U.S. patent application number 13/860872 was filed with the patent office on 2013-08-29 for method and apparatus for development and use of a credit score based on spend capacity.
This patent application is currently assigned to American Express Travel Related Services Company, Inc.. The applicant listed for this patent is American Express Travel Related Services Company, Inc.. Invention is credited to Kathleen Haggerty, Benedict O. Okoh, Peter L. Williamson, Chao M. Yuan.
Application Number | 20130226787 13/860872 |
Document ID | / |
Family ID | 46322207 |
Filed Date | 2013-08-29 |
United States Patent
Application |
20130226787 |
Kind Code |
A1 |
Haggerty; Kathleen ; et
al. |
August 29, 2013 |
METHOD AND APPARATUS FOR DEVELOPMENT AND USE OF A CREDIT SCORE
BASED ON SPEND CAPACITY
Abstract
Share of Wallet ("SOW") is a modeling approach that utilizes
various data sources to provide outputs that describe a consumers
spending capability, tradeline history including balance transfers,
and balance information. These outputs can be appended to data
profiles of customers and prospects and can be utilized to support
decisions involving prospecting, new applicant evaluation, and
customer management across the lifecycle. A SOW score focusing on a
consumer's spending capability can he used in the same manner as a
credit bureau score.
Inventors: |
Haggerty; Kathleen; (Staten
Island, NY) ; Okoh; Benedict O.; (New York, NY)
; Williamson; Peter L.; (Larchmont, NY) ; Yuan;
Chao M.; (Montclair, NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Company, Inc.; American Express Travel Related Services |
|
|
US |
|
|
Assignee: |
American Express Travel Related
Services Company, Inc.
New York
NY
|
Family ID: |
46322207 |
Appl. No.: |
13/860872 |
Filed: |
April 11, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13480910 |
May 25, 2012 |
8438105 |
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13860872 |
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12984801 |
Jan 5, 2011 |
8315942 |
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13480910 |
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12777030 |
May 10, 2010 |
7890420 |
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12984801 |
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11169779 |
Jun 30, 2005 |
7814004 |
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12777030 |
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10978298 |
Oct 29, 2004 |
7788147 |
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11169779 |
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Current U.S.
Class: |
705/38 |
Current CPC
Class: |
G06Q 30/0204 20130101;
G06Q 30/0269 20130101; G06Q 30/0202 20130101; G06Q 40/12 20131203;
G06Q 40/00 20130101; G06Q 40/025 20130101; G06Q 10/067
20130101 |
Class at
Publication: |
705/38 |
International
Class: |
G06Q 40/02 20120101
G06Q040/02 |
Claims
1. A method comprising: identifying, by a computer-based system and
based on payment data, a balance transfer into at least one of a
plurality of accounts, wherein the payment data is related to an
individual consumer, wherein the computer-based system is
programmed to estimate a purchasing ability of a consumer;
discounting, by the computer-based system, spending in at least one
of the plurality of accounts having the identified balance
transfer; and determining, by the computer-based system, a
purchasing ability of the individual consumer based on the
discounting and a model of consumer spending patterns.
2. The method of claim 1, further comprising estimating, by the
computer-based system, credit-related information of the individual
consumer.
3. The method of claim 1, further comprising estimating, by the
computer-based system, credit-related information of the individual
consumer, wherein the credit-related information comprises a spend
amount associated with the individual consumer.
4. The method of claim 1, further comprising estimating, by the
computer-based system, credit-related information of the individual
consumer based on payment data of the individual consumer, a
previous balance transfer of the individual consumer, a paydown
amount on an account of the individual consumer and the model of
consumer spending patterns.
5. The method of claim 1, wherein the determining is further based
on the payment data.
6. The method of claim 1, wherein the identifying is for a period
of time that is prior to a current period of time.
7. The method of claim 1, wherein the identifying is for at least
twelve months prior to a current period of time.
8. The method of claim 1, wherein the discounting is for a portion
of a previous period of time.
9. The method of claim 1, wherein the discounting is for one
month.
10. The method claim 1, wherein the model is derived from at least
one of the payment data, internal customer data and consumer panel
data.
11. The method of claim 1, wherein the determining further
comprises determining at least two categories of customers, the
first category including customers that primarily pay down credit
account balances and the second category including customers that
primarily revolve credit account balances.
12. The method of claim 1, wherein the determining further
comprises assigning one of a first category and a second category
to the individual customer based on the payment data, wherein the
first category includes customers that primarily pay down credit
account balances and the second category includes customers that
primarily revolve credit account balances.
13. The method of claim 12, further comprising changing the terms
of a credit account of the individual based on estimating
credit-related information of the individual consumer and based on
the assigning.
14. The method of claim 13, wherein the changing further comprises
increasing a credit limit of the credit account.
15. The method of claim 14, wherein the changing further comprises
providing a discount on a purchase to the individual consumer.
16. The method of claim 1, further comprising selecting the
individual consumer from a set of customers that do not have a
delinquent account status.
17. A system comprising: a processor programmed to estimate a
purchasing ability of a consumer; a non-transitory storage memory
communicating with the processor; the non-transitory storage memory
having instructions stored thereon that, in response to execution
by the processor, cause the processor to perform operations
comprising: identifying, by the processor and based on payment
data, a balance transfer into at least one of a plurality of
accounts, wherein the payment data is related to an individual
consumer; discounting, by the processor, spending in at least one
of the plurality of accounts having the identified balance
transfer; and determining, by the processor, a purchasing ability
of the individual consumer based on the discounting and a model of
consumer spending patterns.
18. The system of claim 1, further comprising estimating, by the
processor, credit-related information of the individual consumer
based on payment data of the individual consumer, a previous
balance transfer of the individual consumer, a paydown amount on an
account of the individual consumer and the model of consumer
spending patterns.
19. The system of claim 1, wherein the determining further
comprises assigning one of a first category and a second category
to the individual customer based on the payment data, wherein the
first category includes customers that primarily pay down credit
account balances and the second category includes customers that
primarily revolve credit account balances.
20. An article of manufacture including a non-transitory computer
readable storage medium having instructions stored thereon that, in
response to execution by a computer-based system programmed to
estimate a purchasing ability of a consumer, cause the
computer-based system to perform operations comprising:
identifying, by the computer-based system and based on payment
data, a balance transfer into at least one of a plurality of
accounts, wherein the payment data is related to an individual
consumer; discounting, by the computer-based system, spending in at
least one of the plurality of accounts having the identified
balance transfer; and determining, by the computer-based system, a
purchasing ability of the individual consumer based on the
discounting and a model of consumer spending patterns.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 13/480,910 filed May 25, 2012 and entitled
"METHOD AND APPARATUS FOR DEVELOPMENT AND USE OF A CREDIT SCORE
BASED ON SPEND CAPACITY," The '910 application is a continuation of
U.S. patent application Ser. No. 12/984,801, filed Jan. 5, 2011 and
entitled, "Method and Apparatus for Development and Use of a Credit
Score Based on Spend Capacity," which issued as U.S. Pat. No.
8,315,942 on Nov. 20, 2012. The '801 application is a continuation
of U.S. patent application Ser. No. 12/777,030, filed May 10, 2010
and entitled, "Method and Apparatus for Development and Use of a
Credit Score Based on Spend Capacity," which issued as U.S. Pat.
