Note: Descriptions are shown in the official language in which they were submitted.
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SYSTEM AND METHOD FOR PROVIDING PEOPLE-BASED AUDIENCE
PLANNING
Related Applications
[001] This application is a continuation-in-part of Application No.
16/214,769,
filed December 10, 2018, which is a continuation of U.S. Application No.
15/786,551,
filed October 17, 2017 (now U.S. Patent No. 10,181,136), which claims the
priority of
U.S. Provisional Patent Application No. 62/409,374, filed October 17, 2016,
which
are hereby incorporated by references in their entirety.
Technical Field
[002] The present disclosure generally relates to computerized systems and
methods for providing people-based audience planning and targeted advertising.
Background
[003] A vendor may target specific consumers, in a population of consumers,
to address individualized marketplace needs. For example, a vendor may provide
promotions customized for certain potential customers. Such promotional
content
(e.g. advertisements) may be uniquely tailored to different consumers.
Personalizing
promotional content for electronic delivery can lead to an increase in
revenues, but
there are some shortcomings. For example, marketing to address singular
customer
needs may be overly burdensome, time consuming, infeasible due to scalability,
and
expensive.
[004] Consumer needs and wants may overlap with other needs and wants.
Marketing based on dividing a prospective consumer audience into discrete
categories representative of a particular defining characteristic can be
beneficial. For
example, clustering based on select behavioral data, demographic data, and
product
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preferences may improve efficiency and reduce cost. Segmentation according to
these conventional categories, however, may deprive a marketer of the benefits
of
marketing by category. For example, two consumers of the same age may receive
the same advertisement because they are categorized in the same manner based
on
age. However, these consumers may be at different stages of their lives and
thus
have different motivations or values. This can lead to one consumer in the
category
enthusiastically purchasing the advertised product while the other consumer is
vehemently opposed to purchasing the product. Segmenting these two consumers
solely on a single basis (e.g., age) can be inefficient and ineffective.
[005] Conventional segmentation techniques may also cause privacy and
security concerns. For example, it is common for conventional systems to
identify
consumers using identifiers or information that includes personal identifiable
information (e.g., name, email address, phone number or the like). It is also
common for conventional systems to exchange these identifiers over
communication
networks. This can lead to data leaks or losses that can potential expose
personal
identifiable information of the consumers to attackers or other unauthorized
users.
Furthermore, attackers (e.g., hackers) can use the personal identifiable
information
obtained from one attack against the same or additional consumers in
subsequent
attacks (e.g., using techniques such as phishing, social engineering or the
like).
[006] While conventional advertisement platforms allow an advertising client
to supply the client's own consumer data, they are not compatible with or do
not
support the clients' own segmentations. Thus, the advertising client may not
define
their own segments. Moreover, in conventional advertisement platforms, when an
advertising client seeks to publish a list of audiences from a set of consumer
data,
the platforms select the audiences based on a comparison of the set of
consumer
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data with consumer data provided by a publisher to determine the list of
audience
from the set of consumer data. However, when the client seeks to publish the
remainder of the set of consumer data, the platforms compare the entire set of
the
consumer data with consumer data provided by a second publisher, without
excluding the list of audiences that are already published. This causes the
publishing systems to operate inefficiently.
[007] Therefore, there is a need for an improved method of providing people-
based audience planning and targeted advertising.
Summary
[008] One aspect of the present disclosure is directed to a computer-
implemented system for targeted advertising to specific consumers. The system
may include a memory storing instructions; and at least one processor
configured to
execute the instructions to: receive, over a network, consumer data from a
client
device; identify a plurality of client-provided consumers from the consumer
data;
obtain a plurality of unique consumer identifiers corresponding to the
plurality of
client-provided consumers; and identify at least one first overlapping unique
consumer identifier by matching at least one of the plurality of client-
provided
consumers with at least one publisher-provided consumer provided by a first
publisher device of a plurality of publisher devices, the first publisher
device having a
highest priority among the plurality of publisher devices.
[009] Another aspect of the present disclosure is directed to a computer-
implemented method for targeted advertising to specific consumers. The
computer-
implemented method may include: receiving, over a network, consumer data from
a
client device; identifying a plurality of client-provided consumers from the
consumer
data; obtaining a plurality of unique consumer identifiers corresponding to
the
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plurality of client-provided consumers; and identifying at least one first
overlapping
unique consumer identifier by matching at least one of the plurality of client-
provided
consumers with at least one publisher-provided consumer provided by a first
publisher device of a plurality of publisher devices, the first publisher
device having a
highest priority among the plurality of publisher devices.
[0010] Yet another aspect of the present disclosure is directed to a non-
transitory computer-readable medium storing instructions executable by a
processor
to perform a method for targeted advertising to specific consumers. The method
may include: receiving, over a network, consumer data from a client device;
identifying a plurality of client-provided consumers from the consumer data;
obtaining
a plurality of unique consumer identifiers corresponding to the plurality of
client-
provided consumers; and identifying at least one first overlapping unique
consumer
identifier by matching at least one of the plurality of client-provided
consumers with
at least one publisher-provided consumer provided by a first publisher device
of a
plurality of publisher devices, the first publisher device having a highest
priority
among the plurality of publisher devices.
[0011] Other systems, methods, and computer-readable media are also
discussed herein.
Brief Description of the Drawings
[0012] FIG. 1 is a schematic block diagram illustrating an exemplary
embodiment of a system for targeted advertising to specific consumers,
consistent
with the disclosed embodiments.
[0013] FIG. 2 is a diagrammatic illustration of an exemplary target audience
review interface, consistent with the disclosed embodiments.
