U.S. patent application number 13/467247 was filed with the patent office on 2012-09-27 for method and apparatus for collaborative filtering of card member transactions.
This patent application is currently assigned to American Express Travel Related Services Company, Inc.. Invention is credited to Paul A. Herman, Greg M. Keeley, Vernon Marshall, Paula S. Schwalje, Brian J. Yasz.
Application Number | 20120245991 13/467247 |
Document ID | / |
Family ID | 36612928 |
Filed Date | 2012-09-27 |
United States Patent
Application |
20120245991 |
Kind Code |
A1 |
Herman; Paul A. ; et
al. |
September 27, 2012 |
METHOD AND APPARATUS FOR COLLABORATIVE FILTERING OF CARD MEMBER
TRANSACTIONS
Abstract
The disclosed system allows a credit or charge card issuer to
provide its card members with a list of merchants, products,
services, vacation destinations or other offerings that might be of
interest based on the purchases of similar card members. In one
instance, this process looks at all card members that made
purchases at a merchant and then it identifies all other merchants
in the same category where those card members also made purchases.
The associated merchants are ranked based on largest number of
shared card members and the top results may be shared with card
members or merchants in order to enhance promotions, card use and
marketing.
Inventors: |
Herman; Paul A.; (Cave
Creek, AZ) ; Keeley; Greg M.; (New York, NY) ;
Marshall; Vernon; (Montclair, NJ) ; Schwalje; Paula
S.; (Chandler, AZ) ; Yasz; Brian J.; (Cave
Creek, AZ) |
Assignee: |
American Express Travel Related
Services Company, Inc.
New York
NY
|
Family ID: |
36612928 |
Appl. No.: |
13/467247 |
Filed: |
May 9, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12907748 |
Oct 19, 2010 |
8190478 |
|
|
13467247 |
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|
11315262 |
Dec 23, 2005 |
7848950 |
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12907748 |
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Current U.S.
Class: |
705/14.25 ;
705/35 |
Current CPC
Class: |
G06Q 30/0255 20130101;
G06Q 30/0254 20130101; G06Q 30/02 20130101; G06Q 30/0251
20130101 |
Class at
Publication: |
705/14.25 ;
705/35 |
International
Class: |
G06Q 30/02 20120101
G06Q030/02; G06Q 40/00 20120101 G06Q040/00 |
Claims
1. A method comprising: determining, by a computer-based ranking
system, a first value for each pair of, a first merchant of a
plurality of merchants, and each remaining merchant, wherein the
first value corresponds to a number of account affiliates having at
least one financial transaction with the first merchant and the
remaining merchants during a period of time; determining, by the
computer-based system, a second value for each of the remaining
merchants of the plurality of merchants other than the first
merchant, the second value corresponding to the number of account
affiliates having at least one financial transaction with each of
the remaining merchants during the period of time; determining, by
the computer-based system, a third value corresponding to a ratio
of the first value to the second value; determining, by the
computer-based system, a fourth value based on the first value, the
third value and a weighting factor; and ranking, by the
computer-based system, each remaining merchant based on the fourth
value.
2. The method of claim 1, wherein the fourth value corresponds to
the third value multiplied by a corrective factor to account for a
significance of financial transactions.
3. The method of claim 2, wherein the corrective factor comprises
reducing the third value based on a reduced significance of
transactions occurring between the first merchant and merchants
having a number of financial transactions below a threshold number
with both the first merchant and each of the remaining merchants
during the period of time.
4. The method of claim 3, wherein the corrective factor comprises
increasing the third value based on an increased significance of
transactions occurring between the first merchant and merchants
having a number of financial transactions above the threshold
number with both the first merchant and each of the remaining
merchants during the period of time.
5. The method of claim 2, wherein the corrective factor further
comprises a calculated corrective factor based on empirical
data.
6. The method of claim 1, wherein the ranking further comprises
ranking based on ratings of vacation destinations received from
account affiliates.
7. The method of claim 1 further comprising offering a discount on
a purchase from at least one of the remaining merchants to account
affiliates having at least one financial transaction with the first
merchant during the period of time.
8. The method of claim 1, further comprising receiving information
corresponding to a plurality of merchants.
9. The method of claim 1, further comprising determining a group of
the plurality of merchants based on an industry code and a
geographic location.
