent to one skilled in the art from the figures, descriptions, and
claims
included herein.
[0023] FIG. 1 is a block diagram illustrating a payment portfolio optimization
system
10, in accordance with an embodiment of the invention. Payment portfolio
optimization system 10 includes a consumer portfolio 20 having three consumers
22(a), 22(b) and 22(c). Payment portfolio optimization system 10 also includes
portable consumer devices 30(a) and 30(b) in operative communication with
consumers 22(a) and 22(b) and access devices 32(a) and 32(b) for interacting
with
portable consumer devices 30(a) and 30(b). Payment portfolio optimization
system
also includes three merchants 40(a), 40(b), and 40(c). Merchant 40(a) is in
operative communication with access device 32(a) that can interact with
portable
consumer device 30(a). Merchant 40(b) is in operative communication with
access
device 32(b) that can interact with portable consumer device 30(b). Merchant
40(c)
is in operative communication with consumer 22(c) to accept payment in the
form of
checks or cash. Payment portfolio optimization system 10 also includes
acquirers 50
that are associated with merchants 40.
[0024] Payment processing network 60 is in operative communication with
acquirers 50 and issuers 70. In other embodiments, payment processing network
60
is in operative communication with other entities such as other consumers,
other
issuers, marketing analysts, and organizations such as credit bureaus, credit
agencies for collecting information 66 that may be useful in payment portfolio
optimization.
[0025] Payment portfolio optimization system 10 includes a payment processing
network 60 having a diagnostic module 62, a financial modeling module 66, and
a
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database 64 having information 66. Diagnostics module 62 is in communication
with
database 64 for retrieving information 66 used to diagnosis opportunities in
consumer portfolio 20 and for storing information 66 such as the diagnosed
opportunities. Diagnostics module 62 is also in communication with issuer
70(a) to
receive information for payment portfolio optimization. Financial modeling
module 68
is in communication with database 64 for retrieving information 66 used to
develop
targeting tools, design and launch a pilot marketing plan, and roll out a
successful
marketing plan. Financial modeling module 68 is also in communication with
issuer
70(a) to deliver the successful marketing plan to issuer 70(a). The marketing
plan
includes marketing treatments that optimize the potential for maximum revenue
to
issuer 70(a) from consumer portfolio 20. The marketing plan may include
marketing
treatments that optimize the potential for maximum revenue to issuer 70(a)
from
consumer portfolio 20.
[0026] Marketing treatments refer to methods of marketing products to
consumers
22 targeted by financial modeling module 68. In some cases, the products are
customized based on the characteristics of consumers 22. Marketing treatments
can
be of any suitable type. Examples of types of marketing treatments include
solicitations, educational messages, and offers. Marketing treatments can be
given
to consumers 22 by any suitable method (e.g., online). Examples of online
marketing treatments include e-coupons, games, surveys, video streaming, data
management, and search engine marketing.
[0027] Although diagnostics module 62 and financial modeling module 68 are
shown as being part of the payment processing network 60, they may be outside
payment processing network 60 in other embodiments. Diagnostics module 62
and/or financial modeling module 68 may be embodied by software code that
resides
on one or more computers within payment processing network 60. Any of the
functions performed by the diagnostics module 62 and/or financial modeling
module
68 may be embodied by computer code, and/or instructions which may be executed
by one or more processors.
[0028] Payment portfolio optimization system 10 also includes issuers 70 for
issuing portable consumer devices 30(a) to consumer 22(a), issuing portable
consumer device 30(b) to consumer 22(b), and for issuing checks to consumer
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22(c). Consumer 22(a) has a checking account with issuer 70(a) that is
associated
with portable consumer device 30(a) and a checking account with issuer 70(c)
that is
not associated with a portable consumer device 30. Consumer 22(b) has a
checking
account with issuer 70(a) that is associated with portable consumer device
30(b) and
a checking account with issuer 70(b). Consumer 22(c) has a checking account
with
issuer 70(a) and a checking account with issuer 70(c). Although payment
portfolio
optimization system 10 is shown with three issuers 70 and with three consumers
22,
there may be any suitable number of issuers 70 and consumers 22 in payment
portfolio optimization system 10. In addition, issuers 70 may have any
suitable
number or type of account with any suitable number of consumers 22.
[0029] In a typical payment transaction, consumer 22(a) may purchase goods or
services at merchant 40(a) using portable consumer device 30(a) at access
device
32(a) and consumer 22(b) may purchase goods or services at merchant 40(b)
using
portable consumer device 30(b) at access device 32(b). Consumer 22(c) may
purchase goods or services at merchant 40(c) using cash or check.
[0030] Consumers 22 refer to entities that are capable of purchasing goods or
services or making any suitable transaction with merchant 40. In some cases,
consumers 22 may be organizations such as businesses. For example, consumers
22 may be small business owners.
[0031] Consumer portfolio 20 refers to any suitable collection of consumers 22
that
have an account with issuer 70(a). An account may be any suitable type of
account
such as a business account, an individual checking account, an individual
savings
account, etc. Although three consumers 22(a), 22(b), and 22(c) are shown in
consumer portfolio 20, any suitable number of consumers 22 may be present in
consumer portfolio 20. Also, any suitable number of prospective consumers 22
may
be present outside of consumer portfolio 20.