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[0014] FIG. 3 is a diagrammatic illustration of an exemplary performance
report, consistent with the disclosed embodiments.
[0015] FIG. 4 is a flow chart illustrating an exemplary method for targeted
advertising to specific consumers, consistent with the disclosed embodiments.
[0016] FIG. 5 is an exemplary table illustrating a flagged consumer record
indicating a consumer record's enrollment in a class or a segment within
consumer
data, consistent with the disclosed embodiments.
[0017] FIG. 6 is a schematic diagram illustrating a plurality of data provided
by
corresponding plurality of publisher devices and assigned priority of the
data,
consistent with the disclosed embodiments.
[0018] FIG. 7A is a schematic diagram illustrating a first matching test of a
waterfall matching test, consistent with the disclosed embodiments.
[0019] FIG. 7B is a schematic diagram illustrating a second matching test of
the waterfall matching test, consistent with the disclosed embodiments.
[0020] FIG. 8 is a flow diagram illustrating an exemplary method for a
waterfall
matching test, consistent with the disclosed embodiments.
Detailed Description
[0021] The following detailed description refers to the accompanying
drawings. Wherever possible, the same reference numbers are used in the
drawings
and the following description to refer to the same or similar parts. While
several
illustrative embodiments are described herein, modifications, adaptations and
other
implementations are possible. For example, substitutions, additions, or
modifications
may be made to the components and steps illustrated in the drawings, and the
illustrative methods described herein may be modified by substituting,
reordering,
removing, or adding steps to the disclosed methods. Accordingly, the following
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detailed description is not limited to the disclosed embodiments and examples.
Instead, the proper scope of the invention is defined by the appended claims.
[0022] Embodiments of the present disclosure are directed to systems and
methods configured for providing targeted advertising to specific consumers.
For
example, a client device (e.g., an advertiser or a publisher system) may
provide
consumer data to an advertisement agency over a network. Consumer data may
include, for example, personal identifiable information (e.g., name, email
address,
phone number, street address, social security number or the like) and non-
personal
identifiable information (e.g., device identifiers, demographic data,
segments, model
scores or the like). The advertisement agency may process the consumer data
and
assign unique consumer identifiers to the consumers identified in the consumer
data.
In some embodiments, the unique consumer identifiers may not include any
personal
identifiable information. The advertisement agency may then generate a target
audience pool for the client based on the unique consumer identifiers.
Utilizing
unique consumer identifiers, as in certain embodiments of the present
disclosure,
may help improve the efficiency of the target audience pool generation.
Moreover,
utilizing such unique consumer identifiers, as in certain embodiments of the
present
disclosure, may enhance data security, fidelity, and accuracy.
[0023] Referring to FIG. 1, a schematic block diagram depicting an exemplary
embodiment of a system for targeted advertising is shown. As illustrated in
FIG. 1, a
system 100 may include one or more data sources 102, a data processor 104, a
target audience generator 106, an application interface 108, and a data
analyzer
110.
[0024] The data sources 102 may include consumer data 102A provided by
one or more advertisers, consumer data 102B provided by one or more
publishers,
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consumer data 102C provided by one or more third-party data providers, or
consumer data 102D provided by one or more advertisement agencies (e.g.,
agencies that provide targeted advertising services to adversities and
publishers). In
some embodiments, data in one or more of data sources 102 may be provided or
stored as text files, binary files, database records, or various other types
of
computer-readable data formats.
[0025] In some embodiments, advertisers, publishers, third-party data
providers, and advertisement agencies may utilize various types of computing
devices to communicate with each other. Such computing devices may include,
for
example, servers, desktop computers, notebook computers, mobile devices,
tablets,
smartphones, wearable devices such as smart watches, smart bracelets, smart
glasses, or any other devices that can communicate with a wired or wireless
network.
[0026] In some embodiments, consumer data 102A provided by advertisers,
consumer data 102B provided by publishers, consumer data 1020 provided by
third-
party data providers, and consumer data 102D provided by advertisement
agencies
may be stored in physically or logically separated data storage devices to
mitigate
data mixing. For instance, consumer data 102A provided by an advertiser may be
stored in a first data storage device that is physically or logically
separated from a
second data storage device used to store consumer data 102B provided by a
publisher. Similarly, consumer data 1020 provided by a third-party data
provider
may be stored in a third data storage device that is physically or logically
separated
from a fourth data storage device used to store consumer data 102D provided by
an
advertisement agency. In some embodiments, consumer data 102A provided by
different advertisers may be stored in physically or logically separated data
storage
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devices. Similarly, consumer data 102B provided by different publishers and
consumer data 102C provided by different third-party data providers may be
stored
in physically or logically separated data storage devices. Such data storage
devices
may be implemented using any volatile or non-volatile memory including, for
example, magnetic, semiconductor, tape, optical, removable, non-removable, or
any
other types of storage devices or computer-readable mediums.
[0027] The data processor 104 may serve as an entry point for the consumer
data received from the various data sources 102A, 102B, 102C, or 102D. The
data
processor 104 may include one or more dedicated processing units, application-
specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs),
or
various other types of processors or processing units coupled with a non-
transitory
processor-readable memories configured for storing processor-executable code.
When the processor-executable code is executed by the data processor 104, the
data processor 104 may carry out instructions in response to various types of
input
signals received via the wired or wireless network.
