U.S. patent application number 13/438346 was filed with the patent office on 2013-01-24 for method and system for measuring advertising effectiveness using microsegments.
This patent application is currently assigned to MasterCard International Incorporated. The applicant listed for this patent is Curtis VILLARS. Invention is credited to Curtis VILLARS.
Application Number | 20130024274 13/438346 |
Document ID | / |
Family ID | 47556422 |
Filed Date | 2013-01-24 |
United States Patent
Application |
20130024274 |
Kind Code |
A1 |
VILLARS; Curtis |
January 24, 2013 |
METHOD AND SYSTEM FOR MEASURING ADVERTISING EFFECTIVENESS USING
MICROSEGMENTS
Abstract
A method for analyzing advertising effectiveness includes
storing entity information including activity and characteristic
information associated with a plurality of entities; generating a
plurality of microsegments, each microsegment including a subset of
the plurality of entities based on the associated characteristic
information; generating a test audience including a plurality of
first microsegments including entities exposed to an advertisement
for a period of time and a control audience including a plurality
of second microsegments including entities not deliberately exposed
to the advertisement; analyzing the activity information for the
test audience and the control audience to determine spending
behaviors for the associated entities during the period of time;
comparing the spending behaviors determined for the test and
control audiences to determine the effectiveness of the
advertisement; and reporting the effectiveness of the
advertisement.
Inventors: |
VILLARS; Curtis; (Chatham,
NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
VILLARS; Curtis |
Chatham |
NY |
US |
|
|
Assignee: |
MasterCard International
Incorporated
Purchase
NY
|
Family ID: |
47556422 |
Appl. No.: |
13/438346 |
Filed: |
April 3, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61509386 |
Jul 19, 2011 |
|
|
|
Current U.S.
Class: |
705/14.45 |
Current CPC
Class: |
G06Q 10/00 20130101;
G06Q 30/0204 20130101; G06Q 30/0201 20130101; G06Q 30/02 20130101;
G06Q 30/0269 20130101 |
Class at
Publication: |
705/14.45 |
International
Class: |
G06Q 30/02 20120101
G06Q030/02 |
Claims
1. A method of analyzing advertising effectiveness, comprising:
storing, by a database in a processing system, entity information
associated with a plurality of entities, the entity information
including activity information and characteristic information
associated with the corresponding entity; identifying a plurality
of microsegments, each microsegment including a subset of the
plurality of entities based on the associated characteristic
information, wherein no two subsets of the plurality of entities
contains a common entity at the same time; identifying a test
audience including a plurality of first microsegments and a control
audience including a plurality of second microsegments, wherein
each entity in the plurality of first microsegments is exposed to
an advertisement associated with a merchant during a predetermined
period of time and wherein each entity in the plurality of second
microsegments is not deliberately exposed to the advertisement
during the predetermined period of time; analyzing, by a processor
in the processing system, the activity information for the entities
in the plurality of first microsegments and the entities in the
plurality of second microsegments to determine spending behaviors
for the associated entity during the predetermined period of time;
comparing the spending behaviors determined for the entities in the
plurality of first microsegments with the spending behaviors
determined for the entities in the plurality of second
microsegments to determine the effectiveness of the advertisement;
and reporting, by a communication component in the processing
system, the effectiveness of the advertisement.
2. The method of claim 1, further comprising: analyzing, by a
processor in the processing system, the activity information for
the entities in the plurality of first microsegments and the
entities in the plurality of second microsegments to determine
prior spending behaviors for the associated entity during a prior
period of time, wherein the prior period of time is a time
occurring before the predetermined period of time, and wherein the
comparing step further includes comparing the prior spending
behaviors for the entities to further determine the effectiveness
of the advertisement.
3. The method of claim 1, further comprising: analyzing, by a
processor in the processing system, the activity information for
the entities in the plurality of first microsegments and the
entities in the plurality of second microsegments to determine
subsequent spending behaviors for the associated entity during a
subsequent period of time, wherein the subsequent period of time is
a time occurring after the predetermined period of time, and
wherein the comparing step further includes comparing the
subsequent spending behaviors for the entities to further determine
the effectiveness of the advertisement.
4. The method of claim 1, wherein the entity information does not
include any personally identifiable information.
5. The method of claim 1, wherein the activity information includes
financial transaction information, and wherein the characteristic
information includes demographic attributes associated with the
entity.
6. The method of claim 5, wherein the entities in the test audience
and the entities in the control audience have similar associated
demographic attributes
7. The method of claim 5, wherein the financial transaction
information includes information on financial transactions
conducted between the associated entity and the merchant.
8. The method of claim 1, wherein the subset of the plurality of
entities includes at least ten entities.
9. The method of claim 1, wherein the spending behaviors are based
on transactions conducted by the entity with the merchant.
10. The method of claim 1, wherein the spending behaviors are based
on transactions conducted by the entity with a plurality of
competitors of the merchant.
11. A system for analyzing advertising effectiveness, comprising: a
database component configured to store entity information
associated with a plurality of entities, the entity information
including activity information and characteristic information; a
processor configured to identify a plurality of microsegments, each
microsegment including a subset of the plurality of entities based
on the associated characteristic information, wherein no two
subsets of the plurality of entities contains a common entity at
the same time, identify a test audience including a plurality of
first microsegments and a control audience including a plurality of
second microsegments, wherein each entity in the plurality of first
microsegments is exposed to an advertisement associated with a
merchant during a predetermined period of time and wherein each
entity in the plurality of second microsegments is not exposed to
the advertisement during the predetermined period of time, analyze
the activity information for the entities in the plurality of first
microsegments and the entities in the plurality of second
microsegments to determine spending behaviors for the associated
entity during the predetermined period of time, and compare the
spending behaviors determined for the entities in the plurality of
first microsegments with the spending behaviors determined for the
entities in the plurality of second microsegments to determine the
effectiveness of the advertisement; and a communication component
configured to report the effectiveness of the advertisement.
12. The system of claim 11, wherein the processor is further
configured to: analyze the activity information for the entities in
the plurality of first microsegments and the entities in the
plurality of second microsegments to determine prior spending
behaviors for the associated entity during a prior period of time,
wherein the prior period of time is a time occurring before the
predetermined period of time, and wherein comparing the spending
behaviors further includes comparing the prior spending behaviors
for the entities to further determine the effectiveness of the
advertisement.
13. The system of claim 11, wherein the processor is further
configured to: analyze the activity information for the entities in
the plurality of first microsegments and the entities in the
plurality of second microsegments to determine subsequent spending
behaviors for the associated entity during a subsequent period of
time, wherein the subsequent period of time is a time occurring
after the predetermined period of time, and wherein comparing the
spending behaviors further includes comparing the subsequent
spending behaviors for the entities to further determine the
effectiveness of the advertisement.
14. The system of claim 11, wherein the entity information does not
include any personally identifiable information.
15. The system of claim 11, wherein the activity information
includes financial transaction information, and wherein the
characteristic information includes demographic attributes
associated with the entity.
16. The system of claim 15, wherein the entities in the test
audience and the entities in the control audience have similar
associated demographic attributes
17. The system of claim 15, wherein the financial transaction
information includes information on financial transactions
conducted between the associated entity and the merchant.
18. The system of claim 11, wherein the subset of the plurality of
entities includes at least ten entities.
19. The system of claim 11, wherein the spending behaviors are
based on transactions conducted by the entity with the
merchant.
