U.S. patent application number 16/520061 was filed with the patent office on 2019-11-14 for driving behaviors, opinions, and perspectives based on consumer data.
The applicant listed for this patent is U-MVPINDEX LLC. Invention is credited to Michael Baird, Troy Lanier, Higinio O. Maycotte, Rishi Shah, Travis Turner.
Application Number | 20190347696 16/520061 |
Document ID | / |
Family ID | 55180483 |
Filed Date | 2019-11-14 |
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United States Patent
Application |
20190347696 |
Kind Code |
A1 |
Maycotte; Higinio O. ; et
al. |
November 14, 2019 |
DRIVING BEHAVIORS, OPINIONS, AND PERSPECTIVES BASED ON CONSUMER
DATA
Abstract
A method includes storing, in a memory device of a measurement
system comprising at least a processor, correlation data indicating
that a first attribute is correlated with a second attribute. The
method further includes receiving, at a network interface of the
measurement system, a first request to initiate a first messaging
action directed to a target audience segment, the target audience
segment associated with the first attribute. The method further
includes, subsequent to receiving the first request and based on
the correlation data indicating that the first attribute is
correlated to the second attribute, transmitting a second request
to a server via the network interface, the second request to
initiate a second messaging action instead of the first messaging
action. The second messaging action corresponds to a second group
of users associated with the server.
Inventors: |
Maycotte; Higinio O.;
(Austin, TX) ; Baird; Michael; (Austin, TX)
; Shah; Rishi; (Austin, TX) ; Turner; Travis;
(Austin, TX) ; Lanier; Troy; (Austin, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
U-MVPINDEX LLC |
Dallas |
TX |
US |
|
|
Family ID: |
55180483 |
Appl. No.: |
16/520061 |
Filed: |
July 23, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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14448672 |
Jul 31, 2014 |
10373209 |
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16520061 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0201 20130101;
G06Q 30/0242 20130101; G06Q 30/0269 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method comprising: storing, in a memory device of a
measurement system comprising at least a processor, correlation
data indicating that a first attribute is correlated with a second
attribute; receiving, at a network interface of the measurement
system, a first request to initiate a first messaging action
directed to a target audience segment, the target audience segment
associated with the first attribute; and subsequent to receiving
the first request and based on the correlation data indicating that
the first attribute is correlated to the second attribute,
transmitting a second request to a server via the network
interface, the second request to initiate a second messaging action
instead of the first messaging action, the second messaging action
corresponding to a second group of users associated with the
server, wherein each user included in a first group of users is
associated with the first attribute, wherein at least one user
included in the second group is associated with the second
attribute but not the first attribute, and wherein at least one
user is included in both the first and the second groups.
2. The method of claim 1, wherein the first attribute corresponds
to an indication that a user likes a particular beverage, and
wherein the second attribute corresponds to an age range.
3. The method of claim 1, wherein the first attribute corresponds
to an indication that a user likes a particular beverage, and
wherein the second attribute corresponds to an occupation.
4. The method of claim 1, wherein the first messaging action
corresponds to sending an electronic mail (e-mail) message, sending
a text message, sending a push notification, or any combination
thereof, to the first group of users.
5. The method of claim 1, wherein the second messaging action
corresponds to sending an electronic mail (e-mail) message, sending
a text message, sending a push notification, sending a social
networking message, or any combination thereof, to the second group
of users.
6. The method of claim 1, wherein the server is associated with a
social network, an advertising network, an Internet-based network,
a search engine, or any combination thereof, and wherein the first
attribute includes a brand affinity, a behavior, an opinion of a
member of the target audience segment, a perspective of the member
of the target audience segment, or any combination thereof.
7. The method of claim 1, wherein the correlation data further
comprises data indicating that a third attribute is correlated to
the first attribute, the second attribute, or both, the method
further comprising subsequent to transmitting the second request to
the server, transmitting a third request to the server to initiate
a third messaging action, the third messaging action corresponding
to a third group of users associated with the server, wherein at
least one user included in the third group has the third attribute
but neither the first nor the second attributes.
8. The method of claim 7, wherein the correlation data further
comprises data indicating that a fourth attribute is correlated to
the first attribute, the second attribute, the third attribute, or
a combination thereof, the method further comprising subsequent to
transmitting the third request to the server, transmitting a fourth
request to the server to initiate a fourth messaging action, the
fourth messaging action corresponding to a fourth group of users
associated with the server, wherein at least one user included in
the fourth group has the fourth attribute but not the first,
second, or third attributes.
9. The method of claim 1, further comprising transmitting, via the
network interface, a third request to the server, the third request
identifying a third messaging action, wherein the third messaging
action corresponds to a third group of users associated with a
plurality of attributes.
10. The method of claim 1, wherein a message associated with the
first messaging action or the second messaging action is configured
to influence one or more individuals to perform an action, adopt a
particular opinion, join a cause, enroll in a program, watch a
video, read text, or a combination thereof.
11. The method of claim 1, further comprising: receiving presence
data from a radio-frequency identification (RFID) device, the
presence data indicating that an individual has visited a location
associated with the RFID device; and in response to the presence
data, updating profile data associated with the individual to
indicate that the individual is a member of the target audience
segment.
12. The method of claim 1, further comprising: storing, in the
memory device, extension data indicating an approximate number of
additional users associated with the server that are reachable by
utilizing the correlation data indicating that the first attribute
is correlated to the second attribute; and calculating, with the
processor and using the extension data, a number of additional
users that are reachable by initiating the second messaging action
instead of the first messaging action.
13. The method of claim 1, further comprising: detecting, by the
measurement system, a conversion event associated with a particular
user of the second group of users, the conversion event
corresponding to a conversion web page; in response to the
conversion event, sending a retargeting pixel to a web browser
associated with the particular user, wherein the retargeting pixel
enables at least one digital network to identify the particular
user and to send retargeting content to a web browser associated
with the particular user.
14. The method of claim 13, wherein the conversion event
corresponds to the particular user buying a particular product from
a particular location associated with the conversion page.
15. A method comprising: storing, in a memory device of a
measurement system comprising at least a processor, correlation
data indicating that a first attribute is correlated with a second
attribute; receiving, at a network interface of the measurement
system, a first request to initiate a first messaging action
directed to a target audience segment, the target audience segment
associated with the first attribute; and subsequent to receiving
the first request and based on a comparison of a first metric
associated with a first messaging action and a second metric
associated with a second messaging action, based on the first
attribute, and based on the correlation data indicating that the
first attribute is correlated to the second attribute,
transmitting, via the network interface, a second request to a
server, the second request identifying the second messaging action
without identifying the first messaging action, wherein the first
messaging action corresponds to a first group of users associated
with the server and the second messaging action corresponds to a
second group of users associated with the server, wherein at least
one user included in the first group is associated with the first
attribute, wherein at least one user included in the second group
is associated with the second attribute but not the first
attribute, and wherein at least one user is included in both the
first and the second groups.
16. The method of claim 15, wherein the first metric comprises a
first estimated number of actions, a first estimated number of
unique views, or a combination thereof, and wherein the second
metric comprises a second estimated number of actions, a second
estimated number of unique views, or a combination thereof.
17. The method of claim 15, further comprising: after transmitting
the second request, receiving result data associated with the
second messaging action; and based on the result data, adjusting a
purchasing rate associated with the second messaging action.
18. A method comprising: receiving, at a network interface of a
measurement system comprising a processor, a first request to
initiate a first messaging action directed to a target audience
segment, the target audience segment associated with a first
attribute; detecting, by the measurement system, a conversion event
associated with a particular user of a group of users associated
with a server, the conversion event initiated at a conversion web
page; in response to the conversion event, sending retargeting data
to a web browser associated with the particular user, wherein the
retargeting data enables at least one digital network distinct from
the conversion web page to identify the particular user and to send
retargeting content to the web browser; and subsequent to receiving
the first request, transmitting a second request to the server via
the network interface, the second request to initiate a second
messaging action instead of the first messaging action, the
retargeting content associated with the second messaging
action.
19. The method of claim 18, wherein the retargeting data comprises
a retargeting pixel.
20. The method of claim 18, wherein the server is associated with a
social networking service, and wherein the first messaging action
corresponds to sending an electronic mail message, sending a text
message, displaying an advertisement on a web page, sending a push
notification, sending a social networking message, or any
combination thereof, that is targeted to a first group of users.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims priority from, and is a
divisional application of, U.S. patent application Ser. No.
14/448,672, filed Jul. 31, 2014 and entitled "DRIVING BEHAVIORS,
OPINIONS, AND PERSPECTIVES BASED ON CONSUMER DATA," the contents of
each of which are expressly incorporated by reference in their
entirety.
BACKGROUND
[0002] Influencing the behavior, opinions, or perspectives of an
individual may be difficult. Media producers around the world may
spend hundreds of billions of dollars every year to influence
people in certain ways. For example, media producers may desire to
encourage people to consume more of a specific product. Other media
producers may want to affect political leanings or people's
opinions on a topic (e.g., politics, environmentalism, etc.). As
the examples described above demonstrate, a goal of certain kinds
of communication may be to drive a particular kind of response. For
centuries, the primary method of enticing people to take a certain
action was through print media. Then, the method shifted to radio
advertising. Now, there is a brand new digital medium that offers
more promise than previous mediums. The internet and various
associated digital media may have influence in influencing the
actions of individual users, and may enable media producers to
communicate with many people nearly instantly. Despite the strength
of this new medium, if the media producers deliver messages to
"incorrect" audiences, the messages may reduce the likelihood that
those audiences will take a desired response (e.g., make a
purchase, form a particular opinion, etc.).
SUMMARY
[0003] As internet usage increases, a larger volume of data
regarding the interactions of individual users may become
available. However, such data may be difficult to access and
analyze, which may present a significant hurdle to harnessing the
data and developing an understanding of an audience. Advertisers
and other enterprises that are able to effectively use such data to
understand their audience may have a greater chance to influence
the opinions and behaviors of their audience. The present
disclosure presents systems and methods of driving and influencing
actions, behaviors, opinions, and/or perspectives based on consumer
data. For example, a reach extension module may receive an
identification of a target audience segment. The target audience
segment may correspond to a segment of a population that an
advertiser wishes to reach with precise messaging. The messaging
could be delivered via an advertisement, an e-mail communication,
or via another digital communication. As an illustrative,
non-limiting example, if an advertiser is selling tickets to a
football game, the target audience segment may be "audience members
that are likely to buy tickets to football games." Examples of
actions that an advertiser may wish to drive/increase/influence may
include, but are not limited to, purchasing tickets, watching
videos, purchasing retail items, navigating to a particular
website, voting on a particular issue or for a particular
candidate, etc. The reach extension module may identify first
attributes associated with members of the target audience segment.
For example, the reach extension module may identify first
attributes "male" and "age 21-50" in response to determining that
previous buyers of football tickets were largely male and between
the ages of 21-50. The reach extension module may further identify
second attributes that are correlated to the first attributes. For
example, the reach extension module may identify a second attribute
"likes beverage A" in response to determining that, in a population
for which data is available, a large percentage of the men between
the ages of 21-50 have an affinity for beverage A. The first
attributes and/or the second attributes may be "available"
attributes for which targeted communication is available. As an
illustrative, non-limiting example, targeted communication could
include an e-mail, an advertising message delivered via a social
network, or a push notification on a mobile device. Alternatively,
or in addition, the reach extension module may map the first
attributes and/or the second attribute to additional available
attributes. For example, one or more advertising networks may offer
advertising targeted to users with an attribute "man" and/or an
attribute "likes beverage A website." The reach extension module
may map the first attribute "male" to the available attribute "man"
and may map the second attribute "likes beverage A" to the
available attribute "likes beverage A website."