[0028] In some embodiments, the data processor 104 may be configured to
recognize personal identifiable information contained in the consumer data 102
(e.g.,
name, email address, phone number, street address, social security number or
the
like). The data processor 104 may be configured to recognize the personal
identifiable information based on the labels associated with the data fields
contained
in the consumer data 102 (e.g., data fields contained in the consumer data 102
may
be labeled "name," "email address," "phone number" or the like). Additionally
or
alternatively, the data processor 104 may be configured to recognize the
personal
identifiable information based on the format of the data presented (e.g., a 10-
digit
numerical string may be recognized as a phone number and a text string having
an
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"@" symbol may be recognized as an email address). It is to be understood that
the
data processor 104 may be configured to recognize the personal identifiable
information contained in the consumer data 102 using various other techniques
without departing from the scope and spirit of the present disclosure. The
data
processor 104 may then utilize a data split processor 126 (which may be
implemented as a component of the data processor 104) to separate the personal
identifiable information (P II) contained in the consumer data 102 from non-
personal
identifiable information (non-PII) contained in the consumer data 102 (e.g.,
device
identifiers, demographic data, segments, model scores or the like).
[0029] In some embodiments, the Pll contained in the consumer data 102
may be processed separately with respect to the non-Pll contained in the
consumer
data 102. For instance, as illustrated in FIG. 1, the Pll contained in the
consumer
data 102 may be processed by a consumer identification processor 114 (which
may
be implemented as a component of the data processor 104). The consumer
identification processor 114 may be configured to recognize one or more
consumers
identified in the consumer data 102 based on name, email address, phone
number,
street address, social security number or the like. In some embodiments, if
the
advertisement agency has access to a consumer database 102D, the consumer
identification processor 114 may be able to recognize the consumers by
comparing
the consumer data 102A provided by the advertiser (or the consumer data 102B
provided by the publisher) against the consumer database 102D.
[0030] In some embodiments, the consumer identification processor 114 may
implement various types of data formatting, filtering, validation, parsing,
standardization, normalization, or correction techniques to process the
consumer
data 102. In these embodiments, the consumer identification processor 114 may
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also utilize various types of deterministic or probabilistic processing
techniques to
facilitate the consumer recognition process. Suitable deterministic or
probabilistic
processing techniques may include, but are not limited to, consideration of
variations
on name spelling (e.g., "Robert" as "Rob," "Bob," "Bobby," etc.), variations
on
address presentation (e.g., "Road" or "Rd," with or without apartment unit
numbers,
spelling variations on city, etc.), correction of common email address errors
(e.g.,
misspelled or transposed letters in domain names or the like), and deducing
telephone area code based on city and state.
[0031] Consumer identification processor 114 may assign a unique consumer
identifier to one or more consumers that have been recognized in consumer data
102 (e.g., by consumer identification processor 114). In some embodiments, the
unique consumer identifiers assigned by the consumer identification processor
114
may not include any personal identifiable information. In other words, the
unique
consumer identifiers assigned by the consumer identification processor 114 are
pseudonymous identifiers.
[0032] In some embodiments, each pseudonymous identifier assigned by the
consumer identification processor 114 may uniquely identify a particular
consumer at
a particular street address. For instance, distinct identifiers may be
assigned to each
particular address, and likewise, distinct identifiers may be assigned to each
consumer name. Unique pairings of address and consumer identifiers may then be
assigned and exchanged as surrogates for the underlying Pll data records
without
exposing the Pll data in subsequent components. Such pseudonymous identifiers
can provide anonymity compared to P II-based identifiers because the
pseudonymous identifiers, by definition, do not contain personal identifying
information of the consumers. The pseudonymous identifiers can also provide
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improved security, fidelity, and accuracy compared to identifiers such as
those based
on web cookies, device identifiers, or Internet Protocol (IP) addresses (which
typically have multiple consumers mapped to the same identifier, creating
noise and
reducing data fidelity). In some embodiments, the consumer identification
processor
114 may retain a cross-reference 122 between the pseudonymous identifiers and
the
identifiers originally used by the client (e.g., the advertiser or the
publisher). This
cross-reference 122 may be stored in one or more non-transitory processor-
readable
memories accessible to the consumer identification processor 114 (and the data
processor 104 in general).
[0033] The pseudonymous identifiers assigned by the consumer identification
processor 114 may then be merged together with the non-Pll contained in the
consumer data 102 to produce pseudonymous consumer data 116. It is noted that
the pseudonymous consumer data 116 may now contain pseudonymously
identifiable information that can be utilized to generate a target audience
pool for the
client without revealing any personal identifiable information of the
consumers.
[0034] In some embodiments, the target audience pool is generated using a
target audience generator 106. The target audience generator 106 may include
one
or more dedicated processing units, application-specific integrated circuits
(AS ICs),
field-programmable gate arrays (FPGAs), or various other types of processors
or
processing units coupled with a non-transitory processor-readable memories
configured for storing processor-executable code. When the processor-
executable
code is executed by the target audience generator 106, the target audience
generator 106 may carry out instructions to generate a target audience pool.
In
some embodiments, the target audience generator 106 is configured to process
only
the pseudonymous consumer data 116. Utilizing the pseudonymous consumer data
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116 in this manner may help improve the efficiency of the target audience
generator
106.
[0035] For example, suppose that an advertiser wants to run a targeted
advertisement on a platform operated by a publisher. It may be in both
parties'
interest to utilize the target audience generator 106 to generate an audience
pool for
the targeted advertisement. To do so, the advertiser and the publisher may
choose
to provide their corresponding customer base (i.e., consumer data) 102A and
102B
to the target audience generator 106. The advertiser-provided consumer data
102A
and the publisher-provided consumer data 102B may be processed first by the
data
processor 104, which may purge personal identifying information from the data
provided to produce the pseudonymous consumer data 116 as described above.
The target audience generator 106 may then obtain a list of consumers 118
common
to both advertiser-provided consumer data and publisher-provided consumer
data.
