U.S. patent application number 14/458754 was filed with the patent office on 2014-11-27 for systems and methods to match identifiers.
The applicant listed for this patent is Visa U.S.A. Inc.. Invention is credited to Leigh Amaro, Charles Raymond Byce, Edward W. Fordyce, Ryan Hagey, Nurtekin Savas, Michelle Eng Winters.
Application Number | 20140351048 14/458754 |
Document ID | / |
Family ID | 43876785 |
Filed Date | 2014-11-27 |
United States Patent
Application |
20140351048 |
Kind Code |
A1 |
Fordyce; Edward W. ; et
al. |
November 27, 2014 |
SYSTEMS AND METHODS TO MATCH IDENTIFIERS
Abstract
A system includes a transaction handler to process transactions,
a data warehouse to store transaction data recording the
transactions processed at the transaction handler and to store
mapping data between first user identifiers and first account
identifiers, a profile generator to generate a profile of a user
based on the transaction data, and a portal coupled to the
transaction handler to receive a query identifying a second user
identifier used by the first tracker to track online activities of
a user. The system is to identify a second account identifier of
the user from the second user identifier based on the mapping data
between the first user identifiers and the first account
identifiers to facilitate targeted advertising using the profile of
the user and/or to provide information about certain transactions
of the user related to a previously presented advertisement.
Inventors: |
Fordyce; Edward W.;
(Sedalia, CO) ; Amaro; Leigh; (Woodside, CA)
; Winters; Michelle Eng; (Belmont, CA) ; Savas;
Nurtekin; (San Jose, CA) ; Byce; Charles Raymond;
(Mill Valley, CA) ; Hagey; Ryan; (Alameda,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Visa U.S.A. Inc. |
San Francisco |
CA |
US |
|
|
Family ID: |
43876785 |
Appl. No.: |
14/458754 |
Filed: |
August 13, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13277165 |
Oct 19, 2011 |
8843391 |
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14458754 |
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12849798 |
Aug 3, 2010 |
8595058 |
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13277165 |
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61252119 |
Oct 15, 2009 |
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Current U.S.
Class: |
705/14.53 ;
705/39; 705/44 |
Current CPC
Class: |
G06Q 30/02 20130101;
G06Q 30/0269 20130101; G06Q 30/0224 20130101; G06Q 30/0255
20130101; G06Q 30/0239 20130101; G06Q 20/40 20130101; G06Q 20/10
20130101; G06Q 30/0277 20130101 |
Class at
Publication: |
705/14.53 ;
705/39; 705/44 |
International
Class: |
G06Q 20/10 20060101
G06Q020/10; G06Q 30/02 20060101 G06Q030/02; G06Q 20/40 20060101
G06Q020/40 |
Claims
1. A computer-implemented method, comprising: storing, in a
computing device, mapping data generated from matching first user
data configured to track first online activities of users and
second user data configured to track second online activities of
the users, the mapping data identifying mapping between user
identifiers and account identifiers, wherein the user identifiers
are identified in the first user data, and the account identifiers
are identified in the second user data; receiving, in the computing
device, a query identifying a first user identifier; determining,
by the computing device, a first account identifier based on the
mapping data and the first user identifier; identifying, by the
computing device, a transaction profile generated based on
transaction data in one or more payment accounts associated with
the first account identifier; and providing, by the computing
device, the transaction profile as a response to the query.
2. The method of claim 1, wherein the first user data and the
second user data are tracked by different web sites.
3. The method of claim 2, wherein the first user identifier is an
identifier used in a browser cookie; and the first account
identifier is an account number of a payment account.
4. The method of claim 3, wherein the browser cookie is used by a
first web site to track user activities of a user in the first web
site; and the account number of the payment account is an account
number of the user used on a second web site different from the
first web site.
5. The method of claim 3, wherein the mapping data includes
association between the identifier used in the browser cookie and
the account number of the payment account.
6. The method of claim 5, wherein the association between the
identifier used in the browser cookie and the account number of the
payment account is established via correlating data fields of the
first user data tracked by a first website and the second user data
tracked by a second website; the browser cookie is used by the
first website but not the second website; and the account number of
the payment account is used in a payment transaction via the second
website.
7. The method of claim 6, wherein the second website manages
electronic payments for different merchants, including the second
website.
8. The method of claim 6, wherein the second website is operated by
a transaction handler of a payment processing network to verify
authorized use of payment accounts in online transactions.
9. The method of claim 8, further comprising: storing, in the
computing apparatus, transaction data recording transactions
processed by the transaction handler in the payment processing
network; wherein the transaction profile is generated based on
transactions in the one or more payment accounts of the user and
transactions in payment accounts of second users.
10. The method of claim 9, wherein the transaction profile
summarizes transactions of the user using a plurality of factor
values in accordance with factor definitions identified from a
factor analysis of transactions of the second users; the plurality
of factor values are determined by applying the factor definitions
to the transactions of the user.
11. The method of claim 10, wherein the factor definitions specify
ways to combine set of variables to obtain factor values.
12. The method of claim 11, wherein the set of variables aggregates
transactions of the user based on merchant categories.
13. The method of claim 11, wherein the variables include spending
frequency variables and spending amount variables.
14. The method of claim 9, wherein the query is received from the
first website; and the transaction profile is provided to the first
website.
15. The method of claim 14, wherein the first website is configured
to use the transaction profile to identify an advertisement for
presentation to the user.
16. The method of claim 15, wherein the advertisement comprises at
least an offer of a discount, incentive, reward, coupon, gift, cash
back, benefit, product, or service.
17. The method of claim 16, wherein the advertisement is presented
in a context outside any transaction processed by the transaction
handler.
18. The method of claim 16, further comprising: in response to a
user selection of the advertisement, storing an offer provided in
the advertisement in association with a payment account of the
user.
19. A non-transitory computer storage medium storing instructions
configured to instruct a computing device to at least: store, in
the computing device, mapping data generated from matching first
user data configured to track first online activities of users and
second user data configured to track second online activities of
the users, the mapping data identifying mapping between user
identifiers and account identifiers, wherein the user identifiers
are identified in the first user data, and the account identifiers
are identified in the second user data; receive, in the computing
device, a query identifying a first user identifier; determine, by
the computing device, a first account identifier based on the
mapping data and the first user identifier; identify, by the
computing device, a transaction profile generated based on
transaction data in one or more payment accounts associated with
the first account identifier; and provide, by the computing device,
the transaction profile as a response to the query.
20. A system, comprising: at least one microprocessor; and memory
storing instructions configured to instruct the at least one
microprocessor to at least: store, in the computing device, mapping
data generated from matching first user data configured to track
first online activities of users and second user data configured to
track second online activities of the users, the mapping data
identifying mapping between user identifiers and account
identifiers, wherein the user identifiers are identified in the
first user data, and the account identifiers are identified in the
second user data; receive, in the computing device, a query
identifying a first user identifier; determine, by the computing
device, a first account identifier based on the mapping data and
the first user identifier; identify, by the computing device, a
transaction profile generated based on transaction data in one or
more payment accounts associated with the first account identifier;
and provide, by the computing device, the transaction profile as a
response to the query.
Description
RELATED APPLICATIONS
[0001] The present application is a continuation application of
U.S. patent application Ser. No. 13/277,165, filed Oct. 19, 2011
and entitled "Systems and Methods to Match Identifiers," which is a
continuation application of U.S. patent application Ser. No.
12/849,798, filed Aug. 3, 2010 and entitled "Systems and Methods to
Match Identifiers," now U.S. Pat. No. 8,595,058 issued on Nov. 26,
2013, which claims the benefit of provisional U.S. Pat. App. Ser.
No. 61/252,119, filed Oct. 15, 2009 and entitled "Systems and
Methods to Match Identifiers," the disclosures of which
applications are hereby incorporated herein by reference.
FIELD OF THE TECHNOLOGY
[0002] At least some embodiments of the present disclosure relate
to user tracking, the processing of transaction data, such as
records of payments made via credit cards, debit cards, prepaid
cards, etc., and/or providing information based on the processing
of the transaction data.
BACKGROUND
[0003] Millions of transactions occur daily through the use of
payment cards, such as credit cards, debit cards, prepaid cards,
etc. Corresponding records of the transactions are recorded in
databases for settlement and financial record keeping (e.g., to
meet the requirements of government regulations). Such data can be
mined and analyzed for trends, statistics, and other analyses.
Sometimes such data are mined for specific advertising goals, such
as to provide targeted offers to account holders, as described in
PCT Pub. No. WO 2008/067543 A2, published on Jun. 5, 2008 and
entitled "Techniques for Targeted Offers."
[0004] U.S. Pat. App. Pub. No. 2009/0216579, published on Aug. 27,
2009 and entitled "Tracking Online Advertising using Payment
Services," discloses a system in which a payment service identifies
the activity of a user using a payment card as corresponding with
an offer associated with an online advertisement presented to the
user.
[0005] U.S. Pat. No. 6,298,330, issued on Oct. 2, 2001 and entitled
"Communicating with a Computer Based on the Offline Purchase
History of a Particular Consumer," discloses a system in which a
targeted advertisement is delivered to a computer in response to
receiving an identifier, such as cookie, corresponding to the
computer.
[0006] U.S. Pat. No. 7,035,855, issued on Apr. 25, 2006 and
entitled "Process and System for Integrating Information from
Disparate Databases for Purposes of Predicting Consumer Behavior,"
discloses a system in which consumer transactional information is
used for predicting consumer behavior.
[0007] U.S. Pat. No. 6,505,168, issued on Jan. 7, 2003 and entitled
"System and Method for Gathering and Standardizing Customer
Purchase Information for Target Marketing," discloses a system in
which categories and sub-categories are used to organize purchasing
information by credit cards, debit cards, checks and the like. The
customer purchase information is used to generate customer
preference information for making targeted offers.
[0008] U.S. Pat. No. 7,444,658, issued on Oct. 28, 2008 and
entitled "Method and System to Perform Content Targeting,"
discloses a system in which advertisements are selected to be sent
to users based on a user classification performed using credit card
purchasing data.
[0009] U.S. Pat. App. Pub. No. 2005/0055275, published on Mar. 10,
2005 and entitled "System and Method for Analyzing Marketing
Efforts," discloses a system that evaluates the cause and effect of
advertising and marketing programs using card transaction data.
[0010] U.S. Pat. App. Pub. No. 2008/0217397, published on Sep. 11,
2008 and entitled "Real-Time Awards Determinations," discloses a
system for facilitating transactions with real-time awards
determinations for a cardholder, in which the award may be provided
to the cardholder as a credit on the cardholder's statement.
[0011] The disclosures of the above discussed patent documents are
hereby incorporated herein by reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The embodiments are illustrated by way of example and not
limitation in the figures of the accompanying drawings in which
like references indicate similar elements.
[0013] FIG. 1 illustrates a system to provide services based on
transaction data according to one embodiment.
[0014] FIG. 2 illustrates the generation of an aggregated spending
profile according to one embodiment.
[0015] FIG. 3 shows a method to generate an aggregated spending
profile according to one embodiment.
[0016] FIG. 4 shows a system to provide information based on
transaction data according to one embodiment.
[0017] FIG. 5 illustrates a transaction terminal according to one
embodiment.
[0018] FIG. 6 illustrates an account identifying device according
to one embodiment.
[0019] FIG. 7 illustrates a data processing system according to one
embodiment.
[0020] FIG. 8 shows the structure of account data for providing
loyalty programs according to one embodiment.
[0021] FIG. 9 shows a system to obtain purchase details according
to one embodiment.
[0022] FIG. 10 shows a system to target advertisements according to
one embodiment.
[0023] FIGS. 11-12 illustrate systems to map user identifiers to
account identifiers according to some embodiments.
[0024] FIG. 13 shows a method to map user identifiers to account
identifiers according to some embodiments.
DETAILED DESCRIPTION
Introduction
[0025] In one embodiment, transaction data, such as records of
transactions made via credit accounts, debit accounts, prepaid
accounts, bank accounts, stored value accounts and the like, is
processed to provide information for various services, such as
reporting, benchmarking, advertising, content or offer selection,
customization, personalization, prioritization, etc.
[0026] In one embodiment, an advertising network is provided based
on a transaction handler to present personalized or targeted
advertisements/offers on behalf of advertisers. A computing
apparatus of, or associated with, the transaction handler uses the
transaction data and/or other data, such as account data, merchant
data, search data, social networking data, web data, etc., to
develop intelligence information about individual customers, or
certain types or groups of customers. The intelligence information
can be used to select, identify, generate, adjust, prioritize,
and/or personalize advertisements/offers to the customers. In one
embodiment, the transaction handler is further automated to process
the advertisement fees charged to the advertisers, using the
accounts of the advertisers, in response to the advertising
activities.
[0027] In one embodiment, the computing apparatus correlates
transactions with activities that occurred outside the context of
the transaction, such as online advertisements presented to the
customers that at least in part cause the offline transactions. The
correlation data can be used to demonstrate the success of the
advertisements, and/or to improve intelligence information about
how individual customers and/or various types or groups of
customers respond to the advertisements.
[0028] In one embodiment, the computing apparatus correlates, or
provides information to facilitate the correlation of, transactions
with online activities of the customers, such as searching, web
browsing, social networking and consuming advertisements, with
other activities, such as watching television programs, and/or with
events, such as meetings, announcements, natural disasters,
accidents, news announcements, etc.
[0029] In one embodiment, the correlation results are used in
predictive models to predict transactions and/or spending patterns
based on activities or events, to predict activities or events
based on transactions or spending patterns, to provide alerts or
reports, etc.
[0030] In one embodiment, a single entity operating the transaction
handler performs various operations in the services provided based
on the transaction data. For example, in the presentation of the
personalized or targeted advertisements, the single entity may
perform the operations such as generating the intelligence
information, selecting relevant intelligence information for a
given audience, selecting, identifying, adjusting, prioritizing,
personalizing and/or generating advertisements based on selected
relevant intelligence information, and facilitating the delivery of
personalized or targeted advertisements, etc. Alternatively, the
entity operating the transaction handler cooperates with one or
more other entities by providing information to these entities to
allow these entities to perform at least some of the operations for
presentation of the personalized or targeted advertisements.
[0031] In one embodiment, a system is to deliver targeted
advertisements based on mapping online user identifiers to account
identifiers and using the account identifiers to retrieve
transaction-based intelligence information to identify
advertisements for the online users.
[0032] For example, a user as tracked by an online advertisement
provider can be mapped to the account number of a financial
transaction card/account (e.g., credit card, debit card, or banking
card) that has been used by the user. Once the online user is
mapped to the account number (or card number), a transaction
handler can provide intelligence information about the user based
on the transaction data associated with the account number (or card
number). For example, in one embodiment, a personalized or targeted
advertisement is selected, generated, customized, prioritized
and/or adjusted for the user based on the intelligence information.
For example, in one embodiment, after an advertisement is presented
to the user, the transaction handler is to respond to a query to
identify transactions of the user that are related to the
advertisement, such as identifying an offline transaction that is a
result of the online advertisement.
[0033] Further details and examples about mapping online user
identifiers to account identifiers in one embodiment are provided
in the section entitled "COOKIE TO ACCOUNT."
System
[0034] FIG. 1 illustrates a system to provide services based on
transaction data according to one embodiment. In FIG. 1, the system
includes a transaction terminal (105) to initiate financial
transactions for a user (101), a transaction handler (103) to
generate transaction data (109) from processing the financial
transactions of the user (101) (and the financial transactions of
other users), a profile generator (121) to generate transaction
profiles (127) based on the transaction data (109) to provide
information/intelligence about user preferences and spending
patterns, a point of interaction (107) to provide information
and/or offers to the user (101), a user tracker (113) to generate
user data (125) to identify the user (101) using the point of
interaction (107), a profile selector (129) to select a profile
(131) specific to the user (101) identified by the user data (125),
and an advertisement selector (133) to select, identify, generate,
adjust, prioritize and/or personalize advertisements for
presentation to the user (101) on the point of interaction (107)
via a media controller (115).
[0035] In one embodiment, the system further includes a correlator
(117) to correlate user specific advertisement data (119) with
transactions resulting from the user specific advertisement data
(119). The correlation results (123) can be used by the profile
generator (121) to improve the transaction profiles (127).
[0036] In one embodiment, the transaction profiles (127) are
generated from the transaction data (109) in a way as illustrated
in FIGS. 2 and 3. For example, in FIG. 3, an aggregated spending
profile (341) is generated via the factor analysis (327) and
cluster analysis (329) to summarize (335) the spending
patterns/behaviors reflected in the transaction records (301).
[0037] In one embodiment, a data warehouse (149) as illustrated in
FIG. 4 is coupled with the transaction handler (103) to store the
transaction data (109) and other data, such as account data (111),
transaction profiles (127) and correlation results (123). In FIG.
4, a portal (143) is coupled with the data warehouse (149) to
provide data or information derived from the transaction data
(109), in response to a query request from a third party or as an
alert or notification message.
[0038] In FIG. 4, the transaction handler (103) is coupled between
an issuer processor (145) in control of a consumer account (146)
and an acquirer processor (147) in control of a merchant account
(148). An account identification device (141) is configured to
carry the account information (142) that identifies the consumer
account (146) with the issuer processor (145) and provide the
account information (142) to the transaction terminal (105) of a
merchant to initiate a transaction between the user (101) and the
merchant.
[0039] FIGS. 5 and 6 illustrate examples of transaction terminals
(105) and account identification devices (141). FIG. 7 illustrates
the structure of a data processing system that can be used to
implement, with more or fewer elements, at least some of the
components in the system, such as the point of interaction (107),
the transaction handler (103), the portal (143), the data
warehouse, the account identification device (141), the transaction
terminal (105), the user tracker (113), the profile generator
(121), the profile selector (129), the advertisement selector
(133), the media controller (115), etc. Some embodiments use more
or fewer components than those illustrated in FIGS. 1 and 4-7, as
further discussed in the section entitled "VARIATIONS."
[0040] In one embodiment, the transaction data (109) relates to
financial transactions processed by the transaction handler (103);
and the account data (111) relates to information about the account
holders involved in the transactions. Further data, such as
merchant data that relates to the location, business, products
and/or services of the merchants that receive payments from account
holders for their purchases, can be used in the generation of the
transaction profiles (127, 341).
[0041] In one embodiment, the financial transactions are made via
an account identification device (141), such as financial
transaction cards (e.g., credit cards, debit cards, banking cards,
etc.); the financial transaction cards may be embodied in various
devices, such as plastic cards, chips, radio frequency
identification (RFID) devices, mobile phones, personal digital
assistants (PDAs), etc.; and the financial transaction cards may be
represented by account identifiers (e.g., account numbers or
aliases). In one embodiment, the financial transactions are made
via directly using the account information (142), without
physically presenting the account identification device (141).
[0042] Further features, modifications and details are provided in
various sections of this description.
Centralized Data Warehouse
[0043] In one embodiment, the transaction handler (103) maintains a
centralized data warehouse (149) organized around the transaction
data (109). For example, the centralized data warehouse (149) may
include, and/or support the determination of, spend band
distribution, transaction count and amount, merchant categories,
merchant by state, cardholder segmentation by velocity scores, and
spending within merchant target, competitive set and
cross-section.
[0044] In one embodiment, the centralized data warehouse (149)
provides centralized management but allows decentralized execution.
For example, a third party strategic marketing analyst,
statistician, marketer, promoter, business leader, etc., may access
the centralized data warehouse (149) to analyze customer and
shopper data, to provide follow-up analyses of customer
contributions, to develop propensity models for increased
conversion of marketing campaigns, to develop segmentation models
for marketing, etc. The centralized data warehouse (149) can be
used to manage advertisement campaigns and analyze response
profitability.
[0045] In one embodiment, the centralized data warehouse (149)
includes merchant data (e.g., data about sellers),
customer/business data (e.g., data about buyers), and transaction
records (301) between sellers and buyers over time. The centralized
data warehouse (149) can be used to support corporate sales
forecasting, fraud analysis reporting, sales/customer relationship
management (CRM) business intelligence, credit risk prediction and
analysis, advanced authorization reporting, merchant benchmarking,
business intelligence for small business, rewards, etc.
[0046] In one embodiment, the transaction data (109) is combined
with external data, such as surveys, benchmarks, search engine
statistics, demographics, competition information, emails, etc., to
flag key events and data values, to set customer, merchant, data or
event triggers, and to drive new transactions and new customer
contacts.
Transaction Profile
[0047] In FIG. 1, the profile generator (121) generates transaction
profiles (127) based on the transaction data (109), the account
data (111), and/or other data, such as non-transactional data, wish
lists, merchant provided information, address information,
information from social network websites, information from credit
bureaus, information from search engines, and other examples
discussed in U.S. patent application Ser. No. 12/614,603, filed
Nov. 9, 2009 and entitled "Analyzing Local Non-Transactional Data
with Transactional Data in Predictive Models," the disclosure of
which is hereby incorporated herein by reference.
[0048] In one embodiment, the transaction profiles (127) provide
intelligence information on the behavior, pattern, preference,
propensity, tendency, frequency, trend, and budget of the user
(101) in making purchases. In one embodiment, the transaction
profiles (127) include information about what the user (101) owns,
such as points, miles, or other rewards currency, available credit,
and received offers, such as coupons loaded into the accounts of
the user (101). In one embodiment, the transaction profiles (127)
include information based on past offer/coupon redemption patterns.
In one embodiment, the transaction profiles (127) include
information on shopping patterns in retail stores as well as
online, including frequency of shopping, amount spent in each
shopping trip, distance of merchant location (retail) from the
address of the account holder(s), etc.
[0049] In one embodiment, the transaction handler (103) provides at
least part of the intelligence for the prioritization, generation,
selection, customization and/or adjustment of the advertisement for
delivery within a transaction process involving the transaction
handler (103). For example, the advertisement may be presented to a
customer in response to the customer making a payment via the
transaction handler (103).
[0050] Some of the transaction profiles (127) are specific to the
user (101), or to an account of the user (101), or to a group of
users of which the user (101) is a member, such as a household,
family, company, neighborhood, city, or group identified by certain
characteristics related to online activities, offline purchase
activities, merchant propensity, etc.
[0051] In one embodiment, the profile generator (121) generates and
updates the transaction profiles (127) in batch mode periodically.
In other embodiments, the profile generator (121) generates the
transaction profiles (127) in real time, or just in time, in
response to a request received in the portal (143) for such
profiles.
[0052] In one embodiment, the transaction profiles (127) include
the values for a set of parameters. Computing the values of the
parameters may involve counting transactions that meet one or more
criteria, and/or building a statistically-based model in which one
or more calculated values or transformed values are put into a
statistical algorithm that weights each value to optimize its
collective predictiveness for various predetermined purposes.
[0053] Further details and examples about the transaction profiles
(127) in one embodiment are provided in the section entitled
"AGGREGATED SPENDING PROFILE."
Non-Transactional Data
[0054] In one embodiment, the transaction data (109) is analyzed in
connection with non-transactional data to generate transaction
profiles (127) and/or to make predictive models.
[0055] In one embodiment, transactions are correlated with
non-transactional events, such as news, conferences, shows,
announcements, market changes, natural disasters, etc. to establish
cause and effect relations to predict future transactions or
spending patterns. For example, non-transactional data may include
the geographic location of a news event, the date of an event from
an events calendar, the name of a performer for an upcoming
concert, etc. The non-transactional data can be obtained from
various sources, such as newspapers, websites, blogs, social
networking sites, etc.
[0056] In one embodiment, when the cause and effect relationships
between the transactions and non-transactional events are known
(e.g., based on prior research results, domain knowledge,
expertise), the relationships can be used in predictive models to
predict future transactions or spending patterns, based on events
that occurred recently or are happening in real time.
[0057] In one embodiment, the non-transactional data relates to
events that happened in a geographical area local to the user (101)
that performed the respective transactions. In one embodiment, a
geographical area is local to the user (101) when the distance from
the user (101) to locations in the geographical area is within a
convenient range for daily or regular travel, such as 20, 50 or 100
miles from an address of the user (101), or within the same city or
zip code area of an address of the user (101). Examples of analyses
of local non-transactional data in connection with transaction data
(109) in one embodiment are provided in U.S. patent application
Ser. No. 12/614,603, filed Nov. 9, 2009 and entitled "Analyzing
Local Non-Transactional Data with Transactional Data in Predictive
Models," the disclosure of which is hereby incorporated herein by
reference.