[0004] Prior to initiating targeted communication aimed at driving
behavior, the reach extension module may determine estimated cost
and reach of one or more of the available attributes. "Reach" may
be estimated as a number of unique views for an advertisement, a
number of people who interact with the advertisement, a number of
people who take a desired action as a result of the advertisement,
a number of people expected to open an e-mail, number of people
that have installed a mobile application, number of people wearing
a device connected to the internet, or any combination thereof. For
example, the reach extension module may query one or more channels
for prices associated with the one or more available attributes.
The prices may be represented as a cost per thousand impressions, a
cost per click, a cost per e-mail, or a cost per notification. The
type of pricing may depend on the network the targeted messaging is
delivered on. The reach extension module may compare the received
price information to historical data regarding previous targeted
communication and audiences to estimate cost and reach. Based on
the estimated cost and reach of the one or more available
attributes, the reach extension module may reference a library of
strategies and execute targeted communication at the one or more
networks according to the strategy. The reach extension module may
monitor results of the strategies, the number of people completing
the desired behavior, (e.g., reach of targeted communication,
conversions due to targeted communication, cost of communication,
etc.), adjust the strategies according to the results, and store
data regarding the strategies for subsequent use. The described
system and method may thus provide an automatic method to target
communication aimed at driving behaviors. The targeted
communication may exist on paid advertising networks, e-mail
delivery systems, or other ways to digitally intercept a user's
attention and promote a particular action.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 is a diagram to illustrate a particular embodiment of
a system that is operable to initiate a messaging action based on
audience attributes;
[0006] FIG. 2 is a diagram to illustrate automatic targeted
messaging actions;
[0007] FIG. 3 is a diagram to illustrate a particular embodiment of
an attribute mapping used by the system of FIG. 1;
[0008] FIG. 4 is a diagram to illustrate a particular embodiment of
a graphical user interface (GUI) associated with initiating
messaging actions based on audience attributes;
[0009] FIG. 5 is a diagram of another embodiment of a system that
is operable to initiate targeted messaging actions based on
audience attributes;
[0010] FIG. 6 is a flowchart to illustrate a particular embodiment
of a method of initiating targeted messaging actions;
[0011] FIG. 7 is a flowchart to illustrate another particular
embodiment of a method of initiating targeted messaging
actions;
[0012] FIG. 8 is a diagram to illustrate a particular embodiment of
an audience measurement system; and
[0013] FIGS. 9A, 9B, 9C, and 9D are diagrams to illustrate another
particular embodiment of an audience measurement system.
DETAILED DESCRIPTION
[0014] FIG. 1 illustrates a particular embodiment of a system 100
that is operable to initiate a messaging action (e.g., an
advertisement, an e-mail, a text message, a push notification to a
computing device, a social networking message, etc.) based on
audience attributes. The system 100 includes a measurement system
120 that may be implemented using one or more computing devices
(e.g., servers). For example, such computing devices may include
one or more processors or processing logic, memories, and network
interfaces. The memories may include instructions executable by the
processors to perform various functions described herein. The
network interfaces may include wired and/or wireless interfaces
operable to enable communication to local area networks (LANs)
and/or wide area networks (WANs), such as the Internet. In the
illustrated example, the measurement system 120 is communicably
coupled to a network 130.
[0015] The measurement system 120 may include a reach extension
module 124, which may be implemented using instructions executable
by one or more processors at the measurement system 120. In
alternative embodiments, the reach extension module 124 may be
external to and in communication with the measurement system 120
(e.g., functions described herein with reference to the reach
extension module 124 may be executed on a separate computing device
in communication with the measurement system 120). The reach
extension module 124 is executable to initiate messaging actions
based on audience attributes, as further described herein. For
example, a media producer may initiate a messaging action in an
attempt to cause an individual to perform a desired action (e.g.,
buy a product, form a particular opinion, join a cause, enroll in a
program, watch a video, read an article, etc.) The reach extension
module 124 may initiate messaging actions based on attributes of an
audience made of people who have performed the desired action.
[0016] In a particular non-limiting example, initiating a messaging
action corresponds to purchasing targeted advertising. In other
non-limiting examples, initiating a messaging action corresponds to
a different type of communication. In the context of targeted
internet advertising, "purchasing" targeted advertising directed to
a particular attribute refers to placing a bid with an advertising
network (e.g., a social network that advertises to users, a search
engine that inserts advertisements in search results webpages,
etc.) for the opportunity to advertise to members/users of the
advertising network that exhibit the particular attribute. Bids may
be placed in terms of cost per mille (CPM), cost per click (CPC),
or cost per action (CPA), as illustrative non-limiting examples.
For example, an advertiser may place a CPC bid of $1.25 targeting
unmarried users of a social network. If the advertiser's bid is
accepted, the advertiser's advertisement(s) are presented to
unmarried users of the social network and the advertiser is charged
$1.25 each time a user of the social network clicks on the
advertisement(s).
[0017] The measurement system 120 may be coupled via the network
130 to one or more segment databases 140 that store data (e.g.,
consumer data). Although the segment databases 140 are illustrated
in FIG. 1 as being external to the measurement system 120, in
alternative embodiments the segment databases 140 may be part of
(e.g., internal to) the measurement system 120. As used herein, a
"segment" is based on (or corresponds to) a group of people (e.g.,
an audience or a subset thereof). As further described herein, a
set of traits may be determined for each segment. In an
illustrative embodiment, the set of traits for a segment
corresponds to a Digital Genome.RTM. of the segment (Digital Genome
is a registered trademark of Umbel Corporation of Austin, Tex.).
Examples of segments include, but are not limited to, brand
affinity segments (also called brand segments), demographic
segments, geographic segments, social activity segments, employer
segments, educational institution segments, professional group
segments, industry category of employer segments, brand affinity
category segments, professional skills segments, job title
segments, and behavioral segments. In a particular embodiment,
behavioral segments are defined by a client (e.g., property owner
or publisher) or by the measurement system 120, and represent
actions taken on a client's property, such as "watched a video,"
"read an article," "made a purchase," etc. In this context,
"property" refers to a media property, such as media content, a
website, etc.
[0018] Additional examples of segments include segments based on an
advertisement, an advertisement campaign, an advertisement
placement, an advertisement context, a content item, a content
context, content placement, etc. As another example, a segment may
be generated based on a platform (e.g., desktop/laptop computer vs.
mobile phone vs. tablet computer). For example, a "tablet segment"
may include users that viewed a media property using a tablet
computing device. Segments may be used to evaluate characteristics
of an audience, craft a content strategy, generate advertising
leads, create advertising pitches, and respond to inbound
advertising requests.
[0019] The measurement system 120 collects and analyzes data that
describes attributes of audiences of one or more media properties
and stores the data and analysis results in the segment databases
140. In a particular embodiment, audience data is stored in the
segment databases 140 according to media property (e.g., website),
and audience segments are generated and analyzed on demand by the
measurement system 120. Each segment is defined by a relation
between members of a media property's audience (e.g., users) and
one or more attributes. The relations may include a HAS relation,
an AND relation, an OR relation, a NOT relation, or a combination
thereof. For example, a "male" segment may be defined by a relation
"users who have the male attribute" (male={users: has male
attribute}), and a "males who watch video" segment may be defined
by a relation "users who have the male attribute and have the watch
video attribute" (male AND watches video={users: has male
attribute; and has watches video attribute}). Similarly, a "males
who do not watch video" segment may be defined by a relation "users
who have the male attribute and do not have the watches video
attribute" (male AND -watches video={users: has male attribute; and
does not have watches video attribute}). In addition, a "male or
watches video" segment may be defined by a relation "users who have
the male attribute or users that have the watches video attribute"
(male OR watches video={users: has male attribute; or has watches
video attribute}). In a particular embodiment, the measurement
system 120 collects and organizes data regarding audience members
(e.g., users) of various media properties based on event signals
received from user devices (e.g., mobile phones, tablet computers,
laptop/desktop computers, radio-frequency identification (RFID)
tags, etc.), media property web servers, network logs, and/or
third-party signal sources, as further described with reference to
FIGS. 8-9.
[0020] An audience of a media property may be segmented by
demographic attributes, brand affinities, behavioral attributes, or
a combination thereof. For example, as explained above, the "Male"
segment may include all members of an audience of a media property
who are male. As another example, a "Brand X" audience segment may
include all members of the audience who are determined (e.g., based
on statements made in social networks, actions performed on the
media property, etc.) to have an affinity for "Brand X." As a third
example, an audience segment may include all members of the
audience who have performed a particular action related to the
media property (e.g., users who have purchased an item at a
website, watched a video, clicked on an advertisement, etc.).
Further, audience segments may be defined by an AND or an OR
relation between attributes (and associated segments). For example,
a particular audience segment may include all members of an
audience who are female AND who are between the ages of 23 and 27.
As another example, a particular audience segment may include all
members of an audience who are female OR who are between the ages
of 23 and 27. In addition, audience segments may be defined in part
by a NOT relation. For example, a segment may include all members
of an audience who are NOT female.
[0021] The measurement system 120 may be in communication with one
or more digital networks 150 (e.g., advertising networks, social
networks, etc.) via the network 130. In alternative embodiments,
the measurement system 120 may communicate with the digital
networks 150 via a second network (e.g., a local area network
(LAN), a wide area network (WAN) such as the Internet, etc.). The
digital networks 150, or at least a subset thereof, may correspond
to Internet-based networks and may offer the ability to purchase
advertising targeted to specific users 160 and/or specific user
attributes. For example, a social network may enable media
producers (e.g., advertisers, political campaigns, etc.) to
initiate messaging actions (e.g., advertisements, e-mails, text
messages, push notifications, social network messages, etc.)
targeted to users of the social network who have certain attributes
(e.g., affinities, demographic attributes, etc.) using an
auction-based system, as further described herein. The users 160 of
the digital networks 150 may include users of the media properties
(e.g., websites) for which data is collected by the measurement
system 120, although this is not necessary. For example, one of the
digital networks 150 may correspond to a social network. The social
network may include a user "John Smith," who is also a registered
user of a particular blog that is tracked by the measurement system
120. Thus, in this example, John Smith is both a user of a tracked
media property as well as one of the users 160 of the digital
networks 150.
[0022] In a particular example, a digital network, such as a social
network, may solicit bids for messaging actions directed to
specific attributes of members of the social network. To
illustrate, an advertiser may bid to initiate a messaging action to
members of the social network who, according to their social
networking profiles, match the attributes "female," "unmarried,"
and "age 21-30." As further described herein, during initiation of
messaging actions, the measurement system 120 may target individual
users (e.g., John Smith) of a social network if permitted to do so
by the social network.
[0023] Some or all of the attributes tracked by the measurement
system 120 (also referred to herein as "first attributes") may
correspond to some or all of the attributes for which messaging
actions are available at the digital networks 150 (also referred to
herein as "second attributes"). For example, the measurement system
120 may collect data indicating which users of a media property
like a particular television show. One or more of the digital
networks 150 may offer messaging actions targeted to a subset of
the users 160 who are members of a fan group dedicated to the
particular television show. One non-limiting example of a messaging
action is purchasing targeted advertising. Members of a fan group
dedicated to the particular television show may have similar
attributes to users of the media property who like the particular
television show (e.g., may share other affinities, demographic
attributes, etc.). As further described herein, by identifying and
initiating messaging actions directed to the "related" second
attributes instead of or in addition to purchasing advertising
directed to the first attributes, the reach extension module 124
may improve results of a messaging action (e.g., by initiating
messaging actions targeted to users who may share interests,
demographic attributes etc., with a target segment and may
therefore be more likely to respond to the messaging action),
decrease advertising cost, and/or increase advertising reach.