This list of consumers 118 can be obtained very efficiently by matching
pseudonymous identifiers associated with the advertiser-provided consumer data
against pseudonymous identifiers associated the publisher-provided consumer
data
after they have been processed by the data processor 104.
[0036] In some embodiments, the list of consumers 118 common to both
advertiser-provided consumer data and publisher-provided consumer data may
readily be identified as the target audience pool. Alternatively, the list of
consumers
118 may be considered as a basis pool, which may then be expanded utilizing
one
or more lookalike audience models 120. For example, the target audience
generator
106 may analyze the non-personal identifiable information (e.g., demographic
data,
segments, model scores or the like) associated with the consumers identified
in the
list of consumers 118 to obtain one or more top attributes describing such
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consumers. The top attribute(s) identified in this manner may then be utilized
to help
identify additional consumers provided by third-party data providers (e.g.,
data
derived from consumer data 1020) or the advertisement agencies (e.g., data
derived
from consumer data 102D).
[0037] In another example, the advertiser may choose to ask the target
audience generator 106 to process the advertiser-provided consumer data 102A
without having to take into consideration any publisher-provided consumer
data.
The advertiser-provided consumer data 102A may be processed by the data
processor 104, which may produce the pseudonymous consumer data 116 as
described above. The target audience generator 106 may then analyze the
pseudonymous consumer data 116 produced based on the advertiser-provided
consumer data 102A to identify one or more top attributes describing the
advertiser-
provided consumer data 102A. The top attribute(s) identified in this manner
may
then be utilized to help identify additional consumers provided by third-party
data
providers (e.g., data derived from consumer data 1020) or the advertisement
agencies (e.g., data derived from consumer data 102D).
[0038] It is to be understood that the target audience generation techniques
described above are presented as examples and are not meant to be limiting. It
is to
be understood that specific implementations of target audience generation
processes may vary from the examples presented above without departing from
the
scope and spirit of the present disclosure.
[0039] In some embodiments, once a target audience pool is generated, the
target audience generator 106 may deliver the target audience pool (e.g., over
a
network) to the advertiser for review and approval. FIG. 2 is an illustration
depicting
an exemplary review interface. In this example, the target audience pool is
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generated based on consumer data recorded in an electronic consumer database
102D provided by an advertisement agency. The electronic consumer database
102D, in one embodiment, includes millions of records relating to consumers,
each
record having more than 1,000 attributes, including, but not limited to, email
addresses, phone records, vehicle records, IP addresses, mortgage information,
lifestyle/behavioral data, demographics data, transactional cooperative data,
life
events data (e.g., new movers, new homeowners, new parents, tri-bureau credit
triggers and the like), wealth indicators, credit statistics, automotive data
and
automotive statistics, real property data, social media handles/flags, social
influence,
other syndicated research data and the like. Other embodiments of electronic
consumer database 102D are possible as well.
[0040] It is contemplated that the advertiser may utilize the exemplary review
interface shown in FIG. 2 to confirm or to modify the target audience pool.
For
example, the exemplary review interface may include a visual representation
204 of
the target audience pool. The visual representation 204 may include one or
more
graphics indicating the composition of the target audience pool. For example,
the
visual representation 204 may indicate the composition in terms of education
levels,
gender, marital status or the like. The visual representation 204 may also
indicate
the composition in terms of age groups, occupations or the like. The visual
representation 204 may further indicate the estimated reach (and if available,
the
actual reach based on historical/recorded data) of the advertisement if the
advertiser
approves the target audience pool presented.
[0041] The exemplary review interface may also include a control panel 202
configured to receive control input from the advertiser. For example, if the
advertiser
chooses not to target a specific age group 206, the advertiser may select the
age
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group 206 (e.g., by clicking the age group 206 using a computer mouse) and
click
the "DELETE AUDIENCE" button in the control panel 202 to remove that specific
age group 206 from the target audience pool. The modifications made by the
advertiser may be communicated to the target audience generator 106 over the
network, and the target audience generator 106 may adjust the target audience
pool
accordingly. On the other hand, if the advertiser is satisfied with the target
audience
pool, the advertiser may choose to confirm/approve the target audience pool by
clicking the "CONFIRM" button in the control panel 202.
[0042] It is to be understood that the exemplary review interface shown in
FIG.
2 is presented merely as an example and is not meant to be limiting. Once the
advertiser confirms/approves the target audience pool, the application
interface 108
may deliver the target audience pool to one or more publishers upon receipt of
the
advertiser's approval.
[0043] In some embodiments, because the target audience generator 106 is
configured to process only the pseudonymous consumer data 116, the target
audience pool generated by the target audience generator 106 may not contain
certain identifiers required by the publishers. It is therefore noted that, in
some
embodiments, a publisher may require the target audience pool to be converted
according to a publisher-specified conversion protocol so that the target
audience
pool delivered to the publisher may contain the identifiers required by the
publishers.
[0044] In some embodiments, the data processor 104 may be configured to
serve as a controlled exit point for converting/modifying the pseudonymous
identifiers based on publisher specifications as needed. More specifically, in
some
embodiments, the data processor 104 may utilize the cross-reference dataset
122
populated earlier in the pseudonymous identifier generation process (described
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above) to help translate the pseudonymous identifiers contained in the target
audience pool. For example, if the publisher uses web cookies or device
identifiers
to identify its target audience, the data processor 104 may convert the
pseudonymous identifiers contained in the target audience pool to web cookies
or
device identifiers using reference data stored in the cross-reference dataset
122.
Similarly, if the publisher uses hashed emails to identify its target
audience, the data
processor 104 may convert the pseudonymous identifiers contained in the target
audience pool to hashed emails using reference data stored in the cross-
reference
dataset 122. The application interface 108 may then provide the target
audience
pool with converted identifiers to the publishers to carry out the
advertisement
campaign.
