Note: Descriptions are shown in the official language in which they were submitted.
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SYSTEM AND METHOD FOR SCORING AUDIENCE RESPONSIVENESS
AND EXPOSURE TO TELEVISION ADVERTISING
BACKGROUND
1. Field of the Invention
[0001] The present invention relates to paid content or
advertisement ("ad")
placement for viewing over the internet via connected television devices
("CTV") or other
over-the-top ("OTT") delivery mechanisms that facilitate viewing of content
over the
internet. More particularly, the present invention relates to a system and
methods for scoring
audience responsiveness or exposure to viewing of such paid content or
advertising to
optimize advertisement delivery.
2. Description of the Related Art
[0002] With the popularity and use of the Internet for
streaming content, connected
television or "CTV" use has grown dramatically in recent years. A CTV is a
device that can
connect to a TV or a smart TV that facilitates the delivery of streaming video
content over the
internet. A smart TV is a television with a built-in internet connection and
media platform.
No additional equipment is required to stream videos. Instead, videos are most
often streamed
via apps that are downloaded.
[0003] Other connected devices that connect directly to a traditional
television (not a
smart television) and the internet and enable apps that are downloaded for
viewing videos
include Xbox, PlayStation, Roku, Amazon Figure TV, Apple TV, Chromecast, and
more.
Gaming consoles act as the connected devices that provide access to apps from
their app
stores. These are referred to as Over-the-Top ("OTT") devices. The advanced TV
ecosystem
that has emerged is a broad category that includes data-driven linear TV,
addressable TV,
OTT, and CTV.
[0004] This ecosystem is constantly growing and evolving as
new technologies enter
the ecosystem. As recognized by those skilled in the art, OTT can refer to
premium video
content that is streamed over the internet across any app or website, which
may or may not
require users to subscribe to a traditional "pay TV service." Users may access
OTT content
via streaming service aggregators, a standalone app, a virtual multichannel
video
programming distributor ("MVPD") on a TV, desktop, tablet or smartphone. Some
refer to
OTT as digital video, but that does not distinguish video content as premium,
professionally-
produced long-form versus free short-form video content. Four main streaming
companies
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account for about 80% of OTT volume. These are Antazun via its app or website,
Hulu via
its app, Netflix via its app or website and YouTube Premium via its app or
website.
[0005] With this growth there has been an equally dramatic
growth and migration to
CTV, OTT, or like advertising. For example, for consumers, "CTV" is a
different way to
watch TV across multiple types of screens with no cable or satellite
subscription required.
For advertisers, it's an innovative way to reach a new and unique audience.
Today's viewers
are increasingly turning to diverse viewing options that don't necessarily
involve a traditional
television. They are watching smart TVs, laptops, smartphones, game consoles
(Nintendo
switch, Xbox, PlayStation) and other connected devices such as Amazon Fire,
Roku, and
Apple TV. However, programmatic advertising presents a complex eco-system
involving a
complicated interplay between several entities, including content providers,
advertisers (both
informed and uninformed), and users or viewers who browse the internet to view
all types of
streamed content available via websites that are of interest to them.
[0006] With connected TV advertising, advertisers can
typically reach television
viewers that advertisers cannot reach without traditional TV commercials.
Superior targeting
capabilities involve connected television audience targeting, by which
companies can be sure
that marketing dollars are going towards the most valuable and targeted
viewers. In this
industry, programmatic platforms allow measurement of the results of connected
TV
campaigns with both digital and traditional metrics, including video
completion rates.
Growing audience targets millennials and the growing population who do not use
cable TV,
also known as "cord cutters."
[0007] Connected TV, OTT, or like advertising is becoming a
powerful open
platform that caters directly to a variety of new applications and services to
homes, mainly
for the young, middle-aged, and older adult population. Such a robust, tech-
savvy audience
represents incredible marketing opportunities with brands continuously seeking
metrics to
target specific advertising to viewers. Similar to other video advertisements,
connected TV
advertisements may be pre-roll or mid-roll. Pre-roll ads are those shown
before content and
mid-roll ads are those shown in the middle of the content. Considering most
ads on smart
TVs cannot be skipped over and users are highly engaged (having carefully
selected content
that they are most interested in viewing), CTV advertisements are extremely
effective.