20. The system of claim 11, wherein the spending behaviors are
based on transactions conducted by the entity with a plurality of
competitors of the merchant.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the priority benefit of commonly
assigned U.S. Provisional Application 61/509,386, "Protecting
Privacy in Audience Targeting," by Curtis Villars, filed Jul. 19,
2011. The subject matter of the foregoing is herein incorporated by
reference in its entirety.
FIELD
[0002] The present disclosure relates to methods for measuring the
effectiveness of advertisements, specifically measuring
advertisement effectiveness by using microsegments as applied to
exposed and unexposed consumers.
BACKGROUND
[0003] In the ever expanding information age, merchants and
advertisers have a desire to develop more effective and efficient
advertising. Traditionally, methods and systems for measuring
effectiveness of advertising have lacked in detail and efficiency.
Analysis of overall revenue and consumer activity for a particular
merchant may indicate that an advertisement campaign is effective,
but the merchant is unable to deduce if the increased activity is
from consumers exposed to the advertisement. In addition, this type
of high level analysis is unable to provide specific information
regarding advertising effectiveness, such as its effectiveness on
particular demographic groups and the strength of the response,
information which could be beneficial to not only the merchant, but
to the end consumer as well.
[0004] Some traditional methods for detailed measuring advertising
effectiveness include surveying and polling consumers. This type of
analysis has several shortcomings. Surveys and polls require
consumers to volunteer information, which may be inaccurate or
fabricated, especially if the survey or poll is anonymous. The
results of the analysis may be full of uncertainty at whether or
not each consumer was in fact exposed to the advertisement, and
whether or not the consumer's spending behavior was affected. In
addition, surveys or polls take time and require consumer
participation, which may result in a small and/or
non-representative sample of all consumers. Furthermore, increased
consumer concerns for privacy and security of personal information
may result in even less participation and/or more unreliable
information.
[0005] Thus, there is a perceived opportunity to provide a
technical solution for improving measurement of advertising
effectiveness by analyzing actual financial transaction information
for exposed and unexposed consumers, while still maintaining the
privacy and security of consumer information.
SUMMARY
[0006] The present disclosure provides for a system and method for
analyzing advertising effectiveness.
[0007] A method for analyzing advertising effectiveness includes
storing, by a database in a processing system, entity information
associated with a plurality of entities, the entity information
including activity information and characteristic information
associated with the corresponding entity; generating a plurality of
microsegments, each microsegment including a subset of the
plurality of entities based on the associated characteristic
information, wherein no two subsets of the plurality of entities
contains a common entity; and generating a test audience including
a plurality of first microsegments and a control audience including
a plurality of second microsegments, wherein each entity in the
plurality of first microsegments is exposed to an advertisement
associated with a merchant during a predetermined period of time
and wherein each entity in the plurality of second microsegments is
not exposed to the advertisement during the predetermined period of
time. The method also includes analyzing, by a processor in the
processing system, the activity information for the entities in the
plurality of first microsegments and the entities in the plurality
of second microsegments to determine spending behaviors for the
associated entity during the predetermined period of time. The
method further includes comparing the spending behaviors determined
for the entities in the plurality of first microsegments with the
spending behaviors determined for the entities in the plurality of
second microsegments to determine the effectiveness of the
advertisement and reporting, by a communication component in the
processing system, the effectiveness of the advertisement.
[0008] A system for analyzing advertising effectiveness includes a
database component configured to store entity information
associated with a plurality of entities, the entity information
including activity information and characteristic information, a
processor, and a communication component. The processor is
configured to: generate a plurality of microsegments, each
microsegment including a subset of the plurality of entities based
on the associated characteristic information, wherein no two
subsets of the plurality of entities contains a common entity;
generate a test audience including a plurality of first
microsegments and a control audience including a plurality of
second microsegments, wherein each entity in the plurality of first
microsegments is exposed to an advertisement associated with a
merchant during a predetermined period of time and wherein each
entity in the plurality of second microsegments is not exposed to
the advertisement during the predetermined period of time; analyze
the activity information for the entities in the plurality of first
microsegments and the entities in the plurality of second
microsegments to determine spending behaviors for the associated
entity during the predetermined period of time; and compare the
spending behaviors determined for the entities in the plurality of
first microsegments with the spending behaviors determined for the
entities in the plurality of second microsegments to determine the
effectiveness of the advertisement. The communication component is
configured to report the effectiveness of the advertisement
BRIEF DESCRIPTION OF THE DRAWING FIGURES
[0009] Exemplary embodiments are best understood from the following
detailed description when read in conjunction with the accompanying
drawings. Included in the drawings are the following figures:
[0010] FIG. 1 is a block diagram illustrating a high-level view of
system architecture of a financial transaction processing system in
accordance with exemplary embodiments.
[0011] FIG. 2 is a flow chart illustrating a method for generating
microsegments without the use of personally identifiable
information in accordance with exemplary embodiments.
[0012] FIG. 3 is a data set illustrating useable consumer data
without including personally identifiable information in accordance
with exemplary embodiments.
[0013] FIGS. 4A and 4B are data sets illustrating microsegments
created from the data set of FIG. 3 in accordance with exemplary
embodiments.
[0014] FIG. 5 is a block diagram illustrating a data set for use
with the disclosed methods in accordance with exemplary
embodiments.
[0015] FIG. 6 is a block diagram illustrating a system for
analyzing the effectiveness of advertisements in accordance with
exemplary embodiments.
[0016] FIGS. 7 and 8 are flowcharts illustrating methods for
measuring advertisement effectiveness in accordance with exemplary
embodiments.
[0017] FIG. 9 is a flowchart illustrating an exemplary method for
analyzing the effectiveness of an advertisement in accordance with
exemplary embodiments.
[0018] Further areas of applicability of the present disclosure
will become apparent from the detailed description provided
hereinafter. It should be understood that the detailed description
of exemplary embodiments are intended for illustration purposes
only and are, therefore, not intended to necessarily limit the
scope of the disclosure.
DETAILED DESCRIPTION
Financial Transaction Processing System
[0019] FIG. 1 illustrates a financial transaction processing system
100 including a customer (e.g., a consumer) 102, a merchant 104, an
issuer 106, a financial transaction processing agency 108, and a
demographic tracking agency 110.
[0020] The customer 102 may use a payment card at the merchant 104
for payment of a financial transaction. The payment card may be any
type of transaction card used for making payments in a financial
transaction, such as a debit card, credit card, charge card, ATM
card, etc. Each payment card may be assigned a unique identifier
(e.g., an account number) that links the payment card to a
cardholder (e.g., the customer 102).
[0021] The merchant 104 may forward the payment card information
(e.g., the account number) as well as transaction information
(e.g., the amount, merchant information, time and date information,
etc.) to the financial transaction processing agency 108 for
processing. The financial transaction processing agency 108 may be
any service provider for merchants, acquirers, issuers, consumers,
etc. for the processing of transactions involving payment cards,
such as MasterCard or VISA. The financial transaction processing
agency 108 may issue an authorization request from the issuer 106.
The issuer 106 may be an entity (e.g., a bank or the merchant 104)
that issued the payment card used in the transaction, a stand-in
processor configured to act on behalf of the issuer of the payment
card, a credit bureau that has card or consumer related
information, or any other suitable entity.