[0032] In the illustrated embodiment, diagnostics module 62 and financial
modeling
module 68 optimize consumer portfolio 20 of issuer 70(a). In other
embodiments,
diagnostics module 62 and financial modeling module 68 may optimize
opportunities
associated with prospective consumers 22 outside of consumer portfolio 20.
[0033] A consumer characteristic refers to any suitable attribute (e.g. a
spend
behavior) that describes consumer 22, the account associated with consumer 22,
the
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portable consumer device 30 of consumer 22, one or more issuers 70, or other
suitable entity. For example, a consumer characteristic can be the extent of
their
automated clearing house (ACH), cash, and check usage where a relative heavy
usage suggests that there may be an opportunity to migrate the consumer 22 to
a
portable consumer device 30. As another example, a consumer characteristic can
be the amounts and quantities of the transactions made by consumer 22 using
portable consumer device 30. In some cases, a consumer can be characterized as
"light user," "medium user," "heavy user," or "super-heavy user" based on the
amounts and quantities of the transactions. Another example of a consumer
characteristic can be the way in which portable consumer device 30 is used by
consumer 20. In this example, consumer can be characterized as "offline" when
consumer's portable consumer device 30 requires a signature at the point of
sale
(POS) or "online" where the consumer's portable consumer device 30 requires a
PIN
at POS. In yet another example, a consumer characteristic can be whether or
not
transactions are made using portable consumer device 30. A consumer not
associated with portable consumer device 30 is characterized as "uncarded" and
a
consumer associated with portable consumer device 30 is characterized as
"carded."
Another consumer characteristic can be whether portable consumer device 30 of
consumer 20 has been activated. A consumer can be characterized as "active"
when the portable consumer device 30 has been activated and used at a POS or
characterized as "inactive" where the portable consumer device 30 has not been
activated or activated but not used at POS. Another consumer characteristic
can be
whether or not the consumer's account is open. A consumer 20 with an open
account can be characterized as "open" and a consumer 20 with a closed account
can be characterized as "closed." Another consumer characteristic can be their
location such as county, state, or region (e.g., Northeast region of the
U.S.). Another
example of a consumer characteristic may be whether consumer 20 has a rewards
plan associated with their account. Consumers may have any number consumer
characteristics. For example, a consumer can be an "active," "carded," "super-
heavy user," where the consumer has an active account with issuer 70(a) that
is
associated with a portable consumer device 30(a) and that the consumer is a
super-
heavy user of the portable consumer device 30(a).
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[0034] A consumer segment refers to a subset of consumers 22 that share a set
of
consumer characteristics. For example, all consumers 22 in consumer portfolio
20
have an account with issuer 70(a) but only consumer 22(c) does not have a
portable
consumer device 30 associated with their account. One consumer segment may
consist of consumer 22(a) and consumer 22(b) with a set of consumer
characteristics consisting of "carded." Another consumer segment may consist
of
consumer 22(c) with a set of consumer characteristics consisting of
"uncarded."
[0035] Consumer segmentation refers to the separation of consumers into
consumer segments based on segment definitions. In the illustrated embodiment,
only consumers 22 in consumer portfolio 20 are segmented. In other
embodiments,
consumers in consumer portfolio 20 and outside of consumer portfolio 20 are
segmented. A segment definition refers to a set of consumer characteristics
that
define a consumer segment. A segment definition may include any suitable
number
of characteristics. For example, a segment definition may include "carded,
"active"
consumers that is consumers with active cards. In another example, a segment
definition may include "offline," "carded," "super-heavy user," that is the
consumers
with open accounts having an active portable consumer device that requires a
signature at the POS where the consumer is a super-heavy user of their
portable
consumer device. Segment definitions may be defined by issuer 70(a), by
diagnostics module 62, or by any other suitable entity. In some cases, segment
definitions may be based on information 66 that is available over the payment
processing network 60.
[0036] Portable consumer device 30 refers to any suitable device that allows
the
transaction to be conducted with merchant 40 and that is associated with an
account
of issuer 70. Portable consumer device 30 may be in any suitable form. For
example, suitable portable consumer devices 30 can be hand-held and compact so
that they can fit into a consumer's wallet and/or pocket (e.g., pocket-sized).
They
may include smart cards, magnetic stripe cards, keychain devices (such as the
SpeedpassTM commercially available from Exxon-Mobil Corp.), etc. Other
examples
of portable consumer devices 30 include cellular phones, personal digital
assistants
(PDAs), pagers, payment cards, security cards, access cards, smart media,
transponders, and the like.
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[0037] In some embodiments, portable consumer device 30 may comprise a
computer readable medium and a body. The computer readable medium may be on
the body of portable consumer device 30. The body may in the form of a plastic
substrate, a housing, or other structure. The computer readable medium may be
a
memory that stores data and may be in any suitable form. Exemplary computer
readable media may be in any suitable form including a magnetic stripe, a
memory
chip, etc. If portable consumer device 30 is in the form of a card, it may
have an
embossed region (ER) which is embossed with a PAN (primary account number).