[0058] In one embodiment, the non-transactional data is not limited
to local non-transactional data. For example, national
non-transactional data can also be used.
[0059] In one embodiment, the transaction records (301) are
analyzed in frequency domain to identify periodic features in
spending events. The periodic features in the past transaction
records (301) can be used to predict the probability of a time
window in which a similar transaction would occur. For example, the
analysis of the transaction data (109) can be used to predict when
a next transaction having the periodic feature would occur, with
which merchant, the probability of a repeated transaction with a
certain amount, the probability of exception, the opportunity to
provide an advertisement or offer such as a coupon, etc. In one
embodiment, the periodic features are detected through counting the
number of occurrences of pairs of transactions that occurred within
a set of predetermined time intervals and separating the
transaction pairs based on the time intervals. Some examples and
techniques for the prediction of future transactions based on the
detection of periodic features in one embodiment are provided in
U.S. Pat. App. Pub. No. 2010/0280882, entitled "Frequency-Based
Transaction Prediction and Processing," the disclosure of which is
hereby incorporated herein by reference.
[0060] Techniques and details of predictive modeling in one
embodiment are provided in U.S. Pat. Nos. 6,119,103, 6,018,723,
6,658,393, 6,598,030, and 7,227,950, the disclosures of which are
hereby incorporated herein by reference.
[0061] In one embodiment, offers are based on the point-of-service
to offeree distance to allow the user (101) to obtain in-person
services. In one embodiment, the offers are selected based on
transaction history and shopping patterns in the transaction data
(109) and/or the distance between the user (101) and the merchant.
In one embodiment, offers are provided in response to a request
from the user (101), or in response to a detection of the location
of the user (101). Examples and details of at least one embodiment
are provided in U.S. Pat. App. Pub. No. 2008/0319843, entitled
"Supply of Requested Offer Based on Point-of Service to Offeree
Distance," U.S. Pat. App. Pub. No. 2008/0300973, entitled "Supply
of Requested Offer Based on Offeree Transaction History," U.S. Pat.
App. Pub. No. 2009/0076896, entitled "Merchant Supplied Offer to a
Consumer within a Predetermined Distance," U.S. Pat. App. Pub. No.
2009/0076925, entitled "Offeree Requested Offer Based on Point-of
Service to Offeree Distance," and U.S. Pat. App. Pub. No.
2010/0274627, entitled "Receiving an Announcement Triggered by
Location Data," the disclosures of which applications are hereby
incorporated herein by reference.
Targeting Advertisement
[0062] In FIG. 1, an advertisement selector (133) prioritizes,
generates, selects, adjusts, and/or customizes the available
advertisement data (135) to provide user specific advertisement
data (119) based at least in part on the user specific profile
(131). The advertisement selector (133) uses the user specific
profile (131) as a filter and/or a set of criteria to generate,
identify, select and/or prioritize advertisement data for the user
(101). A media controller (115) delivers the user specific
advertisement data (119) to the point of interaction (107) for
presentation to the user (101) as the targeted and/or personalized
advertisement.
[0063] In one embodiment, the user data (125) includes the
characterization of the context at the point of interaction (107).
Thus, the use of the user specific profile (131), selected using
the user data (125), includes the consideration of the context at
the point of interaction (107) in selecting the user specific
advertisement data (119).
[0064] In one embodiment, in selecting the user specific
advertisement data (119), the advertisement selector (133) uses not
only the user specific profile (131), but also information
regarding the context at the point of interaction (107). For
example, in one embodiment, the user data (125) includes
information regarding the context at the point of interaction
(107); and the advertisement selector (133) explicitly uses the
context information in the generation or selection of the user
specific advertisement data (119).
[0065] In one embodiment, the advertisement selector (133) may
query for specific information regarding the user (101) before
providing the user specific advertisement data (119). The queries
may be communicated to the operator of the transaction handler
(103) and, in particular, to the transaction handler (103) or the
profile generator (121). For example, the queries from the
advertisement selector (133) may be transmitted and received in
accordance with an application programming interface or other query
interface of the transaction handler (103), the profile generator
(121) or the portal (143) of the transaction handler (103).
[0066] In one embodiment, the queries communicated from the
advertisement selector (133) may request intelligence information
regarding the user (101) at any level of specificity (e.g., segment
level, individual level). For example, the queries may include a
request for a certain field or type of information in a
cardholder's aggregate spending profile (341). As another example,
the queries may include a request for the spending level of the
user (101) in a certain merchant category over a prior time period
(e.g., six months).
[0067] In one embodiment, the advertisement selector (133) is
operated by an entity that is separate from the entity that
operates the transaction handler (103). For example, the
advertisement selector (133) may be operated by a search engine, a
publisher, an advertiser, an ad network, or an online merchant. The
user specific profile (131) is provided to the advertisement
selector (133) to assist the customization of the user specific
advertisement data (119).
[0068] In one embodiment, advertising is targeted based on shopping
patterns in a merchant category (e.g., as represented by a Merchant
Category Code (MCC)) that has high correlation of spending
propensity with other merchant categories (e.g., other MCCs). For
example, in the context of a first MCC for a targeted audience, a
profile identifying second MCCs that have high correlation of
spending propensity with the first MCC can be used to select
advertisements for the targeted audience.
[0069] In one embodiment, the aggregated spending profile (341) is
used to provide intelligence information about the spending
patterns, preferences, and/or trends of the user (101). For
example, a predictive model can be established based on the
aggregated spending profile (341) to estimate the needs of the user
(101). For example, the factor values (344) and/or the cluster ID
(343) in the aggregated spending profile (341) can be used to
determine the spending preferences of the user (101). For example,
the channel distribution (345) in the aggregated spending profile
(341) can be used to provide a customized offer targeted for a
particular channel, based on the spending patterns of the user
(101).
[0070] In one embodiment, mobile advertisements, such as offers and
coupons, are generated and disseminated based on aspects of prior
purchases, such as timing, location, and nature of the purchases,
etc. In one embodiment, the size of the benefit of the offer or
coupon is based on purchase volume or spending amount of the prior
purchase and/or the subsequent purchase that may qualify for the
redemption of the offer. Further details and examples of one
embodiment are provided in U.S. Pat. App. Pub. No. 2008/0201226,
entitled "Mobile Coupon Method and Portable Consumer Device for
Utilizing Same," the disclosure of which is hereby incorporated
herein by reference.
[0071] In one embodiment, conditional rewards are provided to the
user (101); and the transaction handler (103) monitors the
transactions of the user (101) to identify redeemable rewards that
have satisfied the respective conditions. In one embodiment, the
conditional rewards are selected based on transaction data (109).
Further details and examples of one embodiment are provided in U.S.
Pat. App. Pub. No. 2008/0082418, entitled "Consumer Specific
Conditional Rewards," the disclosure of which is hereby
incorporated herein by reference. The techniques to detect the
satisfied conditions of conditional rewards can also be used to
detect the transactions that satisfy the conditions specified to
locate the transactions that result from online activities, such as
online advertisements, searches, etc., to correlate the
transactions with the respective online activities.
[0072] Further details about targeted offer delivery in one
embodiment are provided in U.S. Pat. App. Pub. No. 2010/0030644,
entitled "Targeted Advertising by Payment Processor History of
Cashless Acquired Merchant Transaction on Issued Consumer Account,"
and in U.S. Pat. App. Pub. No. 2011/0035280, entitled "Systems and
Methods for Targeted Advertisement Delivery," the disclosure of
which is hereby incorporated herein by reference.
Profile Matching
[0073] In FIG. 1, the user tracker (113) obtains and generates
context information about the user (101) at the point of
interaction (107), including user data (125) that characterizes
and/or identifies the user (101). The profile selector (129)
selects a user specific profile (131) from the set of transaction
profiles (127) generated by the profile generator (121), based on
matching the characteristics of the transaction profiles (127) and
the characteristics of the user data (125). For example, the user
data (125) indicates a set of characteristics of the user (101);
and the profile selector (129) selects the user specific profile
(131) that is for a particular user or a group of users and that
best matches the set of characteristics specified by the user data
(125).
[0074] In one embodiment, the profile selector (129) receives the
transaction profiles (127) in a batch mode. The profile selector
(129) selects the user specific profile (131) from the batch of
transaction profiles (127) based on the user data (125).
Alternatively, the profile generator (121) generates the
transaction profiles (127) in real time; and the profile selector
(129) uses the user data (125) to query the profile generator (121)
to generate the user specific profile (131) in real time, or just
in time. The profile generator (121) generates the user specific
profile (131) that best matches the user data (125).
[0075] In one embodiment, the user tracker (113) identifies the
user (101) based on the user activity on the transaction terminal
(105) (e.g., having visited a set of websites, currently visiting a
type of web pages, search behavior, etc.).
[0076] In one embodiment, the user data (125) includes an
identifier of the user (101), such as a global unique identifier
(GUID), a personal account number (PAN) (e.g., credit card number,
debit card number, or other card account number), or other
identifiers that uniquely and persistently identify the user (101)
within a set of identifiers of the same type. Alternatively, the
user data (125) may include other identifiers, such as an Internet
Protocol (IP) address of the user (101), a name or user name of the
user (101), or a browser cookie ID, which identify the user (101)
in a local, temporary, transient and/or anonymous manner. Some of
these identifiers of the user (101) may be provided by publishers,
advertisers, ad networks, search engines, merchants, or the user
tracker (113). In one embodiment, such identifiers are correlated
to the user (101) based on the overlapping or proximity of the time
period of their usage to establish an identification reference
table.
[0077] In one embodiment, the identification reference table is
used to identify the account information (142) (e.g., account
number (302)) based on characteristics of the user (101) captured
in the user data (125), such as browser cookie ID, IP addresses,
and/or timestamps on the usage of the IP addresses. In one
embodiment, the identification reference table is maintained by the
operator of the transaction handler (103). Alternatively, the
identification reference table is maintained by an entity other
than the operator of the transaction handler (103).
[0078] In one embodiment, the user tracker (113) determines certain
characteristics of the user (101) to describe a type or group of
users of which the user (101) is a member. The transaction profile
of the group is used as the user specific profile (131). Examples
of such characteristics include geographical location or
neighborhood, types of online activities, specific online
activities, or merchant propensity. In one embodiment, the groups
are defined based on aggregate information (e.g., by time of day,
or household), or segment (e.g., by cluster, propensity,
demographics, cluster IDs, and/or factor values). In one
embodiment, the groups are defined in part via one or more social
networks. For example, a group may be defined based on social
distances to one or more users on a social network website,
interactions between users on a social network website, and/or
common data in social network profiles of the users in the social
network website.
[0079] In one embodiment, the user data (125) may match different
profiles at a different granularity or resolution (e.g., account,
user, family, company, neighborhood, etc.), with different degrees
of certainty. The profile selector (129) and/or the profile
generator (121) may determine or select the user specific profile
(131) with the finest granularity or resolution with acceptable
certainty. Thus, the user specific profile (131) is most specific
or closely related to the user (101).
[0080] In one embodiment, the advertisement selector (133) uses
further data in prioritizing, selecting, generating, customizing
and adjusting the user specific advertisement data (119). For
example, the advertisement selector (133) may use search data in
combination with the user specific profile (131) to provide
benefits or offers to a user (101) at the point of interaction
(107). For example, the user specific profile (131) can be used to
personalize the advertisement, such as adjusting the placement of
the advertisement relative to other advertisements, adjusting the
appearance of the advertisement, etc.
Browser Cookie
[0081] In one embodiment, the user data (125) uses browser cookie
information to identify the user (101). The browser cookie
information is matched to account information (142) or the account
number (302) to identify the user specific profile (131), such as
aggregated spending profile (341) to present effective, timely, and
relevant marketing information to the user (101), via the preferred
communication channel (e.g., mobile communications, web, mail,
email, POS, etc.) within a window of time that could influence the
spending behavior of the user (101). Based on the transaction data
(109), the user specific profile (131) can improve audience
targeting for online advertising. Thus, customers will get better
advertisements and offers presented to them; and the advertisers
will achieve better return-on-investment for their advertisement
campaigns.
[0082] In one embodiment, the browser cookie that identifies the
user (101) in online activities, such as web browsing, online
searching, and using social networking applications, can be matched
to an identifier of the user (101) in account data (111), such as
the account number (302) of a financial payment card of the user
(101) or the account information (142) of the account
identification device (141) of the user (101). In one embodiment,
the identifier of the user (101) can be uniquely identified via
matching IP address, timestamp, cookie ID and/or other user data
(125) observed by the user tracker (113).
[0083] In one embodiment, a look up table is used to map browser
cookie information (e.g., IP address, timestamp, cookie ID) to the
account data (111) that identifies the user (101) in the
transaction handler (103). The look up table may be established via
correlating overlapping or common portions of the user data (125)
observed by different entities or different user trackers
(113).
[0084] For example, in one embodiment, a first user tracker (113)
observes the card number of the user (101) at a particular IP
address for a time period identified by a timestamp (e.g., via an
online payment process); a second user tracker (113) observes the
user (101) having a cookie ID at the same IP address for a time
period near or overlapping with the time period observed by the
first user tracker (113). Thus, the cookie ID as observed by the
second user tracker (113) can be linked to the card number of the
user (101) as observed by the first user tracker (113). The first
user tracker (113) may be operated by the same entity operating the
transaction handler (103) or by a different entity. Once the
correlation between the cookie ID and the card number is
established via a database or a look up table, the cookie ID can be
subsequently used to identify the card number of the user (101) and
the account data (111).
[0085] In one embodiment, the portal (143) is configured to observe
a card number of a user (101) while the user (101) uses an IP
address to make an online transaction. Thus, the portal (143) can
identify a consumer account (146) based on correlating an IP
address used to identify the user (101) and IP addresses recorded
in association with the consumer account (146).
[0086] For example, in one embodiment, when the user (101) makes a
payment online by submitting the account information (142) to the
transaction terminal (105) (e.g., an online store), the transaction
handler (103) obtains the IP address from the transaction terminal
(105) via the acquirer processor (147). The transaction handler
(103) stores data to indicate the use of the account information
(142) at the IP address at the time of the transaction request.
When an IP address in the query received in the portal (143)
matches the IP address previously recorded by the transaction
handler (103), the portal (143) determines that the user (101)
identified by the IP address in the request is the same user (101)
associated with the account of the transaction initiated at the IP
address. In one embodiment, a match is found when the time of the
query request is within a predetermined time period from the
transaction request, such as a few minutes, one hour, a day, etc.
In one embodiment, the query may also include a cookie ID
representing the user (101). Thus, through matching the IP address,
the cookie ID is associated with the account information (142) in a
persistent way.
[0087] In one embodiment, the portal (143) obtains the IP address
of the online transaction directly. For example, in one embodiment,
a user (101) chooses to use a password in the account data (111) to
protect the account information (142) for online transactions. When
the account information (142) is entered into the transaction
terminal (105) (e.g., an online store or an online shopping cart
system), the user (101) is connected to the portal (143) for the
verification of the password (e.g., via a pop up window, or via
redirecting the web browser of the user (101)). The transaction
handler (103) accepts the transaction request after the password is
verified via the portal (143). Through this verification process,
the portal (143) and/or the transaction handler (103) obtain the IP
address of the user (101) at the time the account information (142)
is used.
[0088] In one embodiment, the web browser of the user (101)
communicates the user provided password to the portal (143)
directly without going through the transaction terminal (105)
(e.g., the server of the merchant). Alternatively, the transaction
terminal (105) and/or the acquirer processor (147) may relay the
password communication to the portal (143) or the transaction
handler (103).
[0089] In one embodiment, the portal (143) is configured to
identify the consumer account (146) based on the IP address
identified in the user data (125) through mapping the IP address to
a street address. For example, in one embodiment, the user data
(125) includes an IP address to identify the user (101); and the
portal (143) can use a service to map the IP address to a street
address. For example, an Internet service provider knows the street
address of the currently assigned IP address. Once the street
address is identified, the portal (143) can use the account data
(111) to identify the consumer account (146) that has a current
address at the identified street address. Once the consumer account
(146) is identified, the portal (143) can provide a transaction
profile (131) specific to the consumer account (146) of the user
(101).
[0090] In one embodiment, the portal (143) uses a plurality of
methods to identify consumer accounts (146) based on the user data
(125). The portal (143) combines the results from the different
methods to determine the most likely consumer account (146) for the
user data (125).
Close the Loop
[0091] In one embodiment, the correlator (117) is used to "close
the loop" for the tracking of consumer behavior across an on-line
activity and an "off-line" activity that results at least in part
from the on-line activity. In one embodiment, online activities,
such as searching, web browsing, social networking, and/or
consuming online advertisements, are correlated with respective
transactions to generate the correlation result (123) in FIG. 1.
The respective transactions may occur offline, in "brick and
mortar" retail stores, or online but in a context outside the
online activities, such as a credit card purchase that is performed
in a way not visible to a search company that facilitates the
search activities.
[0092] In one embodiment, the correlator (117) is to identify
transactions resulting from searches or online advertisements. For
example, in response to a query about the user (101) from the user
tracker (113), the correlator (117) identifies an offline
transaction performed by the user (101) and sends the correlation
result (123) about the offline transaction to the user tracker
(113), which allows the user tracker (113) to combine the
information about the offline transaction and the online activities
to provide significant marketing advantages.
[0093] For example, a marketing department could correlate an
advertising budget to actual sales. For example, a marketer can use
the correlation result (123) to study the effect of certain
prioritization strategies, customization schemes, etc. on the
impact on the actual sales. For example, the correlation result
(123) can be used to adjust or prioritize advertisement placement
on a web site, a search engine, a social networking site, an online
marketplace, or the like.
[0094] In one embodiment, the profile generator (121) uses the
correlation result (123) to augment the transaction profiles (127)
with data indicating the rate of conversion from searches or
advertisements to purchase transactions. In one embodiment, the
correlation result (123) is used to generate predictive models to
determine what a user (101) is likely to purchase when the user
(101) is searching using certain keywords or when the user (101) is
presented with an advertisement or offer. In one embodiment, the
portal (143) is configured to report the correlation result (123)
to a partner, such as a search engine, a publisher, or a merchant,
to allow the partner to use the correlation result (123) to measure
the effectiveness of advertisements and/or search result
customization, to arrange rewards, etc.
[0095] Illustratively, a search engine entity may display a search
page with particular advertisements for flat panel televisions
produced by companies A, B, and C. The search engine entity may
then compare the particular advertisements presented to a
particular consumer with transaction data of that consumer and may
determine that the consumer purchased a flat panel television
produced by Company B. The search engine entity may then use this
information and other information derived from the behavior of
other consumers to determine the effectiveness of the
advertisements provided by companies A, B, and C. The search engine
entity can determine if the placement, the appearance, or other
characteristic of the advertisement results in actual increased
sales. Adjustments to advertisements (e.g., placement, appearance,
etc.) may be made to facilitate maximum sales.
[0096] In one embodiment, the correlator (117) matches the online
activities and the transactions based on matching the user data
(125) provided by the user tracker (113) and the records of the
transactions, such as transaction data (109) or transaction records
(301). In another embodiment, the correlator (117) matches the
online activities and the transactions based on the redemption of
offers/benefits provided in the user specific advertisement data
(119).
[0097] In one embodiment, the portal (143) is configured to receive
a set of conditions and an identification of the user (101),
determine whether there is any transaction of the user (101) that
satisfies the set of conditions, and if so, provide indications of
the transactions that satisfy the conditions and/or certain details
about the transactions, which allows the requester to correlate the
transactions with certain user activities, such as searching, web
browsing, consuming advertisements, etc.
[0098] In one embodiment, the requester may not know the account
number (302) of the user (101); and the portal (143) is to map the
identifier provided in the request to the account number (302) of
the user (101) to provide the requested information. Examples of
the identifier being provided in the request to identify the user
(101) include an identification of an iFrame of a web page visited
by the user (101), a browser cookie ID, an IP address and the day
and time corresponding to the use of the IP address, etc.
[0099] The information provided by the portal (143) can be used in
pre-purchase marketing activities, such as customizing content or
offers, prioritizing content or offers, selecting content or
offers, etc., based on the spending pattern of the user (101). The
content that is customized, prioritized, selected, or recommended
may be the search results, blog entries, items for sale, etc.
[0100] The information provided by the portal (143) can be used in
post-purchase activities. For example, the information can be used
to correlate an offline purchase with online activities. For
example, the information can be used to determine purchases made in
response to media events, such as television programs,
advertisements, news announcements, etc.
[0101] Details about profile delivery, online activity to offline
purchase tracking, techniques to identify the user specific profile
(131) based on user data (125) (such as IP addresses), and targeted
delivery of advertisement/offer/benefit in some embodiments are
provided in U.S. Pat. App. Pub. No. 2011/0035278, entitled "Systems
and Methods for Closing the Loop between Online Activities and
Offline Purchases", the disclosure of which application is
incorporated herein by reference.
Matching Advertisement & Transaction
[0102] In one embodiment, the correlator (117) is configured to
receive information about the user specific advertisement data
(119), monitor the transaction data (109), identify transactions
that can be considered results of the advertisement corresponding
to the user specific advertisement data (119), and generate the
correlation result (123), as illustrated in FIG. 1.
[0103] When the advertisement and the corresponding transaction
both occur in an online checkout process, a website used for the
online checkout process can be used to correlate the transaction
and the advertisement. However, the advertisement and the
transaction may occur in separate processes and/or under control of
different entities (e.g., when the purchase is made offline at a
retail store, while the advertisement is presented outside the
retail store). In one embodiment, the correlator (117) uses a set
of correlation criteria to identify the transactions that can be
considered as the results of the advertisements.
[0104] In one embodiment, the correlator (117) identifies the
transactions linked or correlated to the user specific
advertisement data (119) based on various criteria. For example,
the user specific advertisement data (119) may include a coupon
offering a benefit contingent upon a purchase made according to the
user specific advertisement data (119). The use of the coupon
identifies the user specific advertisement data (119), and thus
allows the correlator (117) to correlate the transaction with the
user specific advertisement data (119).
[0105] In one embodiment, the user specific advertisement data
(119) is associated with the identity or characteristics of the
user (101), such as global unique identifier (GUID), personal
account number (PAN), alias, IP address, name or user name,
geographical location or neighborhood, household, user group,
and/or user data (125). The correlator (117) can link or match the
transactions with the advertisements based on the identity or
characteristics of the user (101) associated with the user specific
advertisement data (119). For example, the portal (143) may receive
a query identifying the user data (125) that tracks the user (101)
and/or characteristics of the user specific advertisement data
(119); and the correlator (117) identifies one or more transactions
matching the user data (125) and/or the characteristics of the user
specific advertisement data (119) to generate the correlation
result (123).
[0106] In one embodiment, the correlator (117) identifies the
characteristics of the transactions and uses the characteristics to
search for advertisements that match the transactions. Such
characteristics may include GUID, PAN, IP address, card number,
browser cookie information, coupon, alias, etc.
[0107] In FIG. 1, the profile generator (121) uses the correlation
result (123) to enhance the transaction profiles (127) generated
from the profile generator (121). The correlation result (123)
provides details on the purchases and/or indicates the
effectiveness of the user specific advertisement data (119).
[0108] In one embodiment, the correlation result (123) is used to
demonstrate to the advertisers the effectiveness of the
advertisements, to process incentive or rewards associated with the
advertisements, to obtain at least a portion of advertisement
revenue based on the effectiveness of the advertisements, to
improve the selection of advertisements, etc.
Coupon Matching
[0109] In one embodiment, the correlator (117) identifies a
transaction that is a result of an advertisement (e.g., 119) when
an offer or benefit provided in the advertisement is redeemed via
the transaction handler (103) in connection with a purchase
identified in the advertisement.
[0110] For example, in one embodiment, when the offer is extended
to the user (101), information about the offer can be stored in
association with the account of the user (101) (e.g., as part of
the account data (111)). The user (101) may visit the portal (143)
of the transaction handler (103) to view the stored offer.