[0022] The issuer 106 may approve or deny the transaction. If the
issuer 106 approves the transaction, the issuer 106 notifies the
financial transaction processing agency 108 of the approval. The
financial transaction processing agency 108 may then notify the
merchant 104 of the approval of the transaction, who may then
finalize the transaction with the customer 102. The issuer 106 may
then bill the customer 102 for payment of the transaction and
report any payments, or lack thereof, to the demographic tracking
agency 110 (e.g., a credit report agency, a marketing and research
firm such as Nielsen, etc.). The demographic tracking agency 110,
therefore, may possess personally identifiable information (PII) of
the customer 102, which may be stored in the external database 114,
though the financial transaction processing agency 108 would not be
in possession of the PII or have access to it.
Personally Identifiable Information
[0023] Personally identifiable information (PII) may be information
that may be used, alone or in conjunction with other sources, to
uniquely identify a single individual (e.g., the customer 102). As
such, there is a benefit to prevent the use and dissemination of
PII in an effort to protect consumer privacy and to prevent against
crimes, such as identity theft. The present disclosure provides for
methods where the financial transaction processing agency 108
(e.g., MasterCard) does not possess any data containing personally
identifiable information in processes that help accurately identify
groups of individuals or businesses having particular interests or
desires across a broad and diverse population of cardholders.
[0024] This is done, viewed at a high level, by enriched data
associated with individuals or businesses (entities), to include
transaction history and demographics, but not PII, as associated by
a unique identifier, and placing like entities, filtered by some
criteria, into small groups. Therefore, third parties that have
contact information for entities can group them and match them to
the enriched data groups. Whether or not the groups from the
combined/enriched data sets and from the data sets have parity,
common members, or no overlap, statistically the matched groups
have predictable behavior, particularly in small groups or
microsegments (as defined below). Having grouped the third party's
data set members into small groups based on selected activities
and/or characteristics (e.g., demographic and geographic
information), the third party can effectively direct communications
of interest to these small groups or microsegments. The third party
may possess contact information, which may include PII, such as
e-mail addresses, phone numbers, etc. In an exemplary embodiment,
the contact information that may include PII may be removed from
the third party data set or made otherwise unavailable to the
financial transaction processing agency 108.
[0025] In some embodiments, bucketing may be used in order to
render potentially identifiable information anonymous. such as by
aggregating information that may otherwise be personally
identifiable (e.g., age, income, etc.) into a bucket (e.g.,
grouping) in order to render the information not personally
identifiable. For example, a consumer of age 26 with an income of
$65,000, which may otherwise be unique in a particular circumstance
to that consumer, may be represented by an age bucket for ages
21-30 and an income bucket for incomes $50,000 to $74,999, which
may represent a large portion of additional consumers and thus no
longer be personally identifiable to that consumer. In other
embodiments, encryption may be used. For example, personally
identifiable information (e.g., an account number) may be encrypted
(e.g., using a one-way encryption) such that the financial
transaction processing agency 108 may not possess the PII or be
able to decrypt the encrypted PII.
[0026] Information that may be considered personally identifiable
may be defined by a third party, such as a governmental agency
(e.g., the U.S. Federal Trade Commission, the European Commission,
etc.), a non-governmental organization (e.g., the Electronic
Frontier Foundation), industry custom, consumers (e.g., through
consumer surveys, contracts, etc.), codified laws, regulations, or
statutes, etc.
Protection of PII in a Financial Transaction Processing System
[0027] As illustrated in FIG. 1, the financial transaction
processing agency 108 may include a database without PII 112 and an
enriched database 116, which also does not include PII. The
demographic tracking agency 110 may include the external database
114, which may include PII not accessible by the financial
transaction processing agency 108.
[0028] The database without PII 112 may store information on a
plurality of consumers (e.g., the customer 102) that is not
personally identifiable. For example, the financial transaction
processing agency 108 may store information relating to financial
transactions processed by the agency as it performs in the system
100, such as transaction amount, transaction time, transaction
location, merchant identification, etc. and do so without the use
of any PII relating to the customer 102 participating in the
transactions. In some embodiments, the database without PII 112 may
store an encrypted unique identifier associated with a consumer,
which may be encrypted using a one-way encryption, such that the
financial transaction processing agency 108 may be unable to
identify the associated consumer. Methods of encryption suitable
for performing the functions as disclosed herein will be apparent
to persons having skill in the relevant art.
[0029] The financial transaction processing agency 108 may
communicate with the demographic tracking agency 110 (e.g., via a
network such as the network 906, discussed below). The financial
transaction processing agency 108 may obtain non-personally
identifiable information included the external database 114.
Non-personally identifiable information included in the external
database 114 may include geographical data, demographic data,
financial data, or any other suitable data as will be apparent to
persons having skill in the relevant art, hereinafter referred to
generally as demographic data. In one embodiment, the information
included in the external database 114 may be bucketed and thus not
personally identifiable. The financial transaction processing
agency 108 may combine the non-personally identifiable information
provided by the demographic tracking agency 110 with information
included in the database without PII 112 into a single data set.
The combined data set may be stored in the enriched database 116.
In some embodiments, the financial transaction processing agency
108 may aggregate (e.g., bucket, group, etc.) data in each of the
external database 114 and the database without PII 112 prior to
combining the information into a single data set. In a further
embodiment, the financial transaction processing agency 108 may
aggregate data to a level of ten prior to combining the information
into a single data set.
[0030] Each of the databases 112, 114, and 116 may be any type of
database suitable for the storage of data as disclosed herein. Each
database may store data in a single database, or may store data
across multiple databases and accessed through a network. Network
configurations as disclosed herein may include a local area network
(LAN), a wide area network (WAN), a wireless network (e.g., WiFi),
a mobile communication network, a satellite network, the Internet,
fiber optic, coaxial cable, infrared, radio frequency (RF) or any
other suitable configuration as would be apparent to persons having
skill in the relevant art.
[0031] Data may be stored on any type of suitable computer readable
media, such as optical storage (e.g., a compact disc, digital
versatile disc, blu-ray disc, etc.) or magnetic tape storage (e.g.,
a hard disk drive). The database may be configured in any type of
suitable database configuration, such as a relational database, a
structured query language (SQL) database, a distributed database,
an object database, etc. Suitable configurations and database
storage types will be apparent to persons having skill in the
relevant art.
[0032] The database without PII 112 and the enriched database 116
may be included as part of the financial transaction processing
agency 108 internally, or externally and accessed through a
network. The external database 114 may be included as part of the
demographic tracking agency 110 internally, or externally and
accessed through a network. Each database may be a single database,
or may comprise multiple databases which may be interfaced together
(e.g., physically or via a network, such as the network 906). In
some embodiments, the database without PII 112 and the enriched
database 116 may be a single database.
[0033] The financial transaction processing agency 108 may include
a processor 102, which may be any type of processing device capable
of performing the functions as disclosed herein, such as a general
purpose computer, a general purpose computer configured as
disclosed herein to become a specific purpose computer, etc. The
processing device may be a single system (e.g., a single specific
purpose computer) or may be comprised of several interconnected
(e.g., physically or through a network) systems or servers (e.g., a
server farm). The processor 102 may be coupled to each of the
databases 112, 114, and 116 either physically (e.g., through a
cable such as a coaxial cable, fiber-optic cable, etc.) or through
a network (e.g., the network 906).