10. The method of claim 6, wherein vacation destinations visited by
the account affiliates are grouped using vacation destination
characteristics.
11. The method of claim 1, further comprising receiving information
corresponding to a number of financial transactions involving
account affiliates during the period of time.
12. The method of claim 1, further comprising determining, for each
merchant of the plurality of merchants, the financial transactions
involving the account affiliates during the period of time.
13. The method claim 1, wherein the plurality of merchants are
restaurateurs.
14. The method claim 1, wherein the plurality of merchants are
restaurateurs offering similar cuisine.
15. The method of claim 1, wherein the fourth value comprises
C=A+(WI*B*(A-W2)); wherein, C=the fourth value A=the first value
B=the third value W1=a first weighting factor W2=a second weighting
factor.
16. An article of manufacture including a non-transitory, tangible
computer readable storage medium having instructions stored thereon
that, in response to execution by a computer-based ranking system,
cause the computer-based system to perform operations comprising:
determining, by the computer-based system, a first value for each
pair of a first merchant of a plurality of merchants, and each
remaining merchant, wherein the first value corresponds to a number
of account affiliates having at least one financial transaction
with the first merchant and the remaining merchants during a period
of time; determining, by the computer-based system, a second value
for each of the remaining merchants of the plurality of merchants
other than the first merchant, the second value corresponding to
the number of account affiliates having at least one financial
transaction with each of the remaining merchants during the period
of time; determining, by the computer-based system, a third value
corresponding to a ratio of the first value to the second value;
determining, by the computer-based system, a fourth value based on
the first value, the third value and a weighting factor; and
ranking, by the computer-based system, each remaining merchant
based on the fourth value.
17. A system comprising: a processor for ranking; a tangible,
non-transitory memory communicating with the processor, the
tangible, non-transitory memory having instructions stored thereon
that, in response to execution by the processor, cause the
processor to perform operations comprising: determining, by the
processor, a first value for each pair of a first merchant of a
plurality of merchants, and each remaining merchant, wherein the
first value corresponds to a number of account affiliates having at
least one financial transaction with the first merchant and the
remaining merchants during a period of time; determining, by the
processor, a second value for each attic remaining merchants of the
plurality of merchants other than the first merchant, the second
value corresponding to the number of account affiliates having at
least one financial transaction with each of the remaining
merchants during the period of time; determining, by the processor,
a third value corresponding to a ratio of the first value to the
second value; determining, by the processor, a fourth value based
on the first value, the third value and a weighting factor; and
ranking, by the processor, each remaining merchant based on the
fourth value.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of, and claims priority
to, U.S. Ser. No. 12/907,748 filed on Oct. 19, 2010 entitled
"Method and Apparatus for Collaborative Filtering of Card Member
Transactions." The '748 application is a continuation of and claims
priority to, U.S. Pat. No. 7,848,950 issued Dec. 7, 2010 (aka U.S.
Ser. No. 11/315,262 filed on Dec. 23, 2005) entitled "Method and
Apparatus for Collaborative Filtering of Card Member Transactions."
The '950 patent claims priority under 35 U.S.C. .sctn.119(e) to
U.S. Provisional Patent Application No. 60/639,472, filed Dec. 28,
2004. All of which are incorporated by reference herein in their
entirety.
BACKGROUND
[0002] 1. Field of the Invention
[0003] This invention generally relates to financial data
processing, and in particular it relates to incentive and
promotional programs.
[0004] 2. Related Art
[0005] Consumers are constantly searching for information on
products and services that may be of interest to them, but with
which they have no actual experience. They typically seek
independent information before making certain purchases. Various
sources provide reports on products and services to satisfy this
consumer demand for information. For example, ZAGATS provides
ratings on restaurants and CONSUMER REPORTS provides detailed
listings on product quality and customer satisfaction. When making
a purchase of a selected product on web sites such as AMAZON.COM,
information is typically provided about other products purchased by
customers who have also purchased the selected product.
[0006] Consumers frequently use credit, debit, stored value or
charge cards (collectively referred to herein as credit
instruments) in transactions with various merchants. This data is
collected and processed en mass by credit providers for billing
purposes and the like. However, little has been done to harness
such card member transaction details for marketing purposes.
BRIEF DESCRIPTION
[0007] Accordingly, the present disclosure introduces a system for
processing financial transaction data, referred to herein as
collaborative filtering, in which transaction data between card
members and merchants is captured and analyzed for marketing
purposes.