[0111] The offer stored in the account of the user (101) may be
redeemed via the transaction handler (103) in various ways. For
example, in one embodiment, the correlator (117) may download the
offer to the transaction terminal (105) via the transaction handler
(103) when the characteristics of the transaction at the
transaction terminal (105) match the characteristics of the
offer.
[0112] After the offer is downloaded to the transaction terminal
(105), the transaction terminal (105) automatically applies the
offer when the condition of the offer is satisfied in one
embodiment. Alternatively, the transaction terminal (105) allows
the user (101) to selectively apply the offers downloaded by the
correlator (117) or the transaction handler (103). In one
embodiment, the correlator (117) sends reminders to the user (101)
at a separate point of interaction (107) (e.g., a mobile phone) to
remind the user (101) to redeem the offer. In one embodiment, the
transaction handler (103) applies the offer (e.g., via statement
credit), without having to download the offer (e.g., coupon) to the
transaction terminal (105). Examples and details of redeeming
offers via statement credit are provided in U.S. Pat. App. Pub. No.
2010/0114686, entitled "Real-Time Statement Credits and
Notifications," the disclosure of which is hereby incorporated
herein by reference.
[0113] In one embodiment, the offer is captured as an image and
stored in association with the account of the user (101).
Alternatively, the offer is captured in a text format (e.g., a code
and a set of criteria), without replicating the original image of
the coupon.
[0114] In one embodiment, when the coupon is redeemed, the
advertisement presenting the coupon is correlated with a
transaction in which the coupon is redeemed, and/or is determined
to have resulted in a transaction. In one embodiment, the
correlator (117) identifies advertisements that have resulted in
purchases, without having to identify the specific transactions
that correspond to the advertisements.
[0115] Details about offer redemption via the transaction handler
(103) in one embodiment are provided in U.S. Pat. App. Pub. No.
2011/0125565, entitled "Systems and Methods for Multi-Channel Offer
Redemption," the disclosure of which is hereby incorporated herein
by reference.
On ATM & POS Terminal
[0116] In one example, the transaction terminal (105) is an
automatic teller machine (ATM), which is also the point of
interaction (107). When the user (101) approaches the ATM to make a
transaction (e.g., to withdraw cash via a credit card or debit
card), the ATM transmits account information (142) to the
transaction handler (103). The account information (142) can also
be considered as the user data (125) to select the user specific
profile (131). The user specific profile (131) can be sent to an
advertisement network to query for a targeted advertisement. After
the advertisement network matches the user specific profile (131)
with user specific advertisement data (119) (e.g., a targeted
advertisement), the transaction handler (103) may send the
advertisement to the ATM, together with the authorization for cash
withdrawal.
[0117] In one embodiment, the advertisement shown on the ATM
includes a coupon that offers a benefit that is contingent upon the
user (101) making a purchase according to the advertisement. The
user (101) may view the offer presented on a white space on the ATM
screen and select to load or store the coupon in a storage device
of the transaction handler (103) under the account of the user
(101). The transaction handler (103) communicates with the bank to
process the cash withdrawal. After the cash withdrawal, the ATM
prints the receipt which includes a confirmation of the coupon, or
a copy of the coupon. The user (101) may then use the coupon
printed on the receipt. Alternatively, when the user (101) uses the
same account to make a relevant purchase, the transaction handler
(103) may automatically apply the coupon stored under the account
of the user (101), or automatically download the coupon to the
relevant transaction terminal (105), or transmit the coupon to the
mobile phone of the user (101) to allow the user (101) to use the
coupon via a display of the coupon on the mobile phone. The user
(101) may visit a web portal (143) of the transaction handler (103)
to view the status of the coupons collected in the account of the
user (101).
[0118] In one embodiment, the advertisement is forwarded to the ATM
via the data stream for authorization. In another embodiment, the
ATM makes a separate request to a server of the transaction handler
(103) (e.g., a web portal) to obtain the advertisement.
Alternatively, or in combination, the advertisement (including the
coupon) is provided to the user (101) at separate, different points
of interactions, such as via a text message to a mobile phone of
the user (101), via an email, via a bank statement, etc.
[0119] Details of presenting targeted advertisements on ATMs based
on purchasing preferences and location data in one embodiment are
provided in U.S. Pat. App. Pub. No. 2010/0114677, entitled "System
Including Automated Teller Machine with Data Bearing Medium," the
disclosure of which is hereby incorporated herein by reference.
[0120] In another example, the transaction terminal (105) is a POS
terminal at the checkout station in a retail store (e.g., a
self-service checkout register). When the user (101) pays for a
purchase via a payment card (e.g., a credit card or a debit card),
the transaction handler (103) provides a targeted advertisement
having a coupon obtained from an advertisement network. The user
(101) may load the coupon into the account of the payment card
and/or obtain a hardcopy of the coupon from the receipt. When the
coupon is used in a transaction, the advertisement is linked to the
transaction.
[0121] Details of presenting targeted advertisements during the
process of authorizing a financial payment card transaction in one
embodiment are provided in U.S. Pat. App. Pub. No. 2008/0275771,
entitled "Merchant Transaction Based Advertising," the disclosure
of which is hereby incorporated herein by reference.
[0122] In one embodiment, the user specific advertisement data
(119), such as offers or coupons, is provided to the user (101) via
the transaction terminal (105) in connection with an authorization
message during the authorization of a transaction processed by the
transaction handler (103). The authorization message can be used to
communicate the rewards qualified for by the user (101) in response
to the current transaction, the status and/or balance of rewards in
a loyalty program, etc. Examples and details related to the
authorization process in one embodiment are provided in U.S. Pat.
No. 7,844,490, entitled "Method and System for Conducting
Promotional Programs," the disclosure of which is hereby
incorporated herein by reference.
[0123] In one embodiment, when the user (101) is conducting a
transaction with a first merchant via the transaction handler
(103), the transaction handler (103) may determine whether the
characteristics of the transaction satisfy the conditions specified
for an announcement, such as an advertisement, offer or coupon,
from a second merchant. If the conditions are satisfied, the
transaction handler (103) provides the announcement to the user
(101). In one embodiment, the transaction handler (103) may auction
the opportunity to provide the announcements to a set of merchants.
Examples and details related to the delivery of such announcements
in one embodiment are provided in U.S. Pat. App. Pub. No.
2010/0274625, entitled "Targeting Merchant Announcements Triggered
by Consumer Activity Relative to a Surrogate Merchant," the
disclosure of which is hereby incorporated herein by reference.
[0124] Details about delivering advertisements at a point of
interaction that is associated with user transaction interactions
in one embodiment are provided in U.S. Pat. App. Pub. No.
2011/0087550, entitled "Systems and Methods to Deliver Targeted
Advertisements to Audience," the disclosure of which is hereby
incorporated herein by reference.
On Third Party Site
[0125] In a further example, the user (101) may visit a third party
website, which is the point of interaction (107) in FIG. 1. The
third party website may be a web search engine, a news website, a
blog, a social network site, etc. The behavior of the user (101) at
the third party website may be tracked via a browser cookie, which
uses a storage space of the browser to store information about the
user (101) at the third party website. Alternatively, or in
combination, the third party website uses the server logs to track
the activities of the user (101). In one embodiment, the third
party website may allow an advertisement network to present
advertisements on portions of the web pages. The advertisement
network tracks the user behavior using its server logs and/or
browser cookies. For example, the advertisement network may use a
browser cookie to identify a particular user across multiple
websites. Based on the referral uniform resource locators (URL)
that cause the advertisement network to load advertisements in
various web pages, the advertisement network can determine the
online behavior of the user (101) via analyzing the web pages that
the user (101) has visited. Based on the tracked online activities
of the user (101), the user data (125) that characterizes the user
(101) can be formed to query the profiler selector (129) for a user
specific profile (131).
[0126] In one embodiment, the cookie identity of the user (101) as
tracked using the cookie can be correlated to an account of the
user (101), the family of the user (101), the company of the user
(101), or other groups that include the user (101) as a member.
Thus, the cookie identity can be used as the user data (125) to
obtain the user specific profile (131). For example, when the user
(101) makes an online purchase from a web page that contains an
advertisement that is tracked with the cookie identity, the cookie
identity can be correlated to the online transaction and thus to
the account of the user (101). For example, when the user (101)
visits a web page after authentication of the user (101), and the
web page includes an advertisement from the advertisement network,
the cookie identity can be correlated to the authenticated identity
of the user (101). For example, when the user (101) signs in to a
web portal of the transaction handler (103) to access the account
of the user (101), the cookie identity used by the advertisement
network on the web portal can be correlated to the account of the
user (101).
[0127] Other online tracking techniques can also be used to
correlate the cookie identity of the user (101) with an identifier
of the user (101) known by the profile selector (129), such as a
GUID, PAN, account number, customer number, social security number,
etc. Subsequently, the cookie identity can be used to select the
user specific profile (131).
Multiple Communications
[0128] In one embodiment, the entity operating the transaction
handler (103) may provide intelligence for providing multiple
communications regarding an advertisement. The multiple
communications may be directed to two or more points of interaction
with the user (101).
[0129] For example, after the user (101) is provided with an
advertisement via the transaction terminal (105), reminders or
revisions to the advertisements can be sent to the user (101) via a
separate point of interaction (107), such as a mobile phone, email,
text message, etc. For example, the advertisement may include a
coupon to offer the user (101) a benefit contingent upon a
purchase. If the correlator (117) determines that the coupon has
not been redeemed, the correlator (117) may send a message to the
mobile phone of the user (101) to remind the user (101) about the
offer, and/or revise the offer.
[0130] Examples of multiple communications related to an offer in
one embodiment are provided in U.S. Pat. App. Pub. No.
2011/0022424, entitled "Successive Offer Communications with an
Offer Recipient," the disclosure of which is hereby incorporated
herein by reference.
Auction Engine
[0131] In one embodiment, the transaction handler (103) provides a
portal to allow various clients to place bids according to clusters
(e.g., to target entities in the clusters for marketing,
monitoring, researching, etc.)
[0132] For example, the cardholders may register in a program to
receive offers, such as promotions, discounts, sweepstakes, reward
points, direct mail coupons, email coupons, etc. The cardholders
may register with issuers, or with the portal (143) of the
transaction handler (103). Based on the transaction data (109) or
transaction records (301) and/or the registration data, the profile
generator (121) is to identify the clusters of cardholders and the
values representing the affinity of the cardholders to the
clusters. Various entities may place bids according to the clusters
and/or the values to gain access to the cardholders, such as the
user (101). For example, an issuer may bid on access to offers; an
acquirer and/or a merchant may bid on customer segments. An auction
engine receives the bids and awards segments and offers based on
the received bids. Thus, the customers can get great deals; and
merchants can get customer traffic and thus sales.
[0133] Some techniques to identify a segment of users (101) for
marketing are provided in U.S. Pat. App. Pub. No. 2009/0222323,
entitled "Opportunity Segmentation," U.S. Pat. App. Pub. No.
2009/0271305, entitled "Payment Portfolio Optimization," and U.S.
Pat. App. Pub. No. 2009/0271327, entitled "Payment Portfolio
Optimization," the disclosures of which applications are hereby
incorporated herein by reference.
Social Network Validation
[0134] In one embodiment, the transaction data (109) is combined
with social network data and/or search engine data to provide
benefits (e.g., coupons) to a consumer. For example, a data
exchange apparatus may identify cluster data based upon consumer
search engine data, social network data, and payment transaction
data to identify like groups of individuals who would respond
favorably to particular types of benefits such as coupons and
statement credits. Advertisement campaigns may be formulated to
target the cluster of cardholders.
[0135] In one embodiment, search engine data is combined with
social network data and/or the transaction data (109) to evaluate
the effectiveness of the advertisements and/or conversion pattern
of the advertisements. For example, after a search engine displays
advertisements about flat panel televisions to a consumer, a social
network that is used by a consumer may provide information about a
related purchase made by the consumer. For example, the blog of the
consumer, and/or the transaction data (109), may indicate that the
flat panel television purchased by the consumer is from company B.
Thus, the search engine data and the social network data and/or the
transaction data (109) can be combined to correlate advertisements
to purchases resulting from the advertisements and to determine the
conversion pattern of the advertisement to the consumer.
Adjustments to advertisements (e.g., placement, appearance, etc.)
can be made to improve the effectiveness of the advertisements and
thus increase sales.
Loyalty Program
[0136] In one embodiment, the transaction handler (103) uses the
account data (111) to store information for third party loyalty
programs. The transaction handler (103) processes payment
transactions made via financial transaction cards, such as credit
cards, debit cards, banking cards, etc.; and the financial
transaction cards can be used as loyalty cards for the respective
third party loyalty programs. Since the third party loyalty
programs are hosted on the transaction handler (103), the consumers
do not have to carry multiple, separate loyalty cards (e.g., one
for each merchant that offers a loyalty program); and the merchants
do not have to spend a large setup and investment fee to establish
the loyalty program. The loyalty programs hosted on the transaction
handler (103) can provide flexible awards for consumers, retailers,
manufacturers, issuers, and other types of business entities
involved in the loyalty programs. The integration of the loyalty
programs into the accounts of the customers on the transaction
handler (103) allows new offerings, such as merchant
cross-offerings or bundling of loyalty offerings.
[0137] In one embodiment, an entity operating the transaction
handler (103) hosts loyalty programs for third parties using the
account data (111) of the users (e.g., 101). A third party, such as
a merchant, a retailer, a manufacturer, an issuer or other entity
that is interested in promoting certain activities and/or
behaviors, may offer loyalty rewards on existing accounts of
consumers. The incentives delivered by the loyalty programs can
drive behavior changes without the hassle of loyalty card creation.
In one embodiment, the loyalty programs hosted via the accounts of
the users (e.g., 101) of the transaction handler (103) allow the
consumers to carry fewer cards and may provide more data to the
merchants than traditional loyalty programs.
[0138] The loyalty programs integrated with the accounts of the
users (e.g., 101) of the transaction handler (103) can provide
tools to enable nimble programs that are better aligned for driving
changes in consumer behaviors across transaction channels (e.g.,
online, offline, via mobile devices). The loyalty programs can be
ongoing programs that accumulate benefits for the customers (e.g.,
points, miles, cash back), and/or programs that provide one time
benefits or limited time benefits (e.g., rewards, discounts,
incentives).
[0139] FIG. 8 shows the structure of account data (111) for
providing loyalty programs according to one embodiment. In FIG. 8,
data related to a third party loyalty program may include an
identifier of the loyalty benefit offeror (183) that is linked to a
set of loyalty program rules (185) and loyalty record (187) for the
loyalty program activities of the account identifier (181). In one
embodiment, at least part of the data related to the third party
loyalty program is stored under the account identifier (181) of the
user (101), such as the loyalty record (187).
[0140] FIG. 8 illustrates the data related to one third party
loyalty program of a loyalty benefit offeror (183). In one
embodiment, the account identifier (181) may be linked to multiple
loyalty benefit offerors (e.g., 183), corresponding to different
third party loyalty programs.
[0141] In one embodiment, a third party loyalty program of the
loyalty benefit offeror (183) provides the user (101), identified
by the account identifier (181), with benefits, such as discounts,
rewards, incentives, cash back, gifts, coupons, and/or
privileges.
[0142] In one embodiment, the association between the account
identifier (181) and the loyalty benefit offeror (183) in the
account data (111) indicates that the user (101) having the account
identifier (181) is a member of the loyalty program. Thus, the user
(101) may use the account identifier (181) to access privileges
afforded to the members of the loyalty programs, such as rights to
access a member only area, facility, store, product or service,
discounts extended only to members, or opportunities to participate
in certain events, buy certain items, or receive certain services
reserved for members.
[0143] In one embodiment, it is not necessary to make a purchase to
use the privileges. The user (101) may enjoy the privileges based
on the status of being a member of the loyalty program. The user
(101) may use the account identifier (181) to show the status of
being a member of the loyalty program.
[0144] For example, the user (101) may provide the account
identifier (181) (e.g., the account number of a credit card) to the
transaction terminal (105) to initiate an authorization process for
a special transaction which is designed to check the member status
of the user (101), as if the account identifier (181) were used to
initiate an authorization process for a payment transaction. The
special transaction is designed to verify the member status of the
user (101) via checking whether the account data (111) is
associated with the loyalty benefit offeror (183). If the account
identifier (181) is associated with the corresponding loyalty
benefit offeror (183), the transaction handler (103) provides an
approval indication in the authorization process to indicate that
the user (101) is a member of the loyalty program. The approval
indication can be used as a form of identification to allow the
user (101) to access member privileges, such as access to services,
products, opportunities, facilities, discounts, permissions, which
are reserved for members.
[0145] In one embodiment, when the account identifier (181) is used
to identify the user (101) as a member to access member privileges,
the transaction handler (103) stores information about the access
of the corresponding member privilege in loyalty record (187). The
profile generator (121) may use the information accumulated in the
loyalty record (187) to enhance transaction profiles (127) and
provide the user (101) with personalized/targeted advertisements,
with or without further offers of benefit (e.g., discounts,
incentives, rebates, cash back, rewards, etc.).
[0146] In one embodiment, the association of the account identifier
(181) and the loyalty benefit offeror (183) also allows the loyalty
benefit offeror (183) to access at least a portion of the account
data (111) relevant to the loyalty program, such as the loyalty
record (187) and certain information about the user (101), such as
name, address, and other demographic data.
[0147] In one embodiment, the loyalty program allows the user (101)
to accumulate benefits according to loyalty program rules (185),
such as reward points, cash back, levels of discounts, etc. For
example, the user (101) may accumulate reward points for
transactions that satisfy the loyalty program rules (185); and the
user (101) may use the reward points to redeem cash, gift,
discounts, etc. In one embodiment, the loyalty record (187) stores
the accumulated benefits; and the transaction handler (103) updates
the loyalty record (187) associated with the loyalty benefit
offeror (183) and the account identifier (181), when events that
satisfy the loyalty program rules occur.
[0148] In one embodiment, the accumulated benefits as indicated in
the loyalty record (187) can be redeemed when the account
identifier (181) is used to perform a payment transaction, when the
payment transaction satisfies the loyalty program rules. For
example, the user (101) may redeem a number of points to offset or
reduce an amount of the purchase price.
[0149] In one embodiment, when the user (101) uses the account
identifier (181) to make purchases as a member, the merchant may
further provide information about the purchases; and the
transaction handler (103) can store the information about the
purchases as part of the loyalty record (187). The information
about the purchases may identify specific items or services
purchased by the member. For example, the merchant may provide the
transaction handler (103) with purchase details at stock-keeping
unit (SKU) level, which are then stored as part of the loyalty
record (187). The loyalty benefit offeror (183) may use the
purchase details to study the purchase behavior of the user (101);
and the profile generator (121) may use the SKU level purchase
details to enhance the transaction profiles (127).
[0150] In one embodiment, the SKU level purchase details are
requested from the merchants or retailers via authorization
responses (e.g., as illustrated in FIG. 9), when the account (146)
of the user (101) is enrolled in a loyalty program that allows the
transaction handler (103) (and/or the issuer processor (145)) to
collect the purchase details.
[0151] In one embodiment, the profile generator (121) may generate
transaction profiles (127) based on the loyalty record (187) and
provide the transaction profiles (127) to the loyalty benefit
offeror (183) (or other entities when permitted).
[0152] In one embodiment, the loyalty benefit offeror (183) may use
the transaction profiles (e.g., 127 or 131) to select candidates
for membership offering. For example, the loyalty program rules
(185) may include one or more criteria that can be used to identify
which customers are eligible for the loyalty program. The
transaction handler (103) may be configured to automatically
provide the qualified customers with the offer of membership in the
loyalty program when the corresponding customers are performing
transactions via the transaction handler (103) and/or via points of
interaction (107) accessible to the entity operating the
transaction handler (103), such as ATMs, mobile phones, receipts,
statements, websites, etc. The user (101) may accept the membership
offer via responding to the advertisement. For example, the user
(101) may load the membership into the account in the same way as
loading a coupon into the account of the user (101).
[0153] In one embodiment, the membership offer is provided as a
coupon or is associated with another offer of benefits, such as a
discount, reward, etc. When the coupon or benefit is redeemed via
the transaction handler (103), the account data (111) is updated to
enroll the user (101) into the corresponding loyalty program.
[0154] In one embodiment, a merchant may enroll a user (101) into a
loyalty program when the user (101) is making a purchase at the
transaction terminal (105) of the merchant.
[0155] For example, when the user (101) is making a transaction at
an ATM, performing a self-assisted check out on a POS terminal, or
making a purchase transaction on a mobile phone or a computer, the
user (101) may be prompted to join a loyalty program, while the
transaction is being authorized by the transaction handler (103).
If the user (101) accepts the membership offer, the account data
(111) is updated to have the account identifier (181) associated
with the loyalty benefit offeror (183).
[0156] In one embodiment, the user (101) may be automatically
enrolled in the loyalty program, when the profile of the user (101)
satisfies a set of conditions specified in the loyalty program
rules (185). The user (101) may opt out of the loyalty program.
[0157] In one embodiment, the loyalty benefit offeror (183) may
personalize and/or target loyalty benefits based on the transaction
profile (131) specific to or linked to the user (101). For example,
the loyalty program rules (185) may use the user specific profile
(131) to select gifts, rewards, or incentives for the user (101)
(e.g., to redeem benefits, such as reward points, accumulated in
the loyalty record (187)). The user specific profile (131) may be
enhanced using the loyalty record (187), or generated based on the
loyalty record (187). For example, the profile generator (121) may
use a subset of transaction data (109) associated with the loyalty
record (187) to generate the user specific profile (131), or
provide more weight to the subset of the transaction data (109)
associated with the loyalty record (187) while also using other
portions of the transaction data (109) in deriving the user
specific profile (131).
[0158] In one embodiment, the loyalty program may involve different
entities. For example, a first merchant may offer rewards as
discounts, or gifts from a second merchant that has a business
relationship with the first merchant. For example, an entity may
allow a user (101) to accumulate loyalty benefits (e.g., reward
points) via purchase transactions at a group of different
merchants. For example, a group of merchants may jointly offer a
loyalty program, in which loyalty benefits (e.g., reward points)
can be accumulated from purchases at any of the merchants in the
group and redeemable in purchases at any of the merchants.
[0159] In one embodiment, the information identifying the user
(101) as a member of a loyalty program is stored on a server
connected to the transaction handler (103). Alternatively or in
combination, the information identifying the user (101) as a member
of a loyalty program can also be stored in the financial
transaction card (e.g., in the chip, or in the magnetic strip).
[0160] In one embodiment, loyalty program offerors (e.g.,
merchants, manufactures, issuers, retailers, clubs, organizations,
etc.) can compete with each other in making loyalty program related
offers. For example, loyalty program offerors may place bids on
loyalty program related offers; and the advertisement selector
(133) (e.g., under the control of the entity operating the
transaction handler (103), or a different entity) may prioritize
the offers based on the bids. When the offers are accepted or
redeemed by the user (101), the loyalty program offerors pay fees
according to the corresponding bids. In one embodiment, the loyalty
program offerors may place an auto bid or maximum bid, which
specifies the upper limit of a bid; and the actual bid is
determined to be the lowest possible bid that is larger than the
bids of the competitors, without exceeding the upper limit.
[0161] In one embodiment, the offers are provided to the user (101)
in response to the user (101) being identified by the user data
(125). If the user specific profile (131) satisfies the conditions
specified in the loyalty program rules (185), the offer from the
loyalty benefit offeror (183) can be presented to the user (101).
When there are multiple offers from different offerors, the offers
can be prioritized according to the bids.
[0162] In one embodiment, the offerors can place bids based on the
characteristics that can be used as the user data (125) to select
the user specific profile (131). In another embodiment, the bids
can be placed on a set of transaction profiles (127).
[0163] In one embodiment, the loyalty program based offers are
provided to the user (101) just in time when the user (101) can
accept and redeem the offers. For example, when the user (101) is
making a payment for a purchase from a merchant, an offer to enroll
in a loyalty program offered by the merchant or related offerors
can be presented to the user (101). If the user (101) accepts the
offer, the user (101) is entitled to receive member discounts for
the purchase.