[0034] The processor 102 may be configured to receive information
from both the database without PII 112 and to receive information
with the PII removed from the external database 114, and to combine
the data to form a combined data set without PII. In some
embodiments, the processor 102 may aggregate the information
received from at least one of the two databases prior to combining
the information into the combined data set. The processor 102 may
also be configured to store the combined data set (e.g., that does
not include PII) in the enriched database 116. The processor 102
may be further configured to review the combined data set or to
select microsegments or audiences based on the combined data set,
as discussed in more detail below. In some embodiments, the
processor 102 may be configured to review selected microsegments
and/or audiences and generate reports therein.
Creation of Microsegments
[0035] FIG. 2 illustrates a method for generating microsegments
without the use of personally identifiable information. The method
is disclosed with reference to the processor 102, the database
without PII 112 and enriched database 116 as part of the financial
transaction processing agency 108, and the external database 114 of
the demographic tracking agency 110.
[0036] Information that is stored in the database without PII 112
may be retrieved (e.g., by the processor 102) in step 202. In one
embodiment, all of the information stored in the database without
PII 112 may be retrieved. In another embodiment, only a single
entry in the database without PII 112 may be retrieved. The
retrieval of information may be performed a single time, or may be
performed multiple times. In an exemplary embodiment, only
information pertaining to a specific microsegment (discussed
further below) may be retrieved from the database without PII
112.
[0037] In step 204, the retrieved information may be associated
with an entity (e.g., a cardholder, a business, a microsegment, any
group or combination thereof, etc.) by the processor 102. In one
embodiment, each entity may be represented by a unique identifier,
such as a unique identification number (e.g., an account number).
In one embodiment, entity information may be encrypted.
[0038] The processor 102 may retrieve, in step 206, information
(e.g., that does not include any personally identifiable
information) from the external database 114. The retrieval
performed in step 206 may be of the same type and retrieve the
corresponding information (e.g., relating to the same microsegment)
as the information retrieved from the database without PII 112 in
step 202. In one embodiment, if the external database 114 includes
PII, the financial transaction processing agency 108 may be
prohibited from accessing the PII. The information retrieved in
this step may, in step 208, then be associated with an entity
(e.g., the same entity from step 202). In step 210, a record may be
created in the enriched database 116. The enriched database 116 may
store the information obtained and associated in the prior steps,
the information not containing any PII. As a result, the financial
transaction processing agency 108 may not be in contact with or
have access to any PII during the process.
[0039] Microsegments (as defined below) may be selected, in step
212, based on the information that was obtained and stored in the
enriched database 116. The selection of information for
representation in the microsegment or microsegments may be
different in every instance. In one embodiment, all of the
information stored in the enriched database 116 may be used for
selecting microsegments. In an alternative embodiment, only a
portion of the information may be used. The selection of
microsegments may be based on specific criteria (e.g., from a
research firm or advertising agency such as the advertiser 118
illustrated in FIG. 6).
[0040] In step 214, information may be reported by the processor
102. Reporting may include the review and/or reporting of the
selected microsegments, of the information stored in the enriched
database 116, or a combination thereof. Reviewing may include a
review of financial account information of the entities in the
microsegments, performing statistical analysis on financial account
information, finding correlations between account information and
consumer behaviors, predicting future consumer behaviors based on
account information, relating information on a financial account
with other financial accounts, or any other method of review
suitable for the particular application of the data, which will be
apparent to persons having skill in the relevant art. In an
exemplary embodiment, statistical analysis may be performed on the
financial data for specific microsegments stored in the enriched
database 116 in order to determine the effectiveness of an
advertisement without the use of any PII, as illustrated in methods
discussed below.
[0041] The report may be transmitted to a third party (e.g., the
advertiser 118) or the financial transaction processing agency 108,
may be displayed (e.g., on a display device), or may be reported in
any other manner suitable for reporting. The reporting may include
a report on a review of the selected microsegments or information,
or any other suitable information, such as an analysis of the
review (e.g., and performed by the financial transaction processing
agency 108). Reporting may be performed visually, aurally,
tactically, or in any other suitable method as will be apparent to
persons having skill in the relevant art.
Microsegment Definition and Creation
[0042] A microsegment is a representation of a group of consumers
that is granular enough to be valuable to advertisers, marketers,
etc., but still maintain a high level of consumer privacy without
the use or obtaining of any personally identifiable
information.
[0043] In step 214, information may be reported by the processor
102. Reporting may include the review and/or reporting of the
selected microsegments, of the information stored in the enriched
database 116, or a combination thereof. Reviewing may include a
review of financial account information of the entities in the
microsegments, performing statistical analysis on financial account
information, finding correlations between account information and
consumer behaviors, predicting future consumer behaviors based on
account information, relating information on a financial account
with other financial accounts, or any other method of review
suitable for the particular application of the data, which will be
apparent to persons having skill in the relevant art. In an
exemplary embodiment, statistical analysis may be performed on the
financial data for specific microsegments stored in the enriched
database 116 in order to determine the effectiveness of an
advertisement without the use of any PII, as illustrated in methods
discussed below.
[0044] The report may be transmitted to a third party (e.g., the
advertiser 118) or the financial transaction processing agency 108,
may be displayed (e.g., on a display device), or may be reported in
any other manner suitable for reporting. The reporting may include
a report on a review of the selected microsegments or information,
or any other suitable information, such as an analysis of the
review (e.g., and performed by the financial transaction processing
agency 108). Reporting may be performed visually, aurally,
tactically, or in any other suitable method as will be apparent to
persons having skill in the relevant art.
Microsegment Definition and Creation
[0045] A microsegment is a representation of a group of consumers
that is granular enough to be valuable to advertisers, marketers,
etc., but still maintain a high level of consumer privacy without
the use or obtaining of any personally identifiable
information.
[0046] Microsegments may be given a minimum or a maximum size. A
minimum size of a microsegment would be at a minimum large enough
so that nowould not result in entity could be personally
identifiable, but small enough to provide the granularity needed in
a particular circumstance. In some instances, the size of a
microsegment may be dependent on the application. An audience based
on a plurality of microsegments, for instance, might have ten
thousand entities, but the microsegments would be aggregated when
forming the audience and would not be discernable to anyone having
access to an audience. As noted elsewhere, the entities in a
microsegment that is used to form an audience might not be members
of a resulting audience at all. In one embodiment, a microsegment
may include at least ten unique entities. Microsegments may be
defined based on geographical or demographical information, such as
age, gender, income, marital status, postal code, income, spending
propensity, familial status, etc. Categories may be bucketed to
avoid the use of PII (e.g., representing age by a range of ages).
In some embodiments, microsegments may be defined by a plurality of
geographical and/or demographical categories. For example, a
microsegment may be defined for any cardholder with an income
between $50,000 and $74,999, that is between the ages of 20 and 29,
and is single.
[0047] In this way, microsegments may be defined in such a way as
to avoid the use of PII. For example, if a preliminary microsegment
is defined for entities with an income between $100,000 and
$149,999 in a particular postal code, and the preliminary
microsegment contains less than a minimum number (e.g., as provided
by the advertiser, governmental regulations, etc.) ofentitiesone
entity, the preliminary microsegment may be combined with another
microsegment (e.g., one corresponding to a neighboring postal code)
as to further protect the personal identity of the entities in the
preliminary microsegment. In this way, microsegments will be
defined in a way so that no entity in any microsegment is
personally identifiable.