No. 7,890,420 on Feb. 15, 2011, The '030 application is a
continuation of U.S. patent application Ser. No. 11/169,779, filed
Jun. 30, 2005 and entitled, "Method and Apparatus for Development
and Use of a Credit Score Based on Spend Capacity," which issued as
U.S. Pat. No. 7,814,004 on Oct. 12, 2010. The '779 application is a
continuation-in-part of U.S. patent application Ser. No.
10/978,298, filed Oct. 29, 2004, and entitled, "Method and
apparatus for estimating the spend capacity of consumers," which
issued as U.S. Pat. No. 7,788,147 on Aug. 31, 2.010. All the above
applications are hereby incorporated by reference herein in their
entirety.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] This disclosure generally relates to financial data
processing, and in particular it relates to credit scoring,
customer profiling, consumer behavior analysis and modeling.
[0004] 2. Background Art
[0005] It is axiomatic that consumers will tend to spend more when
they have greater purchasing power. The capability to accurately
estimate a consumer's spend capacity could therefore allow a
financial institution (such as a credit company, lender or any
consumer services companies) to better target potential prospects
and identify any opportunities to increase consumer transaction
volumes, without an undue increase in the risk of defaults.
Attracting additional consumer spending in this manner, in turn,
would increase such financial institution's revenues, primarily in
the form of an increase in transaction fees and interest payments
received. Consequently, a consumer model that can accurately
estimate purchasing power is of paramount interest to many
financial institutions and other consumer services companies.
[0006] A limited ability to estimate consumer spend behavior from
point-in-time credit data has previously been available. A
financial institution can, for example, simply monitor the balances
of its own customers' accounts. When a credit balance is lowered,
the financial institution could then assume that the corresponding
consumer now has greater purchasing power. However, it is
oftentimes difficult to confirm whether the lowered balance is the
result of a balance transfer to another account. Such balance
transfers represent no increase in the consumer's capacity to
spend, and so this simple model of consumer behavior has its
flaws.
[0007] In order to achieve a complete picture of any consumer's
purchasing ability, one must examine in detail the full range of a
consumer's financial accounts, including credit accounts, checking
and savings accounts, investment portfolios, and the like. However,
the vast majority of consumers do not maintain all such accounts
with the same financial institution and the access to detailed
financial information from other financial institutions is
restricted by consumer privacy laws, disclosure policies and
security concerns.
[0008] There is limited and incomplete consumer information from
credit bureaus and the like at the aggregate and individual
consumer levels. Since balance transfers are nearly impossible to
consistently identify from the face of such records, this
information has not previously been enough to obtain accurate
estimates of a consumer's actual spending ability.
[0009] Accordingly, there is a need for a method and apparatus for
modeling consumer spending behavior which addresses certain
problems of existing technologies.
BRIEF SUMMARY OF THE INVENTION
[0010] A method for modeling consumer behavior can be applied to
both potential and actual customers (who may be individual
consumers or businesses) to determine their spend over previous
periods of time (sometimes referred to herein as the customer's
size of wallet) from tradeline data sources. The share of wallet by
tradeline or account type may also be determined. At the highest
level, the size of wallet is represented by a consumer's or
business' total aggregate spending and the share of wallet
represents how the customer uses different payment instruments.
[0011] In various embodiments, a method and apparatus for modeling
consumer behavior includes receiving individual and aggregated
consumer data for a plurality of different consumers. The consumer
data may include, for example, time series tradeline data, consumer
panel data, and internal customer data. One or more models of
consumer spending patterns are then derived based on the consumer
data for one or more categories of consumer. Categories for such
consumers may be based on spending levels, spending behavior,
tradeline user and type of tradeline.
[0012] In various embodiments, a method and apparatus for
estimating the spending levels of an individual consumer is next
provided, which relies on the models of consumer behavior above.
Size of wallet calculations for individual prospects and customers
are derived from credit bureau data sources to produce outputs
using the models.
[0013] Balance transfers into credit accounts are identified based
on individual tradeline data according to various algorithms, and
any identified balance transfer amount is excluded from the
spending calculation for individual consumers. The identification
of balance transfers enables more accurate utilization of balance
data to reflect consumer spending.
[0014] When consumer spending levels are reliably identified in
this manner, customers may be categorized to more effectively
manage the customer relationship and increase the profitability
therefrom. For example, a share of wallet score focusing on a
consumer's spending capability can be used in the same manner as a
credit bureau score.
BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES
[0015] Further aspects of the present disclosure will be more
readily appreciated upon review of the detailed description of its
various embodiments, described below, when taken in conjunction
with the accompanying drawings, of which:
[0016] FIG. 1 is a block diagram of an exemplary financial data
exchange network over which the processes of the present disclosure
may he performed;
[0017] FIG. 2 is a flowchart of an exemplary consumer modeling
process performed by the financial server of FIG. 1;
[0018] FIG. 3 is a diagram of exemplary categories of consumers
examined during the process of FIG. 2;
[0019] FIG. 4 is a diagram of exemplary subcategories of consumers
modeled during the process of FIG. 2;
[0020] FIG. 5 is a diagram of financial data used for model
generation and validation according to the process of FIG. 2;
[0021] FIG. 6 is a flowchart of an exemplary process for estimating
the spend ability of a consumer, performed by the financial server
of FIG. 1;
[0022] FIG. 7-10 are exemplary timelines showing the rolling time
periods for which individual customer data is examined during the
process of FIG. 6; and
[0023] FIG. 11-19 are tables showing exemplary results and outputs
of the process of FIG. 6 against a sample consumer population,
[0024] FIG. 20 is a flowchart of a method for determining common
characteristics across a particular category of customers.
DETAILED DESCRIPTION OF THE INVENTION
[0025] While specific configurations and arrangements are
discussed, it should be understood that this is done for
illustrative purposes only. A person skilled in the pertinent art
will recognize that other configurations and arrangements can he
used without departing from the spirit and scope of the present
invention. It will be apparent to a person skilled in the pertinent
art that this invention can also be employed in a variety of other
applications.
[0026] As used herein, the following terms shall have the following
meanings. A trade or tradeline refers to a credit or charge vehicle
issued to an individual customer by a credit grantor, Types of
tradelines include, for example and without limitation, bank loans,
credit card accounts, retail cards, personal lines of credit and
car loan/leases. For purposes here, use of the term credit card
shall be construed to include charge cards except as specifically
noted. Tradeline data describes the customer's account status and
activity, including, for example, names of companies where the
customer has accounts, dates such accounts were opened, credit
limits, types of accounts, balances over a period of time and
summary payment histories. Tradeline data is generally available
for the vast majority of actual consumers. Tradeline data, however,
does not include individual transaction data, which is largely
unavailable because of consumer privacy protections. Tradeline data
may be used to determine both individual and aggregated consumer
spending patterns, as described herein.
[0027] Consumer panel data measures consumer spending patterns from
information that is provided by, typically, millions of
participating consumer panelists. Such consumer panel data is
available through various consumer research companies, such as
comScore Networks, Inc. of Reston, Va. Consumer panel data may
typically include individual consumer information such as credit
risk scores, credit card application data, credit card purchase
transaction data, credit card statement views, tradeline types,
balances, credit limits, purchases, balance transfers, cash
advances, payments made, finance charges, annual percentage rates
and fees charged. Such individual information from consumer panel
data, however, is limited to those consumers who have participated
in the consumer panel, and so such detailed data may not be
available for all consumers.