Computer readable medium may electronically store the PAN as well as other
data
such as PIN data.
[0038] Merchant 40 refers to any suitable entity or entities that makes a
transaction
with consumer 22. Merchant 40 may use any suitable method to make the
transaction. For example, merchant 40 may use an e-commerce business to allow
the transaction to be conducted by merchant 40 through the Internet. Other
examples of merchant 40 include a department store, a gas station, a drug
store, a
grocery store, or other suitable business.
[0039] Access device 32 may be any suitable device for communicating with
merchant 40 and for interacting with portable consumer device 30. Access
device
32 can be in any suitable location such as at the same location as merchant
40.
Access device 32 may be in any suitable form. Some examples of access devices
include POS devices, cellular phones, PDAs, personal computers (PCs), tablet
PCs,
handheld specialized readers, set-top boxes, electronic cash registers (ECRs),
automated teller machines (ATMs), virtual cash registers (VCRs), kiosks,
security
systems, access systems, websites, and the like. Access device 32 may use any
suitable contact or contactless mode of operation to send or receive data from
portable consumer devices 30.
[0040] If access device 32 is a POS terminal, any suitable POS terminal may be
used and may include a reader, a processor, and a computer readable medium.
Reader may include any suitable contact or contactless mode of operation. For
example, exemplary card readers can include radio frequency (RF) antennas,
optical
scanners, bar code reader, magnetic stripe readers, etc. to interact with
portable
consumer device 30.
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[0041] Acquirer 50 refers to any suitable entity that has an account with
merchant
40. In some embodiments, issuer 70 may also be acquirer 50.
[0042] Issuer 70 refers to suitable entity that may open and maintain an
account for
consumer 22. Some examples of issuers may be a bank, a business entity such as
a retail store, or a governmental entity. In many cases, issuer 70 may also
issue
portable consumer devices 30 associated with account to consumer 22. For
example, issuer 70(a) issued portable consumer device 30(a) to consumer 22(a)
and
issued portable consumer device 30(b) to consumer 22(b).
[0043] Payment processing network 60 refers to a network of suitable entities
that
have information 66 for payment portfolio optimization. Although payment
processing network 60 is shown with two modules, diagnostics module 62 and
financial modeling module 68, payment processing network 60 may have suitable
number of modules. Payment processing network 60 may also have or operate a
server computer. The server computer may be coupled to database 64 and may
include any hardware, software, other logic, or combination of the preceding
for
servicing the requests from one or more client computers. Server computer may
use
any of a variety of computing structures, arrangements, and compilations for
servicing the requests from one or more client computers. In one embodiment,
the
server computer may be a powerful computer or cluster of computers. For
example,
the server computer can be a large mainframe, a minicomputer cluster, or a
group of
servers functioning as a unit. In one example, the server computer may be a
database server coupled to a Web server. Server computer services the requests
of
one or more client computers.
[0044] Payment processing network 60 may include data processing subsystems,
networks, and operations used to support and deliver authorization services,
exception file services, and clearing and settlement services. An exemplary
payment
processing network 60 may include VisaNetTM. Networks that include VisaNetTM
are
able to process credit card transactions, debit card transactions, and other
types of
commercial transactions. VisaNetTM, in particular, includes a VIP system (Visa
Integrated Payments system) which processes authorization requests and a Base
II
system which performs clearing and settlement services. Payment processing
network 60 may use any suitable wired or wireless network, including the
Internet.
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[0045] Database 64 may include any hardware, software, firmware, or
combination
of the preceding for storing and facilitating retrieval of information 64.
Also, database
64 may use any of a variety of data structures, arrangements, and compilations
to
store and facilitate retrieval of information. In the illustrated embodiment,
database
64 is located in payment processing network 60. Database 64 may be located on
other components of payment portfolio optimization system 10 in other
embodiments. For example, database 64 may be located on a server available
over
payment processing network 60.
[0046] Diagnostics module 62 and financial modeling module 60 store
information
66 to database 64 and retrieve information 66 from database 64. Information 66
refers to any suitable data related to consumers 22 inside and outside
consumer
portfolio 20 that is used in payment portfolio optimization. For example,
information
66 may include transaction information, campaign information, credit
information,
profile information, account information, and other suitable information
related to
processes in payment portfolio optimization system 10. Profile information may
include business profile information such as whether a consumer is a small
business
owner, whether the business is a sole proprietorship, and other suitable
information
related to a business associated with a consumer.
[0047] In the illustrated embodiment, information 66 used in payment portfolio
optimization is provided by issuer 70(a). In another embodiment, information
66
from issuer 70(a) may be pooled with information 66 from other entities such
as
other issuers. One advantage to pooling information 66 is that pooled
information 66
could provide a better statistical basis for developing the propensity models.
Another
technical advantage of pooling information is that pooled information 66 could
be
used to more accurately define opportunities for revenue growth since they are
based on information available from more than one entity.
[0048] In the illustrated embodiment, consumer 22(a) purchases a good or
service
at merchant 40 using portable consumer device 30(a) associated with an account
with issuer 70(a) and consumer 22(c) purchases a good or service at merchant
40
using a check associated with an account with issuer 70(a). Consumer 22(a)
interacts with access device 32(a) such as a POS terminal at merchant 40(a).