[0164] For example, when the user (101) is making a payment for a
purchase from a merchant, a reward offer can be provided to the
user (101) based on loyalty program rules (185) and the loyalty
record (187) associated with the account identifier (181) of the
user (101) (e.g., the reward points accumulated in a loyalty
program). Thus, the user effort for redeeming the reward points can
be reduced; and the user experience can be improved.
[0165] In one embodiment, a method to provide loyalty programs
includes the use of a computing apparatus of a transaction handler
(103). The computing apparatus processes a plurality of payment
card transactions. After the computing apparatus receives a request
to track transactions for a loyalty program, such as the loyalty
program rules (185), the computing apparatus stores and updates
loyalty program information in response to transactions occurring
in the loyalty program. The computing apparatus provides to a
customer (e.g., 101) an offer of a benefit when the customer
satisfies a condition defined in the loyalty program, such as the
loyalty program rules (185).
[0166] Examples of loyalty programs through collaboration between
collaborative constituents in a payment processing system,
including the transaction handler (103) in one embodiment are
provided in U.S. Pat. App. Pub. No. 2008/0059302, entitled "Loyalty
Program Service," U.S. Pat. App. Pub. No. 2008/0059306, entitled
"Loyalty Program Incentive Determination," and U.S. Pat. App. Pub.
No. 2008/0059307, entitled "Loyalty Program Parameter
Collaboration," the disclosures of which applications are hereby
incorporated herein by reference.
[0167] Examples of processing the redemption of accumulated loyalty
benefits via the transaction handler (103) in one embodiment are
provided in U.S. Pat. App. Pub. No. 2008/0059303, entitled
"Transaction Evaluation for Providing Rewards," the disclosure of
which is hereby incorporated herein by reference.
[0168] In one embodiment, the incentive, reward, or benefit
provided in the loyalty program is based on the presence of
correlated related transactions. For example, in one embodiment, an
incentive is provided if a financial payment card is used in a
reservation system to make a reservation and the financial payment
card is subsequently used to pay for the reserved good or service.
Further details and examples of one embodiment are provided in U.S.
Pat. App. Pub. No. 2008/0071587, entitled "Incentive Wireless
Communication Reservation," the disclosure of which is hereby
incorporated herein by reference.
[0169] In one embodiment, the transaction handler (103) provides
centralized loyalty program management, reporting and membership
services. In one embodiment, membership data is downloaded from the
transaction handler (103) to acceptance point devices, such as the
transaction terminal (105). In one embodiment, loyalty transactions
are reported from the acceptance point devices to the transaction
handler (103); and the data indicating the loyalty points, rewards,
benefits, etc. are stored on the account identification device
(141). Further details and examples of one embodiment are provided
in U.S. Pat. App. Pub. No. 2004/0054581, entitled "Network Centric
Loyalty System," the disclosure of which is hereby incorporated
herein by reference.
[0170] In one embodiment, the portal (143) of the transaction
handler (103) is used to manage reward or loyalty programs for
entities such as issuers, merchants, etc. The cardholders, such as
the user (101), are rewarded with offers/benefits from merchants.
The portal (143) and/or the transaction handler (103) track the
transaction records for the merchants for the reward or loyalty
programs. Further details and examples of one embodiment are
provided in U.S. Pat. App. Pub. No. 2008/0195473, entitled "Reward
Program Manager," the disclosure of which is hereby incorporated
herein by reference.
[0171] In one embodiment, a loyalty program includes multiple
entities providing access to detailed transaction data, which
allows the flexibility for the customization of the loyalty
program. For example, issuers or merchants may sponsor the loyalty
program to provide rewards; and the portal (143) and/or the
transaction handler (103) stores the loyalty currency in the data
warehouse (149). Further details and examples of one embodiment are
provided in U.S. Pat. App. Pub. No. 2009/0030793, entitled
"Multi-Vender Multi-Loyalty Currency Program," the disclosure of
which is hereby incorporated herein by reference.
[0172] In one embodiment, an incentive program is created on the
portal (143) of the transaction handler (103). The portal (143)
collects offers from a plurality of merchants and stores the offers
in the data warehouse (149). The offers may have associated
criteria for their distributions. The portal (143) and/or the
transaction handler (103) may recommend offers based on the
transaction data (109). In one embodiment, the transaction handler
(103) automatically applies the benefits of the offers during the
processing of the transactions when the transactions satisfy the
conditions associated with the offers. In one embodiment, the
transaction handler (103) communicates with transaction terminals
(105) to set up, customize, and/or update offers based on market
focus, product categories, service categories, targeted consumer
demographics, etc. Further details and examples of one embodiment
are provided in U.S. Pat. App. Pub. No. 2010-0049620, entitled
"Merchant Device Support of an Integrated Offer Network," the
disclosure of which is hereby incorporated herein by reference.
[0173] In one embodiment, the transaction handler (103) is
configured to provide offers from merchants to the user (101) via
the payment system, making accessing and redeeming the offers
convenient for the user (101). The offers may be triggered by
and/or tailored to a previous transaction, and may be valid only
for a limited period of time starting from the date of the previous
transaction. If the transaction handler (103) determines that a
subsequent transaction processed by the transaction handler (103)
meets the conditions for the redemption of an offer, the
transaction handler (103) may credit the consumer account (146) for
the redemption of the offer and/or provide a notification message
to the user (101). Further details and examples of one embodiment
are provided in U.S. Pat. App. Pub. No. 2010/0114686, entitled
"Real-Time Statement Credits and Notifications," the disclosure of
which is hereby incorporated herein by reference.
[0174] Details on loyalty programs in one embodiment are provided
in U.S. Pat. App. Pub. No. 2011/0087530, entitled "Systems and
Methods to Provide Loyalty Programs," the disclosure of which is
hereby incorporated herein by reference.
SKU
[0175] In one embodiment, merchants generate stock-keeping unit
(SKU) or other specific information that identifies the particular
goods and services purchased by the user (101) or customer. The SKU
information may be provided to the operator of the transaction
handler (103) that processed the purchases. The operator of the
transaction handler (103) may store the SKU information as part of
transaction data (109), and reflect the SKU information for a
particular transaction in a transaction profile (127 or 131)
associated with the person involved in the transaction.
[0176] When a user (101) shops at a traditional retail store or
browses a website of an online merchant, an SKU-level profile
associated specifically with the user (101) may be provided to
select an advertisement appropriately targeted to the user (101)
(e.g., via mobile phones, POS terminals, web browsers, etc.). The
SKU-level profile for the user (101) may include an identification
of the goods and services historically purchased by the user (101).
In addition, the SKU-level profile for the user (101) may identify
goods and services that the user (101) may purchase in the future.
The identification may be based on historical purchases reflected
in SKU-level profiles of other individuals or groups that are
determined to be similar to the user (101). Accordingly, the return
on investment for advertisers and merchants can be greatly
improved.
[0177] In one embodiment, the user specific profile (131) is an
aggregated spending profile (341) that is generated using the
SKU-level information. For example, in one embodiment, the factor
values (344) correspond to factor definitions (331) that are
generated based on aggregating spending in different categories of
products and/or services. A typical merchant offers products and/or
services in many different categories.
[0178] In one embodiment, the user (101) may enter into
transactions with various online and "brick and mortar" merchants.
The transactions may involve the purchase of various items of goods
and services. The goods and services may be identified by SKU
numbers or other information that specifically identifies the goods
and services purchased by the user (101).
[0179] In one embodiment, the merchant may provide the SKU
information regarding the goods and services purchased by the user
(101) (e.g., purchase details at SKU level) to the operator of the
transaction handler (103). In one embodiment, the SKU information
may be provided to the operator of the transaction handler (103) in
connection with a loyalty program, as described in more detail
below. The SKU information may be stored as part of the transaction
data (109) and associated with the user (101). In one embodiment,
the SKU information for items purchased in transactions facilitated
by the operator of the transaction handler (103) may be stored as
transaction data (109) and associated with its associated
purchaser.
[0180] In one embodiment, the SKU level purchase details are
requested from the merchants or retailers via authorization
responses (e.g., as illustrated in FIG. 9), when the account (146)
of the user (101) is enrolled in a program that allows the
transaction handler (103) (and/or the issuer processor (145)) to
collect the purchase details.
[0181] In one embodiment, based on the SKU information and perhaps
other transaction data, the profile generator (121) may create an
SKU-level transaction profile for the user (101). In one
embodiment, based on the SKU information associated with the
transactions for each person entering into transactions with the
operator of the transaction handler (103), the profile generator
(121) may create an SKU-level transaction profile for each
person.
[0182] In one embodiment, the SKU information associated with a
group of purchasers may be aggregated to create an SKU-level
transaction profile that is descriptive of the group. The group may
be defined based on one or a variety of considerations. For
example, the group may be defined by common demographic features of
its members. As another example, the group may be defined by common
purchasing patters of its members.
[0183] In one embodiment, the user (101) may later consider the
purchase of additional goods and services. The user (101) may shop
at a traditional retailer or an online retailer. With respect to an
online retailer, for example, the user (101) may browse the website
of an online retailer, publisher, or merchant. The user (101) may
be associated with a browser cookie to, for example, identify the
user (101) and track the browsing behavior of the user (101).
[0184] In one embodiment, the retailer may provide the browser
cookie associated with the user (101) to the operator of the
transaction handler (103). Based on the browser cookie, the
operator of the transaction handler (103) may associate the browser
cookie with a personal account number of the user (101). The
association may be performed by the operator of the transaction
handler (103) or another entity in a variety of manners such as,
for example, using a look up table.
[0185] Based on the personal account number, the profile selector
(129) may select a user specific profile (131) that constitutes the
SKU-level profile associated specifically with the user (101). The
SKU-level profile may reflect the individual, prior purchases of
the user (101) specifically, and/or the types of goods and services
that the user (101) has purchased.
[0186] The SKU-level profile for the user (101) may also include
identifications of goods and services the user (101) may purchase
in the future. In one embodiment, the identifications may be used
for the selection of advertisements for goods and services that may
be of interest to the user (101). In one embodiment, the
identifications for the user (101) may be based on the SKU-level
information associated with historical purchases of the user (101).
In one embodiment, the identifications for the user (101) may be
additionally or alternatively based on transaction profiles
associated with others. The recommendations may be determined by
predictive association and other analytical techniques.
[0187] For example, the identifications for the user (101) may be
based on the transaction profile of another person. The profile
selector (129) may apply predetermined criteria to identify another
person who, to a predetermined degree, is deemed sufficiently
similar to the user (101). The identification of the other person
may be based on a variety of factors including, for example,
demographic similarity and/or purchasing pattern similarity between
the user (101) and the other person. As one example, the common
purchase of identical items or related items by the user (101) and
the other person may result in an association between the user
(101) and the other person, and a resulting determination that the
user (101) and the other person are similar. Once the other person
is identified, the transaction profile constituting the SKU-level
profile for the other person may be analyzed. Through predictive
association and other modeling and analytical techniques, the
historical purchases reflected in the SKU-level profile for the
other person may be employed to predict the future purchases of the
user (101).
[0188] As another example, the identifications of the user (101)
may be based on the transaction profiles of a group of persons. The
profile selector (129) may apply predetermined criteria to identify
a multitude of persons who, to a predetermined degree, are deemed
sufficiently similar to the user (101). The identification of the
other persons may be based on a variety of factors including, for
example, demographic similarity and/or purchasing pattern
similarity between the user (101) and the other persons. Once the
group constituting the other persons is identified, the transaction
profile constituting the SKU-level profile for the group may be
analyzed. Through predictive association and other modeling and
analytical techniques, the historical purchases reflected in the
SKU-level profile for the group may be employed to predict the
future purchases of the user (101).
[0189] The SKU-level profile of the user (101) may be provided to
select an advertisement that is appropriately targeted. Because the
SKU-level profile of the user (101) may include identifications of
the goods and services that the user (101) may be likely to buy,
advertisements corresponding to the identified goods and services
may be presented to the user (101). In this way, targeted
advertising for the user (101) may be optimized. Further,
advertisers and publishers of advertisements may improve their
return on investment, and may improve their ability to cross-sell
goods and services.
[0190] In one embodiment, SKU-level profiles of others who are
identified to be similar to the user (101) may be used to identify
a user (101) who may exhibit a high propensity to purchase goods
and services. For example, if the SKU-level profiles of others
reflect a quantity or frequency of purchase that is determined to
satisfy a threshold, then the user (101) may also be classified or
predicted to exhibit a high propensity to purchase. Accordingly,
the type and frequency of advertisements that account for such
propensity may be appropriately tailored for the user (101).
[0191] In one embodiment, the SKU-level profile of the user (101)
may reflect transactions with a particular merchant or merchants.
The SKU-level profile of the user (101) may be provided to a
business that is considered a peer with or similar to the
particular merchant or merchants. For example, a merchant may be
considered a peer of the business because the merchant offers goods
and services that are similar to or related to those of the
business. The SKU-level profile reflecting transactions with peer
merchants may be used by the business to better predict the
purchasing behavior of the user (101) and to optimize the
presentation of targeted advertisements to the user (101).
[0192] Details on SKU-level profile in one embodiment are provided
in U.S. Pat. App. Pub. No. 2011/0093335, entitled "Systems and
Methods for Advertising Services Based on an SKU-Level Profile,"
the disclosure of which is hereby incorporated herein by
reference.
Purchase Details
[0193] In one embodiment, the transaction handler (103) is
configured to selectively request purchase details via
authorization responses. When the transaction handler (103) (and/or
the issuer processor (145)) needs purchase details, such as
identification of specific items purchased and/or their prices, the
authorization responses transmitted from the transaction handler
(103) is to include an indicator to request for the purchase
details for the transaction that is being authorized. The merchants
are to determine whether or not to submit purchase details based on
whether or not there is a demand indicated in the authorization
responses from the transaction handler (103).
[0194] For example, in one embodiment, the transaction handler
(103) is configured for the redemption of manufacturer coupons via
statement credits. Manufacturers may provide users (e.g., 101) with
promotional offers, such as coupons for rebate, discounts, cash
back, reward points, gifts, etc. The offers can be provided to
users (e.g., 101) via various channels, such as websites,
newspapers, direct mail, targeted advertisements (e.g., 119),
loyalty programs, etc.
[0195] In one embodiment, when the user (101) has one or more
offers pending under the consumer account (146) and uses the
consumer account (146) to pay for purchases made from a retailer
that supports the redemption of the offers, the transaction handler
(103) is to use authorization responses to request purchase
details, match offer details against the items shown to be
purchased in the purchase details to identify a redeemable offer,
and manage the funding for the fulfillment of the redeemable offer
between the user (101) and the manufacturer that funded the
corresponding offer. In one embodiment, the request for purchase
details is provided in real time with the authorization message;
and the exchange of the purchase details and matching may occur
real-time outside the authorization process, or at the end of the
day via a batch file for multiple transactions.
[0196] In one embodiment, the offers are associated with the
consumer account (146) of the user (101) to automate the processing
of the redemption of the offers. If the user (101) makes a payment
for a purchase using the consumer account (146) of the user (101),
the transaction handler (103) (and/or the issuer processor (145))
processes the payment transaction and automatically identifies the
offers that are qualified for redemption in view of the purchase
and provides the benefit of the qualified offers to the user (101).
In one embodiment, the transaction handler (103) (or the issuer
processor (145)) is to detect the applicable offer for redemption
and provide the benefit of the redeemed offer via statement
credits, without having to request the user (101) to perform
additional tasks.
[0197] In one embodiment, once the user (101) makes the required
purchase according to the requirement of the offer using the
consumer account (146), the benefit of the offer is fulfilled via
the transaction handler (103) (or the issuer processor (145))
without the user (101) having to do anything special at and/or
after the time of checkout, other than paying with the consumer
account (146) of the user (101), such as a credit card account, a
debit card account, a loyalty card account, a private label card
account, a coupon card account, or a prepaid card account that is
enrolled in the program for the automation of offer redemption.
[0198] In one embodiment, the redemption of an offer (e.g., a
manufacturer coupon) requires the purchase of a specific product or
service. The user (101) is eligible for the benefit of the offer
after the purchase of the specific product or service is verified.
In one embodiment, the transaction handler (103) (or the issuer
processor (145)) dynamically requests the purchase details via
authorization response to determine the eligibility of a purchase
for the redemption of such an offer.
[0199] In one embodiment, the methods to request purchase details
on demand via (or in connection with) the authorization process are
used in other situations where the transaction level data is needed
on a case-by-case basis as determined by the transaction handler
(103).
[0200] For example, in one embodiment, the transaction handler
(103) and/or the issuer processor (145) determines that the user
(101) has signed up to receive purchase item detail electronically,
the transaction handler (103) and/or the issuer processor (145) can
make the request on demand; and the purchase details can be stored
and later downloaded into a personal finance software application
or a business accounting software application.
[0201] For example, in one embodiment, the transaction handler
(103) and/or the issuer processor (145) determines that the user
(101) has signed up to automate the process of reimbursements of
health care items qualified under certain health care accounts,
such as a health savings account (HSA), a flexible spending
arrangement (FSA), etc. In response to such a determination, the
transaction handler (103) and/or the issuer processor (145)
requests the purchase details to automatically identify qualified
health care item purchases, capture and reporting evidences showing
the qualification, bookkeeping the receipts or equivalent
information for satisfy rules, regulations and laws reporting
purposes (e.g., as required by Internal Revenue Service), and/or
settle the reimbursement of the funds with the respective health
care accounts.
[0202] FIG. 9 shows a system to obtain purchase details according
to one embodiment. In FIG. 9, when the user (101) uses the consumer
account (146) to make a payment for a purchase, the transaction
terminal (105) of the merchant or retailer sends an authorization
request (168) to the transaction handler (103). In response, an
authorization response (138) is transmitted from the transaction
handler (103) to the transaction terminal (105) to inform the
merchant or retailer of the decision to approve or reject the
payment request, as decided by the issuer processor (145) and/or
the transaction handler (103). The authorization response (138)
typically includes an authorization code (137) to identify the
transaction and/or to signal that the transaction is approved.
[0203] In one embodiment, when the transaction is approved and
there is a need for purchase details (169), the transaction handler
(103) (or the issuer processor (145)) is to provide an indicator of
the request (139) for purchase details in the authorization
response (138). The optional request (139) allows the transaction
handler (103) (and/or the issuer processor (145)) to request
purchase details (169) from the merchant or retailer on demand.
When the request (139) for purchase details is present in the
authorization response (138), the transaction terminal (105) is to
provide the purchase details (169) associated with the payment
transaction to the transaction handler (103) directly or indirectly
via the portal (143). When the request (139) is absent from the
authorization response (138), the transaction terminal (105) does
not have to provide the purchase details (169) for the payment
transaction.
[0204] In one embodiment, when the transaction is approved but
there is no need for purchase details (169), the indicator for the
request (139) for purchase details is not set in the authorization
response (138).
[0205] In one embodiment, prior to transmitting the authorization
response (138), the transaction handler (103) (and/or the issuer
processor (145)) determines whether there is a need for transaction
details. In one embodiment, when there is no need for the purchase
details (169) for a payment transaction, the request (139) for
purchase details (169) is not provided in the authorization
response (138) for the payment transaction. When there is a need
for the purchase details (169) for a payment transaction, the
request (139) for purchase details is provided in the authorization
response (138) for the payment transaction. The merchants or
retailers do not have to send detailed purchase data to the
transaction handler (103) when the authorization response message
does not explicitly request detailed purchase data.
[0206] Thus, the transaction handler (103) (or the issuer processor
(145)) does not have to require all merchants or retailers to send
the detailed purchase data (e.g., SKU level purchase details) for
all payment transactions processed by the transaction handler (103)
(or the issuer processor (145)).
[0207] For example, when the consumer account (146) of the user
(103) has collected a manufacturer coupon for a product or service
that may be sold by the merchant or retailer operating the
transaction terminal (105), the transaction handler (103) is to
request the purchase details (169) via the authorization response
(138) in one embodiment. If the purchase details (169) show that
the conditions for the redemption of the manufacturer coupon are
satisfied, the transaction handler (103) is to provide the benefit
of the manufacturer coupon to the user (101) via credits to the
statement for the consumer account (146). This automation of the
fulfillment of manufacturer coupon releases the merchant/retailer
from the work and complexities in processing manufacturer offers
and improves user experiences. Further, retailers and manufacturers
are provided with a new consumer promotion distribution channel
through the transaction handler (103), which can target the offers
based on the transaction profiles (127) of the user (101) and/or
the transaction data (109). In one embodiment, the transaction
handler (103) can use the offer for loyalty/reward programs.
[0208] In another example, if the user (101) is enrolled in a
program to request the transaction handler (103) to track and
manage purchase details (169) for the user (103), the transaction
handler (103) is to request the transaction details (169) via the
authorization response (138).
[0209] In one embodiment, a message for the authorization response
(138) is configured to include a field to indicate whether purchase
details are requested for the transaction.
[0210] In one embodiment, the authorization response message
includes a field to indicate whether the account (146) of the user
(101) is a participant of a coupon redemption network. When the
field indicates that the account (146) of the user (101) is a
participant of a coupon redemption network, the merchant or
retailer is to submit the purchase details (169) for the payment
made using the account (146) of the user (101).
[0211] In one embodiment, when the request (139) for the purchase
details (169) is present in the authorization response (138), the
transaction terminal (105) of the merchant or retailer is to store
the purchase details (169) with the authorization information
provided in the authorization response (138). When the transaction
is submitted to the transaction handler (103) for settlement, the
purchase details (169) are also submitted with the request for
settlement.
[0212] In one embodiment, the purchase details (169) are
transmitted to the transaction handler (103) via a communication
channel separate from the communication channel used for the
authorization and/or settlement requests for the transaction. For
example, the merchant or the retailer may report the purchase
details to the transaction handler (103) via a portal (143) of the
transaction handler (103). In one embodiment, the report includes
an identification of the transaction (e.g., an authorization code
(137) for the payment transaction) and the purchase details (e.g.,
SKU number, Universal Product Code (UPC)).
[0213] In one embodiment, the portal (143) of the transaction
handler (103) may further communicate with the merchant or the
retailer to reduce the amount of purchase detail data to be
transmitted the transaction handler (103). For example, in one
embodiment, the transaction handler (103) provides an indication of
categories of services or products for which the purchase details
(169) are requested; and the merchant or retailer is to report only
the items that are in these categories. In one embodiment, the
portal (143) of the transaction handler (103) is to ask the
merchant or the retailer to indicate whether the purchased items
include a set of items required for the redemption of the
offers.
[0214] In one embodiment, the merchant or retailer is to complete
the purchase based upon the indication of approval provided in the
authorization response (138). When the indicator (e.g., 139) is
present in the authorization response (138), the merchant (e.g.
inventory management system or the transaction terminal (105)) is
to capture and retain the purchase details (169) in an electronic
data file. The purchase details (169) include the identification of
the individual items purchased (e.g., SKU and/or UPC), their
prices, and/or brief descriptions of the items.
[0215] In one embodiment, the merchant or retailer is to send the
transaction purchase data file to the transaction handler (103) (or
the issuer processor (145)) at the end of the day, or according to
some other prearranged schedule. In one embodiment, the data file
for purchase details (169) is transmitted together with the request
to settle the transaction approved via the authorization response
(138). In one embodiment, the data file for purchase details (169)
is transmitted separately from the request to settle the
transaction approved via the authorization response (138).
[0216] Further details and examples of one embodiment of offer
fulfillment are provided in U.S. Pat. App. Pub. No. 2011/0288918,
and entitled "Systems and Methods for Redemption of Offers," the
disclosure of which is hereby incorporated herein by reference.
Cookie to Account
[0217] FIG. 10 shows a system to target advertisements according to
one embodiment. In FIG. 10, the user (101) is to use the point of
interaction (107) to access the web server (201). The web server
(201) uses the user identifier (215) to track the online activities
of the user (101). The user (101) is to use the account identifier
(211) on the point of interaction (107) to make online purchases.
The transaction handler (103) is to process payments in response to
online payment requests that identify the account identifier (211),
which may be submitted from the point of interaction (107) to the
transaction handler (103) via a number of entities, such as the
merchant website, a web server configured to manage online payments
on behalf of different merchants, and/or acquirer processors (e.g.,
147). The user (101) may also use other transaction terminals (e.g.