[0028] Although the present invention is described as relating to
individual consumers, one of skill in the pertinent art(s) will
recognize that it can also apply to small businesses and
organizations without departing from the spirit and scope of the
present invention.
[0029] I. Consumer panel data and model development/validation
[0030] Technology advances have made it possible to store,
manipulate and model large amounts of time series data with minimal
expenditure on equipment. As will now be described, a financial
institution may leverage these technological advances in
conjunction with the types of consumer data presently available in
the marketplace to more readily estimate the spend capacity of
potential and actual customers. A reliable capability to assess the
size of a consumer's wallet is introduced in which aggregate time
series and raw tradeline data are used to model consumer behavior
and attributes, and identify categories of consumers based on
aggregate behavior. The use of raw trade-line time series data, and
modeled consumer behavior attributes, including but not limited to,
consumer panel data and internal consumer data, allows actual
consumer spend behavior to be derived from point in time balance
information.
[0031] In addition, the advent of consumer panel data provided
through interest channels provides continuous access to actual
consumer spend information for model validation and refinement.
Industry data, including consumer panel information having consumer
statement and individual transaction data, may be used as inputs to
the model and for subsequent verification and validation of its
accuracy. The model is developed and refined using actual consumer
information with the goals of improving the customer experience and
increasing billings growth by identifying and leveraging increased
consumer spend opportunities.
[0032] A credit provider or other financial institution may also
make use of internal proprietary customer data retrieved from its
stored internal financial records. Such internal data provides
access to even more actual customer spending information, and may
be used in the development, refinement and validation of aggregated
consumer spending models, as well as verification of the models'
applicability to existing individual customers on an ongoing
basis.
[0033] While there has long been market place interest in
understanding spend to align offers with consumers and assign
credit line size, the holistic approach of using a size of wallet
calculation across customers lifecycles (that is, acquisitions
through collections) has not previously been provided. The various
data sources outlined above provide the opportunity for unique
model logic development and deployment, and as described in more
detail in the following, various categories of consumers may be
readily identified from aggregate and individual data. In certain
embodiments of the processes disclosed herein, the models may be
used to identify specific types of consumers, nominally labeled
`transactors` and `revolvers,` based on aggregate spending
behavior, and to then identify individual customers and prospects
that fall into one of these categories. Consumers falling into
these categories may then be offered commensurate purchasing
incentives based on the model's estimate of consumer spending
ability.
[0034] Referring now to FIGS. 1-19, wherein similar components of
the present disclosure are referenced in like manner, various
embodiments of a method and system for estimating the purchasing
ability of consumers will now be described in detail.
[0035] Turning now to FIG. 1, there is depicted an exemplary
computer network 100 over which the transmission of the various
types of consumer data as described herein may be accomplished,
using any of a variety of available computing components for
processing such data in the manners described below, Such
components may include an institution computer 102, which may be a
computer, workstation or server, such as those commonly
manufactured by IBM, and operated by a financial institution or the
like. The institution computer 102, in turn, has appropriate
internal hardware, software, processing, memory and network
communication components that enables it to perform the functions
described here, including storing both internally and externally
obtained individual or aggregate consumer data. in appropriate
memory and processing the same according to the processes described
herein using programming instructions provided in any of a variety
of useful machine languages.
[0036] The institution computer 102 may in turn be in operative
communication with any number of other internal or external
computing devices, including for example components 104, 106, 108,
and 110, which may be computers or servers of similar or compatible
functional configuration. These components 104-110 may gather and
provide aggregated and individual consumer data, as described
herein, and transmit the same for processing and analysis by the
institution computer 102. Such data transmissions may occur for
example over the Internet or by any other known communications
infrastructure, such as a local area network, a wide area network,
a wireless network, a fiber-optic network, or any combination or
interconnection of the same. Such communications may also be
transmitted in an encrypted or otherwise secure format, in any of a
wide variety of known manners.
[0037] Each of the components 104-110 may be operated by either
common or independent entities. In one exemplary embodiment, which
is not to be limiting to the scope of the present disclosure, one
or more such components 104-110 may be operated by a provider of
aggregate and individual consumer tradeline data, an example of
which includes services provided by Experian Information Solutions,
Inc. of Costa. Mesa, Calif. ("Experian"). Tradeline level data
preferably includes up to 24 months or more of balance history and
credit attributes captured at which in turn may be used to derive a
broad view of actual aggregated consumer behavioral spending
patterns.
[0038] Alternatively, or in addition thereto, one or more of the
components 104-110 may likewise be operated by a provider of
individual and aggregate consumer panel data, such as commonly
provided by comScore Networks, Inc. of Reston, Va. ("comScore").
Consumer panel data provides more detailed and specific consumer
spending information regarding millions of consumer panel
participants, who provide actual spend data to collectors of such
data in exchange for various inducements. The data collected may
include any one or more of credit risk scores, online credit card
application data, online credit card purchase transaction data,
online credit card statement views, credit trade type and credit
issuer, credit issuer code, portfolio level statistics, credit
bureau reports, demographic data, account balances, credit limits,
purchases, balance transfers, cash advances, payment amounts,
finance charges, annual percentage interest rates on accounts, and
fees charged, all at an individual level for each of the
participating panelists. In various embodiments, this type of data
is used for model development, refinement and verification. This
type of data is further advantageous over tradeline level data
alone .for such purposes, since such detailed information is not
provided at the tradeline level. While such detailed consumer panel
data can be used alone to generate a model, it may not be wholly
accurate with respect to the remaining marketplace of consumers at
large without further refinement. Consumer panel data may also be
used to generate aggregate consumer data for model derivation and
development.
[0039] Additionally, another source of inputs to the model may be
internal spend and payment history of the institution's own.
customers. From such internal data, detailed information at the
level of specificity as the consumer panel data. may be obtained
and used for model development, refinement and validation,
including the categorization of consumers based on identified
transactor and revolver behaviors.
[0040] Turning now to FIG. 2, there is depicted a flowchart of an
exemplary process 200 for modeling aggregate consumer behavior in
accordance with the present disclosure. The process 200 commences
at step 202 wherein individual and aggregate consumer data,
including time-series tradeline data, consumer panel data and
internal customer financial data, is obtained from any of the data
sources described previously as inputs for consumer behavior
models. In certain embodiments, the individual and aggregate
consumer data may be provided in a variety of different data
formats or structures and consolidated to a single useful format or
structure for processing.
[0041] Next, at step 204, the individual and aggregate consumer
data is analyzed to determine consumer spending behavior patterns.
One of ordinary skill in the art will readily appreciate that the
models may include formulas that mathematically describe the
spending behavior of consumers. The particular formulas derived
will therefore highly depend on the values resulting from customer
data used for derivation, as will be readily appreciated. However,
by way of example only and based on the data provided, consumer
behavior may be modeled by first dividing consumers into categories
that may be based on account balance levels, demographic profiles,
household income levels or any other desired categories. For each
of these categories in turn, historical account balance and
transaction information for each of the consumers may be tracked
over a previous period of time, such as one to two years.
Algorithms may then be employed to determine formulaic descriptions
of the distribution of aggregate consumer information over the
course of that period of time for the population of consumers
examined, using any of a variety of known mathematical techniques.