[0024] In a particular embodiment, the measurement system 120
generates, maintains, stores, and/or accesses one or more mapping
databases 126 that store mappings between the first attributes and
the second attributes. For example, such attribute mappings may be
one-to-one, one-to-many, many-to-one, and/or many-to-many relations
that are stored in tables or other data structures. In alternative
embodiments, the mapping databases 126 may be stored externally to
the measurement system 120 or as part of the reach extension module
124. The mappings database 126 may maintain a mapping between
attributes tracked by the measurement system 120 and attributes
associated with each of the digital networks 150 for which
messaging actions are available (e.g., targeted advertising,
e-mail, text message, social networking message, push notification,
etc.). The mappings may be user-defined (e.g., by an administrator
of the measurement system 120) and/or automatically determined
(e.g., based on a fuzzy logic algorithm, a pattern matching
algorithm, an advertising attribute ontology, etc.). An
illustrative example of attribute mappings is further described
with reference to FIG. 3.
[0025] The measurement system 120 may further include or have
access to a messaging action history database 128. The messaging
action history database 128 may be configured to store data
indicating reach, price, yield, or a combination thereof of
messaging actions initiated at the digital networks 150. For
example, the messaging action history database 128 may store data
indicating a number of impressions, a number of unique impressions,
a number of engagements, a number of conversions, or a combination
thereof. As used herein, a number of impressions indicates how many
devices displayed a message as a result of a messaging action, a
number of engagements indicates how many devices interacted with
the message (e.g., a "click" on a hyperlink in the message), and a
number of conversions indicates a number of times a desired action
was taken by devices (or users thereof) after displaying the
message (e.g., a number of purchased a products, a number of new
enrollments in a program, a number of pledges to an organization,
etc.). "Reach" may represent a number of unique views for the
message, a number of people who interact with the message, a number
of people who take the desired action as a result of the message,
or any combination thereof. Yield may represent a total number of
times the desired action is taken in response to the message. The
messaging action history database 128 may store cost data such as
total cost of a messaging action campaign, cost per conversion,
cost per impression, cost per unique impression, or a combination
thereof. While reach, cost, and yield are described to vary for
messaging actions depending on the attribute targeted, it should be
noted that reach, cost, and yield may also vary by type of
messaging action. For example, a text message messaging action may
have a different cost/reach/yield than an e-mail.
[0026] In operation, the measurement system 120 may receive data
identifying a target segment 110. In particular embodiments, the
data identifying the target segment 110 may be received from a web
application or other application that provides a user interface
(UI) that is operable to configure the measurement system 120. For
example, the target segment 110 may correspond to users who have
performed a desired action in the past (e.g., watched a movie, read
a book, indicated a certain opinion, liked a particular product,
etc.) Alternatively, the data identifying the target segment may be
programmatically (e.g., automatically) selected based on a
probability of audience conversion or previous advertising campaign
success, as further described herein. The target segment 110 may
correspond to a segment of one or more media properties tracked by
the measurement system 120. The one or more media properties may or
may not be commonly owned. A client of the measurement system 120
(e.g., an advertiser or other entity) may be interested in
initiating messaging actions to people similar to the target
segment 110 (e.g., to "amplify" or "drive" the action or behavior
corresponding to the target segment 110). For example, the target
segment 110 may correspond to users of one or more media properties
who watched a video, and the client of the measurement system 120
may be interested in initiating messaging actions to people likely
to watch the video or a similar video. As another example, the
advertiser may be a coffee vendor that is determining how to
present a newly created video advertisement for a coffee product.
In this example, the target segment 110 may be a "likes coffee"
segment or a "likes particular coffee brand" segment.
[0027] The reach extension module 124 may determine a first list of
attributes associated with the target segment 110. For example, the
first list of attributes may include attribute(s) tracked by the
measurement system 120 that are associated with at least one member
of the target segment 110. In addition, the reach extension module
124 may determine one or more attributes that are correlated to the
attributes of the first list. For example, the "likes coffee"
target segment 110 may include a large number of users with an
attribute job starts before 7 AM." The reach extension module 124
may identify that an attribute "is a police officer" has a high
degree of correlation with the attribute "job starts before 7 AM."
Thus, the "police officer" attribute may be determined by the reach
extension module 124 to be correlated to the attribute "job starts
before 7 AM." Thus, the reach extension module 124 may determine
that the coffee-related messaging actions (e.g., advertisements)
may be effectively targeted to users in the digital networks 150
that have attributes matching or similar to the "likes coffee,"
"job starts before 7 AM," and/or "police officer" attributes
tracked by the measurement system 120.
[0028] To identify attributes in the digital networks 150 that
match or are similar to "likes coffee," "job starts before 7 AM,"
and "police officer," the reach extension module 124 may access the
mapping databases 126. To illustrate, the mapping databases 126 may
store data indicating that the first list of attributes "likes
coffee," "job starts before 7 AM," and "police officer" maps to a
second list of attributes (e.g., "likes early morning radio,"
"caffeine lovers," "police union," etc.) for which a messaging
action is available at a particular digital network. The reach
extension module 124 may determine metrics, such as an estimated
cost (e.g., cost per conversion), an estimated reach (e.g., a
number of unique views), and an estimated yield (e.g., a number of
desired actions, such as purchases, that are taken) for messaging
actions targeted to different combinations of attributes in the
second list. For example, a particular second list corresponding to
a particular digital network may include the attributes "police
union" (e.g., indicating that a user is a member of a police union
social networking group) and "likes coffee brand M" (e.g.,
indicating that a user is a "fan" of coffee brand M in a social
network). The reach extension module 124 may determine metric(s)
(e.g., estimated cost, estimated reach, estimated yield, or a
combination thereof) for messaging actions in the particular
digital network targeted to the "police union" attribute, the
"likes coffee brand M" attribute, and a combination of both
attributes. It should be noted that the estimated cost may include
lost opportunity costs associated with users who react negatively
to messaging actions. For example, a particular user who may have
bought a product if not for receiving a targeted messaging action
may account for a portion of the estimated cost of the messaging
action. Further, the estimated cost may not be stored as a monetary
value. For example, estimated cost may correspond to votes lost in
a political campaign as a result of initiating targeting messaging
actions.
[0029] In a particular embodiment, the estimated cost, the
estimated reach, the estimated yield, or a combination thereof of
messaging actions directed to a particular attribute (as used
herein, messaging actions directed to a particular attribute refers
to messaging actions directed to users who have, exhibit, or are
associated with the particular attribute) may be calculated based
on data stored in the segment databases 140. Thus, the data in the
segment databases 140 may be used to estimate messaging action
efficacy in the external digital networks 150. For example, the
measurement system 120 may determine a "degree of correlation"
between the target segment 110 (e.g., the "likes coffee" segment)
and each attribute or combination of attributes identified by the
reach extension module 124 (e.g., the "likes coffee" attribute, the
"job starts before 7 AM" attribute, and the "police officer"
attribute). A correlation of the "likes coffee" attribute to the
"likes coffee" segment may be 1, because each user in the "likes
coffee" segment has the attribute "likes coffee"=1 (or true). A
correlation of a "police officer" attribute to the "likes coffee"
segment may be 0.7 based on data in the segment databases 140
indicating that 70% of users in the "police officer" segment are
also in the "likes coffee" segment (e.g., have the attribute "likes
coffee"=1). Correlation data may also be calculated for custom
segments defined using set unions and/or intersections (e.g., a
"police officers who like coffee" segment defined by performing a
set intersection on the "police officer" segment and the "likes
coffee" segment).
[0030] The reach extension module 124 may use the correlation data
to estimate reach and cost for each combination of mapped
attributes in the digital networks 150. To illustrate, in the above
example in which an advertiser is presenting a coffee-related
advertisement, a digital network may have 3,000 users that are
members of a police union. To determine an estimated reach of a
coffee-related advertisement directed to the "police union"
attribute, the reach extension module 124 may apply the previously
determined correlation between "likes coffee" and "police officer"
of 0.7 to the number of members of the police union. Thus, the
reach extension module 124 may determine the estimated reach as
3,000*0.7=2,100.
[0031] In a particular embodiment, the segment databases 140 may
include information indicating a frequency of actions carried out
by users. For example, the segment databases 140 may include
information indicating that, on average, coffee drinkers (e.g.,
user in a "likes coffee" segment) drink 1.5 cups of coffee per day.
Thus, the reach extension module may determine that an estimated
yield of initiating a messaging action (e.g., an advertisement)
designed to encourage coffee drinking directed to members of the
police union is 2,100*1.5=3,150 cups of coffee per day.
[0032] In addition or in the alternative, the reach extension
module 124 may determine estimated cost, estimated reach, and
estimated yield based at least in part on historical data received
from the messaging action history database 128. For example, the
messaging action history database 128 may include information
indicating reach, cost, and yield of a prior advertising campaign
for coffee directed to police officers. The reach extension module
124 may use the advertisement history information as an estimated
cost, estimated reach, and estimated yield or may weight/adjust the
estimated cost, the estimated reach, and the estimated yield based
on the advertisement history information. For example, the reach
extension module 124 may average the estimated cost, the estimated
reach, and the estimated yield with the advertisement history data
to produce a weighted cost, a weighted reach, and a weighted
yield.
[0033] One or more of the attributes identified by the reach
extension module 124 may be used to initiate messaging actions. For
example, the reach extension module 124 may initiate (e.g.,
initiate purchase of, initiate transmission of, etc.) messaging
actions based on an attribute of a target segment, a first
attribute associated with at least one member of the target
segment, a second attribute determined to be related to the first
attribute, or a combination thereof. In the coffee-related
advertisement example, the target segment 110 is "likes coffee,"
the first attribute may be "job starts before 7 AM" and the second
attribute may be "is a police officer." Thus, one or more of the
attributes "likes coffee," "job starts before 7 AM," and "is a
police officer" may be used. At a first digital network, targeted
messaging actions directed to users who like coffee (e.g., directed
to the "likes coffee brand M" attribute at the digital network) may
have a particular estimated cost (e.g., $4 per user reached), a
particular estimated reach (e.g., 1,000 users), and a particular
estimated yield (e.g., 1,500 coffee purchases per day). Similarly,
targeted advertising directed to police officers (e.g., directed to
the "police union" attribute at the digital network) may have an
estimated cost of $1 per user, an estimated reach of 2,100 users,
and an estimated yield of 3,150 coffee purchases per day. Targeted
messaging actions directed to people who start work before 7 AM
(e.g., a "likes early morning radio" attribute at the digital
network) may have an estimated cost of $2 per user reached, an
estimated reach of 800 users, and an estimated yield of 1,200
coffee purchases per day.
[0034] In a particular embodiment, the reach extension module 124
may determine that one or more messaging actions targeted to one
attribute at a digital network is likely to be more effective
(e.g., cost effective) than one or more messaging actions targeted
to another attribute. For example, as described above, a target
segment of coffee drinkers may include people who start work before
7 AM. Being a police officer may be correlated with starting work
before 7 AM. In a particular digital network, messaging actions
directed to police officers may have an estimated cost, an
estimated reach, an estimated yield or a combination thereof, that
is more cost effective (e.g., lower cost, a larger reach, a larger
yield, or a combination thereof) than messaging actions directed to
people who start work before 7 AM. In the above example, the cost
per user reached for messaging actions directed to people who start
work at 7 AM (e.g., the "likes early morning radio" attribute) is
$2 and the cost per user reached directed to police officers (e.g.,
the "police union" attribute) is $1. Therefore, the reach extension
module 124 may automatically initiate messaging actions targeted to
users with attributes corresponding to police officers (e.g., the
"police union" attribute of the digital network) instead of
messaging actions directed to users who start work before 7 AM.
[0035] In a particular embodiment, the reach extension module 124
may initiate messaging actions based on an estimated yield. For
example, the estimated cost per additional cup of coffee sold per
day may be 75 cents for messaging actions directed to people who
start work at 7 AM (e.g., the "likes early morning radio"
attribute) and the estimated cost per additional cup of coffee sold
per day may be 68 cents for messaging actions directed to people
who are police officers (e.g., the "police union" attribute).