105) to make payments offline in retail stores, to make payments
via phone, or via different points of interaction (107) for online
purchases. The transaction handler (103) is to record information
about the payments made via the account identifier (211) as part of
the transaction data (109). The profile generator (121) is to
generate the transaction profiles (127) of different users from the
transaction data (109) recorded by the transaction handler
(103).
[0218] In FIG. 10, the data warehouse (149) associated with the
transaction handler (103) is to store a mapping table (231) that
maps the user identifier (215) to the account identifier (211). To
target the user identified by the user identifier (215), the
advertisement selector (133) is to use the user identifier (215) to
query the portal (143) for transaction-based intelligence
information, such as the user specific profile (131) of the user
(101), or portions of the transaction data (109) of the user (101).
In response to the query that identifies the user identifier (215)
used by the web server (201) to track the online activity of the
user (101), the portal (143) is to use the mapping table (231) to
determine the account identifier (211) from the user identifier
(215) and then identify the user specific profile (131) associated
with the account identifier (211) of the user (101).
[0219] In one embodiment, the portal (143) is to provide the user
specific profile (131) to the advertisement selector (133), as a
response to the query, to allow the advertisement selector (133) to
identify the user specific advertisement data (119) based on a
database of advertisement data (135). In one embodiment, the
advertiser selector (133) is to identify, generate, select,
prioritize, adjust, personalize, and/or target the user specific
advertisement data (119) based on the user specific profile (131).
The web server (201) is to provide the user specific advertisement
data (119) to the user (101) at the point of interaction (107) via
the web server (201).
[0220] In one embodiment, the portal (143) is to provide part of
the transaction data (109) of the user (101) relevant to the query
to the advertisement selector (133) for the identification,
generation, selection, prioritization, adjustment, personalization,
and/or customization of the user specific advertisement data
(119).
[0221] In one embodiment, the advertisement is presented according
to the user specific advertisement data (119) in a web page of the
web server (201). For example, when the web server (201) provides
the services of a search engine, the advertisement can be presented
with search results.
[0222] In one embodiment, the web sever (201) is to present the
advertisement in web pages of other websites. For example, the
websites of a blog, an online marketplace, an online newspaper,
etc., may include a reference to the web server (201) for the
placement of advertisements within the web pages of their websites.
In such an arrangement, the user identifier (215) can be used by
the web server (201), such as the server of an advertisement
network, to track online activities across different websites
operated by different entities. The user identifier (215) can be
used to identify the user (101) in websites where the user (101)
has not used the account identifier (211) to make purchases. When
the user identifier (215) is mapped to account identifier (211) to
identify the user (101), the user specific advertisement data (119)
can be targeted at the user (101) using the intelligence
information afforded by the transaction data (109) associated with
the account identifier (211) even when the user (101) is outside
the context of online payment and browsing as an anonymous Internet
user.
[0223] In one embodiment, the user identifier (215) is used by the
web server (201) to track anonymous users without requiring the
user (101) to register. For example, in one embodiment, the web
server (201) is to use a browser cookie to identify the user (101).
When the browser of the user (101) first visits the web server
(201), the web server (201) assigns the user identifier (215) to
the browser of the user (101); and the browser of the user (101)
stores the user identifier (215) in a browser cookie associated
with the web server (201). When the browser of the user (101)
subsequently revisits the web server (201), the browser of the user
(101) is to provide the user identifier (215) back to the web
server (201) and thus identify the user (101) via the user
identifier (215).
[0224] In some embodiments, the web server (201) may register the
user (101) to obtain further information about the user (101). The
registration information can also be used to link the user
identifier (215) to the account identifier (211) of the user
(101).
[0225] In one embodiment, after the presentation of an
advertisement based on the user specific advertisement data (119)
(or another advertisement delivered without the use of the user
specific profile (131) or related transaction data (109)), the web
server (201) or the advertisement selector (133) may query the
portal (143) using the user identifier (215) to determine purchase
information related to the advertisement.
[0226] For example, the query may request information on whether
the user (101) as identified by the user identifier (215) has made
the purchase according to the advertisement, or made a related
purchase. The portal (143) is to use mapping table (231) to
determine the account identifier (211) of the user (101)
corresponding to the user identifier (215) and to search for
related transaction information requested in the query. The related
transaction information can be used to assess the effectiveness of
the advertisement. For example, in some embodiments, the user (101)
may make an offline purchase after receiving the online
advertisement from the web server (201); and the related
transaction information can correlate the online advertisement to
the offline purchase. Details about correlating purchase
transactions and online activities that lead to the transactions in
one embodiment are provided in the section entitled "CLOSE THE
LOOP."
[0227] In one embodiment, the association relationship between the
user identifier (215) and the account identifier (211) is based on
the same web server (201) that observes the user identifier (215)
and the account identifier (211) in a same web communication, as
illustrated in FIG. 11.
[0228] In one embodiment, the association relationship between the
user identifier (215) and the account identifier (211) is based on
correlating the user data (125) tracked by the user trackers (e.g.,
113) of different web servers (201) which separately observe the
user identifier (215) and the account identifier (211) in separate
communications, as illustrated in FIG. 12.
[0229] FIGS. 11-12 illustrate systems to map user identifiers to
account identifiers according to some embodiments.
[0230] In FIG. 11, a user tracker A (213) is used by an online
payment site (233), or other websites that have access to the
account identifier (211), such as an online merchant site that
registers users and their account identifiers (e.g., 211), or the
portal (143) that is to verify the passwords of the users for their
uses of the account identifiers in making online payments. The user
tracker A (213) is to identify the online user (101) and generate
the user data (220) that can be used by the online payment site
(233) to identify the online user (101). In one embodiment, the
user tracker A (213) is to link multiple Hypertext Transfer
Protocol (HTTP) requests to an identity of an online user (101),
when the online activities of the user (101) include HTTP requests
to view web pages in a web browser and the web pages include
components (e.g., an advertisement, a single-pixel image, a script)
that reside on the online payment site (233).
[0231] In one embodiment, an Internet Protocol (IP) address (221)
of the requester may be used to temporarily identify the online
user (101). For example, it may be assumed that for a period of
time, the IP address (221) of the user (101) does not change and
thus can be used as an identifier of the user (101). However, the
IP address (221) of the user (101) typically changes from time to
time; and it is desirable to track the user (101) even when the IP
address (221) of the user (101) changes, so that activities of the
user (101) at different IP addresses (e.g., 221) with different
timestamps (e.g., 223) can be linked to the same online user (101),
which may be an anonymous user, or a registered user.
[0232] To better track the user (101), the online payment site
(233) is to provide the web browser of the user (101) with a piece
of information to uniquely represent the user (101). The piece of
information is provided to the web browser during one visit to the
online payment site (233); and the web browser is configured to
provide the piece of information back to the online payment site
(233) during subsequent visits to the online payment site (233).
The piece of information may be used as the user identifier (215)
to identify the user (101). The mechanism to communicate this piece
of information can be implemented via a browser cookie, or other
techniques, such as parameters embedded in a Uniform Resource
Locator (URL), hidden form fields in a web page, etc. Thus, the
user identifier (215) can link the online activities of the user
(101) across multiple user IP addresses (e.g., 221).
[0233] In one embodiment, a browser cookie is the piece of
information provided by a web server (e.g., 201) to a web browser;
and the web browser is configured to provide the piece of
information back to the web server (e.g., 201) when the web browser
subsequently visits the web server (e.g., 201) again for the same
web page or different web pages. In one embodiment, the web browser
(e.g., 201) does not present the piece of information as a visible
part of a web page; and this piece of information is not considered
a part of the source code of the web page and is not normally
displayed to the user (101). The user identifier (215) assigned
and/or tracked using the browser cookie mechanism can be referred
to as the cookie ID of the user (101).
[0234] In one embodiment, while the user (101) is tracked via the
user identifier (215), if the user (101) provides the account
identifier (211), such as the account number (302) of a financial
transaction card (or the account information (142)), as part of
payment data (235) to the online payment site (233) for a purchase
(or during other occasions, such as user registration), the online
payment site (233) can associate the account identifier (211) with
the user identifier (215) and other portions of the user data
(220), such as the IP address (221) of the user (101) used at a
time indicated by the timestamp (223) of the visit to the URL
(225). The URL (225) may be the address of the web page on the
online payment site (233), or a web page of a merchant that refers
the user (101) to the online payment site (233).
[0235] In one embodiment, the online payment site (233) is to link
the account identifier (211) and the user identifier (215) in a
mapping table (231) to subsequently map the user identifier (215)
to the account identifier (211) and use the account identifier
(211) as the user data (125) in the system of FIG. 1 to select or
identify the user specific profile (131) and/or to query for other
information, such as transaction data (109) related to the user
specific advertisement data (119).
[0236] In one embodiment, the online payment site (233) is operated
by an entity operating the transaction handler (103). In another
embodiment, the online payment site (233) is operated by an entity
different from the entity operating the transaction handler
(103).
[0237] In one embodiment, user data from different user trackers
are matched to link the account identifier (211) known to one user
tracker A (213), to the user identifier (215) used independently by
another user tracker B (217) to track user online activities, as
illustrated in FIG. 12.
[0238] In one embodiment of FIG. 12, the user tracker B (217) has
no access to the account identifier (211). For example, the user
tracker B (217) may be used by an advertisement provider to place
advertisements; and a typical user would not submit the account
identifier (211) to the advertisement provider. As an advertisement
provider, the user tracker B (217) can observe the URLs of the web
pages in which the advertisements are presented. The user tracker B
(217) may or may not have access to the content of the web pages in
which the advertisements are presented, since access to some of the
web pages may require user authentication.
[0239] In FIG. 12, the common portions of the user data (227) from
the user tracker A (213) and the user data (229) from the user
tracker B (217) are correlated to link the account identifier (211)
and the user identifier (215).
[0240] For example, when the user data (227 and 229) show common
user activities at the same user IP address (221) at the same
timestamp (223) (or timestamps that are close in time), the user
data (227 and 229) can be linked to the same user and thus allow
the association between the account identifier (211), known to the
user tracker A (213), and the user identifier (215) used by another
user tracker B (217). The association relationship between the
account identifier (211) known to the user tracker A (213) and the
user identifier (215) used by another user tracker B (217) can be
stored in the mapping table (231) to allow mapping from the user
identifier (215) to the account identifier (211) to query an entity
operating the transaction handler (103) for transaction data (109)
specific to the account identifier (211), or to select the user
specific profile (131).
[0241] In one embodiment, the user tracker B (217) provides the
user data (229) to the user tracker A (213) to allow the user
tracker A (213) to correlate the user data (227 and 229) and
generate the mapping table (231). In another embodiment, both the
user tracker A (213) and the user tracker B (217) provide the user
data (227 and 229) to a separate correlator, which may be under
control of the entity operating the transaction handler (103) or a
different entity. The correlator generates the mapping table (231)
and provides the services for the transaction handler (103) (and/or
the profile selector (129)) to look up the account identifier (211)
based on the user identifier (215).
[0242] In one embodiment, to improve security, the account
identifier (211) may not be the original account number of the
financial transaction card of the user (101). For example, in one
embodiment, the account identifier (211) is a hash of the original
account number or an encoded or encrypted version of the original
account number. Different account numbers are hashed, encoded, or
encrypted to generate different account identifiers (e.g., 211).
However, to protect the original account number, it is generally
difficult to determine the original account number from the account
identifier (211) (e.g., without the knowledge of the encoding
scheme, the decryption key, etc.). Any known techniques for
hashing, encoding and/or encrypting the account number can be
used.
[0243] In one embodiment, the system does not rely upon the user
trackers (213 and 217) to observe the same activity to correlate
the user data (227 and 229). For example, the user (101) may visit
two web pages tracked separately by the user trackers (213 and
217). Although the timestamps of the IP address for accessing the
two web pages may not be the same, the close proximity of the
timestamps link the IP address to the same user (101). For example,
when the timestamp of an activity observed by the user tracker A
(213) is between two timestamps of activities observed by the user
tracker B (217) at the same IP address (221), these observed
activities can be linked to the same user (101).
[0244] Further, the correlation of the URLs (e.g., 225) can be an
indication of the same user (101). For example, the referral URL
observed by the user tracker A (213) may match the referral URL
observed by the user tracker B (217), which is an indication of the
same user (101).
[0245] In some embodiments, a match between two online activities
as represented by the user data (227 and 229) indicates a link
between the account identifier (211) and the user identifier (215)
with certain probability. When there are a large number of matches,
the probability of the link between the account identifier (211)
and the user identifier (215) increases. When the probability is
above a threshold (e.g., when the number of matches is above a
threshold), the match between the account identifier (211) and the
user identifier (215) can be recorded in the mapping table
(231).
[0246] In some embodiments, the user tracker A (213) and the user
tracker B (217) may observe the same user activity to correlate the
account identifier (211) and the user identifier (215). For
example, the user tracker A (213) may be used by an online payment
site (233) and the user tracker B (217) may be used by an
advertisement provider. When the user (101) visits an online
payment site (233), the online payment site (233) provides the user
identifier (215) used by the user tracker A (213) to the user
tracker B (217) (e.g., via parameters embedded in a URL, form
parameters, API, etc.) to allow the user tracker B (217) to
correlate the user identifier (215) used by the user tracker A
(213) with the user identifier (215) used by the user tracker B
(217). Subsequently, the user identifier (215) can be used to map
the account identifier (211) with the user identifier (215) of the
user tracker B (217).
[0247] Similarly, when an advertisement is presented on the web
page on the online payment site (233), the user tracker B (217) may
provide the user identifier (215) to the user tracker A (213) via a
reference from the advertisement to an element on the online
payment site (233) (e.g., via parameters embedded in a URL, form
parameters, API, etc.). Thus, the user tracker A (213) can
correlate the user identifier (215) used by the user tracker B
(217) with the user identifier (215) used by the user tracker A
(213), and thus to the account identifier (211).
[0248] When the user identifier (215) used by the user tracker B
(217) to track anonymous user online behavior is mapped to the
account identifier (211), the account identifier (211) can be used
to obtain intelligence information about the user (101) for
advertising, based on the transaction data (109).
[0249] For example, an advertisement provider using the user
tracker B (217) may send an inquiry to a profile provider (e.g.,
profile selector (129)) to obtain information specific to the user
(101) having the account identifier (211), such as the percentage
of the user's total spending for a given merchant category, the
segment of users to which the user (101) belongs, etc.
[0250] For example, an online merchant may use the account
identifier (211) to query for transaction data related to an
advertisement. The transaction handler (103) may provide
transaction data (109) specific to the user (101) having the
account identifier (211) to the merchant to match the online
activities of the user (101) at the online merchant with purchase
transactions. For example, the online merchant may specify an
advertisement category and a date of the advertisement; and in
response, a web portal (e.g., 143) of the transaction handler (103)
may provide information on one or more transactions in the
category, indicating the date and amount of the transactions, the
location and the identity of the merchants for the corresponding
transactions, etc.
[0251] FIG. 13 shows a method to map user identifiers to account
identifiers according to some embodiments. In FIG. 13, a computing
apparatus receives (241) first user data (e.g., 229) associated
with first user identifiers (e.g., 215) used by a first user
tracker (217) to track first online activities of users and
receives (243) second user data (e.g., 227) associated with second
online activities of respective first account identifiers (e.g.,
211) that uniquely identify accounts for payment transactions
processed by a transaction handler (103). The computing apparatus
matches (245) the first user data (e.g., 229) with the second user
data (e.g., 227) to identify mapping (e.g., 231) between the first
user identifiers (e.g., 215) and the first account identifiers
(e.g., 211).
[0252] In one embodiment, the computing apparatus includes at least
one of: a data warehouse (149), a portal (143), an advertisement
selector (133), a transaction handler (103), a profile generator
(121), a web server (201), and a correlator.
[0253] In one embodiment, the first user identifiers (e.g., 215)
are browser cookie based identifiers; and the first account
identifiers are account numbers (e.g., 302) of financial
transaction cards. Details about the use of a browser cookie in one
embodiment are provided in the section entitled "BROWSER
COOKIE."
[0254] In one embodiment, the computing apparatus of the
transaction handler (103) stores transaction data (109) related to
a plurality of transactions processed at the transaction handler
(103), receives a second user identifier (215) used by the first
tracker (217) to track online activities of a user (101),
identifies a second account identifier (211) of the user (101)
based on the mapping (e.g., 231), and provides transaction
information based at least in part on a portion of the transaction
data (109) associated with the second account identifier (211).
[0255] In one embodiment, the transaction information provided by
the computing apparatus of the transaction handler (103) is used to
identify a personalized or targeted advertisement for the user
(101), or to correlate with online activities via identifying one
or more purchases related to an advertisement presented to the user
(101) as identified by the second user identifier (211).
[0256] In one embodiment, the first user tracker (217) and the
second user tracker (213) are operated by different web servers;
the first user data (229) and the second user data (227) have a
plurality of common data fields (e.g., 221-225); and the marching
is based on the common data fields (e.g., 221-225), such as
timestamp (223), and Internet Protocol (IP) address (221).
[0257] In one embodiment, the second user tracker (213) is used on
a website of the transaction handler (103) to verify passwords to
use the first account identifiers (e.g., 211) for online
transactions, or a web server (e.g., 201) managing electronic
payments for different merchants.
[0258] In one embodiment, the computing apparatus is to store
transaction data (109) related to a plurality of transactions
processed at the transaction handler (103). Each of the plurality
of transactions is processed to make a payment from an issuer to an
acquirer via the transaction handler (103) in response to an
account identifier (211) of a customer, as issued by the issuer,
being submitted by a merchant to the acquirer. The issuer is to
make the payment on behalf of the customer, and the acquirer is to
receive the payment on behalf of the merchant. Details about the
transaction handler (103) and the portal (143) in one embodiment
are provided in the section entitled "TRANSACTION DATA BASED
PORTAL."
[0259] In one embodiment, the advertisement identified using the
transaction information (109) includes at least an offer identified
based on the transaction information (109), such as a discount,
incentive, reward, coupon, gift, cash back, benefit, product, and
service. Details about targeting advertisement in one embodiment
are provided in the section entitled "TARGETING ADVERTISEMENT."
[0260] In one embodiment, the advertisement is presented on a point
of interaction (107) in a context outside any transaction processed
by the transaction handler (103). Details about the point of
interaction in one embodiment are provided in the section entitled
"POINT OF INTERACTION."
[0261] In one embodiment, in response to a user selection of the
advertisement, the offer provided in the advertisement is stored in
the data warehouse (149) in association with the consumer account
(146) of the user (101); and the transaction handler (103) can
automatically redeem the offer for the user (101) when a purchase
satisfying the redemption requirements of the offer occurs (or
download the offer as a coupon to a mobile phone of the user (101)
for redemption at the time of the purchase paid via the account
identifier (211)). Details about offer redemption in some
embodiments are provided in the section entitled "PURCHASE DETAILS"
and the section entitled "ON ATM & POS TERMINAL."
[0262] In one embodiment, the first tracker (217) is operated by a
web server (201) to track the user (101) as an anonymous user; and
the web server (201) is to deliver advertisements in the web pages
of a plurality of different websites operated by different
entities.
[0263] In one embodiment, the transaction information includes a
profile (e.g., 131 or 341) summarizing transaction data (109) of
the user (101) using a plurality of values (e.g., 343, 344, 346)
representing aggregated spending in various areas; and the values
(e.g., 343, 344, 346) are computed based on factor definitions
(331) identified from a factor analysis (327) of a plurality of
variables (e.g., 311, 313, 315). In one embodiment, the factor
analysis (327) is based on transaction data (109) associated with a
plurality of users; and the variables (e.g., 311, 313, 315)
aggregate the transactions based on merchant categories (e.g. 306).
In one embodiment, the variables (e.g., 311, 313, 315) include
spending frequency variables (313) and spending amount variables
(315).
[0264] In one embodiment, the computing apparatus is to generate
the profile (e.g., 131 or 341) using the transaction data (109) of
the user (101) based on cluster definitions (333) and factor
definitions (331); and the cluster definitions (333) and factor
definitions (331) are generated based on transaction data (109) of
a plurality of users, such as transaction records (301) recorded by
the transaction handler (103). Details about the profile (e.g., 133
or 341) in one embodiment are provided in the section entitled
"TRANSACTION PROFILE" and the section entitled "AGGREGATED SPENDING
PROFILE."
[0265] Details about the system in one embodiment are provided in
the section entitled "SYSTEM," "CENTRALIZED DATA WAREHOUSE" and
"HARDWARE."
Variations
[0266] Some embodiments use more or fewer components than those
illustrated in FIGS. 1 and 4-7. For example, in one embodiment, the
user specific profile (131) is used by a search engine to
prioritize search results. In one embodiment, the correlator (117)
is to correlate transactions with online activities, such as
searching, web browsing, and social networking, instead of or in
addition to the user specific advertisement data (119). In one
embodiment, the correlator (117) is to correlate transactions
and/or spending patterns with news announcements, market changes,
events, natural disasters, etc. In one embodiment, the data to be
correlated by the correlator with the transaction data (109) may
not be personalized via the user specific profile (131) and may not
be user specific. In one embodiment, multiple different devices are
used at the point of interaction (107) for interaction with the
user (101); and some of the devices may not be capable of receiving
input from the user (101). In one embodiment, there are transaction
terminals (105) to initiate transactions for a plurality of users
(101) with a plurality of different merchants. In one embodiment,
the account information (142) is provided to the transaction
terminal (105) directly (e.g., via phone or Internet) without the
use of the account identification device (141).
[0267] In one embodiment, at least some of the profile generator
(121), correlator (117), profile selector (129), and advertisement
selector (133) are controlled by the entity that operates the
transaction handler (103). In another embodiment, at least some of
the profile generator (121), correlator (117), profile selector
(129), and advertisement selector (133) are not controlled by the
entity that operates the transaction handler (103).
[0268] For example, in one embodiment, the entity operating the
transaction handler (103) provides the intelligence (e.g.,
transaction profiles (127) or the user specific profile (131)) for
the selection of the advertisement; and a third party (e.g., a web
search engine, a publisher, or a retailer) may present the
advertisement in a context outside a transaction involving the
transaction handler (103) before the advertisement results in a
purchase.
[0269] For example, in one embodiment, the customer may interact
with the third party at the point of interaction (107); and the
entity controlling the transaction handler (103) may allow the
third party to query for intelligence information (e.g.,
transaction profiles (127), or the user specific profile (131))
about the customer using the user data (125), thus informing the
third party of the intelligence information for targeting the
advertisements, which can be more useful, effective and compelling
to the user (101). For example, the entity operating the
transaction handler (103) may provide the intelligence information
without generating, identifying or selecting advertisements; and
the third party receiving the intelligence information may
identify, select and/or present advertisements.
[0270] Through the use of the transaction data (109), account data
(111), correlation results (123), the context at the point of
interaction, and/or other data, relevant and compelling messages or
advertisements can be selected for the customer at the points of
interaction (e.g., 107) for targeted advertising. The messages or
advertisements are thus delivered at the optimal time for
influencing or reinforcing brand perceptions and revenue-generating
behavior. The customers receive the advertisements in the media
channels that they like and/or use most frequently.
[0271] In one embodiment, the transaction data (109) includes
transaction amounts, the identities of the payees (e.g.,
merchants), and the date and time of the transactions. The
identities of the payees can be correlated to the businesses,
services, products and/or locations of the payees. For example, the
transaction handler (103) maintains a database of merchant data,
including the merchant locations, businesses, services, products,
etc. Thus, the transaction data (109) can be used to determine the
purchase behavior, pattern, preference, tendency, frequency, trend,
budget and/or propensity of the customers in relation to various
types of businesses, services and/or products and in relation to
time.
[0272] In one embodiment, the products and/or services purchased by
the user (101) are also identified by the information transmitted
from the merchants or service providers. Thus, the transaction data
(109) may include identification of the individual products and/or
services, which allows the profile generator (121) to generate
transaction profiles (127) with fine granularity or resolution. In
one embodiment, the granularity or resolution may be at a level of
distinct products and services that can be purchased (e.g.,
stock-keeping unit (SKU) level), or category or type of products or
services, or vendor of products or services, etc.