[0048] Microsegments may also be based on behavioral variables. For
example, the database without PII 112 may store information
relating to financial transactions. The information may be used to
determine an individual's likeliness to spend. An individual's
likeliness to spend may be represented generally, or with respect
to a particular industry (e.g., electronics), retailer (e.g.,
Macy's.RTM.), brand (e.g., Apple.RTM.), or any other criteria which
may be suitable as will be apparent to persons having skill in the
relevant art. An individual's behavior may also be based on
additional factors such as time, location, season, etc. For
example, a microsegment may be based on consumers who are likely to
spend on electronics during the holiday season, or on consumers
whose primary expenses are in a suburb, but are likely to spend on
restaurants located in a major city. The factors and behaviors
identified and used to define microsegments may vary widely and may
be based on the application of the information.
[0049] Behavioral variables may also be applied to generated
microsegments based on the attributes of the entities in the
microsegment. For example, a microsegment of specific geographical
and demographical attributes (e.g., single males in a particular
postal code between the ages of 26-30 with an income between
$100,000 and $149,999) may be analyzed for spending behaviors.
Results of the analysis may be assigned to the microsegment. For
example, the above microsegment may be analyzed and reveal that the
entities in the microsegment have a high spending propensity for
electronics and may be less likely to spend money during the month
of February.
[0050] FIG. 3 illustrates consumer information data that may be
used in the creation of a microsegment. The data represented in the
six leftmost columns may be information that is stored in the
external database 114 at the demographic tracking agency 110, with
any included PII removed or made otherwise inaccessible to the
financial transaction processing agency 108 or the processor 102,
in order to protect consumer privacy. The data represented in the
six rightmost columns may be information that is stored in the
financial transaction processing agency 110 database without PII
112. In the illustrated embodiment, there is a unique identifier
for each consumer that has been encrypted in order to protect the
anonymity of the consumer.
[0051] The data from the external database 114 and the data from
the database without PII 112 may be combined into a single set of
data that does not contain PII, which may be stored in the enriched
database 116. Information may be combined by use of the unique
encrypted identifier for each entity. In one embodiment, if only
one set of data contains a particular identifier, then that data
may be left out of the enriched data set. In some embodiments, only
some of the columns of data may be included in the enriched data
set. For example, the marital status column may not be included
(e.g., because the advertiser does not distinguish consumers based
on marital status).
[0052] The enriched data set may be stored in the enriched database
116. The enriched data may be separated into a plurality of
microsegments, with each microsegment being defined by at least one
geographical or demographical limitation. FIG. 4A illustrates the
data set of individuals in a microsegment MS1, one of a plurality
of microsegments illustrated in FIG. 4B. Microsegment MS1 includes
seven individuals, each with a unique encrypted identifier. As
illustrated in FIG. 4B, microsegment MS1 is defined by individuals
in age group C, income group B, with marital status B, and living
in postal code 12345. Groupings (e.g., age group C) are defined in
bucketed groups in such a manner as to not divulge any personally
identifiable information. In this way, consumers of an ideal age
may be placed into a microsegment (e.g., for advertising) without
the financial transaction processing agency 108 knowing the actual
age of the consumer or even a range of ages, and therefore
protecting the privacy of the consumer. The corresponding values
for the grouping (e.g., ages 25 to 34 corresponding to age group
C), may not be available to the financial transaction processing
agency 108.
[0053] As illustrated in FIG. 4B, preliminary microsegment MS4 only
contains a single individual. As a result, preliminary microsegment
MS4 may be combined with another microsegment in order to protect
the privacy of that individual. For example, preliminary
microsegment MS4 may be combined with microsegment MS1, because
preliminary microsegment MS4 is defined by the same age, income,
and marital groups, and the defined postal code is a neighboring
postal code. It will be apparent to persons having skill in the
relevant art that microsegments may be grouped or combined in any
manner that may be suitable for the particular application. For
example, a retailer may want to advertise to everyone in a
particular postal code without regard for age or income, and
therefore may desire to combine microsegment MS1 and microsegment
MS3, whereas another retailer may want to advertise to a specific
age group without regarding for other factors, and therefore would
want to combine microsegments MS1, MS2, and MS4.
Exemplary Dataset of Microsegments
[0054] FIG. 5 illustrates an exemplary dataset 502 for the storing,
reviewing, and/or reporting of a plurality of microsegments. In one
embodiment, the dataset 502 may be reported in the reporting step
214 of FIG. 2.
[0055] The dataset 502 may contain a plurality of entries (e.g.,
entries 504a, 504b, and 504c). Each entry of the plurality of
entries may include a secure identifier 506, demographic
information 508, and financial information 510. The secure
identifier 506 may include any type of identifier that may be
unique to the particular entry (e.g., entry 504a). The secure
identifier may be encrypted. Suitable encryption methods may
include public key encryption, RSA encryption, XOR encryption,
SHA-2 encryption, symmetric key encryption, etc. In an exemplary
embodiment, the secure identifier may be encrypted using a one-way
encryption process. The secure identifier may be encrypted in such
a way as to make any P11 unavailable to the financial transaction
processing agency 108.
[0056] The demographic information 508 may include any demographic,
geographic, or other suitable information relevant to the
particular application. For example, if a family restaurant is
launching an advertising campaign and is requesting microsegments
of families with a spend propensity on restaurants, then the
demographic information may include familial status, but not age.
If a bar is launching an advertising campaign, then demographic
information may include age, but not familial status. In some
embodiments, the demographic information 808 may be replaced by
geographic or other information. Suitable types of information
relevant for the selecting and supplying of microsegments will be
apparent to persons having skill in the relevant art. Likewise, the
financial information 510 may include any financial information
relevant to the particular application. For example, a dataset
provided to advertisers in the food service industry may contain
entries with financial information that includes a spend propensity
for restaurants, but not a spend propensity for electronics.
System for Measuring Advertising Effectiveness
[0057] FIG. 6 illustrates a system 600 for measuring the
effectiveness of an advertisement. The system 600 may include the
financial transaction processing agency 108, the merchant 104, an
advertiser 118, a test audience 120, and a control audience
122.
[0058] The merchant 104 may communicate with the advertiser 118 to
request advertising, such as for a product or service offered by
the merchant. In some embodiments, the merchant 104 may be the
advertiser 118, or the advertiser may be a third party. The
advertiser 118 may distribute, publisher, or otherwise make
available an advertisement to consumers on behalf of the merchant
104 through print media, online, e-mail, text (e.g., SMS messaging)
or nearly any other type or method of conveyance of advertising
material. In an exemplary embodiment, not all consumers may be
exposed to the advertisement. For example, as illustrated in FIG.
6, only the consumers 102a in the test audience 120 may be exposed
to the advertisement, while the consumers 102b in the control
audience 122 would be deliberately exposed to the advertisement
(though of course incidental exposure by a few might be
expected.
[0059] The test audience 120 may be comprised of consumers 102a
that are deliberately exposed to the advertisement for the merchant
104. In one embodiment, the advertiser 118 may identify the
consumers that are exposed to the advertisement. In an alternative
embodiment, a third party may identify the consumers exposed to the
advertisement. In another alternative embodiment, the financial
transaction processing agency 108 may identify the consumers
exposed to the advertisement (e.g., based on financial transaction
data stored in the enriched database 116). The control audience 122
may be comprised of consumers 102b that are not deliberatively
exposed to the advertisement for the merchant 104. It will be
apparent to persons having skill in the relevant art that the
control audience 122 may be optional, and in fact may be the same
audience but in a temporal sense are both the control and the test
audience (e.g., advertising effectiveness may be measured based on
behavior prior to and subsequent to exposure to the advertisement
without the need for a distinct control group).