These formulas in turn may be used to derive or generate one or
more models (step 206) for each of the categories of consumers
using any of a variety of available trend analysis algorithms. The
models may yield the following types of aggregated consumer
information for each category: average balances, maximum balances,
standard deviation of balances, percentage of balances that change
by a threshold amount, and the like.
[0042] Finally, at step 208, the derived models may be validated
and periodically refined using internal customer data and consumer
panel data from sources such as comScore. In various embodiments,
the model may be validated and refined over time based on
additional aggregated and individual consumer data as it is
continuously received by an institution computer 102 over the
network 100. Actual customer transaction level information and
detailed consumer information panel data may be calculated and used
to compare actual consumer spend amounts for individual consumers
(defined for each month as the difference between the sum of debits
to the account and any balance transfers into the account) and the
spend levels estimated for such consumers using the process 200
above. If a large error is demonstrated between actual and
estimated amounts, the models and the formulas used may be manually
or automatically refined so that the error is reduced. This allows
for a flexible model that has the capability to adapt to actual
aggregated spending behavior as it fluctuates over time.
[0043] As shown in the diagram 300 of FIG. 3, a population of
consumers for which individual and/or aggregated data has been
provided may be divided first into two general categories for
analysis, for example, those that are current on their credit
accounts (representing 132 million consumers in the exemplary data
sample size of 1.78 million consumers) and those that are
delinquent (representing 0.06 million of such consumers), in one
embodiment, delinquent consumers may be discarded from the
populations being modeled,
[0044] In further embodiments, the population of current consumers
is then subdivided into a plurality of further categories based on
the amount of balance information available and the balance
activity of such available data. In the example shown in the
diagram 300, the amount of balance information available is
represented by string of "+" "0" and "?" characters. Each character
represents one month of available data, with the rightmost
character representing the most current months and the leftmost
character representing the earliest month for which data is
available. In the example provided in FIG. 3, a string of six
characters is provided, representing the six most recent months of
data for each category, The "+" character represents a month in
which a credit account balance of the consumer has increased. The
"0" character may represent months where the account balance is
zero. The "?" character represents months for which balance data is
unavailable. Also provided the diagram is number of consumers
fallen to each category and the percentage of the consumer
population they represent in that sample.
[0045] In further embodiments, only certain categories of consumers
may be selected for modeling behavior. The selection may be based
on those categories that demonstrate increased spend on their
credit balances over time. However, it should be readily
appreciated that other categories can be used. FIG. 3 shows the
example of two categories of selected consumers for modeling in
bold. These groups show the availability of at least the three most
recent months of balance data and that the balances increased in
each of those months.
[0046] Turning now to FIG. 4, therein is depicted an exemplary
diagram 400 showing sub categorization of the two categories of
FIG. 3 in bold that are selected for modeling. In the embodiment
shown, the sub-categories may include: consumers having a most
recent credit balance less than $400; consumers having a most
recent credit balance between $400 and $1600; consumers having a
most recent credit balance between $1600 and $5000; consumers whose
most recent credit balance is less than the balance of for example,
three months ago; consumers whose maximum credit balance increase
over, for example, the last twelve months divided by the second
highest maximum balance increase over the same period is less than
2; and consumers whose maximum. credit balance increase over the
last twelve months divided by the second highest maximum balance
increase is greater than 2. It should be readily appreciated that
other subcategories can be used. Each of these sub-categories is
defined by their last month balance level. The number of consumers
from the sample population (in millions) and the percentage of the
population for each category are also Shown in FIG. 4.
[0047] There may be a certain balance threshold established,
wherein if a consumer's account balance is too high, their behavior
may not be modeled, since such consumers are less likely to have
sufficient spending ability. Alternatively, or in addition thereto,
consumers having balances above such threshold may be
sub-categorized yet again, rather than completely discarded from
the sample. In the example shown in FIG. 4, the threshold value may
be $5000, and only those having particular historical balance
activity may be selected, i.e. those consumers whose present
balance is less than their balance three months earlier, or whose
maximum balance increase in the examined period meets certain
parameters. Other threshold values may also be used and may be
dependent on the individual and aggregated consumer data
provided.
[0048] As described in the foregoing, the models generated in the
process 200 may be derived, validated and refined using tradeline
and consumer panel data. An example of tradeline data 500 from
Experian and consumer panel data 502 from comScore are represented
in FIG. 5. Each row of the data 500, 502 represents the record of
one consumer and thousands of such records may be provided at a
time. The statement 500 shows the point-in-time balance of
consumers accounts for three successive months (Balance 1, Balance
2 and Balance 3). The data 502 shows each consumer's purchase
volume, last payment amount, previous balance amount and current
balance. Such information may be obtained, for example, by page
scraping the data (in any of a variety of known manners using
appropriate application programming interfaces) from an Internet
web site or network address at which the data 502 is displayed.
Furthermore, the data 500 and 502 may be matched by consumer
identity and combined by one of the data providers or another third
party independent of the financial institution. Validation of the
models using the combined data 500 and 502 may then be performed,
and such validation may be independent of consumer identity.
[0049] Turning now to FIG. 6, therein is depicted an exemplary
process 600 for estimating the size of an individual consumer's
spending wallet. Upon completion of the modeling of the consumer
categories above, the process 600 commences with the selection of
individual consumers or prospects to be examined (step 602), An
appropriate model derived during the process 200 will then be
applied to the presently available consumer tradeline information
in the following manner to determine, based on the results of
application of the derived models, an estimate of a consumer's size
of wallet. Each consumer of interest may be selected based on their
falling into one of the categories selected for modeling described
above, or may be selected using any of a variety of criteria.
[0050] The process 600 continues to step 604 where, for a selected
consumer, a paydown percentage over a previous period of time is
estimated for each of the consumer's credit accounts. in one
embodiment, the paydown percentage is estimated over the previous
three-month period of time based on available tradeline data, and
may be calculated according to the following formula:
Pay-down %=(The sum of the last three months payments from the
account)/(The sum of three month balances for the account based on
tradeline data).
The paydown percentage may be set to, for example, 2%, for any
consumer exhibiting less than a 5% paydown percentage, and may be
set to 100% if greater than 80%, as a simplified manner for
estimating consumer spending behaviors on either end of the paydown
percentage scale,
[0051] Consumers that exhibit less than a 50% paydown during this
period may be categorized as revolvers, while consumers that
exhibit a 50% paydown or greater may be categorized as transactors.
These categorizations may be used to initially determine what, if
any, purchasing incentives may be available to the consumer, as
described later below,
[0052] The process 600, then continues to step 606, where balance
transfers for a previous period of time are identified from the
available tradeline data for the consumer. The identification of
balance transfers are essential since, although tradeline data may
reflect a higher balance on a credit account over time, such higher
balance may simply be the result of a transfer of a balance into
the account, and are thus not indicative of a true increase in the
consumer's spending. It is difficult to confirm balance transfers
based on tradeline data since the information available is not
provided on a transaction level basis. In addition, there are
typically lags or absences of reporting of such values on tradeline
reports.
[0053] Nonetheless, marketplace analysis using confirmed consumer
panel and internal customer financial records has revealed reliable
ways in which balance transfers into an account may be identified
from imperfect individual tradeline data alone. Three exemplary
reliable methods for identifying balance transfers from credit
accounts, each which is based in part on actual consumer data
sampled, are as follows, it should be readily apparent that these
formulas in this form are not necessary for all embodiments of the
present process and may vary based on the consumer data used to
derive them.