For
example, consumer 22(a) may have swiped their portable consumer device 30(a)
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through an appropriate slot of a cardreader in the POS terminal.
Alternatively, the
POS terminal may be a contactless reader, and portable consumer device 30(a)
may
be a contactless device such as a contactless card. A transaction
authorization
request is sent to acquirer 50(a) who sends it through payment processing
network
60 to issuer 70(a). Issuer 70(a) sends an authorization message through
payment
processing network 60 to acquirer 50(a) indicating that the transaction is
authorized
(or is declined). Acquirer 50(a) forwards the authorization message to
merchant
40(a).
[0049] After merchant 40(a) receives authorization message, access device
32(a)
at merchant 40(a) may then provide authorization message to consumer 22(a).
Authorization message may be displayed by access device 32(a), or may be
printed
out on a receipt.
[0050] At the end of the day, a normal clearing and settlement process can be
conducted on the payment processing network 60. A clearing process is a
process
of exchanging financial details between a merchant 40 and an issuer 70 to
facilitate
posting to a consumer's account and reconciliation of the consumer's
settlement
position. Clearing and settlement can occur simultaneously. Information 66
related
to this transaction is stored in database 64.
[0051] Diagnostics module 62 retrieves information 66 from issuer 70(a) about
consumers 22 in consumer portfolio 20 to identify opportunities in consumer
portfolio
20. An opportunity refers to a possibility of increasing net revenue to issuer
70(a)
based on consumers 22 in consumer portfolio 20 under favorable circumstances.
In
other embodiments, diagnostics module 62 retrieves information 66 about
consumers 22 outside of consumer portfolio 20 to identify opportunities such
as
procuring consumers for consumer portfolio 20.
[0052] Diagnostics module 62 performs consumer segmentation to divide
consumer portfolio 20 into consumer segments based on segment definitions. In
the
illustrated example, diagnostics module 62 may select segment definitions of
"carded" and "uncarded." Based on the first definition, the first segment
consists of
consumers 22(a) and 22(b) that have portable consumer devices 30(a) and 30(b)
associated with their accounts with issuer 70(a). Based on the second segment
definition, the second segment consists of consumer 22(c) that doesn't have a
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portable consumer device 30(b). In another embodiment, diagnostics module 62
divided consumer portfolio 20 into any suitable number of segments.
[0053] Diagnostics module 62 evaluates the performance of consumer portfolio
20
and consumer segments within consumer portfolio 20 based on performance
metrics
to identify potential opportunities for issuer 70(a). Performance metrics
refer to
measures of performance. Some examples of performance metrics include
penetration, attrition rate, activation, usage, average ticket value, and
volume mix.
Penetration of consumer portfolio 20 into a market refers to the percentage
that
consumer portfolio has entered the market. Attrition rate refers to the rate
at which
portable consumer devices 30 associated with accounts of consumers 22 have not
been used. Activation refers to the percentage of portable consumer devices 30
associated with accounts of consumers 22 that have been activated and used
once
at a POS terminal. An active portable consumer device 30 refers to a portable
consumer device 30 that has been activated and used at least once at a POS
terminal. Usage refers to the number of transactions conducted using portable
consumer devices 30 by consumers 22 as compared to other consumers 22 in
consumer portfolio 20. In some cases, consumers are rated on a usage scale.
For
example, consumers 22 may be rated as a "light user," a "medium user," a
"heavy
user," or a "super heavy user." Average ticket value refers to the average
value of
transactions made by portable consumer devices 30 associated with accounts of
consumers 22 in consumer portfolio 20. Performance metrics are determined
based
on input from issuer 70(a) or another suitable entity.
[0054] In some embodiments, diagnostics module 62 may also determine the
penetration of consumer portfolio 20 and/or consumer segments into the market
to
identify potential opportunities. Penetration of consumer portfolio 20 into
the
consumer market is the percentage of consumers in the market that have
accounts
with issuer 70(a). Penetration of a consumer segment into the market is the
percentage of consumers in the portion of the market related to that consumer
segment that have accounts with issuer 70(a). For example, diagnostics module
62
may determine that there are five small business owners in the small business
owner
market, each of these small business owners has two business accounts so that
there are a total of ten business accounts in the small business market where
three
are held by issuer 70(a). Since 30% of all business accounts in the small
business
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market are held issuer 70(a), penetration of the issuer's consumer portfolio
20 into
the small business market is 30%. In this example, diagnostics module 62 may
determine that based on 30% penetration there is an opportunity for revenue
growth
in acquiring new business accounts with small business owners.