[0060] As discussed in more detail below, the test audience 120 and
the control audience 122 may be generated by the financial
transaction processing agency 108. The audience may comprise a
plurality of microsegments as applied to an external data set
(e.g., provided by the advertiser 118). For example, the advertiser
118 may provide characteristic data (e.g., geographical and
demographical data) for a plurality of entities (e.g., consumers).
In one embodiment, the financial transaction processing agency 108
may generate microsegments based on the plurality of entities. In
another embodiment, the financial transaction processing agency 108
may apply the plurality of entities to previously generated
microsegments (e.g., based on the characteristic data in the
enriched database 116 and the received characteristic data). The
test audience 120 may be comprised of entities that have been
exposed to the advertisement, or may be comprised of the
microsegments to which the entities have been applied.
[0061] In some embodiments, the generated microsegments and the
plurality of entities may have no entities in common. In a further
embodiment, the plurality of entities may have no associated
activity data. In these embodiments, activity data for the entities
of the corresponding microsegment may be applied to the entities in
the plurality of entities mapped or applied to that microsegment.
In this way, spending behaviors may be analyzed for the entity in
the plurality of entities by its association in a microsegment of
entities with similar or the same characteristic data.
Methods for Measuring Advertising Effectiveness
[0062] FIG. 7 illustrates a method 700 for measuring advertising
effectiveness using the system 600.
[0063] In step 702, a processor (e.g., the processor 102 of the
financial transaction processing agency 108) may receive (e.g., by
a receiving device) characteristic data for a plurality of entities
(e.g., from the advertiser 118). The characteristic data may
include geographical and/or demographical data associated with the
plurality of entities. In an exemplary embodiment, the
characteristic data may include an indicator of the exposure of an
entity to an advertisement for a merchant (e.g., the merchant 104).
In another exemplary embodiment, the characteristic data may not
include personally identifiable information (PII). In some
embodiments, the processor 102 may also receive from the advertiser
118 a predetermined period of time for which the advertiser 118
requests a measure of the effectiveness of the advertisement, if
the advertiser 118 requests analysis of behaviors before and/or
after the predetermined period of time, if (e.g., and which)
competitors should be analyzed, what spend behaviors are requested,
or if reports during the predetermined period of time are requested
(e.g., and at what intervals).
[0064] In step 704, the processor 102 may generate test and control
audiences (e.g., the test and control audiences 120 and 122). The
test and control audiences 120 and 122 may be generated by applying
the received entities to previously generated microsegments (e.g.,
based on the data in the enriched database 116) based on the
associated characteristic data. The test audience 120 may include
only those entities or corresponding microsegments that were
indicated as exposed or deliberately exposed to the advertisement
(which is not to say the individuals actually saw it or paid
attention to it). The control audience 122 may include only those
entities or corresponding microsegments that were indicated as not
having been deliberately exposed to the advertisement, though of
course some may have seen it. It is again noted that this may be
temporal, meaning that the control audience is the same or
overlapping with the test audience insofar as the control audience
is measured before exposure, and then measured afterwards as the
test audience. In an alternative embodiment, the processor 102 may
receive indicators of exposure to the advertisement for the
plurality of entities from a third party. In another alternative
embodiment, the processor 102 may determine exposure to the
advertisement for each entity based on spending behaviors, as
discussed in more detail below. In one embodiment, all of the
entities may have been exposed to the advertisement, and there may
be no control audience 122.
[0065] In step 706, the processor 102 may determine if the
predetermined time period has ended. If the predetermined time
period has not ended (e.g., the campaign for which the advertiser
118 is requesting effectiveness on is ongoing), then the processor
102 may, in step 708, continue processing financial transactions
for entities in the test and control audiences 120 and 122. In step
710, the processor 102 may analyze financial transactions (e.g.,
only those financial transactions processing since the most recent
analysis as performed). In an exemplary embodiment, the processor
102 may analyze transactions on a weekly basis. In an alternative
embodiment, the advertiser 118 may select a recurring time period
for analysis during the predetermined time period.
[0066] In step 712, the processor 102 may generate a report based
on the analysis performed in step 710. In one embodiment, a report
may be generated every time the analysis is performed, e.g.,
weekly. In another embodiment, a report may be generated when
requested by the advertiser 118. The report may include at least a
report on the financial transactions processed including the
entities or microsegments in the test audience 120 and/or the
control audience 122. In an exemplary embodiment, the report may
include only those financial transactions processed in step 708 and
analyzed in step 710. In an alternative embodiment, the report may
include analysis of financial transactions since the beginning of
the predetermined period of time. Appendix A shows two samples
output measurement reports. The first is a segment comparison
(between different, non-overlapping segments) with measurement
stream data; and the second is a report based on pre-advertisements
and post advertisements to the same or overlapping segments with
measurement stream data.
[0067] After the weekly report is generated (e.g., and transmitted
to the advertiser 118, the merchant 104, or a third party), the
processor 102 may return to step 706 and determine if the
predetermined period of time has ended. If the predetermined period
of time has ended, then, in step 714, the processor 102 may analyze
spend behaviors for the test audience 120 and the control audience
122. The analysis of spend behaviors may include analyzing the
spend behaviors of microsegments in each audience based on activity
data stored in the external database 116. In one embodiment, the
activity data stored in the external database 116 may include
activity data for entities not included in the received plurality
of entities from the advertiser 118. In an alternative embodiment,
the activity data in the external database 116 may be associated
with only entities that are not included in the received plurality
of entities (e.g., the external database 116 and received data have
no entities in common). Activity data of entities in the generated
microsegments may be analyzed and applied to the entities
identified by the advertiser 118 based on similarities in the
corresponding characteristic data. In this way, spending behaviors
of the entities identified by the advertiser 118 may be analyzed by
analyzing the spend behaviors of other entities in the same
microsegment.
[0068] The analysis of spend behaviors may include analyzing
activity data (e.g., financial transactions) for at least one
(e.g., or all) entities in a given microsegment. Spend behaviors
analyzed by the processor 102 may include spending propensities for
a given industry (e.g., the industry of the merchant 104), for a
specific vendor (e.g., the merchant 104 or competitors of the
merchant 104), or any other behavior that may be analyzed based on
available activity data. In one embodiment, spend behaviors
analyzed for the test audience 120 and the control audience 122 may
include spend propensity for the merchant 104 and spend propensity
for a competitive set of the merchant 104 (e.g., competitors in the
same industry and/or geographical location as the merchant 104).
Other types of spend behaviors that may be analyzed will be
apparent to persons having skill in the relevant art and may
include, for example, location type of transaction (e.g., online or
offline, specific merchant location, etc.), number of transactions,
average spending amount, etc.
[0069] In one embodiment, spend behaviors may be analyzed for
activity only during the predetermined period of time. In an
alternative embodiment, spend behaviors may also be analyzed for
activity prior to and/or after the predetermined period of time. In
one embodiment, spend behaviors may be requested by the advertiser
118. In some embodiments, projected spend behaviors may also be
calculated or generated by the processor 102.
[0070] In step 716, the processor 102 may determine the
effectiveness of the advertisement exposed to the entities or
corresponding microsegments of the test audience 120. Methods of
determining the effectiveness of an advertisement based on activity
data will be apparent to persons having skill in the relevant art.