[0054] A first rule identifies a balance transfer for a given
consumer's credit account as follows. The month having the largest
balance increase in the tradeline data, and which satisfies the
following conditions, may be identified as a month in which a
balance transfer has occurred:
[0055] The maximum balance increase is greater than twenty times
the second maximum balance increase for the remaining months of
available data; [0056] The estimated pay-down percent calculated at
step 306 above is less than 40%; and [0057] The largest balance
increase is greater than $1000 based on the available data
[0058] A second rule identifies a balance transfer for a given
consumer's credit account in any month where the balance is above
twelve times the previous month's balance and the next month's
balance differs by no more than 20%.
[0059] A third rule identifies a balance transfer for a given
consumer's credit account in any month where: [0060] the current
balance is greater than 1.5 times the previous month's balance; the
current balance minus the previous month's balance is greater than
$4500; and [0061] the estimated pay-down percent from step 306
above is less than 30%, [0062] The process 600 then continues to
step 608, where consumer spending on each credit account is
estimated over the next, for example, three month period. In
estimating consumer spend, any spending for a month in which a
balance transfer has been identified from individual tradeline data
above is set to zero for purposes of estimating the size of the
consumer's spending wallet, reflecting the supposition that no real
spending has occurred on that account. The estimated spend for each
of the three previous months may then be calculated as follows:
[0062] Estimated spend=(the current balance-the previous month's
balance+(the previous month's balance*the estimated pay-down % from
step 604 above).
[0063] The exact form of the formula selected may be based on the
category in which the consumer is identified from the model
applied, and the formula is then computed iteratively for each of
the three months of the first period of consumer spend.
[0064] Next, at step 610 of the process 600, the estimated spend is
then extended over, for example, the previous three quarterly or
three-month periods, providing a most-recent year of estimated
spend for the consumer.
[0065] Finally, at step 612, this in turn may be used to generate a
plurality of final outputs for each consumer account (step 314).
These may be provided in an output file that may include a portion
or all of the following exemplary information, based on the
calculations above and information available from individual
tradeline data:
[0066] (i) size of previous twelve month spending wallet; (ii) size
of spending wallet for each of the last four quarters; (iii) total
number of revolving cards, revolving balance, and average pay down
percentage for each; (iv) total number of transacting cards, and
transacting balances for each; (v) the number of balance transfers
and total, estimated amount thereof; (vi) maximum revolving balance
amounts and associated credit limits; and (vii) maximum transacting
balance and associated credit limit.
[0067] After step 612, the process 600 ends with respect to the
examined consumer. It should be readily appreciated that the
process 600 may be repeated for any number of current customers or
consumer prospects.
[0068] Referring now to FIGS. 7-10, therein is depicted
illustrative diagrams 700-1000 of how such estimated spending is
calculated in a rolling manner across each previous three month
(quarterly) period. In FIG. 7, there is depicted a first three
month period (i.e., the most recent previous quarter) 702 on a
timeline 710. As well, there is depicted a first twelve-month
period 704 on a timeline 708 representing the last twenty-one
months of point-in-time account balance information available from
individual tradeline data for the consumer's account, Each month's
balance for the account is designated as "B#" B1-B12 represent
actual account balance information available over the past twelve
months for the consumer. B13-B21 represent consumer balances over
consecutive, preceding months.
[0069] :In accordance with the diagram 700, spending in each of the
three months of the first quarter 702 is calculated based on the
balance values B1-B12, the category of the consumer based on
consumer spending models generated in the process 200, and the
formulas used in steps 604 and 606.
[0070] Turning now to FIG. 8, there is shown a diagram 800
illustrating the balance information used for estimating spending
in a second previous quarter 802 using a second twelve-month period
of balance information 804. Spending in each of these three months
of the second previous quarter 802 is based on known balance
information B4-B15.
[0071] Turning now to FIG. 9, there is shown a diagram 900
illustrating the balance information used for estimating spending
in a third successive quarter 902 using a third twelve-month period
of balance information 904. Spending in each of these three months
of the third previous quarter 902 is based on known balance
information B7-B18.
[0072] Turning now to FIG. 10, there is shown a diagram 1000
illustrating the balance information used for estimating spending
in a fourth previous quarter 1002 using a fourth twelve-month
period of balance information 1004. Spending in each of these three
months of the fourth previous quarter 1002 is based on balance
information B10-B21.
[0073] It should he readily appreciated that as the rolling
calculations proceed, the consumer's category may change based on
the outputs that result, and, therefore, different formula
corresponding to the new category may be applied to the consumer
for different periods of time. The rolling manner described above
maximizes the known data used for estimating consumer spend in a
previous twelve month period 1006.
[0074] Based on the final output generated for the customer,
commensurate purchasing incentives may be identified and provided
to the consumer, for example, in anticipation of an increase in the
consumer's purchasing ability as projected by the output file. In
such cases, consumers of good standing, who are categorized as
transactors with a projected increase in purchasing ability, may be
offered a lower financing rate on purchases made during the period
of expected increase in their purchasing ability, or may be offered
a discount or rebate for transactions with selected merchants
during that time.
[0075] in another example, and in the case where a consumer is a
revolver, such consumer with a projected increase in purchasing
ability may be offered a lower annual percentage rate on balances
maintained on their credit account,
[0076] Other like promotions and enhancements to consumers'
experiences are well known and may be used within the processes
disclosed herein.
[0077] Various statistics for the accuracy of the processes 200 and
600 are provided in FIGS. 11-18, for which a consumer sample was
analyzed by the process 200 and validated using 24 months of
historic actual spend data. The table 1100 of FIG. 11 shows the
number of consumers having a balance of $5000 or more for whom the
estimated paydown percentage (calculated in step 604 above) matched
the actual paydown percentage (as determined from internal
transaction data and external consumer panel data).
[0078] The table 1200 of FIG. 12 shows the number of consumers
having a balance of $5000 or more who were expected to be
transactors or revolvers, and who actually turned out to be
transactors and revolvers based on actual spend data. As can be
seen, the number of expected revolvers who turned out to be actual
revolvers (80539) was many times greater than the number of
expected revolvers who turned out to be transactors (1090).
Likewise, the number of expected and actual transactors outnumbered
by nearly four-to-one the number of expected transactors that
turned out to be revolvers.
[0079] The table 1300 of FIG. 13 shows the number of estimated
versus actual instances in the consumer sample of When there
occurred a balance transfer into an account. For instance, in the
period sampled, there were 148,326 instances where no balance
transfers were identified in step 606 above, and for which a
comparison of actual consumer data showed there were in fact no
balance transfers in. This compares to only 9,534 instances where
no balance transfers were identified in step 606, but there were in
fact actual balance transfers,
[0080] The table 1400 of FIG. 14 shows the accuracy of estimated
spending (in steps 608-612) versus actual spending for consumers
with account balances (at the time this sample testing was
performed) greater than $5000. As can be seen, the estimated
spending at each spending level most closely matched the same
actual spending level than for any other spending level in nearly
all instances.