[0273] The profile generator (121) may consolidate transaction data
for a person having multiple accounts to derive intelligence
information about the person to generate a profile for the person
(e.g., transaction profiles (127), or the user specific profile
(131)).
[0274] The profile generator (121) may consolidate transaction data
for a family having multiple accounts held by family members to
derive intelligence information about the family to generate a
profile for the family (e.g., transaction profiles (127), or the
user specific profile (131)).
[0275] Similarly, the profile generator (121) may consolidate
transaction data for a group of persons, after the group is
identified by certain characteristics, such as gender, income
level, geographical location or region, preference, characteristics
of past purchases (e.g., merchant categories, purchase types),
cluster, propensity, demographics, social networking
characteristics (e.g., relationships, preferences, activities on
social networking websites), etc. The consolidated transaction data
can be used to derive intelligence information about the group to
generate a profile for the group (e.g., transaction profiles (127),
or the user specific profile (131)).
[0276] In one embodiment, the profile generator (121) may
consolidate transaction data according to the user data (125) to
generate a profile specific to the user data (125).
[0277] Since the transaction data (109) are records and history of
past purchases, the profile generator (121) can derive intelligence
information about a customer using an account, a customer using
multiple accounts, a family, a company, or other groups of
customers, about what the targeted audience is likely to purchase
in the future, how frequently, and their likely budgets for such
future purchases. Intelligence information is useful in selecting
the advertisements that are most useful, effective and compelling
to the customer, thus increasing the efficiency and effectiveness
of the advertising process.
[0278] In one embodiment, the transaction data (109) are enhanced
with correlation results (123) correlating past advertisements and
purchases that result at least in part from the advertisements.
Thus, the intelligence information can be more accurate in
assisting with the selection of the advertisements. The
intelligence information may not only indicate what the audience is
likely to purchase, but also how likely the audience is to be
influenced by advertisements for certain purchases, and the
relative effectiveness of different forms of advertisements for the
audience. Thus, the advertisement selector (133) can select the
advertisements to best use the opportunity to communicate with the
audience. Further, the transaction data (109) can be enhanced via
other data elements, such as program enrollment, affinity programs,
redemption of reward points (or other types of offers), online
activities, such as web searches and web browsing, social
networking information, etc., based on the account data (111)
and/or other data, such as non-transactional data discussed in U.S.
patent application Ser. No. 12/614,603, filed Nov. 9, 2009 and
entitled "Analyzing Local Non-Transactional Data with Transactional
Data in Predictive Models," the disclosure of which is hereby
incorporated herein by reference.
[0279] In one embodiment, the entity operating the transaction
handler (103) provides the intelligence information in real time as
the request for the intelligence information occurs. In other
embodiments, the entity operating the transaction handler (103) may
provide the intelligence information in batch mode. The
intelligence information can be delivered via online communications
(e.g., via an application programming interface (API) on a website,
or other information server), or via physical transportation of a
computer readable media that stores the data representing the
intelligence information.
[0280] In one embodiment, the intelligence information is
communicated to various entities in the system in a way similar to,
and/or in parallel with the information flow in the transaction
system to move money. The transaction handler (103) routes the
information in the same way it routes the currency involved in the
transactions.
[0281] In one embodiment, the portal (143) provides a user
interface to allow the user (101) to select items offered on
different merchant websites and store the selected items in a wish
list for comparison, reviewing, purchasing, tracking, etc. The
information collected via the wish list can be used to improve the
transaction profiles (127) and derive intelligence on the needs of
the user (101); and targeted advertisements can be delivered to the
user (101) via the wish list user interface provided by the portal
(143). Examples of user interface systems to manage wish lists are
provided in U.S. Pat. App. Pub. No. 2010/0174623, entitled "System
and Method for Managing Items of Interest Selected from Online
Merchants," the disclosure of which is hereby incorporated herein
by reference.
Aggregated Spending Profile
[0282] In one embodiment, the characteristics of transaction
patterns of customers are profiled via clusters, factors, and/or
categories of purchases. The transaction data (109) may include
transaction records (301); and in one embodiment, an aggregated
spending profile (341) is generated from the transaction records
(301), in a way illustrated in FIG. 2, to summarize the spending
behavior reflected in the transaction records (301).
[0283] In one embodiment, each of the transaction records (301) is
for a particular transaction processed by the transaction handler
(103). Each of the transaction records (301) provides information
about the particular transaction, such as the account number (302)
of the consumer account (146) used to pay for the purchase, the
date (303) (and/or time) of the transaction, the amount (304) of
the transaction, the ID (305) of the merchant who receives the
payment, the category (306) of the merchant, the channel (307)
through which the purchase was made, etc. Examples of channels
include online, offline in-store, via phone, etc. In one
embodiment, the transaction records (301) may further include a
field to identify a type of transaction, such as card-present,
card-not-present, etc.
[0284] In one embodiment, a "card-present" transaction involves
physically presenting the account identification device (141), such
as a financial transaction card, to the merchant (e.g., via swiping
a credit card at a POS terminal of a merchant); and a
"card-not-present" transaction involves presenting the account
information (142) of the consumer account (146) to the merchant to
identify the consumer account (146) without physically presenting
the account identification device (141) to the merchant or the
transaction terminal (105).
[0285] In one embodiment, certain information about the transaction
can be looked up in a separate database based on other information
recorded for the transaction. For example, a database may be used
to store information about merchants, such as the geographical
locations of the merchants, categories of the merchants, etc. Thus,
the corresponding merchant information related to a transaction can
be determined using the merchant ID (305) recorded for the
transaction.
[0286] In one embodiment, the transaction records (301) may further
include details about the products and/or services involved in the
purchase. For example, a list of items purchased in the transaction
may be recorded together with the respective purchase prices of the
items and/or the respective quantities of the purchased items. The
products and/or services can be identified via stock-keeping unit
(SKU) numbers, or product category IDs. The purchase details may be
stored in a separate database and be looked up based on an
identifier of the transaction.
[0287] When there is voluminous data representing the transaction
records (301), the spending patterns reflected in the transaction
records (301) can be difficult to recognize by an ordinary
person.
[0288] In one embodiment, the voluminous transaction records (301)
are summarized (335) into aggregated spending profiles (e.g., 341)
to concisely present the statistical spending characteristics
reflected in the transaction records (301). The aggregated spending
profile (341) uses values derived from statistical analysis to
present the statistical characteristics of transaction records
(301) of an entity in a way easy to understand by an ordinary
person.
[0289] In FIG. 2, the transaction records (301) are summarized
(335) via factor analysis (327) to condense the variables (e.g.,
313, 315) and via cluster analysis (329) to segregate entities by
spending patterns.
[0290] In FIG. 2, a set of variables (e.g., 311, 313, 315) are
defined based on the parameters recorded in the transaction records
(301). The variables (e.g., 311, 313, and 315) are defined in a way
to have meanings easily understood by an ordinary person. For
example, variables (311) measure the aggregated spending in super
categories; variables (313) measure the spending frequencies in
various areas; and variables (315) measure the spending amounts in
various areas. In one embodiment, each of the areas is identified
by a merchant category (306) (e.g., as represented by a merchant
category code (MCC), a North American Industry Classification
System (NAILS) code, or a similarly standardized category code). In
other embodiments, an area may be identified by a product category,
a SKU number, etc.
[0291] In one embodiment, a variable of a same category (e.g.,
frequency (313) or amount (315)) is defined to be aggregated over a
set of mutually exclusive areas. A transaction is classified in
only one of the mutually exclusive areas. For example, in one
embodiment, the spending frequency variables (313) are defined for
a set of mutually exclusive merchants or merchant categories.
Transactions falling with the same category are aggregated.
[0292] Examples of the spending frequency variables (313) and
spending amount variables (315) defined for various merchant
categories (e.g., 306) in one embodiment are provided in U.S. Pat.
App. Pub. No. 2010/0306029, entitled "Cardholder Clusters," and in
U.S. Pat. App. Pub. No. 2010/0306032, entitled "Systems and Methods
to Summarize Transaction Data," the disclosures of which
applications are hereby incorporated herein by reference.
[0293] In one embodiment, super categories (311) are defined to
group the categories (e.g., 306) used in transaction records (301).
The super categories (311) can be mutually exclusive. For example,
each merchant category (306) is classified under only one super
merchant category but not any other super merchant categories.
Since the generation of the list of super categories typically
requires deep domain knowledge about the businesses of the
merchants in various categories, super categories (311) are not
used in one embodiment.
[0294] In one embodiment, the aggregation (317) includes the
application of the definitions (309) for these variables (e.g.,
311, 313, and 315) to the transaction records (301) to generate the
variable values (321). The transaction records (301) are aggregated
to generate aggregated measurements (e.g., variable values (321))
that are not specific to a particular transaction, such as
frequencies of purchases made with different merchants or different
groups of merchants, the amounts spent with different merchants or
different groups of merchants, and the number of unique purchases
across different merchants or different groups of merchants, etc.
The aggregation (317) can be performed for a particular time period
and for entities at various levels.
[0295] In one embodiment, the transaction records (301) are
aggregated according to a buying entity. The aggregation (317) can
be performed at account level, person level, family level, company
level, neighborhood level, city level, region level, etc. to
analyze the spending patterns across various areas (e.g., sellers,
products or services) for the respective aggregated buying entity.
For example, the transaction records (301) for a particular account
(e.g., presented by the account number (302)) can be aggregated for
an account level analysis. To aggregate the transaction records
(301) in account level, the transactions with a specific merchant
or merchants in a specific category are counted according to the
variable definitions (309) for a particular account to generate a
frequency measure (e.g., 313) for the account relative to the
specific merchant or merchant category; and the transaction amounts
(e.g., 304) with the specific merchant or the specific category of
merchants are summed for the particular account to generate an
average spending amount for the account relative to the specific
merchant or merchant category. For example, the transaction records
(301) for a particular person having multiple accounts can be
aggregated for a person level analysis, the transaction records
(301) aggregated for a particular family for a family level
analysis, and the transaction records (301) for a particular
business aggregated for a business level analysis.
[0296] The aggregation (317) can be performed for a predetermined
time period, such as for the transactions occurring in the past
month, in the past three months, in the past twelve months,
etc.
[0297] In another embodiment, the transaction records (301) are
aggregated according to a selling entity. The spending patterns at
the selling entity across various buyers, products or services can
be analyzed. For example, the transaction records (301) for a
particular merchant having transactions with multiple accounts can
be aggregated for a merchant level analysis. For example, the
transaction records (301) for a particular merchant group can be
aggregated for a merchant group level analysis.
[0298] In one embodiment, the aggregation (317) is formed
separately for different types of transactions, such as
transactions made online, offline, via phone, and/or "card-present"
transactions vs. "card-not-present" transactions, which can be used
to identify the spending pattern differences among different types
of transactions.
[0299] In one embodiment, the variable values (e.g., 323, 324, . .
. , 325) associated with an entity ID (322) are considered the
random samples of the respective variables (e.g., 311, 313, 315),
sampled for the instance of an entity represented by the entity ID
(322). Statistical analyses (e.g., factor analysis (327) and
cluster analysis (329)) are performed to identify the patterns and
correlations in the random samples.
[0300] For example, a cluster analysis (329) can identify a set of
clusters and thus cluster definitions (333) (e.g., the locations of
the centroids of the clusters). In one embodiment, each entity ID
(322) is represented as a point in a mathematical space defined by
the set of variables; and the variable values (323, 324, . . . ,
325) of the entity ID (322) determine the coordinates of the point
in the space and thus the location of the point in the space.
Various points may be concentrated in various regions; and the
cluster analysis (329) is configured to formulate the positioning
of the points to drive the clustering of the points. In other
embodiments, the cluster analysis (329) can also be performed using
the techniques of Self Organizing Maps (SOM), which can identify
and show clusters of multi-dimensional data using a representation
on a two-dimensional map.
[0301] Once the cluster definitions (333) are obtained from the
cluster analysis (329), the identity of the cluster (e.g., cluster
ID (343)) that contains the entity ID (322) can be used to
characterize spending behavior of the entity represented by the
entity ID (322). The entities in the same cluster are considered to
have similar spending behaviors. Similarities and differences among
the entities, such as accounts, individuals, families, etc., as
represented by the entity ID (e.g., 322) and characterized by the
variable values (e.g., 323, 324, . . . , 325) can be identified via
the cluster analysis (329). In one embodiment, after a number of
clusters of entity IDs are identified based on the patterns of the
aggregated measurements, a set of profiles can be generated for the
clusters to represent the characteristics of the clusters. Once the
clusters are identified, each of the entity IDs (e.g.,
corresponding to an account, individual, family) can be assigned to
one cluster; and the profile for the corresponding cluster may be
used to represent, at least in part, the entity (e.g., account,
individual, family). Alternatively, the relationship between an
entity (e.g., an account, individual, family) and one or more
clusters can be determined (e.g., based on a measurement of
closeness to each cluster). Thus, the cluster related data can be
used in a transaction profile (127 or 341) to provide information
about the behavior of the entity (e.g., an account, an individual,
a family).
[0302] In one embodiment, more than one set of cluster definitions
(333) is generated from cluster analyses (329). For example,
cluster analyses (329) may generate different sets of cluster
solutions corresponding to different numbers of identified
clusters. A set of cluster IDs (e.g., 343) can be used to summarize
(335) the spending behavior of the entity represented by the entity
ID (322), based on the typical spending behavior of the respective
clusters. In one example, two cluster solutions are obtained; one
of the cluster solutions has 17 clusters, which classify the
entities in a relatively coarse manner; and the other cluster
solution has 55 clusters, which classify the entities in a relative
fine manner. A cardholder can be identified by the spending
behavior of one of the 17 clusters and one of the 55 clusters in
which the cardholder is located. Thus, the set of cluster IDs
corresponding to the set of cluster solutions provides a
hierarchical identification of an entity among clusters of
different levels of resolution. The spending behavior of the
clusters is represented by the cluster definitions (333), such as
the parameters (e.g., variable values) that define the centroids of
the clusters.
[0303] In one embodiment, the random variables (e.g., 313 and 315)
as defined by the definitions (309) have certain degrees of
correlation and are not independent from each other. For example,
merchants of different merchant categories (e.g., 306) may have
overlapping business, or have certain business relationships. For
example, certain products and/or services of certain merchants have
cause and effect relationships. For example, certain products
and/or services of certain merchants are mutually exclusive to a
certain degree (e.g., a purchase from one merchant may have a level
of probability to exclude the user (101) from making a purchase
from another merchant). Such relationships may be complex and
difficult to quantify by merely inspecting the categories. Further,
such relationships may shift over time as the economy changes.
[0304] In one embodiment, a factor analysis (327) is performed to
reduce the redundancy and/or correlation among the variables (e.g.,
313, 315). The factor analysis (327) identifies the definitions
(331) for factors, each of which represents a combination of the
variables (e.g., 313, 315).
[0305] In one embodiment, a factor is a linear combination of a
plurality of the aggregated measurements (e.g., variables (313,
315)) determined for various areas (e.g., merchants or merchant
categories, products or product categories). Once the relationship
between the factors and the aggregated measurements is determined
via factor analysis, the values for the factors can be determined
from the linear combinations of the aggregated measurements and be
used in a transaction profile (127 or 341) to provide information
on the behavior of the entity represented by the entity ID (e.g.,
an account, an individual, a family).
[0306] Once the factor definitions (331) are obtained from the
factor analysis (327), the factor definitions (331) can be applied
to the variable values (321) to determine factor values (344) for
the aggregated spending profile (341). Since redundancy and
correlation are reduced in the factors, the number of factors is
typically much smaller than the number of the original variables
(e.g., 313, 315). Thus, the factor values (344) represent the
concise summary of the original variables (e.g., 313, 315).
[0307] For example, there may be thousands of variables on spending
frequency and amount for different merchant categories; and the
factor analysis (327) can reduce the factor number to less than one
hundred (and even less than twenty). In one example, a
twelve-factor solution is obtained, which allows the use of twelve
factors to combine the thousands of the original variables (313,
315); and thus, the spending behavior in thousands of merchant
categories can be summarized via twelve factor values (344). In one
embodiment, each factor is combination of at least four variables;
and a typical variable has contributions to more than one
factor.
[0308] In one example, hundreds or thousands of transaction records
(301) of a cardholder are converted into hundreds or thousands of
variable values (321) for various merchant categories, which are
summarized (335) via the factor definitions (331) and cluster
definitions (333) into twelve factor values (344) and one or two
cluster IDs (e.g., 343). The summarized data can be readily
interpreted by a human to ascertain the spending behavior of the
cardholder. A user (101) may easily specify a spending behavior
requirement formulated based on the factor values (344) and the
cluster IDs (e.g., to query for a segment of customers, or to
request the targeting of a segment of customers). The reduced size
of the summarized data reduces the need for data communication
bandwidth for communicating the spending behavior of the cardholder
over a network connection and allows simplified processing and
utilization of the data representing the spending behavior of the
cardholder.
[0309] In one embodiment, the behavior and characteristics of the
clusters are studied to identify a description of a type of
representative entities that are found in each of the clusters. The
clusters can be named based on the type of representative entities
to allow an ordinary person to easily understand the typical
behavior of the clusters.
[0310] In one embodiment, the behavior and characteristics of the
factors are also studied to identify dominant aspects of each
factor. The clusters can be named based on the dominant aspects to
allow an ordinary person to easily understand the meaning of a
factor value.
[0311] In FIG. 2, an aggregated spending profile (341) for an
entity represented by an entity ID (e.g., 322) includes the cluster
ID (343) and factor values (344) determined based on the cluster
definitions (333) and the factor definitions (331). The aggregated
spending profile (341) may further include other statistical
parameters, such as diversity index (342), channel distribution
(345), category distribution (346), zip code (347), etc., as
further discussed below.
[0312] In one embodiment, the diversity index (342) may include an
entropy value and/or a Gini coefficient, to represent the diversity
of the spending by the entity represented by the entity ID (322)
across different areas (e.g., different merchant categories (e.g.,
306)). When the diversity index (342) indicates that the diversity
of the spending data is under a predetermined threshold level, the
variable values (e.g., 323, 324, . . . , 325) for the corresponding
entity ID (322) may be excluded from the cluster analysis (329)
and/or the factor analysis (327) due to the lack of diversity. When
the diversity index (342) of the aggregated spending profile (341)
is lower than a predetermined threshold, the factor values (344)
and the cluster ID (343) may not accurately represent the spending
behavior of the corresponding entity.
[0313] In one embodiment, the channel distribution (345) includes a
set of percentage values that indicate the percentages of amounts
spent in different purchase channels, such as online, via phone, in
a retail store, etc.
[0314] In one embodiment, the category distribution (346) includes
a set of percentage values that indicate the percentages of
spending amounts in different super categories (311). In one
embodiment, thousands of different merchant categories (e.g., 306)
are represented by Merchant Category Codes (MCC), or North American
Industry Classification System (NAICS) codes in transaction records
(301). These merchant categories (e.g., 306) are classified or
combined into less than one hundred super categories (or less than
twenty). In one example, fourteen super categories are defined
based on domain knowledge.
[0315] In one embodiment, the aggregated spending profile (341)
includes the aggregated measurements (e.g., frequency, average
spending amount) determined for a set of predefined, mutually
exclusive merchant categories (e.g., super categories (311)). Each
of the super merchant categories represents a type of products or
services a customer may purchase. A transaction profile (127 or
341) may include the aggregated measurements for each of the set of
mutually exclusive merchant categories. The aggregated measurements
determined for the predefined, mutually exclusive merchant
categories can be used in transaction profiles (127 or 341) to
provide information on the behavior of a respective entity (e.g.,
an account, an individual, or a family).
[0316] In one embodiment, the zip code (347) in the aggregated
spending profile (341) represents the dominant geographic area in
which the spending associated with the entity ID (322) occurred.
Alternatively or in combination, the aggregated spending profile
(341) may include a distribution of transaction amounts over a set
of zip codes that account for a majority of the transactions or
transaction amounts (e.g., 90%).
[0317] In one embodiment, the factor analysis (327) and cluster
analysis (329) are used to summarize the spending behavior across
various areas, such as different merchants characterized by
merchant category (306), different products and/or services,
different consumers, etc. The aggregated spending profile (341) may
include more or fewer fields than those illustrated in FIG. 2. For
example, in one embodiment, the aggregated spending profile (341)
further includes an aggregated spending amount for a period of time
(e.g., the past twelve months); in another embodiment, the
aggregated spending profile (341) does not include the category
distribution (346); and in a further embodiment, the aggregated
spending profile (341) may include a set of distance measures to
the centroids of the clusters. The distance measures may be defined
based on the variable values (323, 324, . . . , 325), or based on
the factor values (344). The factor values of the centroids of the
clusters may be estimated based on the entity ID (e.g., 322) that
is closest to the centroid in the respective cluster.
[0318] Other variables can be used in place of, or in additional
to, the variables (311, 313, 315) illustrated in FIG. 2. For
example, the aggregated spending profile (341) can be generated
using variables measuring shopping radius/distance from the primary
address of the account holder to the merchant site for offline
purchases. When such variables are used, the transaction patterns
can be identified based at least in part on clustering according to
shopping radius/distance and geographic regions. Similarly, the
factor definition (331) may include the consideration of the
shopping radius/distance. For example, the transaction records
(301) may be aggregated based on the ranges of shopping
radius/distance and/or geographic regions. For example, the factor
analysis can be used to determine factors that naturally combine
geographical areas based on the correlations in the spending
patterns in various geographical areas.
[0319] In one embodiment, the aggregation (317) may involve the
determination of a deviation from a trend or pattern. For example,
an account makes a certain number of purchases a week at a merchant
over the past 6 months. However, in the past 2 weeks the number of
purchases is less than the average number per week. A measurement
of the deviation from the trend or pattern can be used (e.g., in a
transaction profile (127 or 341) as a parameter, or in variable
definitions (309) for the factor analysis (327) and/or the cluster
analysis) to define the behavior of an account, an individual, a
family, etc.
[0320] FIG. 3 shows a method to generate an aggregated spending
profile according to one embodiment. In FIG. 3, computation models
are established (351) for variables (e.g., 311, 313, and 315). In
one embodiment, the variables are defined in a way to capture
certain aspects of the spending statistics, such as frequency,
amount, etc.
[0321] In FIG. 3, data from related accounts are combined (353).
For example, when an account number change has occurred for a
cardholder in the time period under analysis, the transaction
records (301) under the different account numbers of the same
cardholder are combined under one account number that represents
the cardholder. For example, when the analysis is performed at a
person level (or family level, business level, social group level,
city level, or region level), the transaction records (301) in
different accounts of the person (or family, business, social
group, city or region) can be combined under one entity ID (322)
that represents the person (or family, business, social group, city
or region).
[0322] In one embodiment, recurrent/installment transactions are
combined (355). For example, multiple monthly payments may be
combined and considered as one single purchase.
[0323] In FIG. 3, account data are selected (357) according to a
set of criteria related to activity, consistency, diversity,
etc.
[0324] For example, when a cardholder uses a credit card solely to
purchase gas, the diversity of the transactions by the cardholder
is low. In such a case, the transactions in the account of the
cardholder may not be statistically meaningful to represent the
spending pattern of the cardholder in various merchant categories.
Thus, in one embodiment, if the diversity of the transactions
associated with an entity ID (322) is below a threshold, the
variable values (e.g., 323, 324, . . . , 325) corresponding to the
entity ID (322) are not used in the cluster analysis (329) and/or
the factor analysis (327). The diversity can be examined based on
the diversity index (342) (e.g., entropy or Gini coefficient), or
based on counting the different merchant categories in the
transactions associated with the entity ID (322); and when the
count of different merchant categories is fewer than a threshold
(e.g., 5), the transactions associated with the entity ID (322) are
not used in the cluster analysis (329) and/or the factor analysis
(327) due to the lack of diversity.