Moreover, CTV adds are far more measurable than traditional TV advertising.
With access to
data, advertisers can quickly adjust they strategy based on evaluating what
has or has not
been working for their campaigns.
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[0008] It should be recognized CTV has many advantages over
linear TV (i.e., cable,
satellite, antenna) is its inherent precision. It unlocks a level of insight
that allows advertisers
to run ads and know exactly how many people viewed them, all the way down to
the last
digit. CTV also provides advertisers insights into completion rates, by
providing an exact
understanding of how many people saw the ad from start to finish, and how many
dropped
out.
[0009] Performance marketers expect much more. As with other
performance
marketing channels, such as paid search and social, performance marketers
desire a full view
into the customer journey to truly understand the impact of their CTV
campaigns.
Mechanisms for CTV measurement begins after an ad is displayed. Ways of
monitoring may
include measuring traffic to the advertiser's website after a CTV ad is shown.
It's able to
identify other devices visiting the site from the same household that saw the
ad, which allows
determining site visits driven by that ad impression. In addition, monitoring
the advertiser's
site to see if the users that originated from the CTV campaign eventually
convert is important
to direct-response TV advertisers. This is recognized as a way of attributing
purchases to the
TV ads they run ¨ while delivering an ad measurement experience familiar to
all performance
marketers.
[00101 Performance marketers find value in having their
campaign data funneled into
their 3rd party analytics or campaign management solution of choice. It allows
them to
understand the performance of their marketing efforts across disparate
channels.
I Tnfortunately, this is an area where CTV platforms can fall short ¨ they
tend to rely on siloed
measurement only available through their own platforms.
[00111 There is a need in the industry for continuous
improvements that can gauge
audience responsiveness to CTV or OTT advertising and to create soring
mechanisms that
can he used to create more meaningful experiences for viewers.
SUMMARY
[0012] The present technology overcomes the deficiencies and
limitations of prior
systems and methods, at least in part by, providing systems and methods for
scoring audience
responsiveness or exposure to television advertising of all types (e.g.,
display or home audio)
on connected television devices ("CTV") or over-the-top ("OTT") delivery
mechanisms, for
providing advertising analytics and creating an index designed to optimize
advertising
delivery to households.
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[0013] In some embodiments of the present invention, the
audience-responsiveness
scoring system may be provided in a demand-side platform ("DSP") or integrated
with a DSP
platform or an integrated network used to provide CTV, OTT or like
advertising. The
audience-responsiveness or exposure scoring system in accordance with the
present invention
determines and designates unique household identification data or identifiers
("ID") by
obtaining and using a combination of signals including signals representative
of a timestamp,
CTV identification or ID, "IP" address and physical location etc. As is
recognized in the
online industry, an Internet Protocol ("IP") address is a numerical label
assigned to each
device connected to a computer network that uses the Internet Protocol for
communication.
An IP address serves two main functions: host or network interface
identification and
location addressing.
[0014] In accordance with some embodiments of the present
invention, the audience-
responsiveness scoring system has a Household ID ("HH-ID") collection engine
configured
to collect Household ID signals from a particular advertiser. By way of
example, a particular
advertiser for illustration purposes is designated as "Advertiser A."
Therefore, the
"Advertiser A- CTV Ad Delivery, via a combination VAST Tag lx1 IMG Tracker and
Ad
Server Logs records to a platform file called "Ad Exposure File," creating
exposed household
records. For example, an example record may be designated as -Exposed
Household Record
001." Here, the system records any one or more of the IP address, location,
time stamp,
Device ID, UA, advertiser, category, product, price point, offer, and call to
action.
[0015] In sonic embodiments of the present invention,
additional Household devices
are appended to "Exposed Household Record 123" via Device ID anchored by IP
address,
Timestamp, location cues, etc., and recording new identifiers including Device
ID.
[0016] in some embodiments of !he present invennon, "Website
Visit Data" includes
the initial visit, purchase or other desirable action on "Advertiser A's"
Website or the like,
which is recorded by a JavaScript tracking pixel or s2s post back and stored
in the
"Advertiser Outcome File," thereby, creating "Visitor Record 001," with a
recording of the IP
address, location, time stamp, event increment, value of conversion,
conversion type, or the
like.