Therefore, the reach extension module 124 may automatically
initiate messaging actions targeted to more users with attributes
corresponding to police officers (e.g., the "police union"
attribute of the digital network) than users who start work before
7 AM.
[0036] Alternately, the reach extension module 124 may utilize a
first portion of a budget to initiate targeted messaging actions
directed to coffee drinkers, a second portion of the budget to
initiate targeted messaging actions directed to users who start
work before 7 AM, and/or a third portion of the budget to initiate
targeted messaging actions directed to police officers. The ratios
of the first portion, the second portion, and/or the third portion
of the budget may be based on the estimated costs, the estimated
reaches, the estimated yields, or a combination thereof of the
corresponding targeted messaging action. The ratios may also be
determined based on historical success of previous advertising
campaigns. To illustrate, lower-cost and/or higher-reach/yield
attributes may be purchased in larger proportions than higher-cost
and/or lower-reach/yield attributes.
[0037] In a particular embodiment, the reach extension module 124
may make messaging decisions by determining that a messaging action
satisfies message criteria 112. The message criteria 112 may be
received from a client application (e.g., a web application, a
mobile phone application, or any other application). The message
criteria 112 may indicate that targeted messaging actions directed
to an attribute should be initiated at a digital network when a
particular number of members of the digital network exhibit the
attribute. In addition or in the alternative, the message criteria
112 may indicate that the targeted messaging action should be
initiated when an estimated cost (e.g., monetary cost, negative
reactions, loss of good will, etc.) of the targeted messaging
action satisfies (e.g., is less than) a cost threshold. Further,
the message criteria 112 may indicate that the targeted messaging
action should be initiated when an estimated reach of the targeted
advertising satisfies (e.g., is greater than) a reach threshold. In
addition or in the alternative, the message criteria 112 may
indicate that the targeted messaging action should be initiated
when an estimated yield of the targeted messaging action satisfies
(e.g., is greater than) a yield threshold). In a particular
embodiment, the message criteria 112 indicates that when a
combination of estimated reach, estimated cost, and estimated yield
(e.g., cost per audience member reached or cost per action caused)
satisfies a threshold, the targeted messaging action should be
initiated. In a particular embodiment, targeted messaging actions
directed to more than one attribute may satisfy the message
criteria. The reach extension module 124 may evaluate each
potential targeted messaging action that satisfies the message
criteria 112 and select a combination of the messaging actions
based on estimated cost, estimated reach, estimated yield or a
combination thereof. For example, the reach extension module 124
may initiate targeted messaging actions to achieve a highest
estimated reach at a cost that is less than or equal to a budget.
Further, the reach extension module 124 may initiate targeted
messaging actions to achieve a highest estimate yield at a cost
that is less than or equal to the budget. The estimated yield may
be a function of the estimated reach.
[0038] In a particular embodiment, the reach extension module 124
develops a messaging strategy based on the estimated cost, the
estimated reach, the estimated yield, or a combination thereof of
targeted messaging actions available for one or more attributes and
initiates targeted messaging actions based on the messaging
strategy (e.g., transmits bids for targeted advertising to one or
more advertising networks, initiates transmission of e-mails,
initiates transmission of text messages, etc.). The messaging
strategy may be further based on the message criteria 112. The
message criteria 112 may indicate a number of members (e.g., users)
of a digital network that exhibit a particular attribute (e.g.,
only purchase advertising directed to the attribute "likes
television show x" when at least 1,000 members are part of a social
networking fan club for television show x). The messaging strategy
may improve effectiveness of targeted messaging actions across the
digital networks 150. The messaging strategy may be designed to
achieve a highest estimated reach and/or estimated yield within the
budget. The reach extension module 124 may continue initiating
targeted messaging actions according to the messaging strategy
until a budget is spent.
[0039] As targeted messaging actions are initiated and messages are
transmitted to the users 160 via the digital networks 150, the
reach extension module 124 may monitor actual cost, actual reach,
and actual yield of the initiated targeted messaging actions, and
may update the messaging action history database 128 based on the
monitoring. For example, the messaging action history database 128
may include data regarding some or all previous messaging
campaigns, a top 10 indexing strategies, a top 10 brand strategies,
etc. When a client (e.g., advertiser) wishes to influence a
particular behavior, the described techniques may include
identifying target segment(s) corresponding to the behavior,
checking the messaging action history database 128 to see if any
previous strategies regarding the same or similar target segments
exist, and then deploying a strategy/data combination that is
predicted to have at least a desired cost/reach/yield. The reach
extension module 124 may update an in-progress or stored messaging
strategy (e.g., a previously used messaging strategy for a
coffee-related messaging action campaign) based on the updated
messaging action history database 128. For example, the updated
messaging action history database 128 may indicate that messaging
actions targeted to a particular attribute have reached fewer users
than estimated, have generated fewer desired actions than estimated
(i.e., have a lower yield than estimated), or have cost more than
anticipated. The reach extension module 124 may issue an alert
(e.g., an e-mail, a text message, a pop-up notification, etc. to a
client or administrator of the measurement system 120) and/or
adjust the messaging strategy to stop or slow initiation of
messaging actions targeted to the particular attribute in response
to the updated messaging action history database 128.
Alternatively, the updated messaging action history database 128
may indicate that messaging actions directed to a certain attribute
cost less, have reached more users than anticipated, or have
generated more desired actions than estimated (i.e., have a higher
yield than estimated). The reach extension module 124 may issue an
alert to a client or administrator and/or adjust the messaging
strategy to accelerate initiation of messaging actions targeted to
the particular attribute. Examples of alerts and modification of
messaging strategy are further described with reference to FIG.
4.
[0040] In a particular embodiment, the reach extension module 124
may recursively or iteratively refine and/or expand a messaging
strategy. For example, the reach extension module 124 may generate
a messaging strategy for a coffee-related messaging action
campaign. Thus, in this example, the initial target segment is
coffee drinkers. The messaging strategy may include initiating
messaging actions directed to one or more attributes at one or more
digital networks, where the one or more attributes are determined
to be correlated to the segment of coffee drinkers. For example,
the messaging strategy may include initiating targeted messaging
actions directed to police officers, people who start work before 7
AM, light house operators, people who subscribe to a newspaper, and
people who attend a yoga studio. These attributes can be considered
"first-degree" attributes of the coffee drinkers target segment.
The reach extension module 124 may receive information (e.g., from
the one or more digital networks, such as a social network) related
to performance of the targeted messaging actions directed to each
of the one or more attributes and may expand and refine the
messaging strategy based on the performance. For example, messaging
actions targeted to police officers may be performing "well" (e.g.,
meeting or exceeding a particular cost, yield, or reach threshold
or performing better than targeted messaging actions directed to
the other attributes). In response, the reach extension module 124
may identify attributes exhibited by members of the police officers
segment (e.g., subscribes to a particular magazine, has a high
stress job, likes comfortable shoes, etc.) These attributes can be
considered "second-degree" attributes for the original coffee
drinkers target segment. The reach extension module 124 may expand
and refine the advertising strategy by purchasing advertising
targeted to one or more of the second-degree attributes. The reach
extension module 124 may continue expanding and refining the
advertising strategy in this fashion (e.g., by determining
third-degree attributes, fourth-degree attributes, etc.) to extend
reach for the coffee drinkers target segment. Thus, the described
techniques may be used to find people with non-obvious
characteristics while maintaining quality (e.g., a user acquisition
cost).
[0041] Thus, the reach extension module 124 may enable automatic
initiation of targeted messaging actions at multiple digital
networks 150, where the initiated targeted messaging actions are
directed to attributes that are highly correlated to the target
segment 110. Further, the reach extension module 124 may monitor
the initiated targeted messaging actions and adjust messaging
strategies based on performance of the targeted messaging actions.
Moreover, the reach extension module 124 may maintain mappings
between first attributes tracked by the measurement system 120
(e.g., "likes coffee," "job starts before 7 AM," and "police
officer") and second attributes available for purchase at each of
multiple digital networks (e.g., "likes coffee brand M," "likes
early morning radio," and "police union") so that targeted
messaging actions may be initiated at multiple digital networks
based on a common list of attributes (e.g., the first
attributes).
[0042] In some embodiments, data regarding users (e.g., audience
members) may be used internally within the measurement system 120
instead of, or in addition to, being used externally with respect
to an advertising network, push notification system, e-mail system,
etc. For example, based on data regarding users that have performed
a particular behavior, similar users may be identified by internal
data or an internal system 129, such as a consumer relationship
management (CRM) system, an e-mail list, a transaction log, etc.
Thus, the reach extension module 124 may be used to identify "new"
users that an enterprise does not have a relationship with as well
as "known" users that the enterprise has an existing relationship
with (e.g., users that the measurement system 120 is aware of). As
an illustrative non-limiting example, the reach extension module
124 may determine that a large percentage of coffee drinkers age
44-51 have performed a behavior of interest (e.g., read an
article). Based on such information, the reach extension module 124
may identify other users from an internal e-mail list that are also
coffee drinkers age 44-51, and are therefore likely to perform the
behavior (e.g., read the article or a different article). The reach
extension module 124 may use such data (e.g., "coffee drinkers" and
"age 44-51") to initiate an internal messaging action (e.g., send a
targeted e-mail) to drive the desired behavior. For example, the
targeted e-mail may be sent by the measurement system 120 (or a
component thereof) to other users tracked by the measurement system
120 that have not performed the target behavior.
[0043] FIG. 2 is a diagram 200 illustrating automatic targeted
messaging actions. In an illustrative embodiment, the automatic
initiation of targeted messaging actions may be performed by a
reach extension module, such as the reach extension module 124 of
FIG. 1.
[0044] In operation, a reach extension module may receive data
identifying a target segment (or identification thereof). The
target segment may be associated with a desired behavior that a
content producer wishes to drive. In the illustrated example, the
target segment includes users who like a product D (e.g., a content
producer may wish to drive purchases of product D). The reach
extension module may generate a first list 202 of attributes
associated with at least one member of the target segment. For
example, in the segment databases 140, users x, y, and z may be
members of a likes product D segment. The first list 202 may
include attributes of the users x, y, and/or z (e.g., if one or
more of the users x, y, and z are male, then the first list 202 may
include an attribute "male"). The attributes of the first list 202
may include demographic attributes (e.g., gender, age, income,
etc.), brand affinities (e.g., brands "liked" by the users of the
target segment), behavioral attributes (e.g., actions performed by
the users of the target segment), or any combination thereof.
[0045] In particular embodiments, the reach extension module may
also identify one or more attributes that are related to the
attributes in the first list 202. For example, a related attribute
203 "likes TV show A" is related to an attribute "likes TV network
C" of the first list 202. In FIG. 2, the relation between the
attribute "likes TV show A" and the attribute "likes TV network C"
is illustrated by a dashed line. The related attribute 203 may be
identified based on a correlation between users liking TV network C
and users liking TV show A, as described with reference to FIG. 1.
Further, although one related attribute 203 is shown in FIG. 2, it
will be understood that in alternate embodiments multiple related
attributes may be identified.
[0046] The reach extension module may map the first list 202 and
the related attribute 203 to attributes 204 for which targeted
messaging actions are available at a particular digital network to
generate a second list 206 of attributes. For example, the reach
extension module 124 of FIG. 1 may receive a map from the mapping
databases 126. The map may identify relationships between
attributes of the first list 202 and the attributes 204. The second
list 206 of attributes may thus include attributes for which
targeted messaging actions are available at the digital network and
are predicted to be successful in reaching the target segment.