[0045] It is to be understood that the conversion described above is not
always required. In certain embodiments, for example, the publisher may
partner
with the advertisement agency and may therefore have shared access to the
pseudonymous identifiers. In such embodiments, the application interface 108
may
provide the target audience pool to the publisher directly without conversion,
and the
publisher may identify the consumers in the target audience pool using the
pseudonymous identifiers and carry out the targeted advertisement campaign.
[0046] In some embodiments, the performance data associated with the
advertisement campaign may be collected and analyzed by the system 100. For
instance, some publishers may provide log level details associated with their
advertisement campaigns. The log level details may include information
regarding
the advertiser, the publisher, the advertisement campaign, the audience, the
date,
time, and location where the advertisements appeared, as well as the
impression
and click counts associated with the advertisement campaigns. The system 100
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may utilize a data analyzer 110 to collect the log level details in a storage
area 124
(commonly referred to as a staging area or a data landing zone). The data
analyzer
110 may then use the log level details collected in the storage area 124 to
facilitate
data analysis.
[0047] For example, the data analyzer 110 may use the log level details
collected in the storage area 124 to determine performance metrics, including,
but
not limited to, impressions, click-through rate, completion rates, percentage
complete, engagement time, engagement rate and the like. The data analyzer 110
may then provide a report 128 containing the performance metrics to the
advertiser
or the publisher to evaluate the effectiveness of the advertisement campaign.
In
some embodiments, the data analyzer 110 may present the performance metrics to
the advertisement agency, the advertiser, or the publisher through an
interactive
user interface (e.g., a web page or a mobile device application).
Alternatively or
additionally, the data analyzer 110 may present the performance metrics to the
advertisement agency, the advertiser, or the publisher as periodical reports.
In some
embodiments, the presentation of the performance metrics (whether through an
interactive user interface or through periodical reports) may include text or
graphical
representation as shown in FIG. 3.
[0048] It is noted that FIG. 3 is merely a simplified example depicting an
exemplary format for presenting performance metrics. For example, a panel 302
may provide the user a list of publishers involved in a particular
advertisement
campaign. In an interactive user interface, the user may select one of the
publishers
from the panel 302 and a display area 304 may display the performance metrics
associated with the selected publisher. The display area 304 may display the
performance metrics in various formats, including line charts, pie charts, bar
charts,
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or text descriptions. In some embodiments, while the performance metrics may
be
aggregated, the aggregated performance metrics may be further analyzed against
segments and demographic attributes made available in the pseudonymous
consumer data 116 to provide additional insights.
[0049] Referring now to FIG. 4, a flow diagram illustrating an exemplary
method 400 for targeted advertising to specific consumers consistent with the
disclosed embodiments is shown. While the exemplary method 400 is described
herein as a series of steps, it is to be understood that the order of the
steps may vary
in other implementations. In particular, steps may be performed in any order,
or in
parallel. It is to be understood that each step of method 400 may be performed
by
one or more processors, computers, servers, controllers or the like.
[0050] In some embodiments, the method 400 may be performed by the
system 100 (as depicted in FIG. 1). At step 402, the method 400 may include
receiving, by the system 100, over a network, client-provided data from a
client
device. The client may be an advertiser or a publisher. The client may provide
its
customer base (i.e., its consumer data) to the system 100. The consumer data
may
include personal identifiable information (P11) as well as non-personal
identifiable
information (non-PII) about the consumers. The consumer data may also include
client-assigned identifiers.
[0051] At step 404, the method 400 may include identifying one or more
consumers identified in the client-provided data. The consumers may be
identified
by matching the client-provided data against consumer data recorded in an
electronic consumer database. In some embodiments, the electronic consumer
database may include millions of records relating to consumers, each record
having
more than 1,000 attributes, including, but not limited to, email addresses,
phone
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records, vehicle records, IP addresses, mortgage information,
lifestyle/behavioral
data, demographics data, transactional cooperative data, life events data
(e.g., new
movers, new homeowners, new parents, tri-bureau credit triggers and the like),
wealth indicators, credit statistics, automotive data and automotive
statistics, real
property data, social media handles/flags, social influence, other syndicated
research data and the like. It is to be understood that the electronic
consumer
database may be expanded to include consumers based in other regions as well.
[0052] At step 406, the method 400 may assign unique consumer identifiers to
the consumers identified in the client-provided data. In some embodiments, the
unique consumer identifiers assigned to the consumers do not include personal
identifiable information originally contained in the client-provided data. In
other
words, the unique consumer identifiers assigned in this manner are
pseudonymous
identifiers. In some embodiments, a cross-reference between the pseudonymous
identifiers and the client-assigned identifiers originally provided by the
client is
retained. This cross-reference may be utilized later to help convert the
pseudonymous identifiers to the client-assigned identifiers if such a
conversion is
required by the client.
[0053] At step 408, the method 400 may include generating a target audience
pool. As described above with respect to FIG. 1, the system 100 may generate
the
target audience pool using consumer data provided by an advertiser alone, or
in
conjunction with consumer data provided by one or more publishers, third-party
data
providers, as well as advertisement agencies. It is noted that the basis of
the target
audience pool generation process is the pseudonymous identifiers. In other
words,
in some embodiments, the step 408 does not directly compare the consumer data
provided by the advertiser against the consumer data provided by the
publisher.
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Rather, in those embodiment, the step 408 may be configured to generate the
target
audience pool by matching pseudonymous identifiers associated with the
advertiser-
provided consumer data against pseudonymous identifiers associated the
publisher-
provided consumer data.