For example, the effectiveness may be based on an increase in
activity of the test audience 120 during the predetermined period
of time, repeat business by entities or corresponding microsegments
in the test audience 120 during or after the predetermined period
of time, and/or first-time consumers transacting with the merchant
104 during the predetermined period of time. In step 718, a report
on the effectiveness of the advertisement may be generated by the
processor 102 (e.g., and transmitted to the advertiser 118, the
merchant 104, and/or a third party). Useful data, metrics, and
analysis that may be included in the report will be apparent to
persons having skill in the relevant art.
[0071] FIG. 8 illustrates an alternative embodiment of a method 800
for measuring advertisement effectiveness using the system 600.
[0072] In step 802, a processor (e.g., the processor 102 of the
financial transaction processing agency 108) may receive (e.g., by
a receiving device) characteristic data for a plurality of entities
(e.g., from the advertiser 118). The characteristic data may
include geographical and/or demographical data associated with the
plurality of entities. In an exemplary embodiment, the
characteristic data may include an indicator of the exposure of an
entity to an advertisement for a merchant (e.g., the merchant 104).
In another exemplary embodiment, the characteristic data may not
include personally identifiable information (PII). The processor
102 may also receive a selected predetermined period of time from
the advertiser 118 for which the advertiser 118 requests a measure
of the effectiveness of the advertisement.
[0073] In step 804, the processor 102 may generate a test audience
(e.g., the test audience 120) and a control audience (e.g., the
control audience 122), as discussed above with respect to step 704
illustrated in FIG. 7. In one embodiment, the test and control
audiences 120 and 122 may include entities corresponding to the
received characteristic data from the advertiser 118. In another
embodiment, the test and control audiences 120 and 122 may include
microsegments that share at least some (e.g., all) characteristic
attributes with the plurality of entities received from the
advertiser 118.
[0074] In step 806, the processor 102 may analyze spend behaviors
for the merchant 104 by analyzing activity data (e.g., stored in
the enriched database 116) for the corresponding microsegments of
the test audience 120 and/or the control audience 122 that occurred
prior to the predetermined period of time. Spend behaviors analyzed
may include spending propensities for a given industry (e.g., the
industry of the merchant 104), for a specific vendor (e.g., the
merchant 104), or any other behavior that may be analyzed based on
available activity data. In some embodiments, the advertiser 118 or
the merchant 104 may identify the spend behaviors for analysis. In
step 808, the spend behavior analysis may be performed for activity
data corresponding to a competitor set (e.g., competitors in the
same industry, geographic location, etc. of the merchant 104). In
one embodiment, the competitor set may be identified by the
advertiser 118 or the merchant 104.
[0075] In steps 810 and 812, the processor 102 may analyze spend
behaviors of activity data for the entities or corresponding
microsegments of the test audience 120 and the control audience 122
for financial transactions including the merchant 104 or the
competitor set, respectively, that occur during the predetermined
period of time. In steps 814 and 816, the processor 102 may perform
the analysis for transactions that occur after the predetermined
period of time.
[0076] In step 818, the processor 102 may determine the
effectiveness of the advertisement using the spend behaviors
analyzed in steps 806-816. Methods of determining advertising
effectiveness based on analyzed spend behaviors will be apparent to
persons having skill in the relevant art. For example, the
effectiveness of the advertisement may be based on an increase in
spend propensity for the merchant 104 during the predetermined
period of time (e.g., as compared to the spend propensity prior to
the predetermined period of time), a decrease in spend propensity
for the competitor set during the predetermined period of time, an
increased spend propensity for the merchant 104 after the
predetermined period of time (e.g., as compared to the spend
propensity prior to the predetermined period of time), a greater
spend propensity for the merchant 104 than for the competitor set
during and/or after the predetermined period of time, etc.
[0077] In step 820, a report on the determination performed in step
818 may be generated by the processor 102 (e.g., and transmitted to
the advertiser 118, the merchant 104, and/or a third party). In one
embodiment, the report may also include results of the analysis
performed in at least one of steps 806-816.
[0078] The use of microsegments to determine advertising
effectiveness as disclosed herein may provide more efficient and
more accurate measurements. Furthermore, if the enriched database
116 and the received characteristic data for the plurality of
entities contains no personally identifiable information, than the
advertising effectiveness may be measured while maintaining
consumer privacy and security. The analysis of spend behaviors
without the use of P11 may be performed by applying the entities
received from the advertiser 118 to microsegments generated by the
processor 102 based on the data in the enriched database 116. The
analysis (e.g., in steps 806-818) may be performed on activity data
for the entities in the corresponding microsegments, which may then
be applied to the received entities.
Exemplary Method for Measuring Effectiveness of an
Advertisement
[0079] FIG. 9 illustrates an exemplary method 900 for determining
the effectiveness of an advertisement.
[0080] In step 902, entity information associated with a plurality
of entities may be stored in a database (e.g., by a processor such
as the processor 102 of the financial transaction processing agency
108). The entity information may include activity information and
characteristic information associated with the corresponding
entity. In one embodiment, the activity information may include
transaction details for financial transactions including the
corresponding entity. In one embodiment, the characteristic
information may include demographic information associated with the
corresponding entity. In a further embodiment, the demographic
information may include demographical, geographical, or other
information associated with the corresponding entity. In an
exemplary embodiment, the activity and characteristic information
may not include personally identifiable information. In a further
embodiment, the characteristic data may be bucketed or aggregated
as to render it not personally identifiable.
[0081] In step 904, a plurality of microsegments may be generated
(e.g., by the processor 102), each microsegment including a subset
of the plurality of entities based on the associated characteristic
information, wherein no two subsets of the plurality of entities
contains a common entity. In one embodiment, each entity in a
subset of the plurality of entities may have similar characteristic
information. In a further embodiment, each entity in a subset of
the plurality of entities may have the same characteristic
information. In one embodiment, each subset of the plurality of
entities may contain at least two entities. In an exemplary
embodiment, each subset of the plurality of entities may contain at
least ten entities.
[0082] In step 906, a test audience including a plurality of first
microsegments and a control audience including a plurality of
second microsegments may be generated (e.g., by the processor 102).
Each entity in the plurality of first microsegments may be exposed
to an advertisement associated with a merchant (e.g., the merchant
104) during a predetermined period of time, and each entity in the
plurality of second microsegments may not be exposed to the
advertisement during the predetermined period of time. Then, in
step 908, a processor (e.g., the processor 102) may analyze the
activity information for the entities in the plurality of first
microsegments and the entities in the plurality of second
microsegments to determine spending behaviors for the associated
entity during the predetermined period of time.
[0083] In one embodiment, the spending behaviors may be based on
financial transactions between the associated entity and the
merchant. In another embodiment, the spending behaviors may be
based on financial transactions between the associated entity and a
competitor of the merchant. In one embodiment, step 908 may further
include analyzing the activity information to determine spending
behaviors for the associated entity during a period of time prior
to the predetermined period of time. In an alternative embodiment,
step 908 may further include determining spending behaviors for the
associated entity during a period of time after the predetermined
period of time. In a further embodiment, the processor may analyze
the spending behaviors for the associated entity prior to, during,
and after the predetermined period of time.
[0084] In step 910, the spending behaviors determined for the
entities in the plurality of first microsegments may be compared
(e.g., by the processor 102) with the spending behaviors determined
for the entities in the plurality of second microsegments to
determine the effectiveness of the advertisement. Then, in step
912, the effectiveness of the advertisement may be transmitted by a
communication component (e.g., of the financial transaction
processing agency 108). In one embodiment, the effectiveness of the
advertisement may be transmitted to the merchant (e.g., the
merchant 104).