[0047] For each attribute and each combination of attributes of the
second list 206, the reach extension module may calculate an
estimated cost, an estimated reach, and an estimated yield. The
reach extension module may store the estimated cost and the
estimated reach of each attribute and each combination (e.g., in a
data structure, such as illustrative table 208). In a particular
embodiment, the estimated cost is an estimated cost per user
reached and the estimated reach indicates an estimated number of
unique views for an advertisement. The estimated yield may
correspond to an estimated number of desired actions taken (e.g.,
purchases of product D) in response to a messaging action. In
alternate embodiments, different cost, reach, and yield metrics may
be used.
[0048] In a particular embodiment, cost and reach may be estimated
by determining a correlation between each attribute of the first
list 202 with the target segment. The correlations may correspond
to correlations between the attributes and the target segment in a
segment database, such as the segment databases 140. The
correlations may be applied to data regarding the attributes of the
second list 206 received from the digital network. For example,
data regarding estimated audience size and estimated cost per view
for each attribute of the second list 206 may be received from the
digital network. The correlations may be used to predict user
behavior and may be combined with the data received from the
digital network to generate estimated cost and estimated reach for
each combination of attributes of the second list 206. For example,
an attribute A of the first list 202 may have a 0.5 correlation to
a target segment X. An attribute B of the second list 206 may be
mapped to (i.e., correspond to) the attribute A and may also have a
0.5 correlation to the target segment X. The reach extension module
124 may receive data indicating that a digital network has 20 users
with attribute B and that a cost per unique view of advertising
directed to users with attribute B is $2. Based on the 0.5
correlation, the reach extension module 124 may predict that 10 of
the 20 users with attribute B would, if tracked by the measurement
system 120, be classified into target segment X. Therefore, the
reach extension module 124 may determine that 10 target users may
be reached for $40 if the reach extension module initiates targeted
messaging actions directed to attribute B.
[0049] The segment database may further include frequency
information indicating how often a particular action is taken. For
example, the segment database may indicate that users who like
product D buy product D, on average, 1.5 times per year. The
frequency information may be used to determine an estimated yield
of targeting messaging action directed to each attribute of the
first list 202. Thus, the estimated yield for targeted messaging
actions directed to attribute B may be 15 purchases per year.
[0050] In addition or in the alternative, the estimated cost,
estimated reach, and the estimated yield may be based on messaging
action history data such as messaging action history data stored in
the messaging action history database 128 of FIG. 1. For example,
the messaging action history data may include information related
to actual reach, actual cost, and actual yield of past targeted
messaging actions.
[0051] The reach extension module may initiate targeted messaging
actions based on the table 208. For example, the reach extension
module may compare estimated costs, estimated reaches, and
estimated yields to decrease an estimated cost per conversion,
increase an estimated reach, increase an estimated yield, or a
combination thereof. For example, the first list 202 may include a
"likes product D" attribute with a corresponding "likes product D"
attribute available for targeted messaging actions at the digital
network. The "likes product D" attribute of the first list 202 may
be shared by 100% of the "likes product D" target segment.
Therefore, the table 208 may include a relatively high estimated
reach for messaging actions targeted to users who like product D.
However, the reach extension module may further identify that a
likes TV network C attribute of the first list 202 is shared by a
large percentage of the likes product D target segment. In
addition, a corresponding TV network C fan group member attribute
available for targeted messaging actions at the digital network may
have a relatively lower estimated cost. Based on comparisons
between the estimated costs, estimated reaches, and estimated
yields of messaging actions directed to users who like product D
and messaging actions directed to TV network C fan group members,
the reach extension module may determine that initiating messaging
actions targeted to TV network C fan group members is predicted to
be the more cost/reach/yield-effective option. The reach extension
module may thus initiate messaging actions targeted to TV network C
fan group members at the digital network. Similarly, the reach
extension module may determine to initiate targeted messaging
actions based on the related attribute 203 "likes TV show A" rather
than or in addition to messaging actions based on the first list
202 of attributes based on the comparisons.
[0052] In a particular embodiment, the reach extension module may
compare estimated costs/reaches/yields to threshold
costs/reaches/yields. When the estimated cost, reach, yield or a
combination thereof for a particular targeted messaging action
meets a threshold, the reach extension module may automatically
initiate the particular targeted messaging action. For example, the
reach extension module may automatically initiate targeted
messaging actions if an associated estimated reach or yield is
above a particular threshold or if an estimated cost is below a
particular threshold. Further, the reach extension module may
receive updates from the digital network or from servers indicating
user interactions with the initiated messaging actions as well as
actual cost data. Additionally, the reach extension module may
receive updates from a segment database indicating that a
correlation between an attribute tracked by the measurement system
and the target segment has changed. The reach extension module may
update the table 208 based on the received updates and modify
messaging strategies based on the updated table 208. For example,
the reach extension module may issue an alert or may stop or slow
initiation of messaging actions targeted to TV network C fan group
members when the updated table 208 indicates an increased price, a
decreased reach, a decreased yield or a combination thereof. In
another example, the reach extension module may initiate messaging
actions (or additional messaging actions) targeted to TV network C
fan group members at an increased rate when the updated table 208
indicates a decreased price, an increased reach, an increased
yield, or a combination thereof.
[0053] Thus, as illustrated in FIG. 2, a reach extension module
(e.g., the reach extension module 124 of FIG. 1) may automatically
initiate messaging actions targeted to users having attributes that
are similar to a target segment. The reach extension module may
further monitor the success of the initiated messaging actions and
adjust future messaging actions accordingly (e.g., by increasing
messaging actions or decreasing messaging actions based on the
success).
[0054] Referring to FIG. 3, a diagram 300 illustrating mappings
between attributes tracked by a measurement system and attributes
for which targeted messaging actions are available at particular
digital networks is shown. In an illustrative embodiment, the
measurement system may be the measurement system 120 of FIG. 1 and
the digital networks may be the digital networks 150 of FIG. 1. The
mappings illustrated in FIG. 3 may be stored in one or more mapping
databases, such as the mapping databases 126 of FIG. 1. As
illustrated in FIG. 3, attribute mappings may be one-to-one,
one-to-many, and/or many-to-one. Attribute mappings may be
automatically generated and/or manually created by a
user/administrator. As described with reference to FIGS. 1-2, a
reach extension module (e.g., the reach extension module 124 of
FIG. 1) may identify a first list of attributes (e.g., "likes TV
network C") that are tracked by the measurement system 120 and that
are associated with a target segment. The reach extension module
may use the attribute mappings of FIG. 3 to identify corresponding
attributes (e.g., "member of TV network C fan group", "works at TV
Network C," etc.) available for targeted messaging actions at
various digital networks. In a particular embodiment, one or more
attributes may be mapped to a "Free Text Search" option at a
digital network. In the example of FIG. 3, the attributes "Income
[110-120 k]" and "Likes Coffee Shop A" in the measurement system do
not have corresponding attributes at Digital Network B. Instead,
the attributes are mapped to a "Free Text Search," indicating that
when initiating messaging actions directed to such attributes, a
reach extension module would initiate a text-based search of user
profiles in Digital Network B to identify users of interest.
[0055] Referring to FIG. 4, a messaging action campaign graphical
user interface (GUI) 400 is illustrated. In an illustrative
embodiment, the GUI 400 may be displayed by a web application in
communication with a measurement system, such as the measurement
system 120 of FIG. 1. For example, the GUI 400 may be displayed to
an advertiser, a media property owner, an administrator of the
measurement system 120 of FIG. 1, etc. to track the performance of
messaging action campaigns. The GUI 400 may display statistics for
a plurality of messaging action campaigns (e.g., "Product D,"
"Restaurant Z," and "Bookstore Y"). In FIG. 4, statistics 402 are
displayed for the selected "Product D" campaign. The statistics 402
may include aggregated information regarding a plurality of
messaging actions targeted to a plurality of attributes initiated
by a reach extension module, such as the reach extension module 124
of FIG. 1. In FIG. 4, the statistics 402 for the "Product D"
campaign are displayed using a line graph 404 that illustrates
conversions (e.g., yield) over time and includes information
regarding actions taken by the reach extension module. In the
example of FIG. 4, an alert was issued at a first time 406 in
response to a declining number of conversions. Initiation of
targeted messaging actions was stopped at a second time 408 and at
a fourth time 412 (e.g., when a number of conversions met a
threshold number for a particular period of time). Initiation of
targeted messaging actions was increased at a third time 410 in
response to a rising number of conversions.
[0056] As shown in FIG. 4, the statistics 402 for a messaging
action campaign may also include a number of conversions (e.g.,
100), a cost per acquisition (CPA) (e.g., $5.60), a reach (e.g.,
500), an amount of money earned from the conversions (e.g.,
$12,000), an amount of campaign budget spent/remaining (e.g., $560
of $900 spent), demographic information about an advertising
audience (e.g., 51% male, 49% female, average age of 24), and/or
device information (e.g., 57% mobile phones/tablet computers and
43% desktop/laptop computers). The statistics may also display a
number of impressions (e.g., 1,000) and a number of engagements
(e.g., 300) associated with the messaging action campaign. In
alternate embodiments, a messaging action campaign GUI may
illustrate, more, less, and/or different types of messaging action
statistics.
[0057] FIG. 5 illustrates another embodiment of a system 500
operable to initiate targeted messaging actions based on audience
attributes. The system 500 includes a measurement system 520, which
may, in an illustrative embodiment, correspond to the measurement
system 120 of FIG. 1. The system 500 further includes a reach
extension module 524 and a monitoring module 550, which may
partially or collectively correspond to the reach extension module
124 of FIG. 1.
[0058] The measurement system 520 may track data regarding audience
segments for one or more media properties (e.g., a website) and may
store the data in a data store 538. In a particular embodiment, the
data store 538 corresponds to the segment databases 140 of FIG. 1.
Each audience segment may have a relation to one or more
demographic attributes, brand affinities, behaviors, or any
combination thereof (e.g., each member of a segment HAS an
attribute, each member of the segment does NOT have the attribute,
each member of the segment has a first attribute AND a second
attribute, each member of the segment has the first attribute OR
the second attribute, etc.).
[0059] In operation, the measurement system 520 may receive data
indicating selection of a segment 532 from a client application 510
(e.g., a web browser executing at a computing device associated
with an advertiser). The segment 532 may be associated with one or
more media properties. The selection of the segment 532 may
indicate that a client (e.g., an advertiser) wishes to initiate
messaging actions targeted to users similar to members of the
segment 532 (e.g., to drive a behavior associated with the members
of the segment 532). For example, the segment 532 may correspond to
users who watched a previous coffee-related advertisement. Based on
a low cost, a high yield, a high reach, or a combination thereof of
the previous advertisement, an advertiser may select the segment
532 to target the same (or similar) users with a new advertisement
for a new coffee-related product. In response to the selection, the
measurement system 520 may identify first attributes 534 (e.g.,
demographic attributes and interests) for each member of the
segment 532. Data indicating the first attributes 534 may be sent
to the reach extension module 524 to create a messaging action
campaign 526. In a particular embodiment, data identifying second
attributes that are correlated to the first attributes 534 is also
sent to the reach extension module 524 to create the messaging
action campaign 526, as described with reference to FIGS. 1-2.
[0060] The messaging action campaign 526 includes message groups
528. The message groups 528 may include a message group for each
combination of the first and second attributes for each of the
digital networks 530. Each message group of the message groups 528
may include one or more third attributes for which targeted
messaging actions are available.
[0061] The reach extension module 524 may analyze each message
group of the message groups 528 to determine an estimated cost, an
estimated reach, and an estimated yield of messaging actions based
on the message group. The estimated cost, the estimated reach, and
the estimated yield may be estimated based on data stored in the
data store 538 (e.g., past cost, past reach data, past yield data,
correlation data, etc.) and messaging strategies stored in a
relational database 536. When the estimated cost, the estimated
reach, the estimated yield, or a combination thereof, of a
particular message group meets a threshold, the reach extension
module 524 may create messaging strategy to automatically initiate
targeted messaging actions. The targeted messaging actions may
correspond to the particular message group at a particular digital
network of the digital networks 530. The reach extension module 524
may store information regarding the message strategy in the
relational database 536 of the measurement system 520. The
information may identify the segment 532, an estimated cost, an
estimated reach, an estimated yield, specific targeted messaging
actions to be initiated, or a combination thereof.