[0081] The table 1500 of FIG. 15 shows the accuracy of estimated
spending (in steps 608-612) versus actual spending for consumers
having most recent account balances between $1600 and $5000. As can
be readily seen, the estimated spending at each spending level most
closely matched the same actual spending level than for any other
spending level in all instances,
[0082] The table 1600 of FIG. 16 shows the accuracy of estimated
spending versus actual spending for all consumers in the sample. As
can be readily seen, the estimated spending at each spending level
most closely matched the same actual spending level than for any
other actual spending level in all instances.
[0083] The table 1700 of FIG. 17 shows the rank order of estimated
versus actual spending for all consumers in the sample. This table
1700 readily shows that the number of consumers expected to be in
the bottom 10% of spending most closely matched the actual number
of consumers in that category, by 827,716 to 22,721. The table 1700
thither shows that the number of consumers expected to he in the
top 10% of spenders most closely matched the number of consumers
who were actually in the top 10%, by 71,773 to 22,721.
[0084] The table 1800 of FIG. 18 shows estimated versus actual
annual spending for all consumers in the sample over the most
recent year of available data. As can be readily seen, the expected
number of consumers at each spending level most closely matched the
same actual spending level than any other level in all
instances.
[0085] Finally, the table 1900 of FIG. 19 shows the rank order of
estimated versus actual total annual spending for all the consumers
over the most recent year of available data. Again, the number of
expected consumers in each rank most closely matched the actual
rank than any other rank.
[0086] Prospective customer populations used for modeling and/or
later evaluation may be provided from any of a plurality of
available marketing groups, or may be culled from credit bureau
data, targeted advertising campaigns or the like. Testing and
analysis may be continuously performed to identify the optimal
placement and required frequency of such sources for using the size
of spending wallet calculations. The processes described herein may
also be used to develop models for predicting a size of wallet for
an individual consumer in the future.
[0087] institutions adopting the processes disclosed herein may
expect to more readily and profitably identify opportunities for
prospect and customer offerings, which in turn provides enhanced
experiences across all parts of a customer's lifecycle. In the case
of a credit provider, accurate identification of spend
opportunities allows for rapid provisioning of card member
offerings to increase spend that, in turn, results in increased
transaction fees, interest charges and the like, The careful
selection of customers to receive such offerings reduces the
incidence of fraud that may occur in less disciplined card member
incentive programs. This, in turn, reduces overall operating
expenses for institutions.
[0088] II. Model Output
[0089] As mentioned above, the process described may also be used
to develop models for predicting a size of wallet for an individual
consumer in the future. The capacity a consumer has for spending in
a variety of categories is the share of wallet. The model used to
determine share of wallet for particular spend categories using the
processes described herein is the share of wallet ("SoW") model.
The SOW model provides estimated data and/or characteristics
information that is more indicative of consumer spending power than
typical credit bureau data or scores. The SoW model may output,
with sufficient accuracy, data that is directly related to the
spend capacity of an individual consumer. One of skill in the art
will recognize that any one or combination of the following data
types, as well as other data types, may be output by the SoW model
without altering the spirit and scope of the present invention,
[0090] The size of a consumer's twelve-month spending wallet is an
example output of the SoW model. This type of data is typically
output as an actual or rounded dollar amount. The size of a
consumer's spending wallet for each of several consecutive
quarters, for example, the most recent four quarters, may also be
output.
[0091] The SoW model output may include the total number of
revolving cards held by a consumer, the consumer's revolving
balance, and/or the consumer's average pay-down percentage of the
revolving cards. The maximum revolving balance and associated
credit limits can be determined for the consumer, as well as the
size of the consumer's revolving spending.
[0092] Similarly, the SoW model output may include the total number
of a consumer's transacting cards and/or the consumer's transacting
balance. The SoW model may additionally output the maximum
transacting balance, the associated credit limit, and/or the size
of transactional spending of the consumer.
[0093] These outputs, as well as any other outputs from the SoW
model, may be appended to data profiles of a company's customers
and prospects. This enhances the company's ability to make
decisions involving prospecting, new applicant evaluation, and
customer relationship management across the customer lifecycle.
[0094] Additionally or alternatively, the output of the model can
be calculated to equal a SoW score, much like credit bureau data is
used to calculate a credit rating. Credit bureau scores are
developed from data available in a consumer's file, such as the
amount of lines of credit, payment performance, balance, and number
of tradelines. This data is used to model the risk of a consumer
over a period of time using statistical regression analysis. Those
data elements that are found to be indicative of risk are weighted
and combined to determine the credit score. For example, each data
element may be given a score, with the final credit score being the
sum of the data element scores.
[0095] A SoW score, based on the SoW model, may provide a higher
level of predictability regarding spend capacity and
creditworthiness. The SoW score can focus, for example, on total
spend, plastic spend and/or a consumer's spending trend. Using the
processes described above, balance transfers are factored out of a
consumer's spend capacity. Further, when correlated with a risk
score, the SoW score may provide more insight into behavior
characteristics of relatively low-risk consumers and relatively
high-risk consumers.
[0096] The SoW score may be structured in one of several ways, For
instance, the score may be a numeric score that reflects a
consumer's spend in various ranges over a given time period, such
as the last quarter or year. As an example, a score of 5000 might
indicate that a consumer spent between $5000 and $6000 in the given
time period.
[0097] Alternatively or additionally, the score may include a range
of numbers or a numeric indicator, such as an exponent, that
indicates the trend of a consumer's spend over a given time period.
For example, a trend score of +4 may indicate that a consumer's
spend has increased over the previous 4 months, while a trend score
of -4 may indicate that a consumer's spend has decreased over the
previous 4 months.
[0098] In addition to determining an overall SoW score, the SoW
model outputs may each be given individual scores and used as
attributes for consideration in credit score development by, for
example, traditional credit bureaus, As discussed above, Credit
scores are traditionally based on information in a customer's
credit bureau file. Outputs of the SoW model, such as balance
transfer information, spend capacity and trend, and revolving
balance information, could be more indicative of risk than some
traditional data elements, Therefore, a company may use scored SoW
outputs in addition to or in place of traditional data elements
when computing a final credit score. This information may be
collected, analyzed, and/or summarized in a scorecard. This would
be useful to, for example and without limitation, credit bureaus,
major credit grantors, and scoring companies, such as Fair Isaac
Corporation of Minneapolis, Minn.
[0099] The SoW model outputs for individual consumers or small
businesses can also be used to develop various consumer models to
assist in direct marketing campaigns, especially targeted direct
marketing campaigns. For example, "best customer" or "preferred
customer" models may be developed that correlate characteristics
from the SoW model outputs, such as plastic spend, with certain
consumer groups. If positive correlations are identified, marketing
and customer relationship management strategies may be developed to
achieve more effective results.
[0100] In an example embodiment, a company may identify a group of
customers as its "best customers." The company can process
information about those customers according to the SoW model. This
may identify certain consumer characteristics that are common to
members of the best customer group. The company can then profile
prospective customers using the SoW model, and selectively target
those who have characteristics in common with the company's best
consumer model.
[0101] FIG. 20 is a flowchart of a method 2000 for using model
outputs to improve customer profiling. In step 2002, customers are
segmented into various categories, Such categories may include, for
example and without limitation, best customers, profitable
customers, marginal customers, and other customers.
[0102] In step 2004, model outputs are created for samples of
customers from each category.
[0103] The customers used in step 2004 are those for Whom detailed
information is known.