[0085] Where methods described above indicate certain events
occurring in certain orders, the ordering of certain events may be
modified. Moreover, while a process depicted as a flowchart, block
diagram, etc. may describe the operations of the system as
occurring concurrently, it should be understood that many of the
system's operations can occur in a sequential manner or in a
different order. For example, although the spend behavior analysis
for before, during, and after the predetermined time period (steps
806, 810, and 814) is illustrated as occurring concurrently, the
analysis may be performed in a sequential manner such that the
behaviors prior to the predetermined period of time are analyzed
before the behaviors during the predetermined period of time, or
vice versa.
[0086] Techniques consistent with the present disclosure provide,
among other features, a system and method for protecting consumer
privacy in the creation of microsegments and audiences. While
various exemplary embodiments of the disclosed system and method
have been described above it should be understood that they have
been presented for purposes of example only, not limitations. It is
not exhaustive and does not limit the disclosure to the precise
form disclosed. Modifications and variations are possible in light
of the above teachings or may be acquired from practicing of the
disclosure, without departing from the breadth or scope.
TABLE-US-00001 APPENDIX A Sample Output for Measurement Clients
Output file returned to Measurement Partner: Segment Comparison
Measurement Data Stream: Segment Comparison Wk1 Wk2 Wk3 Wk4 Wk5
Total Index (with Spend Merchant Segment A 100.00 101.00 102.01
103.03 104.06 510.10 awareness) - Segment B 100.00 102.00 104.04
105.08 106.13 517.25 Wk1 Base Segment C 100.00 101.00 102.01 104.05
106.13 513.19 Transactions Segment A 100.00 101.00 102.01 103.03
105.09 511.13 Segment B 100.00 101.00 102.01 103.03 105.09 511.13
Segment C 100.00 102.00 104.04 106.12 108.24 520.40 Transaction
Size Segment A 100.00 100.00 100.00 100.00 99.02 99.80 Segment B
100.00 100.99 101.99 101.99 100.99 101.20 Segment C 100.00 99.02
98.05 98.05 98.05 98.61 Index (with Spend Merchant Segment A 100.00
100.00 100.00 100.00 100.00 100.00 awareness) - Segment B 80.00
80.79 81.59 81.59 81.59 81.12 Seg A Base Segment C 120.00 120.00
120.00 121.19 122.39 120.73 Transactions Segment A 100.00 100.00
100.00 100.00 100.00 100.00 Segment B 90.00 90.00 90.00 90.00 90.00
90.00 Segment C 120.00 121.19 122.39 123.60 123.60 122.18
Transaction Size Segment A 100.00 100.00 100.00 100.00 100.00
100.00 Segment B 88.89 89.77 90.66 90.66 90.66 90.14 Segment C
100.00 99.02 98.05 98.05 99.02 98.81 Actuals (with Spend direct
Merchant Segment A $10,000 $10,100 $10,201 $10,303 $10,406 $51,010
permission) Segment B $8,000 $8,160 $8,323 $8,406 $8,490 $41,380
Segment C $12,000 $12,120 $12,241 $12,486 $12,736 $61,583
Transactions Segment A 50 51 51 52 53 256 Segment B 45 45 46 46 47
230 Segment C 60 61 62 64 65 312 Transaction Size Segment A $200
$200 $200 $200 $198 $200 Segment B $178 $180 $181 $181 $180 $180
Segment C $200 $198 $196 $196 $196 $197 Pre/Post Comparison
Measurement Data Stream: Pre/Post Comparison Wk-4 Wk-3 Wk-2 Wk-1
Wk-1 Index (with Spend Merchant Segment A 100.00 102.00 103.02
105.08 106.13 awareness) - Segment B 100.00 101.00 102.01 104.05
105.09 Wk1 Base Segment C 100.00 102.00 104.04 106.12 108.24
Transactions Segment A 100.00 102.00 103.02 105.08 107.18 Segment B
100.00 102.00 104.04 105.08 107.18 Segment C 100.00 102.00 104.04
105.08 107.18 Transaction Size Segment A 100.00 100.00 100.00
100.00 99.02 Segment B 100.00 99.02 98.05 99.02 98.05 Segment C
100.00 100.00 100.00 100.99 100.99 Index (with Spend Merchant
Segment A 100.00 100.00 100.00 100.00 100.00 Awareness) - Segment B
80.00 79.22 79.22 79.22 79.22 Seg A Base Segment C 120.00 120.00
121.19 121.19 122.39 Transactions Segment A 100.00 100.00 100.00
100.00 100.00 Segment B 90.00 90.00 90.89 90.00 90.00 Segment C
120.00 120.00 121.19 120.00 120.00 Transaction Size Segment A
100.00 100.00 100.00 100.00 100.00 Segment B 88.89 88.02 87.15
88.02 88.02 Segment C 100.00 100.00 100.00 100.99 101.99 Actuals
(with Spend direct Merchant Segment A $10,000 $10,200 $10,302
$10,508 $10,613 permission) Segment B $8,000 $8,080 $8,161 $8,324
$8,407 Segment C $12,000 $12,240 $12,485 $12,734 $12,989
Transactions Segment A 50 51 52 53 54 Segment B 45 46 47 47 48
Segment C 60 61 62 63 64 Transaction Size Segment A $200 $200 $200
$200 $198 Segment B $178 $176 $174 $176 $174 Segment C $200 $200
$200 $202 $202 Pre/Post Comparison Measurement Data Stream:
Pre/Post Comparison Wk2 Wk3 Wk4 Pre Total Post Total Index (with
Spend Merchant Segment A 108.25 110.42 111.52 410.10 436.33
awareness) - Segment B 107.19 108.26 109.35 407.06 429.89 Wk1 Base
Segment C 109.33 110.42 111.52 412.16 439.51 Transactions Segment A
108.25 109.34 110.43 410.10 435.20 Segment B 108.25 110.42 111.52
411.12 437.38 Segment C 108.25 109.34 110.43 411.12 435.20
Transaction Size Segment A 100.00 100.99 100.99 100.00 100.26
Segment B 99.02 98.05 98.05 99.01 98.29 Segment C 100.99 100.99
100.99 100.25 100.99 Index (with Spend Merchant Segment A 100.00
100.00 100.00 100.00 100.00 Awareness) - Segment B 79.22 78.44
78.44 79.41 78.82 Seg A Base Segment C 121.19 120.00 120.00 120.60
120.88 Transactions Segment A 100.00 100.00 100.00 100.00 100.00
Segment B 90.00 90.89 90.89 90.22 90.45 Segment C 120.00 120.00
120.00 120.30 120.00 Transaction Size Segment A 100.00 100.00
100.00 100.00 100.00 Segment B 88.02 86.30 86.30 88.01 87.14
Segment C 100.99 100.00 100.00 100.25 100.73 Actuals (with Spend
direct Merchant Segment A $10,825 $11,042 $11,152 $41,010 $43,633
permission) Segment B $8,575 $8,661 $8,748 $32,565 $34,392 Segment
C $13,119 $13,250 $13,383 $49,459 $52,741 Transactions Segment A 54
55 55 205 218 Segment B 49 50 50 185 197 Segment C 65 66 66 247 261
Transaction Size Segment A $200 $202 $202 $200 $201 Segment B $176
$174 $174 $176 $175 Segment C $202 $202 $202 $201 $202
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