[0062] In the illustrated example, the initiated targeted messaging
actions include an advertisement 542 to be presented to a user
(e.g., an individual viewing a website) via a user browser 540. In
other examples, the messaging actions may include other types of
messaging actions in addition to or instead of the advertisement
542. The particular digital network may count impressions (e.g., a
number of times the advertisement 542 has been seen by users)
resulting from the targeted advertising. When the user clicks on
the advertisement 542, the user browser 540 may display a
conversion page 544. The conversion page 544 may correspond to a
web page of the client. The conversion page 544 may also be
displayed responsive to an action within a mobile application
(e.g., a purchase) or an action at a specific venue (e.g., a RFID
"check-in"). In particular embodiments, the particular digital
network counts a number of clickthroughs (e.g., a number of times
users click on the advertisement 542).
[0063] From the conversion page 544, the user may initiate a
conversion event (e.g., buying an advertised product or service
from the client, watching a video, etc.). The conversion event may
result in a conversion "pixel" (e.g., message or data) being sent
to the particular digital network notifying the particular digital
network that the conversion event occurred. In addition, an event
capture module 531 may capture the conversion event and update the
first attributes 534 accordingly (examples of an event capture
module capturing events are described in reference to FIG. 8).
Thus, the conversion event can be associated with users of certain
demographics and interests, which may enable a system (e.g., the
measurement system 120 of FIG. 1) to find additional users that are
similar to users that are converting. The updated first attributes
534 may in turn be used to update the message groups 528.
[0064] In a particular embodiment, the user browser 540 may receive
a retargeting "pixel" 546 from the conversion page 544. The
retargeting pixel 546 may be observed by one or more of the digital
networks 530 and used to send particular advertising to the user
browser 540. To illustrate, one of the benefits of attributing
conversion(s) to a specific audience is an ability to value the
audience for retargeting. For example, different bids and budgets
may be used for different retargeting segments.
[0065] A digital network monitor 556 of the monitoring module 550
may receive updates from the digital networks 530 (e.g., the
digital networks 530 may send updates periodically or when a
particular message is viewed or clicked on). The updates may
include performance data related to particular targeted messaging
actions, such as cost per message, a number of impressions, a
number of conversions, total money spent, or a combination thereof.
The digital network monitor 556 may update the data store 538 based
on the performance data.
[0066] A campaign monitor 552 of the monitoring module 550 may
monitor the performance data stored in the data store 538. Based on
the performance data, alert logic 554 of the campaign monitor 552
may issue commands to the reach extension module 524 to adjust the
message strategy. For example, the alert logic 554 may issue a
command to increase targeted messaging actions based on a
particular message group of the message groups 528 in response to
detecting that a reach, a number of conversions (e.g., a yield), a
cost, or a combination thereof, associated with the messaging
actions based on the particular message group meets a first
threshold. Alternatively, the alert logic 554 may issue a command
to decrease messaging actions, stop messaging actions, issue a
warning, or a combination thereof in response to detecting that the
reach, number of conversions, cost, or a combination thereof,
associated with the messaging actions based on the particular
message group meets a second threshold. In response to the command
from the alert logic 554, the reach extension module 524 may alter
the message strategy and update (e.g., by changing the message
strategy to increase, decrease, or stop initiation of messaging
actions) the advertising strategy stored in the relational database
536. The reach extension module 524 may continue to initiate
messaging actions according to the updated message strategy. When
the alert logic 554 of the campaign monitor 552 issues a stop
command, the reach extension module 524 may stop initiating
messaging actions. In a particular embodiment, the reach extension
module 524 may stop initiating messaging actions for a period of
time before automatically resuming initiating messaging actions in
response to the period of time elapsing. In an alternate
embodiment, the reach extension module 524 may stop initiating
messaging actions until a command is received to continue
initiating messaging actions (e.g., from the alert logic 554 or
from a content producer).
[0067] Thus, the system 500 may automatically initiate targeted
messaging actions that are more likely to reach users with certain
attributes. The targeted messaging actions may thus be sent to
users more likely to perform a desired action than a random
sampling of people. The targeted messaging actions may be based on
estimated costs and effectiveness data (e.g., how well an audience
performs a desired behavior), which may be updated as messaging
actions are initiated. The updated cost and effectiveness data may
be used to update a message strategy.
[0068] Referring to FIG. 6, a method 600 of initiating targeted
messaging actions is shown. In an illustrative embodiment, the
method 600 may be performed by a measurement system, such as the
measurement system 120, the measurement system 520 of FIG. 5, or a
measurement system as further described with reference to FIGS.
8-9.
[0069] The method 600 includes receiving input identifying a target
audience segment, at 602. For example, the measurement system 120
of FIG. 1 may receive input (e.g., from a client application, such
as a web client, in response to user selection) identifying the
target segment 110. As another example, the measurement system 520
may receive input identifying the segment 532 from the client
application 510 (e.g., in response to a user selection). The target
segment may be associated with a particular behavior, perspective,
or opinion. To illustrate, referring to FIG. 2, the target segment
is a segment of users who like product D.
[0070] The method 600 further includes identifying a first
attribute measured by a measurement system (e.g., the measurement
system 120 or the measurement system 520), at 604. The first
attribute is determined to correlate to users tracked by the
measurement system and that belong to the target audience segment.
For example, the measurement system 120 may identify the target
segment 110 in the segment databases 140 and determine a first list
of attributes associated with at least one member of the target
segment 110. In the example illustrated in FIG. 2, the first list
202 includes attributes associated with at least one member of the
target segment of users who like product D. As a further example,
the measurement system 520 may identify the first attributes 534
(e.g., demographics and interests) of users of the segment 532.
[0071] The method 600 further includes identifying a second
attribute that corresponds to the first attribute, where a
messaging action directed to the first attribute and/or the second
attribute is available for purchase at a digital network, at 606.
For example, the reach extension module 124 may use the mapping
databases 126 to map the first list of attributes associated with
the target segment 110 to a second list of attributes for which
messaging actions are available at the digital networks 150. In the
example shown in FIG. 2, attributes of the first list 202 are
mapped to the attributes 204 for which messaging actions are
available to generate the second list 206. As another example, the
first attributes 534 of FIG. 5 (e.g., demographics and interests)
may be mapped to attributes for which messaging actions are
available at the digital networks 530 to form the message groups
528. The method 600 further includes initiating the messaging
action at the digital network directed to the first attribute
and/or the second attribute, at 608.
[0072] In some embodiments, instead of or in addition to
identifying the second attribute (at 606) and initiating the
messaging action at the digital network (at 608), the method 600
may include initiating an internal messaging action, at 610. The
internal messaging action may be directed to other users tracked by
the measurement system that do not belong to the target segment
(e.g., have not performed a target behavior) but that may have the
first attribute and/or a related attribute, as described with
reference to the internal data/system(s) 129 of FIG. 1.
[0073] Referring to FIG. 7, another method 700 of initiating
targeted messaging actions is shown. In an illustrative embodiment,
the method 700 may be performed by a measurement system, such as
the measurement system 120, the measurement system 520 of FIG. 5,
or a measurement system as further described with reference to
FIGS. 8-9.
[0074] The method 700 includes receiving input identifying a target
audience segment that corresponds to a particular behavior, at 702.
For example, the measurement system 120 of FIG. 1 may receive input
identifying the target segment 110. As another example, the
measurement system 520 may receive input identifying the segment
532 from the client application 510. To illustrate, referring to
FIG. 2, the target segment is a segment of users who like product D
(e.g., buy or use product D).
[0075] The method 700 further includes identifying a first
attribute measured by a measurement system, where the first
attribute is determined to correlate to users tracked by the
measurement system and that belong to the target audience segment,
at 704. For example, the measurement system 120 may identify the
target segment 110 in the segment databases 140 and determine a
first list of attributes associated with at least one member of the
target segment 110. In the example illustrated in FIG. 2, the first
list 202 includes attributes associated with at least one member of
the target segment of users who like product D (e.g., buy or use
product D). As a further example, the measurement system 520 may
identify the first attributes 534 (e.g., demographics and
interests) of users of the segment 532.
[0076] The method 700 further includes identifying a second
attribute measured by the measurement system, where the second
attribute is determined to correlate to users indicated by the
measurement system as having the first attribute, at 706. For
example, the measurement system 120 may identify attributes related
to attributes of the first list. As shown in FIG. 2, the related
attribute 203 may be determined to be related to an attribute of
the first list 202. The relation may be based on correlation
between the related attribute and an attribute on the first
list.
[0077] The method 700 further includes comparing estimated yields,
estimated costs, estimated reaches, or a combination thereof of a
first messaging action available for purchase at one or more
digital networks and estimated yields, estimated costs, estimated
reaches, or a combination thereof of a second messaging action
available at the one or more digital networks, at 708. A target of
the first messaging action is based on the first attribute and a
target of the second messaging action is based on the second
attribute. For example, the table 208 of FIG. 2 may be used to
compare estimated cost/reach of messaging actions based on the
related attribute 203 "Likes TV Show A" (e.g., a second attribute)
to estimated cost/reach of messaging actions based on an attribute
"Likes Product D" (e.g., a first attribute) from the first list
202. The method 700 further includes initiating the second
messaging action at based on the comparisons, at 710.
[0078] As described herein, to perform "intelligent" evaluation of
targeted messaging actions to improve
cost/reach/yield-effectiveness, a measurement system may rely on
data that has been collected about audiences of media properties.
FIGS. 8-9 illustrate examples such measurement systems.
[0079] FIG. 8 illustrates an embodiment of a measurement system
840, and is generally designated 800. For example, the measurement
system 840 may include, correspond to, or be included within the
measurement system 120 of FIG. 1 or the measurement system 520 of
FIG. 5. The measurement system 840 may be communicatively coupled
to one or more user devices (e.g., illustrative user devices 812,
814, and 816), to one or more content delivery networks (CDNs)
(e.g., illustrative CDN 822), and to media properties (e.g.,
websites) 832 and 834. In FIG. 8, the media properties 832 and 834
are illustrated by corresponding servers (e.g., web servers). The
measurement system 840 may be implemented using one or more
computing devices (e.g., servers). For example, such computing
devices may include one or more processors or processing logic,
memories, and network interfaces. The memories may include
instructions executable by the processors to perform various
functions described herein. The network interfaces may include
wired and/or wireless interfaces operable to enable communication
to local area networks and/or wide area networks (e.g., the
Internet).
[0080] The user devices 812-816 may be associated with various
users. For example, the desktop computing device 812 and the tablet
computing device 814 may be associated with a first user 802, and
the mobile telephone device (e.g., smartphone) 816 may be
associated with a second user 804. It should be noted that the user
devices 812-816 are shown for example only and are not to be
considered limiting. In alternate embodiments, fewer, additional,
and/or different types of user devices may be present in the system
800. For example, a radio-frequency identification (RFID)-enabled
device may be carried by a user and may transmit a signal in
response to detecting that the user is visiting a particular
physical location. In a particular embodiment, the user devices
812-816 may execute applications that are operable to access the
media properties 832 and 834. For example, the user devices 812-816
may include applications developed using a mobile software
development kit (SDK) that includes support for audience
measurement functions. To illustrate, when the SDK-based
applications interact with the media properties 832 and 834, the
applications may generate first event signals 810 that are
transmitted by the user devices 812-816 to the measurement system
840.