[0104] In step 2006, it is determined whether there is any
correlation between particular model outputs and the customer
categories.
[0105] Alternatively, the SoW model can be used to separate
existing customers on the basis of spend capacity. This allows
separation into groups based on spend capacity, A company can then
continue with method 2000 for identifying correlations, or the
company may look to non-credit-related characteristics of the
consumers in a category for correlations.
[0106] If a correlation is found, the correlated model output(s) is
deemed to be characteristic and/or predictive of the related
category of customers. This output can then be considered when a
company looks for customers who fit its best customer model.
[0107] III. Applicable market segments/industries
[0108] Outputs of the SoW model can be used in any business or
market segment that extends credit or otherwise needs to evaluate
the creditworthiness or spend capacity of a particular customer,
These businesses will be referred to herein as falling into one of
three categories: financial services companies, retail companies,
and other companies.
[0109] The business cycle in each category may be divided into
three phases: acquisition, retention, and disposal. The acquisition
phase occurs when a business is attempting to gain new customers.
This includes, for example and without limitation, targeted
marketing, determining what products or services to offer a
customer, deciding whether to lend to a particular customer and
what the line size or loan should be, and deciding whether to buy a
particular loan. The retention phase occurs after a customer is
already associated with the business. in the retention phase, the
business interests shift to managing the customer relationship
through, for example, consideration of risk, determination of
credit lines, cross-sell opportunities, increasing business from
that customer, and increasing the company's assets under
management. The disposal phase is entered when a business wishes to
dissociate itself from a customer or otherwise end the customer
relationship. This can occur, for example, through settlement
offers, collections, and sale of defaulted or near-default
loans.
[0110] A. Financial services companies
[0111] Financial services companies include, for example and
without limitation: banks and lenders, mutual fund companies,
financiers of leases and sales, life insurance companies, online
brokerages, and loan buyers.
[0112] Banks and lenders can utilize the SoW model in all phases of
the business cycle. One exemplary use is in relation to home equity
loans and the rating given to a particular bond issue in the
capital market, Although not specifically discussed herein, the SoW
model would apply to home equity lines of credit and automobile
loans in a similar manner.
[0113] if the holder of a home equity loan, for example, borrows
from the capital market, the loan holder issues asset-backed
securities ("ABS"), or bonds, which are backed by receivables. The
loan holder is thus an ABS issuer. The ABS issuer applies for an
ABS rating, which is assigned based on the credit quality of the
underlying receivables. One of skill in the art will recognize that
the ABS issuer may apply for the ABS rating through any application
means without altering the spirit and scope of the present
invention. In assigning a rating, the rating agencies weigh a
loan's probability of default by considering the lender's
underwriting and portfolio management processes, Lenders generally
secure higher ratings by credit enhancement. Examples of credit
enhancement include over-collateralization, buying insurance (such
as wrap insurance), and structuring ABS (through, for example,
senior/subordinate bond structures, sequential pay vs. pari passu,
etc.) to achieve higher ratings. Lenders and rating agencies take
the probability of default into consideration when determining the
appropriate level of credit enhancement.
[0114] During the acquisition phase of a loan, lenders may use the
SoW model to improve their lending decisions. Before issuing the
loan, lenders can evaluate a consumer's spend capacity for making
payments on the loan. This leads to fewer bad loans and a reduced
probability of default for loans in the lender's portfolio. A lower
probability of default means that, for a given loan portfolio that
has been originated using the SoW model, either a higher rating can
be obtained with the same degree of over-collateralization, or the
degree of over-collateralization can be reduced for a given debt
rating. Thus, using the SoW model at the acquisition stage of the
loan reduces the lender's overall borrowing cost and loan loss
reserves.
[0115] During the retention phase of a loan, the SoW model can be
used to track a Customer's spend. Based on the SoW outputs, the
lender can make various decisions regarding the customer
relationship. For example, a lender may use the SoW model to
identify borrowers who are in financial difficulty. The credit
lines of those borrowers which have not fully been drawn down can
then be reduced. Selectively revoking unused lines of credit may
reduce the probability of default for loans in a given portfolio
and reduce the lender's borrowing costs. Selectively revoking
unused lines of credit may also reduce the lender's risk by
minimizing further exposure to a borrower that may already be in
financial distress.
[0116] During the disposal phase of a loan, the SoW model enables
lenders to better predict the likelihood that a borrower will
default. Once the lender has identified customers who are in danger
of default, the lender may select those likely to repay and extend
settlement offers. Additionally, lenders can use the SoW model to
identify which customers are unlikely to pay and those who are
otherwise not worth extending a settlement offer.
[0117] The SoW model allows lenders to identify loans with risk of
default, allowing lenders, prior to default, to begin anticipating
a course of action to take if default occurs. Because freshly
defaulted loans fetch a higher sale price than loans that have been
non-performing for longer time periods, lenders may sell these
loans earlier in the default period, thereby reducing the lender's
costs.
[0118] The ability to predict and manage risk before default
results in a lower likelihood of default for loans in the lender's
portfolio. Further, even in the event of a defaulted loan, the
lender can detect the default early and thereby recoup a higher
percentage of the value of that
[0119] loan, A lender using the SoW model can thus show to the
rating agencies that it uses a combination of tight underwriting
criteria and robust post-lending portfolio management processes.
This enables the lender to increase the ratings of the ABS that are
backed by a given pool or portfolio of loans and/or reduce the
level of over-collateralization or credit enhancement required in
order to obtain a particular rating.
[0120] Turning to mutual funds, the SoW model may be used to manage
the relationship with customers who interact directly with the
company. During the retention phase, if the mutual fund company
concludes that a customer's spending capacity has increased, the
company can conclude that either or both of the cutomer's
discretionary and disposable income has increased, The company can
then market additional funds to the customer. The company can also
cross-sell other services that the customer's increased spend
capacity would support.
[0121] Financiers of leases or sales, such as automobile lease or
sale financiers, can benefit from SoW outputs in much the same way
as a bank or lender, as discussed above, in typical product
financing, however, the amount of the loan or lease is based on the
value of the product being financed, Therefore, there is generally
no credit limit that needs to he revisited during the course of the
loan. For this reason, the SoW model is most useful to lease/sales
finance companies during the acquisition and disposal phases of the
business cycle.
[0122] Life insurance companies can primarily benefit from the SoW
model during the acquisition and retention phases of the business
cycle. During the acquisition phase, the SoW model allows insurance
companies to identify those people with adequate spend capacity for
paying premiums. This allows the insurance company to selectively
target its marketing efforts to those most likely to purchase life
insurance. For example, the insurance company could model consumer
behavior in a similar manner as the "best customer" model described
above. During the retention phase, an insurance company can use the
SoW model to determine which of its existing clients have increased
their spend capacity and would have a greater capability to
purchase additional life insurance. In this way, those existing
customers could be targeted at a time during which they would most
likely be willing to purchase without overloading them with
materials when they are not likely to purchase.
[0123] The SoW model is most relevant to brokerage and wealth
management companies during the retention phase of the business
cycle, Due to convenience factors, consumers typically trade
through primarily one brokerage house. The more incentives extended
to as customer by a company, the more likely the customer will use
that company for the majority of its trades. A brokerage house may
thus use the SoW model to determine the capacity or trend of a
particular customer's spend and then use that data to cross-sell
other products and/or as the basis for an incentive program. For
example, based on the SoW outputs, a particular customer may become
eligible for additional services offered by the brokerage house,
such as financial planning, wealth management, and estate planning
services.