[0008] According to various embodiments of the disclosed processes,
a plurality of merchants having transactions with card members are
grouped based on an industry code and their geographic location.
For each merchant, the system determines its total number of
financial transactions involving card members over a period of
time. The system then selects a first merchant from the group and
identifies all card members that have had at least one financial
transaction with the first merchant over the period of time. The
system next determines, for each remaining merchant in the group,
the number of card members having at least one financial
transaction with both the first merchant and the remaining merchant
over the period of time. The system then ranks each remaining
merchant based on a ratio of: (i) the number of card members having
at least one financial transaction with both the first merchant and
the remaining merchant over the period of time to (ii) the number
of financial transactions involving card members over the period of
time. A corrective factor may be introduced to eliminate merchants
having relatively few card member transactions. The system then
reports one or more of the highest-ranked remaining merchants in
which card members have had at least one financial transaction with
both merchants over the period of time. The ranking of merchants
may then be provided to card members or interested merchants for
marketing purposes.
[0009] In various examples, the processes disclosed herein are
particularly useful for identifying restaurants or vacation
destinations that may be of interest to card members, but may be
applied to any of a variety of products, services and
offerings.
[0010] Further features and advantages of the present invention as
well as the structure and operation of various embodiments of the
present invention are described in detail below with reference to
the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The features of the present invention will become more
apparent from the detailed description set forth below when taken
in conjunction with the drawings. The left-most digit of a
reference number identifies the drawing in which the reference
number first appears.
[0012] FIG. 1 is a block diagram of an exemplary computer network
over which the processes of the present disclosure may be
performed.
[0013] FIG. 2 is a flowchart of an exemplary collaborative
filtering process performed over the network of FIG. 1.
[0014] FIG. 3 is a diagram of an exemplary collaborative filtering
database for use with the process of FIG. 2,
[0015] FIG. 4 is a diagram of exemplary ranking results using the
collaborative filtering process of FIG. 2.
DETAILED DESCRIPTION
Overview
[0016] The collaborative filtering processes now introduced allows
a credit, debit, stored value or charge card provider, such as
AMERICAN EXPRESS, to analyze financial transaction data involving
its card members (i.e., holders of a particular brand of credit
instrument) that is typically used only for billing purposes. The
system also harnesses such information in order to assist consumers
in making other purchases at merchants that have appealed to other
(similarly-situated) card members. According to such processes,
merchants who accept a particular brand of credit instrument are
grouped by location and industry code. Financial transactions
between card members and the various merchants within one or more
groups are analyzed to identify those merchants with common card
member patronage, and to further rank merchants within a group that
have had common patronage among card members. The ranking of
similar merchants may be reported to card members who have made at
least one purchase from a merchant within the group in order to
assist the card members in making their purchases at similar
merchants. Such reporting may be accompanied by discounts on
purchases at such other merchants, if desired. The ranking of
merchants may also be communicated to the merchants themselves (in
a manner such that card member privacy without is not violated),
who may then properly use such information for their marketing
purposes.
Exemplary Systems and Processes
[0017] Referring now to FIGS. 1-4, wherein similar components of
the present disclosure are referenced in like manner, and wherein
various embodiments of a method and system for collaborative
filtering of card member transactions are disclosed.
[0018] Turning to FIG. 1, there is depicted an exemplary computer
network 100 over which the transmission of financial transaction
data as described herein may be accomplished, using any of a
variety of available computing components for processing such data.
Such components may include a credit provider server 102, which may
be a computer, such as an enterprise server of the type commonly
manufactured by SUN MICROSYSTEMS. The credit provider server 102
has appropriate internal hardware, software, processing, memory and
network communication components, which enables it to perform the
functions described herein. General software applications may
include the SOLARIS operating system and SYBASE IQ data management
and analysis tools. The credit provider server 102 stores financial
transaction data in appropriate memory and processes the same
according to the processes described herein using programming
instructions that may be provided in any of a variety of useful
machine programming languages. It should be readily apparent that
any number of other computing systems and software may be used to
accomplish the processes described herein.