[0055] Once diagnostics module 62 has identified potential opportunities,
diagnostics module 62 evaluates or sizes the opportunities by assessing the
profitability of the identified opportunities. Profitability refers to the
potential to
generate net revenue to issuer 70(a). Net revenue is the gross revenue less
expenses. Some expenses include marketing costs, account management costs,
and rewards program costs. In some embodiments, diagnostics module 62
assesses the profitability of opportunities by consumer segment. In one
embodiment, diagnostics module 62 performs a sensitivity analysis to assess
the
profitability of opportunities by consumer segment. A sensitivity analysis
predicts the
increased net revenue to issuer 70(a) if a given percentage of consumers in
the
consumer segment associated with the opportunity increases. For example,
diagnostics module 62 may determine that there is potential for an increase in
net
revenue of $1 M to issuer 70(a) if 1 % of its consumers that are "uncarded"
were to
become "carded." Based on the results of the profitability assessment,
diagnostics
module 62 prioritizes and selects consumer segments with the most profitable
opportunities. Diagnostics module 62 stores the selected consumer segments
with
the most profitable opportunities and information 66 related to these
opportunities to
database 64.
[0056] Financial modeling module 68 retrieves the selected consumer segments
with the most profitable opportunities and information related these
opportunities
from database 64. Financial modeling module 68 develops one or more propensity
models to determine the likelihood that consumers 22 in the selected consumer
segments will respond favorably to marketing treatments so as to actualize the
opportunities. Each propensity model is based on multiple performance metrics.
An
exemplary propensity model is based on penetration, activation, usage and
attrition.
Financial modeling module 68 ranks the selected consumer segments into deciles
based on the likelihood that the consumers in the segments will perform in the
most
favorable way based on the multiple performance metrics. In some embodiments,
the top tier of deciles consists of those consumer segments with the most
promising
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consumers for maximizing revenue to issuer 70(a) and for targeting in a
marketing
plan.
[0057] Financial modeling module 68 designs marketing treatments that target
the
top tier of deciles resulting from the one or more propensity models.
Financial
modeling module 68 tests the marketing treatments based on the effects of
multiple
factors simultaneously to determine an optimal set of marketing treatments.
Any
suitable number or type of factor may be used. Some examples of factors
include
channel, rewards, pricing and creative. The channel factor may be direct mail
or
telemarketing. The rewards factor may be cash back or premium rewards. The
pricing factor may be waive over-draft fee or don't waive over-draft fee. The
creative
factor may be zero liability or online reporting.
[0058] In some embodiments, financial modeling module 68 uses a factorial
design
to test the pilot model. A factorial design tests the effects of multiple
factors
simultaneously while reducing the number of test groups by half by pairing
factors
together in the test groups. For example, issuer 70(a) may want to test the
performance of factors such as channel (direct mail or telemarketing), rewards
(cash
back or merchant offer), and pricing (waive overdraft fee or waive business
account
fee). For these three factors, there are 8 (2 x 2 x 2) possible combinations
of
marketing treatments. Using the factorial design, financial modeling module 68
can
pair factors together, test 4 (2 x 2) combinations of the marketing treatments
with
paired factors, and extrapolate the results to the untested combinations.
[0059] The test results are used to determine a successful combination of
marketing treatments that target the consumer characteristics of the top tier
of
deciles. A successful marketing plan with the successful marketing treatments
is
then delivered to issuer 70(a). The improved marketing plan may be delivered
in any
suitable form. In some cases, the improved marketing plan is delivered in a
report to
issuer 70(a). The report may be in any suitable form.
[0060] Modifications, additions, or omissions may be made to payment portfolio
optimization system 10 without departing from the scope of the disclosure. The
components of payment portfolio optimization system 10 may be integrated or
separated according to particular needs. Moreover, the operations of payment
portfolio optimization system 10 may be performed by more, fewer, or other
system
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modules. Additionally, operations of payment portfolio optimization system 10
may
be performed using any suitable logic comprising software, hardware, other
logic, or
any suitable combination of the preceding.
[0061] FIG. 2 is a flow chart illustrating a method of payment portfolio
optimization
that includes diagnosing opportunities in consumer portfolio 20 (step 110),
developing targeting tools (step 120), designing and launching a pilot
marketing plan
(step 130), and rolling out a successful marketing plan (step 140), in
accordance
with an embodiment of the invention.
[0062] Diagnostics module 62 diagnoses opportunities in consumer portfolio 20
(step 110) to identify and evaluate potential opportunities in consumer
portfolio 20 for
increasing net revenue to issuer 70(a). In diagnosing opportunities,
diagnostics
module 62 performs consumer segmentation, segment/portfolio penetration into
consumer market, analyzes consumer portfolio 20, determines key volume and
profitability drivers, analyzes average ticket, and performs opportunity
sizing, in any
suitable order. In other embodiments, some, none, or all of these analyses may
be
performed by diagnostics module 62 when diagnosing opportunities.
[0063] Diagnostics module 62 performs consumer segmentation to divide
consumer portfolio 20 into consumer segments based on segment definitions
provided by issuer 70 or another suitable entity. In some embodiments,
diagnostics
module 62 may define segment definitions using historical data in information
66
retrieved from database 64. Diagnostics module 62 may use any appropriate
method of segmentation. Some example methods of segmentation include the
waterfall method of separating one or more segments from consumer portfolio 20
using corresponding segment definitions.