[0325] For example, when a cardholder uses a credit card only
sporadically (e.g., when running out of cash), the limited
transactions by the cardholder may not be statistically meaningful
in representing the spending behavior of the cardholder. Thus, in
one embodiment, when the numbers of transactions associated with an
entity ID (322) is below a threshold, the variable values (e.g.,
323, 324, . . . , 325) corresponding to the entity ID (322) are not
used in the cluster analysis (329) and/or the factor analysis
(327).
[0326] For example, when a cardholder has only used a credit card
during a portion of the time period under analysis, the transaction
records (301) during the time period may not reflect the consistent
behavior of the cardholder for the entire time period. Consistency
can be checked in various ways. In one example, if the total number
of transactions during the first and last months of the time period
under analysis is zero, the transactions associated with the entity
ID (322) are inconsistent in the time period and thus are not used
in the cluster analysis (329) and/or the factor analysis (327).
Other criteria can be formulated to detect inconsistency in the
transactions.
[0327] In FIG. 3, the computation models (e.g., as represented by
the variable definitions (309)) are applied (359) to the remaining
account data (e.g., transaction records (301)) to obtain data
samples for the variables. The data points associated with the
entities, other than those whose transactions fail to meet the
minimum requirements for activity, consistency, diversity, etc.,
are used in factor analysis (327) and cluster analysis (329).
[0328] In FIG. 3, the data samples (e.g., variable values (321))
are used to perform (361) factor analysis (327) to identify factor
solutions (e.g., factor definitions (331)). The factor solutions
can be adjusted (363) to improve similarity in factor values of
different sets of transaction data (109). For example, factor
definitions (331) can be applied to the transactions in the time
period under analysis (e.g., the past twelve months) and be applied
separately to the transactions in a prior time period (e.g., the
twelve months before the past twelve months) to obtain two sets of
factor values. The factor definitions (331) can be adjusted to
improve the correlation between the two set of factor values.
[0329] The data samples can also be used to perform (365) cluster
analysis (329) to identify cluster solutions (e.g., cluster
definitions (333)). The cluster solutions can be adjusted (367) to
improve similarity in cluster identifications based on different
sets of transaction data (109). For example, cluster definitions
(333) can be applied to the transactions in the time period under
analysis (e.g., the past twelve months) and be applied separately
to the transactions in a prior time period (e.g., the twelve months
before the past twelve months) to obtain two sets of cluster
identifications for various entities. The cluster definitions (333)
can be adjusted to improve the correlation between the two set of
cluster identifications.
[0330] In one embodiment, the number of clusters is determined from
clustering analysis. For example, a set of cluster seeds can be
initially identified and used to run a known clustering algorithm.
The sizes of data points in the clusters are then examined. When a
cluster contains less than a predetermined number of data points,
the cluster may be eliminated to rerun the clustering analysis.
[0331] In one embodiment, standardizing entropy is added to the
cluster solution to obtain improved results.
[0332] In one embodiment, human understandable characteristics of
the factors and clusters are identified (369) to name the factors
and clusters. For example, when the spending behavior of a cluster
appears to be the behavior of an internet loyalist, the cluster can
be named "internet loyalist" such that if a cardholder is found to
be in the "internet loyalist" cluster, the spending preferences and
patterns of the cardholder can be easily perceived.
[0333] In one embodiment, the factor analysis (327) and the cluster
analysis (329) are performed periodically (e.g., once a year, or
six months) to update the factor definitions (331) and the cluster
definitions (333), which may change as the economy and the society
change over time.
[0334] In FIG. 3, transaction data (109) are summarized (371) using
the factor solutions and cluster solutions to generate the
aggregated spending profile (341). The aggregated spending profile
(341) can be updated more frequently than the factor solutions and
cluster solutions, when the new transaction data (109) becomes
available. For example, the aggregated spending profile (341) may
be updated quarterly or monthly.
[0335] Various tweaks and adjustments can be made for the variables
(e.g., 313, 315) used for the factor analysis (327) and the cluster
analysis (329). For example, the transaction records (301) may be
filtered, weighted or constrained, according to different rules to
improve the capabilities of the aggregated measurements in
indicating certain aspects of the spending behavior of the
customers.
[0336] For example, in one embodiment, the variables (e.g., 313,
315) are normalized and/or standardized (e.g., using statistical
average, mean, and/or variance).
[0337] For example, the variables (e.g., 313, 315) for the
aggregated measurements can be tuned, via filtering and weighting,
to predict the future trend of spending behavior (e.g., for
advertisement selection), to identify abnormal behavior (e.g., for
fraud prevention), or to identify a change in spending pattern
(e.g., for advertisement audience measurement), etc. The aggregated
measurements, the factor values (344), and/or the cluster ID (343)
generated from the aggregated measurements can be used in a
transaction profile (127 or 341) to define the behavior of an
account, an individual, a family, etc.
[0338] In one embodiment, the transaction data (109) are aged to
provide more weight to recent data than older data. In other
embodiments, the transaction data (109) are reverse aged. In
further embodiments, the transaction data (109) are seasonally
adjusted.
[0339] In one embodiment, the variables (e.g., 313, 315) are
constrained to eliminate extreme outliers. For example, the minimum
values and the maximum values of the spending amounts (315) may be
constrained based on values at certain percentiles (e.g., the value
at one percentile as the minimum and the value at 99 percentile as
the maximum) and/or certain predetermined values. In one
embodiment, the spending frequency variables (313) are constrained
based on values at certain percentiles and median values. For
example, the minimum value for a spending frequency variable (313)
may be constrained at P.sub.1-k.times.(M-P.sub.1), where P.sub.1 is
the one percentile value, M the median value, and k a predetermined
constant (e.g., 0.1). For example, the maximum value for a spending
frequency variable (313) may be constrained at
P.sub.99+a.times.(P.sub.99-M), where P.sub.99 is the 99 percentile
value, M the median value, and k a predetermined constant (e.g.,
0.1).
[0340] In one embodiment, variable pruning is performed to reduce
the number of variables (e.g., 313, 315) that have less impact on
cluster solutions and/or factor solutions. For example, variables
with standard variation less than a predetermined threshold (e.g.,
0.1) may be discarded for the purpose of cluster analysis (329).
For example, analysis of variance (ANOVA) can be performed to
identify and remove variables that are no more significant than a
predetermined threshold.
[0341] The aggregated spending profile (341) can provide
information on spending behavior for various application areas,
such as marketing, fraud detection and prevention, creditworthiness
assessment, loyalty analytics, targeting of offers, etc.
[0342] For example, clusters can be used to optimize offers for
various groups within an advertisement campaign. The use of factors
and clusters to target advertisement can improve the speed of
producing targeting models. For example, using variables based on
factors and clusters (and thus eliminating the need to use a large
number of convention variables) can improve predictive models and
increase efficiency of targeting by reducing the number of
variables examined. The variables formulated based on factors
and/or clusters can be used with other variables to build
predictive models based on spending behaviors.
[0343] In one embodiment, the aggregated spending profile (341) can
be used to monitor risks in transactions. Factor values are
typically consistent over time for each entity. An abrupt change in
some of the factor values may indicate a change in financial
conditions, or a fraudulent use of the account. Models formulated
using factors and clusters can be used to identify a series of
transactions that do not follow a normal pattern specified by the
factor values (344) and/or the cluster ID (343). Potential
bankruptcies can be predicted by analyzing the change of factor
values over time; and significant changes in spending behavior may
be detected to stop and/or prevent fraudulent activities.
[0344] For example, the factor values (344) can be used in
regression models and/or neural network models for the detection of
certain behaviors or patterns. Since factors are relatively
non-collinear, the factors can work well as independent variables.
For example, factors and clusters can be used as independent
variables in tree models.
[0345] For example, surrogate accounts can be selected for the
construction of a quasi-control group. For example, for a given
account A that is in one cluster, the account B that is closest to
the account A in the same cluster can be selected as a surrogate
account of the account B. The closeness can be determined by
certain values in the aggregated spending profile (341), such as
factor values (344), category distribution (346), etc. For example,
a Euclidian distance defined based on the set of values from the
aggregated spending profile (341) can be used to compare the
distances between the accounts. Once identified, the surrogate
account can be used to reduce or eliminate bias in measurements.
For example, to determine the effect of an advertisement, the
spending pattern response of the account A that is exposed to the
advertisement can be compared to the spending pattern response of
the account B that is not exposed to the advertisement.
[0346] For example, the aggregated spending profile (341) can be
used in segmentation and/or filtering analysis, such as selecting
cardholders having similar spending behaviors identified via
factors and/or clusters for targeted advertisement campaigns, and
selecting and determining a group of merchants that could be
potentially marketed towards cardholders originating in a given
cluster (e.g., for bundled offers). For example, a query interface
can be provided to allow the query to identify a targeted
population based on a set of criteria formulated using the values
of clusters and factors.
[0347] For example, the aggregated spending profile (341) can be
used in a spending comparison report, such as comparing a
sub-population of interest against the overall population,
determining how cluster distributions and mean factor values
differ, and building reports for merchants and/or issuers for
benchmarking purposes. For example, reports can be generated
according to clusters in an automated way for the merchants. For
example, the aggregated spending profile (341) can be used in
geographic reports by identifying geographic areas where
cardholders shop most frequently and comparing predominant spending
locations with cardholder residence locations.
[0348] In one embodiment, the profile generator (121) provides
affinity relationship data in the transaction profiles (127) so
that the transaction profiles (127) can be shared with business
partners without compromising the privacy of the users (101) and
the transaction details.
[0349] For example, in one embodiment, the profile generator (121)
is to identify clusters of entities (e.g., accounts, cardholders,
families, businesses, cities, regions, etc.) based on the spending
patterns of the entities. The clusters represent entity segments
identified based on the spending patterns of the entities reflected
in the transaction data (109) or the transaction records (301).
[0350] In one embodiment, the clusters correspond to cells or
regions in the mathematical space that contain the respective
groups of entities. For example, the mathematical space
representing the characteristics of users (101) may be divided into
clusters (cells or regions). For example, the cluster analysis
(329) may identify one cluster in the cell or region that contains
a cluster of entity IDs (e.g., 322) in the space having a plurality
of dimensions corresponding to the variables (e.g., 313 and 315).
For example, a cluster can also be identified as a cell or region
in a space defined by the factors using the factor definitions
(331) generated from the factor analysis (327).
[0351] In one embodiment, the parameters used in the aggregated
spending profile (341) can be used to define a segment or a cluster
of entities. For example, a value for the cluster ID (343) and a
set of ranges for the factor values (344) and/or other values can
be used to define a segment.
[0352] In one embodiment, a set of clusters are standardized to
represent the predilection of entities in various groups for
certain products or services. For example, a set of standardized
clusters can be formulated for people who have shopped, for
example, at home improvement stores. The cardholders in the same
cluster have similar spending behavior.
[0353] In one embodiment, the tendency or likelihood of a user
(101) being in a particular cluster (i.e. the user's affinity to
the cell) can be characterized using a value, based on past
purchases. The same user (101) may have different affinity values
for different clusters.
[0354] For example, a set of affinity values can be computed for an
entity, based on the transaction records (301), to indicate the
closeness or predilection of the entity to the set of standardized
clusters. For example, a cardholder who has a first value
representing affinity of the cardholder to a first cluster may have
a second value representing affinity of the cardholder to a second
cluster. For example, if a consumer buys a lot of electronics, the
affinity value of the consumer to the electronics cluster is
high.
[0355] In one embodiment, other indicators are formulated across
the merchant community and cardholder behavior and provided in the
profile (e.g., 127 or 341) to indicate the risk of a
transaction.
[0356] In one embodiment, the relationship of a pair of values from
two different clusters provides an indication of the likelihood
that the user (101) is in one of the two cells, if the user (101)
is shown to be in the other cell. For example, if the likelihood of
the user (101) to purchase each of two types of products is known,
the scores can be used to determine the likelihood of the user
(101) buying one of the two types of products if the user (101) is
known to be interested in the other type of products. In one
embodiment, a map of the values for the clusters is used in a
profile (e.g., 127 or 341) to characterize the spending behavior of
the user (101) (or other types of entities, such as a family,
company, neighborhood, city, or other types of groups defined by
other aggregate parameters, such as time of day, etc.).
[0357] In one embodiment, the clusters and affinity information are
standardized to allow sharing between business partners, such as
transaction processing organizations, search providers, and
marketers. Purchase statistics and search statistics are generally
described in different ways. For example, purchase statistics are
based on merchants, merchant categories, SKU numbers, product
descriptions, etc.; and search statistics are based on search
terms. Once the clusters are standardized, the clusters can be used
to link purchase information based merchant categories (and/or SKU
numbers, product descriptions) with search information based on
search terms. Thus, search predilection and purchase predilection
can be mapped to each other.
[0358] In one embodiment, the purchase data and the search data (or
other third party data) are correlated based on mapping to the
standardized clusters (cells or segments). The purchase data and
the search data (or other third party data) can be used together to
provide benefits or offers (e.g., coupons) to consumers. For
example, standardized clusters can be used as a marketing tool to
provide relevant benefits, including coupons, statement credits, or
the like to consumers who are within or are associated with common
clusters. For example, a data exchange apparatus may obtain cluster
data based on consumer search engine data and actual payment
transaction data to identify like groups of individuals who may
respond favorably to particular types of benefits, such as coupons
and statement credits.
[0359] Details about aggregated spending profile (341) in one
embodiment are provided in U.S. Pat. App. Pub. No. 2010/0306032,
entitled "Systems and Methods to Summarize Transaction Data," the
disclosure of which is hereby incorporated herein by reference.
Transaction Data Based Portal
[0360] In FIG. 1, the transaction terminal (105) initiates the
transaction for a user (101) (e.g., a customer) for processing by a
transaction handler (103). The transaction handler (103) processes
the transaction and stores transaction data (109) about the
transaction, in connection with account data (111), such as the
account profile of an account of the user (101). The account data
(111) may further include data about the user (101), collected from
issuers or merchants, and/or other sources, such as social
networks, credit bureaus, merchant provided information, address
information, etc. In one embodiment, a transaction may be initiated
by a server (e.g., based on a stored schedule for recurrent
payments).
[0361] Over a period of time, the transaction handler (103)
accumulates the transaction data (109) from transactions initiated
at different transaction terminals (e.g., 105) for different users
(e.g., 101). The transaction data (109) thus includes information
on purchases made by various users (e.g., 101) at various times via
different purchases options (e.g., online purchase, offline
purchase from a retail store, mail order, order via phone,
etc.)
[0362] In one embodiment, the accumulated transaction data (109)
and the corresponding account data (111) are used to generate
intelligence information about the purchase behavior, pattern,
preference, tendency, frequency, trend, amount and/or propensity of
the users (e.g., 101), as individuals or as a member of a group.
The intelligence information can then be used to generate, identify
and/or select targeted advertisements for presentation to the user
(101) on the point of interaction (107), during a transaction,
after a transaction, or when other opportunities arise.
[0363] FIG. 4 shows a system to provide information based on
transaction data (109) according to one embodiment. In FIG. 4, the
transaction handler (103) is coupled between an issuer processor
(145) and an acquirer processor (147) to facilitate authorization
and settlement of transactions between a consumer account (146) and
a merchant account (148). The transaction handler (103) records the
transactions in the data warehouse (149). The portal (143) is
coupled to the data warehouse (149) to provide information based on
the transaction records (301), such as the transaction profiles
(127) or aggregated spending profile (341). The portal (143) may be
implemented as a web portal, a telephone gateway, a file/data
server, etc.
[0364] In one embodiment, the portal (143) is configured to receive
queries identifying search criteria from the profile selector
(129), the advertisement selector (133) and/or third parties and in
response, to provide transaction-based intelligence requested by
the queries.
[0365] For example, in one embodiment, a query is to specify a
plurality of account holders to request the portal (143) to deliver
the transaction profiles (127) of account holders in a batch
mode.
[0366] For example, in one embodiment, a query is to identify the
user (101) to request the user specific profile (131), or the
aggregated spending profile (341), of the user (101). The user
(101) may be identified using the account data (111), such as the
account number (302), or the user data (125) such as browser cookie
ID, IP address, etc.
[0367] For example, in one embodiment, a query is to identify a
retail location; and the portal (143) is to provide a profile
(e.g., 341) that summarizes the aggregated spending patterns of
users who have shopped at the retail location within a period of
time.
[0368] For example, in one embodiment, a query is to identify a
geographical location; and the portal (143) is to provide a profile
(e.g., 341) that summarizes the aggregated spending patterns of
users who have been to, or who are expected to visit, the
geographical location within a period of time (e.g., as determined
or predicted based on the locations of the point of interactions
(e.g., 107) of the users).
[0369] For example, in one embodiment, a query is to identify a
geographical area; and the portal (143) is to provide a profile
(e.g., 341) that summarizes the aggregated spending patterns of
users who reside in the geographical area (e.g., as determined by
the account data (111), or who have made transactions within the
geographical area with a period of time (e.g., as determined by the
locations of the transaction terminals (e.g., 105) used to process
the transactions).
[0370] In one embodiment, the portal (143) is configured to
register certain users (101) for various programs, such as a
loyalty program to provide rewards and/or offers to the users
(101).
[0371] In one embodiment, the portal (143) is to register the
interest of users (101), or to obtain permissions from the users
(101) to gather further information about the users (101), such as
data capturing purchase details, online activities, etc.
[0372] In one embodiment, the user (101) may register via the
issuer; and the registration data in the consumer account (146) may
propagate to the data warehouse (149) upon approval from the user
(101).
[0373] In one embodiment, the portal (143) is to register merchants
and provide services and/or information to merchants.
[0374] In one embodiment, the portal (143) is to receive
information from third parties, such as search engines, merchants,
web sites, etc. The third party data can be correlated with the
transaction data (109) to identify the relationships between
purchases and other events, such as searches, news announcements,
conferences, meetings, etc., and improve the prediction capability
and accuracy.
[0375] In FIG. 4, the consumer account (146) is under the control
of the issuer processor (145). The consumer account (146) may be
owned by an individual, or an organization such as a business, a
school, etc. The consumer account (146) may be a credit account, a
debit account, or a stored value account. The issuer may provide
the consumer (e.g., user (101)) an account identification device
(141) to identify the consumer account (146) using the account
information (142). The respective consumer of the account (146) can
be called an account holder or a cardholder, even when the consumer
is not physically issued a card, or the account identification
device (141), in one embodiment. The issuer processor (145) is to
charge the consumer account (146) to pay for purchases.
[0376] In one embodiment, the account identification device (141)
is a plastic card having a magnetic strip storing account
information (142) identifying the consumer account (146) and/or the
issuer processor (145). Alternatively, the account identification
device (141) is a smartcard having an integrated circuit chip
storing at least the account information (142). In one embodiment,
the account identification device (141) includes a mobile phone
having an integrated smartcard.
[0377] In one embodiment, the account information (142) is printed
or embossed on the account identification device (141). The account
information (142) may be printed as a bar code to allow the
transaction terminal (105) to read the information via an optical
scanner. The account information (142) may be stored in a memory of
the account identification device (141) and configured to be read
via wireless, contactless communications, such as near field
communications via magnetic field coupling, infrared
communications, or radio frequency communications. Alternatively,
the transaction terminal (105) may require contact with the account
identification device (141) to read the account information (142)
(e.g., by reading the magnetic strip of a card with a magnetic
strip reader).
[0378] In one embodiment, the transaction terminal (105) is
configured to transmit an authorization request message to the
acquirer processor (147). The authorization request includes the
account information (142), an amount of payment, and information
about the merchant (e.g., an indication of the merchant account
(148)). The acquirer processor (147) requests the transaction
handler (103) to process the authorization request, based on the
account information (142) received in the transaction terminal
(105). The transaction handler (103) routes the authorization
request to the issuer processor (145) and may process and respond
to the authorization request when the issuer processor (145) is not
available. The issuer processor (145) determines whether to
authorize the transaction based at least in part on a balance of
the consumer account (146).
[0379] In one embodiment, the transaction handler (103), the issuer
processor (145), and the acquirer processor (147) may each include
a subsystem to identify the risk in the transaction and may reject
the transaction based on the risk assessment.
[0380] In one embodiment, the account identification device (141)
includes security features to prevent unauthorized uses of the
consumer account (146), such as a logo to show the authenticity of
the account identification device (141), encryption to protect the
account information (142), etc.
[0381] In one embodiment, the transaction terminal (105) is
configured to interact with the account identification device (141)
to obtain the account information (142) that identifies the
consumer account (146) and/or the issuer processor (145). The
transaction terminal (105) communicates with the acquirer processor
(147) that controls the merchant account (148) of a merchant. The
transaction terminal (105) may communicate with the acquirer
processor (147) via a data communication connection, such as a
telephone connection, an Internet connection, etc. The acquirer
processor (147) is to collect payments into the merchant account
(148) on behalf of the merchant.
[0382] In one embodiment, the transaction terminal (105) is a POS
terminal at a traditional, offline, "brick and mortar" retail
store. In another embodiment, the transaction terminal (105) is an
online server that receives account information (142) of the
consumer account (146) from the user (101) through a web
connection. In one embodiment, the user (101) may provide account
information (142) through a telephone call, via verbal
communications with a representative of the merchant; and the
representative enters the account information (142) into the
transaction terminal (105) to initiate the transaction.
[0383] In one embodiment, the account information (142) can be
entered directly into the transaction terminal (105) to make
payment from the consumer account (146), without having to
physically present the account identification device (141). When a
transaction is initiated without physically presenting an account
identification device (141), the transaction is classified as a
"card-not-present" (CNP) transaction.
[0384] In one embodiment, the issuer processor (145) may control
more than one consumer account (146); the acquirer processor (147)
may control more than one merchant account (148); and the
transaction handler (103) is connected between a plurality of
issuer processors (e.g., 145) and a plurality of acquirer
processors (e.g., 147). An entity (e.g., bank) may operate both an
issuer processor (145) and an acquirer processor (147).
[0385] In one embodiment, the transaction handler (103), the issuer
processor (145), the acquirer processor (147), the transaction
terminal (105), the portal (143), and other devices and/or services
accessing the portal (143) are connected via communications
networks, such as local area networks, cellular telecommunications
networks, wireless wide area networks, wireless local area
networks, an intranet, and Internet. In one embodiment, dedicated
communication channels are used between the transaction handler
(103) and the issuer processor (145), between the transaction
handler (103) and the acquirer processor (147), and/or between the
portal (143) and the transaction handler (103).
[0386] In one embodiment, the transaction handler (103) uses the
data warehouse (149) to store the records about the transactions,
such as the transaction records (301) or transaction data (109). In
one embodiment, the transaction handler (103) includes a powerful
computer, or cluster of computers functioning as a unit, controlled
by instructions stored on a computer readable medium.
[0387] In one embodiment, the transaction handler (103) is
configured to support and deliver authorization services, exception
file services, and clearing and settlement services. In one
embodiment, the transaction handler (103) has a subsystem to
process authorization requests and another subsystem to perform
clearing and settlement services.
[0388] In one embodiment, the transaction handler (103) is
configured to process different types of transactions, such credit
card transactions, debit card transactions, prepaid card
transactions, and other types of commercial transactions.
[0389] In one embodiment, the transaction handler (103) facilitates
the communications between the issuer processor (145) and the
acquirer processor (147).
[0390] In one embodiment, the transaction handler (103) is coupled
to the portal (143) (and/or the profile selector (129), the
advertisement selector (133), the media controller (115)) to charge
the fees for the services of providing the transaction-based
intelligence information and/or advertisement.
[0391] For example, in one embodiment, the system illustrated in
FIG. 1 is configured to deliver advertisements to the point of
interaction (107) of the user (101), based on the transaction-based
intelligence information; and the transaction handler (103) is
configured to charge the advertisement fees to the account of the
advertiser in communication with the issuer processor in control of
the account of the advertiser. The advertisement fees may be
charged in response to the presentation of the advertisement, or in
response to the completion of a pre-determined number of
presentations, or in response to a transaction resulted from the
presentation of the advertisement. In one embodiment, the
transaction handler (103) is configured to a periodic fee (e.g.,
monthly fee, annual fee) to the account of the advertiser in
communication with the respective issuer processor that is similar
to the issuer processor (145) of the consumer account (146).