[0019] The credit provider server 102 may, in turn, be in operative
communication with any number of other external servers 104, which
may be computers or servers of similar or compatible functional
configuration. These external servers 104 may gather and provide
financial transactions data, as described herein, and transmit the
same for processing and analysis by the credit provider server 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. Each of the external servers 104 may be operated by either
common or independent entities, and in certain embodiment may
represent point-of-sale terminals where card member transactions
are initiated, or may be servers operated by credit card
clearinghouses that typically process credit transactions.
[0020] Turning now to FIG. 2, therein is depicted an exemplary
collaborative filtering process 200 performed by the credit
provider server 102 using the financial transaction data obtained
by and transmitted from the external servers 104.
[0021] The process 200 commences with the capture of financial
transaction data involving a plurality of card members and
merchants over a period of time (step 202). Such financial
transaction data may include, but is not limited to, an
identification of the card member (such as by name and/or account
number), an identification of the merchant (such as by name or
merchant identification number), a financial amount of the
transaction, and the date of the transaction. The period of time
may be for example a month, a quarter, a year or any other desired
period of time. The credit provider server 102 may store such
received data in a suitable database format for analysis as
described herein.
[0022] Next, the credit provider server 102 groups similar
merchants having transactions with card members according to their
geographic location and an applicable industry code (step 204). The
grouping of the merchants by geographic location may be
accomplished according to the zip code, street address, city,
metropolitan area (MSA) or county in which the merchants reside,
which is typically readily available to credit providers. The
grouping by geographic location ensures that card members who have
frequented a merchant in that location may be amenable to visiting
other merchants in the same location.
[0023] The further grouping of merchants by industry code ensures
that the merchants within the group offer similar products and
services. The industry code may be a Standard Industry
Classification (SIC) code that may be assigned to merchants by a
government agency. The industry code may further be a proprietary
classification code assigned by a credit provider, issuer, or
acquiror to a class of merchants to uniquely identify the products
or services offered by such merchants.
[0024] Next, at step 206, a group of merchants is selected for
analysis wherein the number of financial transactions with card
members is determined and stored for each merchant in the group.
This information may then be stored by the credit provider server
102 in a database, such as database 300 described below with
respect to FIG. 3.
[0025] Next, at step 208, a merchant is selected from the group,
and each card member having at least one transaction with that
merchant is identified from the stored financial transaction
data.
[0026] Next, at step 210, it is determined whether any other
merchants within that group have had transaction with any of the
card members identified in step 208 above. Those merchants having
transactions with common card members are identified and ranked
based on a ratio of the number of number of transactions with
common card members to the total number of transactions with all
card members determined in step 206 above.
[0027] One exemplary method for ranking merchants will now be
described with reference to FIG. 3 wherein an exemplary merchant
ranking database 300 used by the collaborative filtering process
200 is depicted. The database 300 has a number of fields
represented by columns in FIG. 3 and a number of database records,
represented as rows within FIG. 3. This database 300 may include:
(i) a first merchant identifier field 302 for storing an
identification of a first merchant being analyzed by the
collaborative filtering process 200; (ii) a second merchant
identifier field 200 for storing an identification of similar
merchants having transactions with common cardholders; (iii) a
number of common card members field 306 for storing a number of
card members who have frequented both the merchants identified in
fields 302 and 304; a number of total transactions field 308 for
storing the total number of card member transactions involving the
second merchant identified in field 304; a ratio field 310 for
storing the ratio of the value stored in field 306 to the value
stored in field 308; and a ranking field that stores the result of
a ranking applied to the data in fields 306-308.
[0028] A problem exists with simply using the ratio value 310 to
directly rank similar merchants within a group. This problem
becomes apparent when the number of shared card members is low, or
is close to the total number of transactions at the second
merchant. Either scenario, or a combination of the two, would
result in a ratio of nearly 1:1. However, particularly in the case
where there are few shared card member transactions, simply using
the highest ratios may not be representative of a true correlation
between the patronage of the first and second merchants.
[0029] Accordingly, a mathematical solution may be applied that
discounts such problematic data. One such solution may be expressed
as follows:
C=A+(10*B*(A-3)) [0030] where: [0031] C is the value stored in
field 312; [0032] A is the value stored in field 306; [0033] B is
the ratio value stored in field 310, obtained by dividing the value
stored in field 306 by the value stored in field 308; and [0034]
the value of (A-3) is set to zero if it results in zero or a
negative number.