[0064] Using the waterfall method, consumer portfolio 20 is first divided into
two or
more segments based on a first segment definition. Each of these segments is
then
divided into two or more segments based on other segment definitions. Each of
these segments may then be further divided into two or more segments based on
other segment definitions. This process continues until a hierarchy of
segments
based on segment definitions is created from consumer portfolio 20. For
example,
consumer portfolio 20 may first be divided into segments consisting of
consumers 22
that are "carded" or "uncarded." The segment consisting of consumers 22 that
are
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"carded" may be further separated into "active," or "inactive." The segment
with
consumers 22 that are "active" may be further separated into "light user,"
"medium
user," "heavy user," or "super-heavy user." The segment with consumers 22 that
are "inactive" may be separated into "potential user," or "non-user." Using
this
method, the following seven segments may result: 1) "open," "carded,"
"active," and
"light users;" 2) "open," "carded," "active," and "medium users," 3) "open,"
"carded,"
"active," and "heavy user;" 4) "open," "carded," "active," and "super-heavy
user;" 5)
"open," "carded," "inactive," and "potential users;" 6) "open," "carded,"
"inactive," and
"non-user;" 7) "open" and "uncarded."
[0065] Using the second method of segmentation, one or more segments can be
separated from consumer portfolio 20 based on one or more segment definitions.
For example, issuer 70(a) may provide the segment definition: "consumers
located in
the Northeast region of the United States." Based on the provided definition,
diagnostics module 62 separates a consumer segment consisting of consumers in
consumer portfolio 20 that are located in the Northeast region of the United
States.
In another example, diagnostics module 62 may divide consumer portfolio 20
into
two segments based on two separate segment definitions of having and not
having a
rewards plan. The first segment consists of consumers 22 having accounts with
rewards plans. The second segment consists of consumers 22 having accounts
that
are not associated with rewards plans.
[0066] Diagnostics module 62 determines the segment/portfolio penetration into
the
consumer market to identify potential opportunities in each consumer segment
for
increased net revenue to issuer 70(a). In other embodiments, segment/portfolio
penetration may identify potential opportunities that lie outside consumer
portfolio 20.
For example, diagnostics module 62 may determine that one of issuer's consumer
segments e.g. "heavy users" penetrates 10% of the "heavy users" market
associated
with that consumer segment. Based on this result, diagnostics module 62 may
determine that issuer 70(a) has a potential opportunity to increase revenue by
marketing to "heavy users" outside of consumer's portfolio 20 that do not yet
have an
account with issuer 70(a).
[0067] Diagnostics module 62 also analyzes consumer portfolio 20 of issuer
70(a)
based on various performance metrics. For example, financial modeling module
68
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may analyze the attrition rate of accounts in consumer portfolio 20 is 90%.
Based on
this analysis, diagnostics module 62 may determine that issuer 70(a) has a
problem
with attrition and that there is an opportunity to reduce attrition rates in
its consumer
portfolio 20.
[0068] Diagnostics module 62 also determines key volume drivers, key
profitability
drivers, and average ticket values. Volume refers to the total dollar amount
of
completed transactions by consumers 22 in consumer portfolio over a time
period.
A volume driver refers to consumer characteristics that control volume. Some
examples of volume drivers are how many consumers 22 have portable consumer
devices 30 and how many of the portable consumer devices 30 are activated. For
example, diagnostics module 62 may analyze information 66 and determine that
85% of the volume is generated by business accounts associated with portable
consumer devices 30. Based on this information, diagnostics module 62 may
determine that its main volume driver is whether the business account is
"carded."
A profitability driver refers to those consumer characteristics that control
profitability.
An example of a profitability driver is whether the account is associated with
a
rewards program that has provided rewards which is an expense to the issuer
and
decreases net revenues.
[0069] Diagnostics module 62 sizes or evaluates the opportunities in each
consumer segment or segment opportunities based on the volume drivers, the
profitability drivers, and the average ticket values. In some embodiments,
diagnostics module 62 performs a sensitivity analysis to predict the increased
net
revenue to issuer 70(a) if a certain percentage of consumers in each segment
were
to increase. Based on the sensitivity analysis, diagnostics module 62
prioritizes and
selects consumer segments as input to a propensity model developed by
financial
modeling module 68.
[0070] Financial modeling module 68 develops targeting tools (step 120) to
identify
the most promising consumer segments to be targeted by the marketing plan.
Financial modeling module 68 develops a propensity model that addresses
penetration, activation, and usage. In other embodiments, other suitable
propensity
models could be used and other suitable performance metrics could be assessed.
The propensity model predicts the likelihood that each consumer in the
selected
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consumer segments will open an account with a portable consumer device 30
(penetration), activate that portable consumer device 30 (activation), and
then spend
with the portable consumer device 30 (usage) that they activated. The
propensity
model also predicts the ROI for the selected consumer segments. Financial
modeling module 68 ranks the consumers into deciles based on the predicted
likelihood and the predicted return on investment. Based on these analyses,
financial modeling module 68 selects the top tier of deciles to be targeted in
the
marketing plan.
[0071] Financial modeling module 68 designs and launches a pilot marketing
plan
(step 130). The pilot plan includes a group of marketing treatments that
target the
consumer segments in the top tier of deciles resulting from the propensity
model.
Designing and launching the pilot plan includes designing the pilot plan,
launching
the pilot, testing the pilot plan, and measuring the test results.