[0124] Just as the SoW model can help loan holders determine that
a. particular loan is nearing default, loan buyers can use the
model to evaluate the quality of a prospective purchase during the
acquisition phase of the business cycle. This assists the loan
buyers in avoiding or reducing the sale prices of loans that are in
likelihood of default.
[0125] B. Retail companies
[0126] Aspects of the retail industry for which the SoW model would
be advantageous include, for example and without limitation: retail
stores having private label cards, on-line retailers, and mail
order companies.
[0127] There are two general types of credit and charge cards in
the marketplace today:
[0128] multipurpose cards and private label cards. A third type of
hybrid card is emerging. Multipurpose cards are cards that can be
used at multiple different merchants and service providers. For
example, American Express, Visa, Mastercard, and Discover are
considered multipurpose card issuers. Multipurpose cards are
accepted by merchants and other service providers in what is often
referred to as an "open network." This essentially means that
transactions are routed from a point-of-sale ("POS") through a
network for authorization, transaction posting, and settlement. A
variety of intermediaries play different roles in the process.
These include merchant processors, the brand networks, and issuer
processors. This open network is often referred to as an
interchange network. Multipurpose cards include a range of
different card types, such as charge cards, revolving cards, and
debit cards, which are linked to a consumer's demand deposit
account ("DDA") or checking account.
[0129] Private label cards are cards that can be used for the
purchase of goods and services from a single merchant or service
provider. Historically, major department stores were the
originators of this type of card. Private label cards are now
offered by a wide range of retailers and other service providers.
These cards are generally processed on a closed network, with
transactions flowing between the merchant's POS and its own
backoffice or the processing center for a third-party processor.
These transactions do not flow through an interchange network and
are not subject to interchange fees.
[0130] Recently, a type of hybrid card has evolved. This is a card
that, when used at a particular merchant, is that merchant's
private label card, but when used elsewhere, becomes a multipurpose
card. The particular merchant's transactions are processed in the
proprietary private label network. Transactions made with the card
at all other merchants and service providers are processed through
an interchange network.
[0131] Private label card issuers, in addition to multipurpose card
issuers and hybrid card issuers, can apply the SoW model in a
similar way as described above with respect to credit card
companies. That is, knowledge of a consumer's spend capability, as
well as knowledge of the other SoW outputs, could be used by card
issuers to improve performance and profitability across the entire
business cycle.
[0132] Online retail and mail order companies can use the SoW model
in both the acquisition and retention phases of the business cycle.
During the acquisition phase, for example, the companies can base
targeted marketing strategies on SoW outputs. This could
substantially reduce costs, especially in the mail order industry,
where catalogs are typically sent to a wide variety of individuals.
During the retention phase, companies can, for example, base
cross-sell strategies or credit line extensions on SoW outputs.
[0133] C. Other companies
[0134] Types of companies which also may make use of the SoW model
include, for example and without limitation: the gaming industry,
charities and universities, communications providers, hospitals,
and the travel industry.
[0135] The gaming industry can use the SoW model in, for example,
the acquisition and retention phases of the business cycle. Casinos
often extend credit to their wealthiest and/or most active players,
also known as "high rollers." The casinos can use the SoW model in
the acquisition phase to determine whether credit should be
extended to an individual. Once credit has been extended, the
casinos can use the SoW model to periodically review the customer's
spend capacity. If there is a change in the spend capacity, the
casinos may alter the customer's credit line to be more
commensurate with the customer's spend capacity.
[0136] Charities and universities rely heavily on donations and
gifts. The SoW model allows charities and universities to use their
often limited resources more effectively by timing their
solicitations to coincide with periods when donors have had an
increase in disposable/discretionary income and are thus better
able to make donations, The SoW model also allows charities and
universities to review existing donors to determine whether they
should be targeted for additional support.
[0137] Communications providers, such as telephone service
providers often contract into service plans with their customers.
In addition to improving their targeted marketing strategies,
communications providers can use the SoW outputs during the
acquisition phase to determine whether a potential customer is
capable of paying for the service under the contract.
[0138] The SoW model is most applicable to hospitals during the
disposal phase of the business cycle. Hospitals typically do not
get to choose or manage the relationship with their patients.
Therefore, they are often in the position of trying to collect for
their services from patients with whom there was no prior customer
relationship. There are two ways that a hospital can collect its
fees. The hospital may run the collection in-house, or the hospital
may turn over responsibility for the collection to a collection
agent. Although the collection agent often takes fees for such a
service, it can be to the hospital's benefit if the collection is
time-consuming and/or difficult.
[0139] The SoW model can be used to predict which accounts are
likely to pay with minimal persuasion, and which ones are not. The
hospital can then select which accounts to collect in-house, and
which accounts to outsource to collection agencies. For those that
are retained in-house, the hospital can further segment the
accounts into those that require simple reminders and those
requiring more attention. This allows the hospital to optimize the
use of its in-house collections staff. By selectively outsourcing
collections, the hospital and other lenders reduces the contingency
fees that it pays to collection agencies, and maximizes the amount
collected by the in-house collection team.
[0140] Members of the travel industry can make use of the SoW data
in the acquisition and retention stages of the business cycle. For
example, a hotelier typically has a brand of hotel that is
associated with a particular "star-level" or class of hotel. In
order to capture various market segments, hoteliers may be
associated with several hotel brands that are of different classes,
During the acquisition phase of the business cycle, a hotelier may
use the SoW method to target individuals that have appropriate
spend capacities for various classes of hotels. During the
retention phase, the hotelier may use the SoW method to determine,
for example, when a particular individual's spend capacity
increases. Based on that determination, the hotelier can market a
higher class of hotel to the consumer in an attempt to convince the
consumer to upgrade.
[0141] One of skill in the relevant art(s) will recognize that many
of the above-described SoW applications may be utilized by other
industries and market segments without departing from the spirit
and scope of the present invention. For example, the strategy of
using SoW to model an industry's "best customer" and targeting
individuals sharing characteristics of that best customer can be
applied to nearly all industries.
[0142] SoW data can also be used across nearly all industries to
improve customer loyalty by reducing the number of payment
reminders sent to responsible accounts. Responsible accounts are
those who are most likely to pay even without being contacted by a
collector. The reduction in reminders may increase customer
loyalty, because the customer will not feel that the lender or
service provider is unduly aggressive, The lender's or service
provider's collection costs are also reduced, and resources are
freed to dedicate to accounts requiring more persuasion.
[0143] Additionally, the SoW model may be used in any company
having a large customer service call center to identify specific
types of customers. Transcripts are typically made for any call
from a customer to a call center. These transcripts may be scanned
for specific keywords or topics, and combined with the SoW model to
determine the consumer's characteristics. For example, a bank
having a large customer service center may scan service calls for
discussions involving bankruptcy. The bank could then use the SoW
model with the indications from the call center transcripts to
evaluate the customer.
[0144] Although the best methodologies of the disclosure have been
particularly described.
[0145] above, it is to be understood that such descriptions have
been provided for purposes of illustration only, and that other
variations both in form and in detail can be made by those skilled
in the art without departing from the spirit and scope thereof,
which is defined first and foremost by the appended claims.
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