[0035] It should be noted that, in one embodiment, the corrective
factor 10*B*(A-3) has been incorporated to discount coincidences
from merchants having relatively few numbers of common card member
transactions, and add weight to those with higher ratios and
numbers of common transactions. The variables in the corrective
factor were determined to be suitable based on experimental data
and may be altered or adjusted based on empirical data resulting
from actual use of the collaborative filtering processes. Other
suitable corrective factors may also be applied.
[0036] Returning to the process 200, upon completion of the
analysis in step 210 above, each of the remaining merchants within
the group are ranked according to the value stored in field 312 for
them, wherein the highest-ranked second merchant has the highest
ranking value and the lowest-ranked second merchant has the lowest
ranking value. The credit provider server may rank only a threshold
number of second merchants, such as the top five merchants.
[0037] These merchants may then be stored in a merchant ranking
database 400 shown in FIG. 4. An exemplary merchant ranking
database 400 includes the following fields: (i) a primary merchant
field 402 for storing an identification of a first merchant in a
group; (ii) a highest ranked merchant field 404 for storing the
highest ranked merchant in the group based on its ranking; (iii) a
second-highest ranked merchant field 404 for storing an
identification of the second highest ranked merchant in the group;
and (iv) third though fifth highest-ranked merchant fields 408-412
for storing the respective appropriate merchant
identifications.
[0038] The merchant rankings stored in database 400 may be reported
to card members in any of a variety of manners. In one example, the
collaborative filtering processes could be used to identify
restaurants that a card member may wish to try. Suppose Card member
A recently ate at a Sushi Restaurant in Manhattan. The
collaborative filtering process 200 could be used to identify and
report the highest-ranked restaurants (based on similar industry
codes, and therefore, similar services) where other card members
who have dined at the Sushi Restaurant have also dined. Based on
Card member A's patronage of the Sushi Restaurant, a report of
these highest ranked restaurants may be provided with Card member
A's billing statement or otherwise communicated to the card member
by, for example, a separate mailing, electronic means (e.g.,
e-mail) or telemarketing means.
[0039] in another example of the collaborative filtering process,
card members who are identified as having vacationed in a certain
destination could be informed of other top vacation destinations by
other card members who have also vacationed at that destination.
Such vacation destinations may or may not be grouped by similar
geographic location or similar merchants, but instead may simply be
based on overall card member preferences.
[0040] In one embodiment, the ranking information produced by the
collaborative filtering process described herein may be provided to
merchants themselves. For example, a restaurateur may learn that
many customers of a competing restaurant also tend to frequent
their establishment. The restaurateur may then offer to accept
coupons from that competitor in order to attract new customers.
[0041] The disclosed collaborative filtering processes solve
several problems by allowing a credit provider and merchants to
customize promotions or marketing offers, while at the same time
providing a value-added benefit for card members, by providing them
with meaningful information about merchants they may want to
patronize due to patronage from other (presumably
similarly-situated) card members who carry and utilize the same
particular brand of credit instrument. The process leverages the
ability to personalize information based on purchases already made
by card members. By providing such personalized information to card
members, a credit provider can expect to experience an increase in
revenues due to transactions that are encouraged by the
collaborative filtering process.
CONCLUSION
[0042] While various embodiments of the present invention have been
described above, it should be understood that they have been
presented by way of example, and not limitation. It will be
apparent to persons skilled in the relevant art(s) that various
changes in form and detail can be made therein without departing
from the spirit and scope of the present invention (e.g., packaging
and activation of other transaction cards and/or use of batch
activation processes). Thus, the present invention should not be
limited by any of the above described exemplary embodiments, but
should be defined only in accordance with the following claims and
their equivalents.
[0043] In addition, it should be understood that the figures and
screen shots illustrated in the attachments, which highlight the
functionality and advantages of the present invention, are
presented for example purposes only. The architecture of the
present invention is sufficiently flexible and configurable, such
that it may be utilized (and navigated) in ways other than that
shown in the accompanying figures.
[0044] Further, the purpose of the following Abstract is to enable
the U.S. Patent and Trademark Office and the public generally, and
especially the scientists, engineers and practitioners in the art
who are not familiar with patent or legal terms or phraseology, to
determine quickly from a cursory inspection the nature and essence
of the technical disclosure of the application. The Abstract is not
intended to be limiting as to the scope of the present invention in
any way,
* * * * *