[0072] Financial modeling module 68 tests the marketing treatments in the
pilot
plan based on a factorial design to determine the optimal combination of
marketing
treatments. The factorial design tests the effects of multiple factors
simultaneously
to determine an optimal set of marketing treatments. In one embodiment,
financial
modeling module 68 uses the four factors: channel (direct mail or
telemarketing),
rewards (cash back or merchant offer), pricing (waive overdraft fee or waive
business account fee), and creative (zero liability, online reporting). Based
on these
four factors, there are 16 (2 x 2 x 2 x 2) possible combinations of marketing
treatments. Financial modeling module 68 pairs two levels of factors: cash
back and
waive overdraft fee, cash back and waive business account fee, and merchant
offer
and waive overdraft fee, merchant offer and waive business account fee, zero
liability and direct mail, online reporting and telemarketing, online
reporting and direct
mail, zero liability and telemarketing. Based on these pairings, eight test
groups are
designed. Each paired group acts as a control group for the others. The test
results
for the eight paired groups are extrapolated to the eight untested
combinations.
[0073] Financial modeling module 68 rolls out a successful plan (step 140)
with the
optimal combination of marketing treatments. Rolling out a successful plan
involves
analyzing the pilot plan test results, rolling out a successful plan, and
developing
scalable processes. Financial modeling module 68 analyzes the test results
from the
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factorial design to determine an optimal combination of marketing treatments
that
target consumers in the top tier of deciles. Financial modeling module 68
develops a
successful marketing plan that includes the optimal combination of marketing
treatments. Financial modeling module 68 rolls out the successful marketing
plan.
[0074] Financial modeling module 68 delivers to issuer 70(a) the marketing
plan
with the optimal combination of marketing treatments that target only the most
promising consumers in the top tier of deciles. In one case, the marketing
plan is
delivered in the form of a report.
[0075] Modifications, additions, or omissions may be made to the method
without
departing from the scope of the disclosure. The method may include more,
fewer, or
other steps. Additionally, steps may be performed in any suitable order
without
departing from the scope of the disclosure.
[0076] FIG. 3 is a flowchart illustrating a method of payment portfolio
optimization,
in accordance with an embodiment of the invention. As shown, the method of
payment portfolio optimization begins with diagnostics module 62 collecting
consumer information 66 (step 210) associated with consumers from issuer 70(a)
over payment processing network 60. Consumer information 66 is associated with
current consumers 22 that have open accounts with issuer 70(a) and that are in
consumer portfolio 20. In other embodiments, consumer information 66 is
associated with both current consumers 22 of issuer 70(a) in consumer
portfolio 20
and prospective consumers 22 of issuer 70(a) outside of consumer portfolio 20.
[0077] Diagnostics module 62 segments consumer portfolio 20 into consumer
segments based on segment definitions (step 220). In other embodiments,
diagnostics module 62 also segments prospective consumers 22 outside of
consumer portfolio 20. Issuer 70(a) provides diagnostics module 62 with
segment
definitions. In another embodiment, diagnostics module 62 uses historical data
from
collected information 66 to determine suitable segment definitions for
segmenting
consumer portfolio 20.
[0078] After segmenting consumer portfolio 20, diagnostics module 62
identifies
opportunities in consumer segments (step 230). Diagnostics module 62 evaluates
the performance of consumer segments and consumer portfolio 20 based on one or
more performance metrics. For example, diagnostics module 62 may evaluate the
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penetration of consumer portfolio 20 into the market, the penetration of the
consumer
segments, the attrition rates by consumer segment, the activation rates by
consumer
segment, and the usage of portable consumer devices 30 by consumer segments.
This evaluation is used to identify opportunities in the segments and the
consumer
portfolio 20 as a whole.
[0079] In some embodiments, diagnostics module 62 may perform benchmark
analyses to assess if individual consumer segments or the entire consumer
portfolio
20 can be improved and where they can be improved. Benchmarking analyses can
be used to determine additional opportunities to issuer 70(a). In one case,
diagnostics module 62 may compare the relative performance of consumer
segments to each other to benchmark the performance of consumer segments. For
example, diagnostics module 62 may determine that the "super heavy users"
consumer segment generates twice the net revenue per customer of "super heavy
users." Based on this benchmarking analysis, diagnostics module 62 may
determine that there is an opportunity for revenue growth in the "super heavy
users"
segment. In another case, diagnostics module 62 may compare the relative
performance of consumer portfolio 20 to those of peer issuers.
[0080] Once the opportunities have been identified, diagnostics module 62
evaluates the potential profitability of each of the opportunities by consumer
segment. In some embodiments, sensitivity analyses are used to evaluate
potential
profitability. For example, diagnostics module 62 may use a sensitivity
analysis to
predict the increased net revenue if the number of consumers in each segment
were
to increase by 1%. In another example, diagnostics module 62 may use a
sensitivity
analysis to predict volume growth if the number of consumers in each segment
were
to increase by 1 %.