[0017] In some embodiments of the present invention, at regular intervals
data from
the AEF ("Ad Exposure File") and AOF ("Advertiser Outcome File") are compared
to
identify matches via overlapping combinations of IP Address/DID, Timestamp
(for
confirmation of appropriate recency) recording a conversion event to the
"Exposed
Household Record." of the appropriate household on the "Ad Exposure File." The
records of
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both files ("Ad Exposure File" and "Advertise' Outcome File") are appended
with the ID
from the other file for future analysis.
[0018] In some embodiments, the system and method in
accordance with the present
invention collects "lifetime value metrics" from the various advertisers and
adds them to each
household record that is created. In some embodiments, the system and method
in
accordance with the present invention tracks the sequence, frequency and cross-
channel
exposure of advertising.
[0019] In yet other embodiments, the system and method
identify the category of the
advertising offer (e.g., against one of the Interactive Advertising Bureau
("TAB") categories
or other hierarchy) and measure response rates for each ad against each
household.
[0020] In yet other embodiments, the system and method of the
present invention
determine the index responsiveness based on frequency prior to action, total
seconds exposed
prior to action, a count of actions across distinct advertisers in the same
category or the like.
[0021] In some embodiments, the system and method of the
present invention
determine responsiveness by creative type or outcome event data, for example,
including the
length of commercial and other variables such as visual components, audio
components, and
elements in the ad creative or outcome (e.g., color scheme, actor gender,
etc.)
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The present invention is illustrated by way of example,
and not by way of
limitation in the figures of the accompanying drawings in which like reference
numerals are
used to refer to the same or similar elements.
[0023] Figure lA is high-level block diagram, illustrating an
example system for
scoring audience-responsiveness and/or exposure according to some
implementations of the
present technology.
[0024] Figure 1B is a high-level block diagram illustrating
the infrastructure and
architecture of the example system with the flow of event and outcome data.
[0025] Figure 2 is a high-level block diagram, illustrating an
example, configured to
operate with a market floor engine and auction event store operating a floor
auction for CTV
ad placement.
[0026] Figure 3 is a block diagram, illustrating an example
CTV audience-
responsiveness scoring system and its hardware components.
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[0027] Figure 4 is an example flow chart of operations and
functions performed by
the CTV audience-responsiveness scoring system.
[0028] Figure 5 continues the example flow chart of Figure 4
illustrating additional
operation and functions performed by the audience-responsiveness scoring
system in
accordance with the present system.
[0029] Figure 6 continues the example flow chart of Figure 4
illustrating additional
operation and functions performed by the audience-responsiveness scoring
system in
accordance with the present system.
[0030] Figure 7 continues the example flow chart of Figure 4
illustrating additional
operation and functions performed by the audience-responsiveness scoring
system in
accordance with the present system.
[0031] Figure 8 is a flow chart illustrating various example
metrics used to determine
extent and type of advertising exposure.
[00321 Figure 9 is a flow chart illustrating various other
example metrics used to
determine extent and type of advertising exposure.
[0033] Figure 10 is a block diagram, illustrating a flow chart
of the approach to
creating and using the CTV/OTT advertising audience responsiveness analytics
index.
[0034] Figure 11 illustrates an example index.
[0035] Figure 12 is a high-level block diagram, illustrating
an example Scientific
demand-side platform, configured to operate with a market floor engine and
auction event
store operating a floor auction for CTV ad placement.
[0036] Figure 13 is a flow chart representing an example
method including operations
for executing the Scientific approach by the Scientific demand-side platform.
[0037] Figure 14 is a flow chart representing an example
testing and optimization
process for television advertisement creative variants.
[0038] Figure 15 is a flow chart of a process for linking
video-game activation on
consoles to CTV/OTT Ad delivery.
[0039] Figure 16 is a continued flow chart of the process for
linking video-game
activation on consoles to CTV/OTT Ad delivery.
[0040] Figure 17 is a flow chart on Ad exposure.
[0041] Figure 18 is a flow chart on illustration inventory
tracking.