[0081] The first event signals 810 may include information
identifying specific interactions by the users 802-804 via the user
devices 812-816 (e.g., what action was taken at a media property,
when the action was taken, for how long the action was taken,
etc.). The user interactions may include interactions with
advertisements presented by the media property and/or interactions
with content presented by the media property. The event signals 810
may also include an identifier, such as a browser identifier
(browser ID) generated by the SDK. In a particular embodiment,
browser identifiers are unique across software installations and
devices. For example, a first installation of a SDK-based
application at the desktop computing device 812 and a second
installation of the same SDK-based application at the tablet
computing device 814 may use different browser IDs, even though
both installations are associated with the same user 802.
[0082] In another particular embodiment, Browser IDs may remain
consistent until applications or web browsers are "reset" (e.g.,
caches/cookies are cleared). In some embodiments, the user devices
812-816 may execute applications other than browser applications,
such as downloadable mobile applications, that generate the event
signals 810 based on user interactions with advertisements and/or
content presented by the applications.
[0083] The user devices 812-816 may access content provided by the
media properties 832 and 834 directly or via the CDN 822. The CDN
822 may provide distributed, load-balanced access to audio, video,
graphics, and web pages associated with the media properties 832
and 834. For example, the CDN 822 may include geographically
distributed web servers and media servers that serve Internet
content in a load-balanced fashion. The CDN 822 may send second
event signals 820 to the measurement system 840. The second event
signals 820 may include information identifying interactions with
media properties and browser IDs provided to the CDN 822 by the
user devices 812-816 and/or the media properties 832 and 834. For
example, the second event signals 820 may include CDN logs or data
from CDN logs.
[0084] The media properties 832 and 834 may be controlled by the
same entity (e.g., may be part of a federated property) or by
different entities. The media properties 832 and 834 may send third
event signals 830 to the measurement system 840. The third event
signals 830 may include information identifying interactions with
the media properties and browser IDs provided by the user devices
812-816 during communication with the media properties 832 and 834
(e.g., communication via hypertext transfer protocol (HTTP),
transport control protocol/internet protocol (TCP/IP), or other
network protocols).
[0085] In a particular embodiment, the third event signals 830 may
include server logs or data from server logs. Alternately, or in
addition, the third event signals 830 may be generated by SDK-based
(e.g., web SDK-based) applications executing at the media
properties 832 and 834, such as scripts embedded into web pages
hosted by the media properties 832 and 834.
[0086] In a particular embodiment, the media properties 832 and 834
may send data to the measurement system 840 and receive data from
the measurement system 840 regarding advertisements and/or content
presented by the media properties 832 and 834. Such communication
is illustrated in FIG. 8 as advertisement/content communication
860. For example, an advertisement (or software associated with the
advertisement that is executing on a client device, such as web
server, a computer, a mobile phone, a tablet device, etc.) may
collect and transmit data on a per-advertisement, per-user basis.
The data may include or identify a profile of a user, a duration
that the user viewed the advertisement, action(s) performed by the
user with respect to the advertisement, etc. As another example, a
content item or software associated therewith may collect and
transmit data regarding user interactions with the content
item.
[0087] In a particular embodiment, the measurement system 840
includes a data filtering module 842, a data processing module 844,
a data reporting module 846, and a reach extension module 847
(e.g., the reach extension module 124 of FIG. 1 or the reach
extension module 524 of FIG. 5). In a particular embodiment, each
of the modules 842-847 is implemented using instructions executable
by one or more processors at the measurement system 840.
[0088] The data filtering module 842 may receive the event signals
810, 820, and 830. The data filtering module 842 may check the
event signals 810, 820, and 830 for errors and may perform data
cleanup operations when errors are found. The data filtering module
842 may also receive and perform cleanup operations on
advertisement measurement data and content measurement data
received from the media properties 832 and 834 and from
applications executing on the user devices 812-816. In a particular
embodiment, the data filtering module 842 may implement various
application programming interfaces (APIs) for event signal
collection and inspection. The data filtering module 842 may store
authenticated/verified event signals in a database, event cache or
archive, such as in data storage 848 and/or cloud storage 852. In a
particular embodiment, the measurement system 840 includes or has
access to a brand database that tracks brands. For example, "raw"
data corresponding to the brand database and other collected data
may be stored in the cloud storage 852. Signals received from the
media properties 832 and 834 and from applications executing the
user devices 812-816 may identify a brand that matches one of the
brands in the brand database. The measurement system 840 may thus
track advertisements/content for various brands across multiple
media properties.
[0089] The data processing module 844 may associate received event
signals (and interactions represented thereby) with user profiles
of users. For example, when an event signal having a particular
browser ID is a social networking registration event (e.g., when a
user logs into a website using a Facebook.RTM. account, a
Twitter.RTM. account, a LinkedIn.RTM. account, or some other social
networking account), the data processing module 844 may retrieve a
corresponding social networking profile or other user profile data
from third party data sources 850. Facebook is a registered
trademark of Facebook, Inc. of Menlo Park, Calif. Twitter is a
registered trademark of Twitter, Inc. of San Francisco, Calif.
LinkedIn is a registered trademark of LinkedIn Corp. of Mountain
View, Calif. In a particular embodiment, the social networking
profile or other user profile data is received after an
authentication process. For example, the measurement system 840 may
receive a user token. The user token may enable the measurement
system 840 to request a social network for information associated
with a corresponding user.
[0090] It will be appreciated that interactions that were
previously associated only with the particular browser ID (i.e.,
"impersonal" alphanumeric data) may be associated with an actual
person (e.g., John Smith) after retrieval of the social networking
profile or user profile. Associating interactions with individuals
may enable qualitative analysis of the audiences of media
properties. For example, if John Smith is a fan of a particular
sports team, the measurement system 840 may indicate that at least
one member of the audience of the first media property 832 or the
second property 834 is a fan of the particular sports team. When a
large percentage of a media property's audience shares a particular
characteristic or interest, the media property may use such
information in selecting and/or generating advertising or content.
User profiles (e.g., a profile of the user John Smith) and audience
profiles (e.g., profiles for the media properties associated with
the media properties 832 and 834) may be stored in the data storage
848, the cloud storage 852, and/or in another database, as further
described with reference to FIG. 9. An audience profile for a
particular media property may be generated by aggregating the user
profiles of the individual users (e.g., including John Smith) that
interacted with the particular media property.
[0091] Audience profiles may be generated using as few as one or
two user profiles, although any number of user profiles may be
aggregated. In a particular embodiment, audience profiles may be
updated periodically (e.g., nightly, weekly, monthly, etc.), in
response to receiving updated data for one or more users in the
audience, in response to receiving a request for audience profile
data, or any combination thereof. Audience profiles may similarly
be generated for audiences of a particular mobile application based
on signals generated by installations of the mobile application on
various user devices.
[0092] The data reporting module 846 may generate various
interfaces, such as the GUI 400 of FIG. 4. The data reporting
module 846 may also support an application programming interface
(API) that enables external devices to view and analyze data
collected and stored by the measurement system 840. In a particular
embodiment, the data reporting module 846 is configured to segment
the data. In a particular embodiment, the measurement system 840
may be operable to define "new" segments based on performing
logical operations (e.g., logical OR operations and logical AND
operations).
[0093] The data processing module 844 may also be configured to,
upon receiving an event signal, parse the event signal to identify
what user and media property the event signal corresponds to. The
data processing module 844 may store data corresponding to the
event signal in one or more databases (e.g., the cloud storage 852,
the data storage 848, a user profile database, etc.). If the user
is a new audience member for the media property, the data
processing module 844 may assign a new ID to the user.
[0094] During operation, the users 802-804 may interact with the
media properties 832 and 834 and with applications executing on the
user devices 812-816. In response to the interactions, the
measurement system 840 may receive the event signals 810, 820, 830,
and/or 860. Each event signal may include a unique identifier, such
as a browser ID and/or an audience member ID. If the user is a
"new" audience member, the data processing module 844 may create a
user profile. Data for the user profile may be stored in the cloud
storage 852 and/or the data storage 848. In a particular
embodiment, data for the user profile may be retrieved from the
third party data sources 850.
[0095] For example, the data processing module 844 may retrieve and
store data from one or more social network profiles of the user.
The data may include demographic information associated with the
user (e.g., a name, an age, a geographic location, a marital/family
status, a homeowner status, etc.), social information associated
with the user (e.g., social networking activity of the user, social
networking friends/likes/interests of the user, etc.), and other
types of data. The data processing module 844 may also collect and
store data associated with advertisements and content served by the
media properties 832 and 834 and by applications executing on the
user devices 812-816. In a particular embodiment, the measurement
system 840 is further configured to receive offline data from
external data sources. For example, the measurement system 840 may
receive data regarding transactions (e.g., purchases) made by an
audience and may use the transaction data to generate additional
signals that contribute to a set of traits of an audience, brand,
property, etc. Another example of offline data may be a "data dump"
of data collected by an RFID-enabled device or an RFID detector.
Offline data may be stored in one or more computer-readable files
that are provided to the measurement system 840. In a particular
embodiment, offline data can include previously collected data
regarding users or audience members (e.g., names, addresses,
etc.).
[0096] The data reporting module 846 may report data collected by
the measurement system 840. For example, the data reporting module
846 may generate reports based on an audience profile of a media
property (or application), where the audience profile is based on
aggregating user profiles of users that interacted with the media
property (or application). To illustrate, the data reporting module
846 may generate an interface indicating demographic attributes of
the audience as a whole (e.g., a percentage of audience members
that are male or female, percentages of audience members in various
age brackets, percentages of audience members in various income
bracket, most common audience member cities/states of residence,
etc.). The interface may also indicate social attributes of the
audience as a whole (e.g., the most popular movies, sports teams,
etc. amongst members of the audience). Audience profiles may also
be segmented and/or aggregated with other audience profiles.
Audience profiles may further be segmented based on advertisement,
advertisement campaign, brand, content item, etc. Audience profiles
may also be constructed by combining segments.
[0097] In a particular embodiment, the system 800 may also receive
event signals based on measurements (e.g., hardware measurements)
made at a device. For example, an event signal from the tablet
computing device 814 or the mobile telephone device 816 may include
data associated with a hardware measurement at the tablet computing
device 814 or the mobile telephone device 816, such as an
accelerometer or gyroscope measurement indicating an orientation, a
tilt, a movement direction, and/or a movement velocity of the
tablet computing device 814 or the mobile telephone device 816. As
another example, the system 800 may receive a signal in response to
an RFID device detecting that a user is visiting a particular
physical location. The system 800 of FIG. 8 may also link
interactions with user profiles of users. This may provide
information of "how many" viewers and "how long" the viewers
watched a particular video (e.g., as in direct response measurement
systems), and also "who" watched the particular video (e.g.,
demographic, social, and behavioral attributes of the viewers).
[0098] FIG. 9A illustrates a particular embodiment of a system 900
in accordance with the present disclosure. The system 900 includes
a data collection tier (e.g., subsystem) 910, an event processing
tier 950, a monitoring tier 970, and a reach extension module 990
(e.g., the reach extension module 124 of FIG. 1, the reach
extension module 524 of FIG. 5, or the reach extension module 847
of FIG. 8). Components of the data collection tier 910 are
illustrated in further detail in FIG. 9B. Components of the event
processing tier 950 are illustrated in further detail in FIG. 9C.
Components of the monitoring tier are illustrated in further detail
in FIG. 9D. As further described with reference to FIG. 9D, the
monitoring tier includes a penetration monitor 974 that is
illustrated using horizontal and vertical hatching, a system
monitor 978 that is shown using diagonal hatching, and a ping
monitor 984 that is shown using horizontal-only hatching. Various
other components in FIG. 9 include indicators with hatching
corresponding to their respective monitor(s). For example, capture
servers 926 include indicators to illustrate that the capture
servers are monitored by both the penetration monitor 974 and the
system monitor 978.