[0081] Diagnostics module 62 prioritizes the opportunities associated with the
consumer segments based on the evaluated profitability (step 240). Diagnostics
module 62 selects consumer segments associated with the most profitable
opportunities for further analysis (step 250). Diagnostics module 62 stores
consumer segments associated with the most profitable opportunities in
database
64 (step 250). In another embodiment, diagnostics module 62 may also deliver a
report with the selected consumer segments to issuer 70(a).
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[0082] In one embodiment, diagnostics module 62 updates information 66 of
issuer
70(a) associated with a particular consumer based on more current information
66
collected from another entity. Diagnostics module 62 compares information 66
from
issuer 70(a) associated with the consumer with information 66 associated with
the
same consumer collected from another entity. If information 66 from the other
entity
is more current than information 66 from issuer 70(a), diagnostics module 62
may
update the information of issuer 70(a). For example, at the time that
diagnostics
module 62 collects information 66, issuer 70(a) may have business profile
information that shows that consumer does not own a business. Business profile
information collected from another entity may show that the consumer became a
small business owner the day before diagnostics module 62collected information
66.
Diagnostics module 62 may update the business profile information of issuer
70(a) in
database 66 to reflect that consumer is now a small business owner.
[0083] FIG. 4 is a flowchart illustrating a method of payment portfolio
optimization,
in accordance with an embodiment of the invention. The embodiment illustrated
in
Fig. 4 can be used either in conjunction with or separately from the
embodiment
illustrated in Fig. 3. As shown, the method of payment portfolio optimization
begins
with financial modeling module 68 retrieving consumer segments with profitable
opportunities from a database 64 (step 450).
[0084] Financial modeling module 68 develops a propensity model for the
retrieved
consumer segments (step 460) based on one or more performance metrics such as
penetration, activation, usage, and attrition. In one example embodiment, the
propensity model is based on penetration into the business account market,
activation of portable consumer device 30, and usage of portable consumer
device
30. In this example, the propensity model predicts the likelihood that each
consumer
in the consumer segments will open a business account with a portable consumer
device 30, activate the portable consumer device 30, and then spend with the
portable consumer device 30.
[0085] Financial modeling module 68 ranks the consumer segments into deciles
based on the predicted likelihoods developed in the propensity model (step
470).
Financial modeling module 68 selects the top N deciles for a marketing plan
(step
280). In one embodiment, financial modeling module 68 selects the top 3
deciles
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(N=3) for the marketing plan. In this embodiment, financial modeling module 68
is
selecting the top 30% of the consumers that are most likely to open an
account,
activate their portable consumer devices 30, and then spend.
[0086] Financial modeling module 68 designs marketing treatments that target
the
top N deciles resulting from the propensity model (step 490). Financial
modeling
module 68 tests the marketing treatments using factorial design (step 500).
These
tests result in an optimal combination of marketing treatments. In one
embodiment,
Financial modeling module 68 tests the marketing treatments with four factors
that
include channel, rewards, pricing and creative and each of these factors has
two
levels. Based on these four factors, there are sixteen possible combinations
of
marketing treatments. In this embodiment, financial modeling module 68 pairs
factors together based on the factorial design so that there are eight
possible
combinations marketing treatments to test and the results of these eight tests
are
extrapolated to the other eight possible combinations. Financial modeling
module 68
selects the combination with the most favorable test results as the optimal
combination of marketing treatments.
[0087] Financial modeling module 68 develops the marketing plan based on the
test results (step 510). Financial modeling module 68 uses the results of the
tests to
develop a marketing plan with the optimal combination of marketing treatments
that
target the top N deciles. After developing the marketing plan, financial
modeling
module 68 delivers a report with the marketing plan to issuer 70(a) (step
520).
[0088] Modifications, additions, or omissions may be made to the method
without
departing from the scope of the disclosure. The method may include more,
fewer, or
other steps. Additionally, steps may be performed in any suitable order
without
departing from the scope of the disclosure.
[0089] It should be understood that the present disclosure as described above
can
be implemented in the form of control logic using computer software in a
modular or
integrated manner. Based on the disclosure and teachings provided herein, a
person of ordinary skill in the art will know and appreciate other ways and/or
methods to implement the present disclosure using hardware and a combination
of
hardware and software.
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[0090] Any of the software components or functions described in this
application,
may be implemented as software code to be executed by a processor using any
suitable computer language such as, for example, Java, C++ or Perl using, for
example, conventional or object-oriented techniques. The software code may be
stored as a series of instructions, or commands on a computer readable medium,
such as a random access memory (RAM), a read only memory (ROM), a magnetic
medium such as a hard-drive or a floppy disk, or an optical medium such as a
CD-
ROM. Any such computer readable medium may reside on or within a single
computational apparatus, and may be present on or within different
computational
apparatuses within a system or network.
[0091] A recitation of "a", "an" or "the" is intended to mean "one or more"
unless
specifically indicated to the contrary.
[0092] The above description is illustrative and is not restrictive. Many
variations of
the disclosure will become apparent to those skilled in the art upon review of
the
disclosure. The scope of the disclosure should, therefore, be determined not
with
reference to the above description, but instead should be determined with
reference
to the pending claims along with their full scope or equivalents.
[0093] One or more features from any embodiment may be combined with one or
more features of any other embodiment without departing from the scope of the
disclosure.
26