[0054] At step 410, the method 400 may include delivering, by the system
100, over a network, the target audience pool to the client device to
facilitate
targeted advertising to specific consumers. The step 410 may deliver the
target
audience pool to an advertiser for review and approval. The advertiser may
request
a change to the target audience pool if needed. Otherwise, the advertiser may
approve the target audience pool, in which case the advertiser may proceed
with a
purchase of the targeted advertising.
[0055] In some embodiments, the method 400 may include a step 412
configured to convert the pseudonymous identifiers used to generate the target
audience pool to identifiers recognized by a publisher. This conversion may be
facilitated using the cross-reference previously mentioned. In some
embodiments,
the step 412 may convert the pseudonymous identifiers to web cookie-based
identifiers, device identifiers, or hashed email-based identifiers. It is to
be
understood that the step 412 may convert the pseudonymous identifiers to other
types of client-assigned identifiers without departing from the spirit and
scope of the
present disclosure.
[0056] In some embodiments, the method 400 may also include a step 414
configured to provide performance analysis of the targeted advertising. For
instance,
some publishers may provide log level details associated with their
advertisement
campaigns. The log level details may include information regarding the
advertiser,
the publisher, the advertisement campaign, the audience, the date, time, and
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location where the advertisements appeared, as well as the impression and
click
counts associated with the advertisement campaigns. The step 414 may collect
the
log level details and use the collected log level details to provide data
analysis as
previously described.
[0057] Referring to FIG. 1, in some embodiments, the advertiser-provided
consumer data 102A, the publisher-provided consumer data 102B, the third-party
provided consumer data 102C, and the advertisement-agency provided consumer
data 102D may include at least one flagged consumer record indicating a
consumer
record's enrollment in a class or a segment within the consumer data. In some
embodiments, the at least one flagged consumer record may be flagged with a
binary digit "0" or "1". In another embodiment, the at least one flagged
consumer
record may be flagged with a "yes" or "no". In another embodiment, the at
least one
flagged consumer record is flagged with a "true" or "false".
[0058] FIG. 5 is an exemplary table 500 illustrating flagged consumer record
indicating a consumer record's enrollment in a class or a segment within
consumer
data, consistent with the disclosed embodiments. For example, in table 500,
the
class is a high lifetime value consumer class and the at least one flagged
consumer
record is flagged with indication that the consumer is a high lifetime value
consumer
and not a recent purchaser. As shown in FIG. 5, table 500 includes a column on
the
leftmost side indicating the consumers using personal identifiable information
such
as an email address (xyz@yahoo.com), a name (John Doe), a phone number
((202)123-4567), etc. However, the indication of the consumers is not so
limited,
and can be other personal identifiable information, such as street addresses
or social
security numbers of consumers, or non-personal identifiable information such
as
device identifiers, demographic data, segments, or model scores of the
consumers.
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Table 500 includes a column in the middle including flags (true or false)
indicating
whether the consumers listed on the leftmost column are high lifetime value
consumers or not. For example, the consumer identified with the email address
(xyz@yahoo.com) and the consumer identified with the phone number ((202)123-
4567) are high lifetime value consumers, while the consumer identified with
the
name (John Doe) is not a high lifetime value consumer. Table 500 also includes
a
column on the rightmost side including flags (true or false) indicating
whether the
consumers listed on the leftmost column are recent purchasers or not. For
example,
the consumer identified with the email address (xyz@yahoo.com) and the
consumer
identified with the phone number ((202)123-4567) are not recent purchasers,
while
the consumer identified with the name (John Doe) is a recent purchaser.
[0059] In some embodiments, the system 100 (e.g., the data processor 104 or
the target audience generator 106) may apply a set of modeling techniques and
an
automated fashion to identify one or more consumers whose data profiles are
statistically similar to data profiles of a seed set of consumers. For
example, the
system 100 may identify one or more consumers whose data profiles are
statistically
similar to data profiles of a seed set of consumers. The seed set of consumers
may
be provided by a customer base of an advertiser.
[0060] In some embodiments, system 100 (e.g., the data processor 104 or the
target audience generator 106) may segment or sub-segment consumers into one
or
more subsets, each of the one or more subsets including one or more
statistically
similar consumers. For example, in developing an audience pool of "car
lovers", the
system 100 may divide consumers into effectively identical populations, and
test to
see whether the population favors a particular type of offer or message or
creative
treatment or an alternative option. Statistical similarity of two consumers
may be
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determined based on event statistics of the consumers, for example, how many
times the consumers bought the same type of car, how many times the consumers
clicked on the same advertisement, how many times the consumers skipped the
same advertisement, or how many times the consumers visited the same car
dealer,
etc. The degree of similarity may also be quantitively determined based on the
statistical data collection and analysis. In this way, an automatic segmenter
functionality is introduced to divide a given audience population into
statistically
similar subsets, leading to increased efficiency.
[0061] In some embodiments, system 100 (e.g., the data processor 104 or the
target audience generator 106) may generate a unique audience list record in
which
audiences are curated based on an advertiser specification. The system 100 may
further generate identity keys for the audiences in the unique audience list
and send
the generated identity keys to a second advertisement platform and/or a
programmatic partner. In this way, the second advertisement platform or the
programmatic partner may recognize the feature of the list of the audiences
without
performing detailed analysis on the consumer data, leading to an enhanced
efficiency.
[0062] Referring to FIG. 1, in some embodiments, the publisher-provided
consumer data 102B may include data provided by a plurality of publisher
devices.