[0392] For example, in one embodiment, the portal (143) is
configured to provide transaction-based intelligence information in
response to the queries received in the portal (143). The portal
(143) is to identify the requesters (e.g., via an authentication,
or the address of the requesters) and instruct the transaction
handler (103) to charge the consumer accounts (e.g., 146) of the
respective requesters for the transaction-based intelligence
information. In one embodiment, the accounts of the requesters are
charged in response to the delivery of the intelligence information
via the portal (143). In one embodiment, the accounts of the
requesters are charged a periodic subscription fee for the access
to the query capability of the portal (143).
[0393] In one embodiment, the information service provided by the
system illustrated in FIG. 1 includes multiple parties, such as one
entity operating the transaction handler (103), one entity
operating the advertisement data (135), one entity operating the
user tracker (113), one entity operating the media controller
(115), etc. The transaction handler (103) is used to generate
transactions to settle the fees, charges and/or divide revenues
using the accounts of the respective parties. In one embodiment,
the account information of the parties is stored in the data
warehouse (149) coupled to the transaction handler (103). In some
embodiments, a separate billing engine is used to generate the
transactions to settle the fees, charges and/or divide
revenues.
[0394] In one embodiment, the transaction terminal (105) is
configured to submit the authorized transactions to the acquirer
processor (147) for settlement. The amount for the settlement may
be different from the amount specified in the authorization
request. The transaction handler (103) is coupled between the
issuer processor (145) and the acquirer processor (147) to
facilitate the clearing and settling of the transaction. Clearing
includes the exchange of financial information between the issuer
processor (145) and the acquirer processor (147); and settlement
includes the exchange of funds.
[0395] In one embodiment, the issuer processor (145) is to provide
funds to make payments on behalf of the consumer account (146). The
acquirer processor (147) is to receive the funds on behalf of the
merchant account (148). The issuer processor (145) and the acquirer
processor (147) communicate with the transaction handler (103) to
coordinate the transfer of funds for the transaction. In one
embodiment, the funds are transferred electronically.
[0396] In one embodiment, the transaction terminal (105) may submit
a transaction directly for settlement, without having to separately
submit an authorization request.
[0397] In one embodiment, the portal (143) provides a user
interface to allow the user (101) to organize the transactions in
one or more consumer accounts (146) of the user with one or more
issuers. The user (101) may organize the transactions using
information and/or categories identified in the transaction records
(301), such as merchant category (306), transaction date (303),
amount (304), etc. Examples and techniques in one embodiment are
provided in U.S. Pat. App. Pub. No. 2007/0055597, entitled "Method
and System for Manipulating Purchase Information," the disclosure
of which is hereby incorporated herein by reference.
[0398] In one embodiment, the portal (143) provides transaction
based statistics, such as indicators for retail spending
monitoring, indicators for merchant benchmarking, industry/market
segmentation, indicators of spending patterns, etc. Further
examples can be found in U.S. Pat. App. Pub. No. 2009/0048884,
entitled "Merchant Benchmarking Tool," the disclosure of which
application is hereby incorporated herein by reference.
Transaction Terminal
[0399] FIG. 5 illustrates a transaction terminal according to one
embodiment. In FIG. 5, the transaction terminal (105) is configured
to interact with an account identification device (141) to obtain
account information (142) about the consumer account (146).
[0400] In one embodiment, the transaction terminal (105) includes a
memory (167) coupled to the processor (151), which controls the
operations of a reader (163), an input device (153), an output
device (165) and a network interface (161). The memory (167) may
store instructions for the processor (151) and/or data, such as an
identification that is associated with the merchant account
(148).
[0401] In one embodiment, the reader (163) includes a magnetic
strip reader. In another embodiment, the reader (163) includes a
contactless reader, such as a radio frequency identification (RFID)
reader, a near field communications (NFC) device configured to read
data via magnetic field coupling (in accordance with ISO standard
14443/NFC), a Bluetooth transceiver, a WiFi transceiver, an
infrared transceiver, a laser scanner, etc.
[0402] In one embodiment, the input device (153) includes key
buttons that can be used to enter the account information (142)
directly into the transaction terminal (105) without the physical
presence of the account identification device (141). The input
device (153) can be configured to provide further information to
initiate a transaction, such as a personal identification number
(PIN), password, zip code, etc. that may be used to access the
account identification device (141), or in combination with the
account information (142) obtained from the account identification
device (141).
[0403] In one embodiment, the output device (165) may include a
display, a speaker, and/or a printer to present information, such
as the result of an authorization request, a receipt for the
transaction, an advertisement, etc.
[0404] In one embodiment, the network interface (161) is configured
to communicate with the acquirer processor (147) via a telephone
connection, an Internet connection, or a dedicated data
communication channel.
[0405] In one embodiment, the instructions stored in the memory
(167) are configured at least to cause the transaction terminal
(105) to send an authorization request message to the acquirer
processor (147) to initiate a transaction. The transaction terminal
(105) may or may not send a separate request for the clearing and
settling of the transaction. The instructions stored in the memory
(167) are also configured to cause the transaction terminal (105)
to perform other types of functions discussed in this
description.
[0406] In one embodiment, a transaction terminal (105) may have
fewer components than those illustrated in FIG. 5. For example, in
one embodiment, the transaction terminal (105) is configured for
"card-not-present" transactions; and the transaction terminal (105)
does not have a reader (163).
[0407] In one embodiment, a transaction terminal (105) may have
more components than those illustrated in FIG. 5. For example, in
one embodiment, the transaction terminal (105) is an ATM machine,
which includes components to dispense cash under certain
conditions.
Account Identification Device
[0408] FIG. 6 illustrates an account identifying device according
to one embodiment. In FIG. 6, the account identification device
(141) is configured to carry account information (142) that
identifies the consumer account (146).
[0409] In one embodiment, the account identification device (141)
includes a memory (167) coupled to the processor (151), which
controls the operations of a communication device (159), an input
device (153), an audio device (157) and a display device (155). The
memory (167) may store instructions for the processor (151) and/or
data, such as the account information (142) associated with the
consumer account (146).
[0410] In one embodiment, the account information (142) includes an
identifier identifying the issuer (and thus the issuer processor
(145)) among a plurality of issuers, and an identifier identifying
the consumer account among a plurality of consumer accounts
controlled by the issuer processor (145). The account information
(142) may include an expiration date of the account identification
device (141), the name of the consumer holding the consumer account
(146), and/or an identifier identifying the account identification
device (141) among a plurality of account identification devices
associated with the consumer account (146).
[0411] In one embodiment, the account information (142) may further
include a loyalty program account number, accumulated rewards of
the consumer in the loyalty program, an address of the consumer, a
balance of the consumer account (146), transit information (e.g., a
subway or train pass), access information (e.g., access badges),
and/or consumer information (e.g., name, date of birth), etc.
[0412] In one embodiment, the memory includes a nonvolatile memory,
such as magnetic strip, a memory chip, a flash memory, a Read Only
Memory (ROM), etc. to store the account information (142).
[0413] In one embodiment, the information stored in the memory
(167) of the account identification device (141) may also be in the
form of data tracks that are traditionally associated with credits
cards. Such tracks include Track 1 and Track 2. Track 1
("International Air Transport Association") stores more information
than Track 2, and contains the cardholder's name as well as the
account number and other discretionary data. Track 1 is sometimes
used by airlines when securing reservations with a credit card.
Track 2 ("American Banking Association") is currently most commonly
used and is read by ATMs and credit card checkers. The ABA
(American Banking Association) designed the specifications of Track
1 and banks abide by it. It contains the cardholder's account
number, encrypted PIN, and other discretionary data.
[0414] In one embodiment, the communication device (159) includes a
semiconductor chip to implement a transceiver for communication
with the reader (163) and an antenna to provide and/or receive
wireless signals.
[0415] In one embodiment, the communication device (159) is
configured to communicate with the reader (163). The communication
device (159) may include a transmitter to transmit the account
information (142) via wireless transmissions, such as radio
frequency signals, magnetic coupling, or infrared, Bluetooth or
WiFi signals, etc.
[0416] In one embodiment, the account identification device (141)
is in the form of a mobile phone, personal digital assistant (PDA),
etc. The input device (153) can be used to provide input to the
processor (151) to control the operation of the account
identification device (141); and the audio device (157) and the
display device (155) may present status information and/or other
information, such as advertisements or offers. The account
identification device (141) may include further components that are
not shown in FIG. 6, such as a cellular communications
subsystem.
[0417] In one embodiment, the communication device (159) may access
the account information (142) stored on the memory (167) without
going through the processor (151).
[0418] In one embodiment, the account identification device (141)
has fewer components than those illustrated in FIG. 6. For example,
an account identification device (141) does not have the input
device (153), the audio device (157) and the display device (155)
in one embodiment; and in another embodiment, an account
identification device (141) does not have components (151-159).
[0419] For example, in one embodiment, an account identification
device (141) is in the form of a debit card, a credit card, a
smartcard, or a consumer device that has optional features such as
magnetic strips, or smartcards.
[0420] An example of an account identification device (141) is a
magnetic strip attached to a plastic substrate in the form of a
card. The magnetic strip is used as the memory (167) of the account
identification device (141) to provide the account information
(142). Consumer information, such as account number, expiration
date, and consumer name may be printed or embossed on the card. A
semiconductor chip implementing the memory (167) and the
communication device (159) may also be embedded in the plastic card
to provide account information (142) in one embodiment.
[0421] In one embodiment, the account identification device (141)
has the semiconductor chip but not the magnetic strip.
[0422] In one embodiment, the account identification device (141)
is integrated with a security device, such as an access card, a
radio frequency identification (RFID) tag, a security card, a
transponder, etc.
[0423] In one embodiment, the account identification device (141)
is a handheld and compact device. In one embodiment, the account
identification device (141) has a size suitable to be placed in a
wallet or pocket of the consumer.
[0424] Some examples of an account identification device (141)
include a credit card, a debit card, a stored value device, a
payment card, a gift card, a smartcard, a smart media card, a
payroll card, a health care card, a wrist band, a keychain device,
a supermarket discount card, a transponder, and a machine readable
medium containing account information (142).
Point of Interaction
[0425] In one embodiment, the point of interaction (107) is to
provide an advertisement to the user (101), or to provide
information derived from the transaction data (109) to the user
(101).
[0426] In one embodiment, an advertisement is a marketing
interaction which may include an announcement and/or an offer of a
benefit, such as a discount, incentive, reward, coupon, gift, cash
back, or opportunity (e.g., special ticket/admission). An
advertisement may include an offer of a product or service, an
announcement of a product or service, or a presentation of a brand
of products or services, or a notice of events, facts, opinions,
etc. The advertisements can be presented in text, graphics, audio,
video, or animation, and as printed matter, web content,
interactive media, etc. An advertisement may be presented in
response to the presence of a financial transaction card, or in
response to a financial transaction card being used to make a
financial transaction, or in response to other user activities,
such as browsing a web page, submitting a search request,
communicating online, entering a wireless communication zone, etc.
In one embodiment, the presentation of advertisements may be not a
result of a user action.
[0427] In one embodiment, the point of interaction (107) can be one
of various endpoints of the transaction network, such as point of
sale (POS) terminals, automated teller machines (ATMs), electronic
kiosks (or computer kiosks or interactive kiosks), self-assist
checkout terminals, vending machines, gas pumps, websites of banks
(e.g., issuer banks or acquirer banks of credit cards), bank
statements (e.g., credit card statements), websites of the
transaction handler (103), websites of merchants, checkout websites
or web pages for online purchases, etc.
[0428] In one embodiment, the point of interaction (107) may be the
same as the transaction terminal (105), such as a point of sale
(POS) terminal, an automated teller machine (ATM), a mobile phone,
a computer of the user for an online transaction, etc. In one
embodiment, the point of interaction (107) may be co-located with,
or near, the transaction terminal (105) (e.g., a video monitor or
display, a digital sign), or produced by the transaction terminal
(e.g., a receipt produced by the transaction terminal (105)). In
one embodiment, the point of interaction (107) may be separate from
and not co-located with the transaction terminal (105), such as a
mobile phone, a personal digital assistant, a personal computer of
the user, a voice mail box of the user, an email inbox of the user,
a digital sign, etc.
[0429] For example, the advertisements can be presented on a
portion of media for a transaction with the customer, which portion
might otherwise be unused and thus referred to as a "white space"
herein. A white space can be on a printed matter (e.g., a receipt
printed for the transaction, or a printed credit card statement),
on a video display (e.g., a display monitor of a POS terminal for a
retail transaction, an ATM for cash withdrawal or money transfer, a
personal computer of the customer for online purchases), or on an
audio channel (e.g., an interactive voice response (IVR) system for
a transaction over a telephonic device).
[0430] In one embodiment, the white space is part of a media
channel available to present a message from the transaction handler
(103) in connection with the processing of a transaction of the
user (101). In one embodiment, the white space is in a media
channel that is used to report information about a transaction of
the user (101), such as an authorization status, a confirmation
message, a verification message, a user interface to verify a
password for the online use of the account information (142), a
monthly statement, an alert or a report, or a web page provided by
the portal (143) to access a loyalty program associated with the
consumer account (146) or a registration program.
[0431] In other embodiments, the advertisements can also be
presented via other media channels which may not involve a
transaction processed by the transaction handler (103). For
example, the advertisements can be presented on publications or
announcements (e.g., newspapers, magazines, books, directories,
radio broadcasts, television, digital signage, etc., which may be
in an electronic form, or in a printed or painted form). The
advertisements may be presented on paper, on websites, on
billboards, on digital signs, or on audio portals.
[0432] In one embodiment, the transaction handler (103) purchases
the rights to use the media channels from the owner or operators of
the media channels and uses the media channels as advertisement
spaces. For example, white spaces at a point of interaction (e.g.,
107) with customers for transactions processed by the transaction
handler (103) can be used to deliver advertisements relevant to the
customers conducting the transactions; and the advertisement can be
selected based at least in part on the intelligence information
derived from the accumulated transaction data (109) and/or the
context at the point of interaction (107) and/or the transaction
terminal (105).
[0433] In general, a point of interaction (e.g., 107) may or may
not be capable of receiving inputs from the customers, and may or
may not co-located with a transaction terminal (e.g., 105) that
initiates the transactions. The white spaces for presenting the
advertisement on the point of interaction (107) may be on a portion
of a geographical display space (e.g., on a screen), or on a
temporal space (e.g., in an audio stream).
[0434] In one embodiment, the point of interaction (107) may be
used to primarily to access services not provided by the
transaction handler (103), such as services provided by a search
engine, a social networking website, an online marketplace, a blog,
a news site, a television program provider, a radio station, a
satellite, a publisher, etc.
[0435] In one embodiment, a consumer device is used as the point of
interaction (107), which may be a non-portable consumer device or a
portable computing device. The consumer device is to provide media
content to the user (101) and may receive input from the user
(101).
[0436] Examples of non-portable consumer devices include a computer
terminal, a television set, a personal computer, a set-top box, or
the like. Examples of portable consumer devices include a portable
computer, a cellular phone, a personal digital assistant (PDA), a
pager, a security card, a wireless terminal, or the like. The
consumer device may be implemented as a data processing system as
illustrated in FIG. 7, with more or fewer components.
[0437] In one embodiment, the consumer device includes an account
identification device (141). For example, a smart card used as an
account identification device (141) is integrated with a mobile
phone, or a personal digital assistant (PDA).
[0438] In one embodiment, the point of interaction (107) is
integrated with a transaction terminal (105). For example, a
self-service checkout terminal includes a touch pad to interact
with the user (101); and an ATM machine includes a user interface
subsystem to interact with the user (101).
Hardware
[0439] In one embodiment, a computing apparatus is configured to
include some of the modules or components illustrated in FIGS. 1
and 4, such as the transaction handler (103), the profile generator
(121), the media controller (115), the portal (143), the profile
selector (129), the advertisement selector (133), the user tracker
(113), the correlator, and their associated storage devices, such
as the data warehouse (149).
[0440] In one embodiment, at least some of the modules or
components illustrated in FIGS. 1 and 4, such as the transaction
handler (103), the transaction terminal (105), the point of
interaction (107), the user tracker (113), the media controller
(115), the correlator (117), the profile generator (121), the
profile selector (129), the advertisement selector (133), the
portal (143), the issuer processor (145), the acquirer processor
(147), and the account identification device (141), can be
implemented as a computer system, such as a data processing system
illustrated in FIG. 7, with more or fewer components. Some of the
modules may share hardware or be combined on a computer system. In
one embodiment, a network of computers can be used to implement one
or more of the modules.
[0441] Further, the data illustrated in FIG. 1, such as transaction
data (109), account data (111), transaction profiles (127), and
advertisement data (135), can be stored in storage devices of one
or more computers accessible to the corresponding modules
illustrated in FIG. 1. For example, the transaction data (109) can
be stored in the data warehouse (149) that can be implemented as a
data processing system illustrated in FIG. 7, with more or fewer
components.
[0442] In one embodiment, the transaction handler (103) is a
payment processing system, or a payment card processor, such as a
card processor for credit cards, debit cards, etc.
[0443] FIG. 7 illustrates a data processing system according to one
embodiment. While FIG. 7 illustrates various components of a
computer system, it is not intended to represent any particular
architecture or manner of interconnecting the components. One
embodiment may use other systems that have fewer or more components
than those shown in FIG. 7.
[0444] In FIG. 7, the data processing system (170) includes an
inter-connect (171) (e.g., bus and system core logic), which
interconnects a microprocessor(s) (173) and memory (167). The
microprocessor (173) is coupled to cache memory (179) in the
example of FIG. 7.
[0445] In one embodiment, the inter-connect (171) interconnects the
microprocessor(s) (173) and the memory (167) together and also
interconnects them to input/output (I/O) device(s) (175) via I/O
controller(s) (177). I/O devices (175) may include a display device
and/or peripheral devices, such as mice, keyboards, modems, network
interfaces, printers, scanners, video cameras and other devices
known in the art. In one embodiment, when the data processing
system is a server system, some of the I/O devices (175), such as
printers, scanners, mice, and/or keyboards, are optional.
[0446] In one embodiment, the inter-connect (171) includes one or
more buses connected to one another through various bridges,
controllers and/or adapters. In one embodiment the I/O controllers
(177) include a USB (Universal Serial Bus) adapter for controlling
USB peripherals, and/or an IEEE-1394 bus adapter for controlling
IEEE-1394 peripherals.
[0447] In one embodiment, the memory (167) includes one or more of:
ROM (Read Only Memory), volatile RAM (Random Access Memory), and
non-volatile memory, such as hard drive, flash memory, etc.
[0448] Volatile RAM is typically implemented as dynamic RAM (DRAM)
which requires power continually in order to refresh or maintain
the data in the memory. Non-volatile memory is typically a magnetic
hard drive, a magnetic optical drive, an optical drive (e.g., a DVD
RAM), or other type of memory system which maintains data even
after power is removed from the system. The non-volatile memory may
also be a random access memory.
[0449] The non-volatile memory can be a local device coupled
directly to the rest of the components in the data processing
system. A non-volatile memory that is remote from the system, such
as a network storage device coupled to the data processing system
through a network interface such as a modem or Ethernet interface,
can also be used.
[0450] In this description, some functions and operations are
described as being performed by or caused by software code to
simplify description. However, such expressions are also used to
specify that the functions result from execution of the
code/instructions by a processor, such as a microprocessor.
[0451] Alternatively, or in combination, the functions and
operations as described here can be implemented using special
purpose circuitry, with or without software instructions, such as
using Application-Specific Integrated Circuit (ASIC) or
Field-Programmable Gate Array (FPGA). Embodiments can be
implemented using hardwired circuitry without software
instructions, or in combination with software instructions. Thus,
the techniques are limited neither to any specific combination of
hardware circuitry and software, nor to any particular source for
the instructions executed by the data processing system.
[0452] While one embodiment can be implemented in fully functioning
computers and computer systems, various embodiments are capable of
being distributed as a computing product in a variety of forms and
are capable of being applied regardless of the particular type of
machine or computer-readable media used to actually effect the
distribution.
[0453] At least some aspects disclosed can be embodied, at least in
part, in software. That is, the techniques may be carried out in a
computer system or other data processing system in response to its
processor, such as a microprocessor, executing sequences of
instructions contained in a memory, such as ROM, volatile RAM,
non-volatile memory, cache or a remote storage device.
[0454] Routines executed to implement the embodiments may be
implemented as part of an operating system or a specific
application, component, program, object, module or sequence of
instructions referred to as "computer programs." The computer
programs typically include one or more instructions set at various
times in various memory and storage devices in a computer, and
that, when read and executed by one or more processors in a
computer, cause the computer to perform operations necessary to
execute elements involving the various aspects.
[0455] A machine readable medium can be used to store software and
data which when executed by a data processing system causes the
system to perform various methods. The executable software and data
may be stored in various places including for example ROM, volatile
RAM, non-volatile memory and/or cache. Portions of this software
and/or data may be stored in any one of these storage devices.
Further, the data and instructions can be obtained from centralized
servers or peer to peer networks. Different portions of the data
and instructions can be obtained from different centralized servers
and/or peer to peer networks at different times and in different
communication sessions or in a same communication session. The data
and instructions can be obtained in entirety prior to the execution
of the applications. Alternatively, portions of the data and
instructions can be obtained dynamically, just in time, when needed
for execution. Thus, it is not required that the data and
instructions be on a machine readable medium in entirety at a
particular instance of time.
[0456] Examples of computer-readable media include but are not
limited to recordable and non-recordable type media such as
volatile and non-volatile memory devices, read only memory (ROM),
random access memory (RAM), flash memory devices, floppy and other
removable disks, magnetic disk storage media, optical storage media
(e.g., Compact Disk Read-Only Memory (CD ROMS), Digital Versatile
Disks (DVDs), etc.), among others. The computer-readable media may
store the instructions.
[0457] The instructions may also be embodied in digital and analog
communication links for electrical, optical, acoustical or other
forms of propagated signals, such as carrier waves, infrared
signals, digital signals, etc. However, propagated signals, such as
carrier waves, infrared signals, digital signals, etc. are not
tangible machine readable medium and are not configured to store
instructions.
[0458] In general, a machine readable medium includes any mechanism
that provides (i.e., stores and/or transmits) information in a form
accessible by a machine (e.g., a computer, network device, personal
digital assistant, manufacturing tool, any device with a set of one
or more processors, etc.).
[0459] In various embodiments, hardwired circuitry may be used in
combination with software instructions to implement the techniques.
Thus, the techniques are neither limited to any specific
combination of hardware circuitry and software nor to any
particular source for the instructions executed by the data
processing system.
Other Aspects
[0460] The description and drawings are illustrative and are not to
be construed as limiting. The present disclosure is illustrative of
inventive features to enable a person skilled in the art to make
and use the techniques. Various features, as described herein,
should be used in compliance with all current and future rules,
laws and regulations related to privacy, security, permission,
consent, authorization, and others. Numerous specific details are
described to provide a thorough understanding. However, in certain
instances, well known or conventional details are not described in
order to avoid obscuring the description. References to one or an
embodiment in the present disclosure are not necessarily references
to the same embodiment; and, such references mean at least one.
[0461] The use of headings herein is merely provided for ease of
reference, and shall not be interpreted in any way to limit this
disclosure or the following claims.
[0462] Reference to "one embodiment" or "an embodiment" means that
a particular feature, structure, or characteristic described in
connection with the embodiment is included in at least one
embodiment of the disclosure. The appearances of the phrase "in one
embodiment" in various places in the specification are not
necessarily all referring to the same embodiment, and are not
necessarily all referring to separate or alternative embodiments
mutually exclusive of other embodiments. Moreover, various features
are described which may be exhibited by one embodiment and not by
others. Similarly, various requirements are described which may be
requirements for one embodiment but not other embodiments. Unless
excluded by explicit description and/or apparent incompatibility,
any combination of various features described in this description
is also included here.
[0463] The disclosures of the above discussed patent documents are
hereby incorporated herein by reference.
[0464] In the foregoing specification, the disclosure has been
described with reference to specific exemplary embodiments thereof.
It will be evident that various modifications may be made thereto
without departing from the broader spirit and scope as set forth in
the following claims. The specification and drawings are,
accordingly, to be regarded in an illustrative sense rather than a
restrictive sense.
* * * * *