[0099] The system 900 includes (or has access to) an authentication
provider 932, third party data sources 934, an audience web
application 946, a first framework 944, a second framework 942, a
database 948, an interrogator 938, a data store 936, and an index
940. In an illustrative embodiment, the third party data sources
934 are the third party data sources 850 of FIG. 8, and the event
processing tier 950 and the interrogator 938 correspond to the data
processing module 844 of FIG. 8. In a particular embodiment,
information from the third party data sources 934 is mapped to
information collected by the system 900 by using personally
identifiable information as a key to the third party data sources
934. For example, personally identifiable information may include
an e-mail address, first/last name, a mailing or residential
address, etc. To illustrate, when the system 900 has an e-mail
address for a user, the system 900 may request the third party data
sources 934 for additional information associated with the e-mail
address.
[0100] The data collection tier 910 includes a content management
system (CMS) 912, cloud storage 916, content delivery networks 918,
client browsers 920, and client servers 922. The data collection
tier 910 may further include an application programming interface
(API) 921. The API 921 includes a load balancer 924, the capture
servers 926, and cloud storage 930.
[0101] The event processing tier 950 includes a job queues module
951, an anonymous buffer 960, and an event bundle buffer 962. The
job queues module 951 includes an authentication token handler 952,
an event dispatch 956, and an event bundle handler 958. In
alternate embodiments, the job queues module 951 may include more,
fewer, and/or different handlers than illustrated in FIG. 9.
[0102] The monitoring tier 970 includes an internal monitoring
module 972, the ping monitor 984, and a notifications module 982.
The internal monitoring module 972 includes the penetration monitor
974, a performance analysis module 976, the system monitor 978, and
an alert rules module 980.
[0103] During operation, the content management system 912 may be
used to generate a client specific script (e.g., webscript) 914 for
various clients (e.g., media properties). The client specific
script 914 may be stored in the cloud storage 916 and replicated to
the content delivery networks 918. As audience members register and
interact with a media property, the content delivery networks 918
may deliver the client specific script 914, along with property
content, to the client browsers 920. Based on the client specific
script 914, the client browsers 920 may generate tags (e.g., a tag
corresponding to a particular user activity, such as watching a
video) or tokens (e.g., a social networking registration token).
The tags or tokens may be sent to the load balancer 924. The client
servers 922 may also generate tags or tokens to send to the load
balancer 924 based on user registrations and user activity at media
properties. The tags or tokens from the client servers 922 may be
authenticated by the authentication provider 932.
[0104] The load balancer 924 may send the tags or tokens to the
capture servers 926 based on a load balancing algorithm. The
capture servers 926 may generate event data (e.g., event signals)
based on the tags or tokens. The capture servers 926 may store the
event data in event logs 928 in the cloud storage 930 and send the
event data to the job queues module 951.
[0105] The job queues module 951 may distribute the event data to
different event handler(s) based on the type of the event data. For
example, event data including an authentication token may be sent
to the authentication token handler 952. In addition, event data
requiring additional information from social media sources may be
sent to the authentication token handler 952. The handler 952 may
perform asynchronous event collection operations based on the
received event data. For example, when a new user registers with a
media property using a social networking profile, a token may be
provided by the data collection tier to the authentication token
handler 952. The handler 952 may use the token to retrieve
demographic and brand affinity data for the user from the user's
social networking profile.
[0106] Event signals may also be sent to the event dispatch 956,
which determines whether the event signals correspond to known or
unknown users. When event data corresponds to an unknown user, the
event dispatch 956 buffers the event data in the anonymous buffer
960. After a period of time (e.g., three days), event data from the
anonymous buffer 960 may be sent to the job queues module 951 to be
processed again.
[0107] When event data corresponds to a "known" user (e.g., a user
that has already been assigned a user ID), the event dispatch 956
may send the event data to the event bundles buffer 962. The event
bundle handler 958 may retrieve event data stored in the event
bundles buffer 962 every bundling period (e.g., one hour). The
event bundles processor 958 may bundle event data received each
bundling period into an event bundle that is sent to the
interrogator 938.
[0108] The interrogator 938 may parse the event bundle and update
the data store 936, the database 948 (e.g., a relational database),
and/or the index 940. In a particular embodiment, the database 948
corresponds to a profiles database that is accessible the first
framework 944 to the audience web application 946. For example, the
first framework 944 may be a database-driven framework that is
operable to dynamically generate webpages based on data in the
database 948. The audience web application may be operable to
generate various graphical user interfaces (e.g., the GUI 400 of
FIG. 4) to analyze the data collected by the system 900. The index
940 may be accessible to the audience web application 946 via the
second framework 942. In one example, the second framework 942
supports representational state transfer (REST)-based data access
and webpage navigation. Although not shown, in particular
embodiments, the data store 936 may also be accessible to the
audience web application 946.
[0109] The monitoring tier 970 may monitor the various components
of the system 900 during operation to detect errors, bottlenecks,
network intrusions, and other issues. For example, the penetration
monitor 974 may collect data indicating unauthorized access to or
from the capture servers 926 and the first framework 944. The
penetration monitor 974 may provide the data to the alert rules
module 980. Similarly, the system monitor 978 may collect
performance data from the capture servers 926, from the second
framework 942, and from the data store 936. The system monitor 978
may provide the performance data to the performance analysis module
976, which may analyze the data and send the analyzed data to the
alert rules module 980. The alert rules module 980 may compare
received data to alert rules and, based on the comparison, send an
alert to the notifications module 982. For example, the alert rules
module 980 may determine that an intruder has accessed components
of the system 900 or that the system 900 is not operating at a
desired level of efficiency, and may send an alert to the
notifications module 982.
[0110] The notifications module 982 may also receive alerts from
the ping monitor 984. The ping monitor 984 may monitor the load
balancer 924 and the audience web application 946 and collect data
regarding uptime, downtime, and performance, and provide alerts to
the notification module 982.
[0111] The notification module 982 may send notifications (e.g.,
via short message service (SMS), e-mail, instant messaging, paging,
etc.) to one or more technical support staff members 964 to enable
timely response in the event of errors, performance bottlenecks,
network intrusion, etc.
[0112] In accordance with various embodiments of the present
disclosure, the methods, functions, and modules described herein
may be implemented by hardware, software programs executable by a
computer system, or a combination thereof. Further, in an exemplary
embodiment, implementations can include distributed processing,
component/object distributed processing, and parallel processing.
Alternatively, virtual computer system processing can be
constructed to implement one or more of the methods or
functionality as described herein.
[0113] Particular embodiments can be implemented using a computer
system executing a set of instructions that cause the computer
system to perform any one or more of the methods or computer-based
functions disclosed herein. A computer system may include a laptop
computer, a desktop computer, a mobile phone, a tablet computer, a
set-top box, a media player, or any combination thereof. The
computer system may be connected, e.g., using a network, to other
computer systems or peripheral devices. For example, the computer
system or components thereof can include or be included within any
one or more devices, modules, and/or components illustrated in
FIGS. 1-9. In a networked deployment, the computer system may
operate in the capacity of a server or as a client user computer in
a server-client user network environment, or as a peer computer
system in a peer-to-peer (or distributed) network environment. The
term "system" can include any collection of systems or sub-systems
that individually or jointly execute a set, or multiple sets, of
instructions to perform one or more computer functions.
[0114] In a particular embodiment, the instructions can be embodied
in a computer-readable or a processor-readable device. The terms
"computer-readable device" and "processor-readable device" include
a single storage device or multiple storage devices, such as a
centralized or distributed database, and/or associated caches and
servers that store one or more sets of instructions. The terms
"computer-readable device" and "processor-readable device" also
include any device that is capable of storing a set of instructions
for execution by a processor or that cause a computer system to
perform any one or more of the methods or operations disclosed
herein. For example, a computer-readable or processor-readable
device or storage device may include random access memory (RAM),
flash memory, read-only memory (ROM), programmable read-only memory
(PROM), erasable programmable read-only memory (EPROM),
electrically erasable programmable read-only memory (EEPROM),
registers, a hard disk, a removable disk, a disc-based memory
(e.g., compact disc read-only memory (CD-ROM)), or any other form
of storage device. A computer-readable or processor-readable device
is not a signal.
[0115] In accordance with at last one described embodiment, a
method includes receiving an input identifying a target audience
segment. The method further includes identifying a first attribute
measured by a measurement system, where the first attribute is
determined to correlate to users tracked by the measurement system
and that belong to the target audience segment. The method further
includes identifying a second attribute that corresponds to the
first attribute. A messaging action directed to the first
attribute, the second attribute, or a combination thereof is
available at a digital network. The method further includes
initiating the messaging action at the digital network directed to
the first attribute, the second attribute, or a combination
thereof.
[0116] In another particular embodiment, a method includes
receiving an input identifying a target audience segment that
corresponds to a particular behavior. The method further includes
identifying a first attribute measured by a measurement system,
where the first attribute is determined to correlate to users
tracked by the measurement system that have exhibited the
particular behavior and that belong to the target audience segment.
The method further includes identifying a second attribute measured
by the measurement system, where the second attribute is determined
to correlate to users indicated by the measurement system as having
the first attribute. The method further includes determining a
first metric associated with a first messaging action that is
available at one or more digital networks, where the first
messaging action is based on the first attribute, and where the
first metric includes a first estimated yield, a first estimated
cost, a first estimated reach, or a combination thereof. The method
includes determining a second metric associated with a second
messaging action that is available at the one or more digital
networks and that is directed to the second attribute, where a
target of the second messaging action is based on the second
attribute, and where the second metric includes a second estimated
yield, a second estimated cost, a second estimated reach, or a
combination thereof. The method also includes initiating the second
messaging action based on a comparison of the first metric to the
second metric.
[0117] In another particular embodiment, a computer readable
storage device stores instructions that when executed by a
processor cause the processor to perform operations. The operations
include identifying, based on received input identifying a target
audience behavior, a first attribute measured by a measurement
system, where the first attribute is correlated to users identified
by the measurement system as having performed the target behavior.
The operations further include initiating a messaging action
directed to a second attribute at a digital network, where the
second attribute corresponds to the first attribute.
[0118] The illustrations of the embodiments described herein are
intended to provide a general understanding of the structure of the
various embodiments. The illustrations are not intended to serve as
a complete description of all of the elements and features of
apparatus and systems that utilize the structures or methods
described herein. Many other embodiments may be apparent to those
of skill in the art upon reviewing the disclosure. Other
embodiments may be utilized and derived from the disclosure, such
that structural and logical substitutions and changes may be made
without departing from the scope of the disclosure. Accordingly,
the disclosure and the figures are to be regarded as illustrative
rather than restrictive.
[0119] Although specific embodiments have been illustrated and
described herein, it should be appreciated that any subsequent
arrangement designed to achieve the same or similar purpose may be
substituted for the specific embodiments shown. This disclosure is
intended to cover any and all subsequent adaptations or variations
of various embodiments. Combinations of the above embodiments, and
other embodiments not specifically described herein, will be
apparent to those of skill in the art upon reviewing the
description.
[0120] The Abstract of the Disclosure is submitted with the
understanding that it will not be used to interpret or limit the
scope or meaning of the claims. In addition, in the foregoing
Detailed Description, various features may be grouped together or
described in a single embodiment for the purpose of streamlining
the disclosure. This disclosure is not to be interpreted as
reflecting an intention that the claimed embodiments require more
features than are expressly recited in each claim. Rather, as the
following claims reflect, inventive subject matter may be directed
to less than all of the features of any of the disclosed
embodiments.
[0121] The above-disclosed subject matter is to be considered
illustrative, and not restrictive, and the appended claims are
intended to cover all such modifications, enhancements, and other
embodiments, which fall within the true scope of the present
disclosure. Thus, to the maximum extent allowed by law, the scope
of the present disclosure is to be determined by the broadest
permissible interpretation of the following claims and their
equivalents, and shall not be restricted or limited by the
foregoing detailed description.
* * * * *