The system 100, for example, the data processor 104 or the target audience
generator 106, may assign priority to consumer data provided by the plurality
of
publisher devices, for example, based on importance or relevance of the
consumer
data. FIG. 6 is a schematic diagram illustrating a plurality of data provided
by
corresponding plurality of publisher devices and assigned priority of the
data,
consistent with the disclosed embodiments. As an example, FIG. 6 shows
consumer
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data 102B provided by fifty different publisher devices, and the priority of
the data
listed in a descending order. For example, the consumer data 601 provided by a
first
publisher device has the top priority (priority 1), the consumer data 602
provided by a
second publisher device has a second priority (priority 2) and the consumer
data
provided by 50th publisher has 50th priority (priority 50).
[0063] In some embodiments, the consumer data provided by the plurality of
different publisher devices may be stored in physically or logically separated
data
storage devices to mitigate data mixing. For instance, consumer data 601
provided
by the first publisher device may be stored in a first data storage device
that is
physically or logically separated from a second data storage device used to
store
consumer data 602 provided by the second publisher device and a 50th data
storage
device used to store consumer data 650 provided by the 50th publisher device.
FIG.
6 shows consumer data provided by fifty different publisher devices. However,
the
number of publishers is not so limited, and can be any number that is smaller
or
greater than fifty.
[0064] In some embodiments, in identifying target audiences using the
consumer data provided by the plurality of different publishers as shown in
FIG. 6,
the target audience generator 106 may utilize a waterfall matching test, as
described
with regard to FIGs. 7A, 7B, and 8. In this test, the target audience
generator 106
may receive, over a network, consumer data from an advertiser device, and
identify
a plurality of advertiser-provided consumers from the consumer data. For
example,
the target audience generator 106 may identify the plurality of advertiser-
provided
consumers by comparing the consumer data received from the advertiser device
against consumer data recorded in an electronic consumer database of the
system
100, as discussed above. The target audience generator 106 may obtain a
plurality
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of unique consumer identifiers corresponding to the plurality of advertiser-
provided
consumers. The plurality of unique consumer identifiers may not include
personal
identifiable information. The target audience generator 106 may then identify
at least
one first overlapping unique consumer identifier by matching at least one of
the
plurality of advertiser-provided consumers with at least one publisher-
provided
consumer provided by a first publisher device of the plurality of publisher
devices,
the first publisher device having a highest priority among the plurality of
publisher
devices.
[0065] FIG. 7A is a schematic diagram illustrating a first matching test of a
waterfall matching test consistent with the disclosed embodiments. As shown in
FIG. 7A, the target audience generator 106 may match a plurality of advertiser-
provided consumers (indicated by the left circle of FIG. 7A) with the
consumers
provided by publisher 1 having the highest priority and obtain a target
audience pool
1 common to both advertiser-provided consumer data and the publisher-provided
consumer data. The target audience pool 1 can be obtained by matching
pseudonymous identifiers associated with the advertiser-provided consumer data
against pseudonymous identifiers associated the consumer data provided by
publisher 1 after they have been processed by the data processor 104.
[0066] After the first matching test, the target audience generator 106 may
determine whether a number of unmatched consumers of the plurality of
advertiser-
provided consumers is greater than a threshold number. For example, in FIG.
7A,
the portion of the advertiser-provided consumers excluding the target audience
pool
1 indicates the unmatched consumers. The threshold number may be a number
predetermined by the system 100. If the number of the unmatched consumers is
greater than the threshold number, the target audience generator 106 may
identify at
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least one second overlapping unique consumer identifier by matching at least
one of
the unmatched consumers with at least one publisher-provided consumer provided
by a second publisher device of the plurality of publisher devices. The second
publisher device has a second highest priority among the plurality of
publisher
devices.
[0067] FIG. 7B is a schematic diagram illustrating a second matching test of
the waterfall matching test consistent with the disclosed embodiments. As
shown in
FIG. 7B, the target audience generator 106 may match the unmatched advertiser-
provided consumers (indicated by the left circle of FIG. 7B) with the
consumers
provided by publisher 2 having a second highest priority and obtain a target
audience pool 2 common to both unmatched advertiser-provided consumer data and
the consumer data provided by publisher 2. The target audience pool 2 can be
obtained by matching pseudonymous identifiers associated with the unmatched
advertiser-provided consumer data against pseudonymous identifiers associated
the
consumer data provided by publisher 2 after they have been processed by the
data
processor 104.
[0068] After the second matching, again, the target audience generator 106
may determine whether a number of unmatched consumers (the advertiser-provided
consumers excluding both the target audience pool 1 and the target audience
pool 2)
is greater than a threshold number. If the number of the unmatched consumers
is
greater than the threshold number, the target audience generator 106 may
perform a
third matching test. The target audience generator 106 may iterate matching
remaining consumers of the plurality of advertiser-provided consumers with
consumers provided by a publisher having highest priority among unmatched
publishers (the publishers whose consumer data were not compared with the
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advertiser-provided consumer data) until a number of the remaining consumers
is
smaller than the threshold number. The target audience generator 106 may
select a
publisher device among the plurality of publisher devices based on a
descending
order of priority of the plurality of publisher devices. For example, the
target
audience generator 106 may select a publisher device based on the table in
FIG. 6.
[0069] In some embodiments, after each matching test, the target audience
generator 106 may further identify one or more consumers whose data profiles
are
statistically similar to data profiles of a seed set of consumers. In some
embodiments, after each matching, the target audience generator 106 may
further
segment or sub-segment consumers into one or more subsets, each of the one or
more subsets including one or more statistically similar consumers.
[0070] In some embodiments, after each matching test, the target audience
generator 106 may generate a unique audience list record in which audiences
are
curated based on an advertiser specification, and further generate identity
keys for
the audiences in the unique audience list and send the generated identity keys
to a
second advertisement platform and/or a programmatic partner. For example,
after
the first matching, the target audience generator 106 may generate a unique
audience list record using the target audience pool 1 (FIG. 7A) in which
audiences
are curated based on an advertiser specification. The target audience
generator 106
may also generate identity keys for the audiences in the unique audience list
and
send the generated identity keys to an advertisement platform and a
programmatic
partner.
[0071] In some embodiments, after each matching test, the target audience
generator 106 may receive an advertiser approval of the purchase of media
advertising and deliver a pool of the target audience obtained from the
matching to a
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corresponding publisher device upon receipt of the advertiser approval. For
example, after the first matching, the target audience generator 106 may
receive an
advertiser approval of the purchase of media advertising and deliver the
target
audience pool 1 to the first publisher device upon receipt of the advertiser
approval.
The target audience generator 106 may convert the at least one first unique
consumer identifier contained in the target audience pool 1 according to a
publisher-
specified conversion protocol prior to delivery of the target audience pool to
the first
publisher device. For example, the target audience generator 106 may convert
the
at least one first unique consumer identifier contained in the target audience
pool 1
to at least one of: a web cookie-based identifier, a television-based
identifier, a
hashed email-based identifier, or a device identifier, prior to delivery of
the target
audience pool 1 to the first publisher device.
[0072] In some embodiments, after each matching, the target audience
generator 106 may generate a target audience pool and deliver the target
audience
pool to the advertiser device. For example, after the first matching, the
target
audience generator 106 may generate the target audience pool 1 and deliver the
target audience pool 1 (FIG. 7A) to the advertiser device to facilitate
targeted
advertising to specific consumers. Similarly, after the second matching, the
target
audience generator 106 may generate a target audience pool 2 (FIG. 7B) and
deliver
the target audience pool 2 to the advertiser device to facilitate targeted
advertising to
specific consumers.
[0073] FIG. 8 is a flow diagram illustrating an exemplary method 800 for
waterfall matching test consistent with the disclosed embodiments. It is to be
understood that each step of method 800 may be performed by one or more
processors, computers, servers, controllers or the like. In some embodiments,
the
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method 800 may be performed by the system 100, for example, by the target
audience generator 106, as depicted in FIG. 1.
[0074] At step 802, the method 800 may include matching unmatched
advertiser-provided consumers with publisher-provided consumers provided by a
publisher having highest priority among unmatched publishers. For example, for
a
first matching test, the unmatched consumers are the original advertiser-
provided
consumers, for example, as indicated by left circle of FIG. 7A. For any
subsequent
matching test, the unmatched consumers are a portion of the original
advertiser-
provided consumers after excluding all matched consumers in the previous
matching
test(s), for example, the unmatched advertiser-provided consumers indicated by
the
left circle of FIG. 7B.
[0075] At step 804, the method 800 may include generating a target audience
pool based on overlapping of unique consumer identifiers. For example, the
target
audience pool may be generated by obtaining a group of consumers common to
both the unmatched advertiser-provided consumer data and the publisher-
provided
consumer data. The target audience can be obtained by matching pseudonymous
identifiers associated with the advertiser-provided consumer data against
pseudonymous identifiers associated the consumer data provided by the
publisher,
as discussed above.
[0076] At step 806, the method 800 may include delivering target audience
pool to the client device. For example, the generated target audience pool may
be
delivered to the advertiser device to facilitate targeted advertising to
specific
consumers.
[0077] At step 808, the method 800 may include determining whether a
remainder of the advertiser-provided consumers is greater than a threshold
number.
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If the remainder of the advertiser-provided consumers is greater than the
threshold
number, the method 800 returns to step 802 and iterates the steps 802, 804,
806,
and 808 until the remainder of the advertiser-provided consumers provided does
not
exceed the threshold number.
[0078] At step 810, the method 800 may include ending the waterfall matching
test if the remainder of the consumers provided by the client device does not
exceed
the threshold number.
[0079] While the present disclosure has been shown and described with
reference to particular embodiments thereof, it will be understood that the
present
disclosure can be practiced, without modification, in other environments. The
foregoing description has been presented for purposes of illustration. It is
not
exhaustive and is not limited to the precise forms or embodiments disclosed.
Modifications and adaptations will be apparent to those skilled in the art
from
consideration of the specification and practice of the disclosed embodiments.
Additionally, although aspects of the disclosed embodiments are described as
being
stored in memory, one skilled in the art will appreciate that these aspects
can also be
stored on other types of computer readable media, such as secondary storage
devices, for example, hard disks or CD ROM, or other forms of RAM or ROM, USB
media, DVD, Blu-ray, or other optical drive media.
[0080] Computer programs based on the written description and disclosed
methods are within the skill of an experienced developer. Various programs or
program modules can be created using any of the techniques known to one
skilled in
the art or can be designed in connection with existing software. For example,
program sections or program modules can be designed in or by means of .Net
Framework, .Net Compact Framework (and related languages, such as Visual
Basic,
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C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX combinations, XML, or HTML
with included Java applets.
[0081] Moreover, while illustrative embodiments have been described herein,
the scope of any and all embodiments having equivalent elements,
modifications,
omissions, combinations (e.g., of aspects across various embodiments),
adaptations
and/or alterations as would be appreciated by those skilled in the art based
on the
present disclosure. The limitations in the claims are to be interpreted
broadly based
on the language employed in the claims and not limited to examples described
in the
present specification or during the prosecution of the application. The
examples are
to be construed as non-exclusive. Furthermore, the steps of the disclosed
methods
may be modified in any manner, including by reordering steps and/or inserting
or
deleting steps. It is intended, therefore, that the specification and examples
be
considered as illustrative only, with a true scope and spirit being indicated
by the
following claims and their full scope of equivalents.
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