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DETAILED DESCRIPTION
[0042] The systems and methods of this technology are
configured to be implemented
in a CTV, OTT, or like Audience-Responsiveness-Scoring platform that measures
audience
responsiveness and scores it. The systems and methods of this technology are
configured to
be implemented in a demand-side platform ("DSP") for CTV, OTT, or like
advertising. A
typical demand-side platform integrates with multiple sources such as DMPs, ad
exchanges,
supply sources etc., in an infrastructure that provides secure, elastic,
compute capacity in the
"cloud" that comprises computers from Virtual Machines and Bare Metal servers
to HPC
(High Performance Computing), CPU (Graphics Processing Unit), container
orchestration
and management etc.
[0043] A demand-side platform is typically integrated in three
phases, the first,
involving research and set up. The second phase is the development stage and
the final phase
is testing the integration. Demand-side platforms typically support cross-
channel platforms
and integrate with all the main ad exchanges. The bidding process is one of
the key features
of a DSP. This is performed by a component called "bidder- which is
responsible for placing
bids on inventory during real-time bidding auctions. Usually, multiple bidders
will be there to
manage all real-time demands simultaneously.
[0044] An Ad Server is an essential tool for creative or
outcome management and for
storing an ad creative or outcome and displaying to a user/viewer when
required. Some DSPs
have their own ad servers while others may connect to external ad servers
depending on the
architecture. A Campaign tracker helps to record the data regarding the
performance of a
particular campaign. The data includes clicks, impressions and spends. Once
the data is
recorded, it will be transferred to the reporting dashboard. The campaign
tracker helps the
user/viewer to determine the performance of a particular campaign. A reporting
database
stores all the data received from the campaign tracker. Users/viewers can
generate reports by
making use of this information. User/viewer data is an important part of the
DSP, which
helps in processing and storing important information about the
user/viewer/audience.
User/viewer data may include information regarding buying habits, interests,
age groups,
demographic details etc. Marketers can make use of this information to improve
the
effectiveness of their campaigns and bring in better results.
[0045] A user interface is the dashboard where the marketers
work on creating,
managing and optimizing their campaigns. Ads in DSPs are sold in a few ways,
depending on
the DSP. DSPs specifically built for performance campaigns such as app-
installs, charge a fee
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based on CPI (Cost per Install) or CPV (Cost per View) for video advertising
campaigns.
Prices of ad impressions in DSPs are determined by a real-time bidding (RTB)
process, that
takes place within milliseconds, as a user loads content or interacts with an
app.
[0046] DSPs are unique as they offer the same capabilities as
what ad networks used
to provide, with an addition to a suite of audience targeting options. The
advantage of DSPs
over ad networks is that they provide advertisers with the ability to do real-
time bidding on
ads, serve ads to a multitude of platforms, track and optimize ¨ all under a
single interface.
Some targeting options offered by a DSP include - demographic targeting
(targets based on
demographic features such as age (or age group), job title, gender, education
etc.), device
targeting (shows viewers ads on specific devices to improve the
personalization), re-targeting
(targeting existing customers) and so on. DSPs are also used for retargeting
campaigns. This
is possible because they are able to manage large volumes of ad inventories
and recognize ad
requests with an ideal target audience, targeted by the advertiser. The DSP in
accordance
with the present invention offers a self-serve platform, which is an excellent
way to manage
ad campaigns. This offers targeting, bidding, budgeting and optimizing of ad
campaigns. A
DSP can integrate with a data management platform (DMP) that stores audience
data, usually
coming from multiple sources. It allows advertisers to create target audiences
for their
campaign based on 1st party and 3rd party audience data. A DMP acts as a
single platform
that consolidates online and offline data from various advertisers, creating
demographics, behavioral and affinity segments which are then used as
targeting options in
digital advertising. Performance data from live campaigns are then fed back
into the DMP,
improving the accuracy of the data. DMPs allow advertisers to reach their
specific target
markets while reducing wastage in advertising. A DSP provides global reach and
effective
targeting. Through the present DSP, advertisers can connect to different
segments of
audiences by applying various targeting criteria.
[00471 Some portions of the detailed descriptions that follow
are presented in terms of
algorithms and symbolic representations of operations on data bits within a
computer
memory. These algorithmic descriptions and representations are the means used
by those
knowledgeable in the data processing arts to most effectively convey the
substance of their
work to others in the art. An algorithm is here, and generally, conceived to
be a self-
consistent sequence of steps leading to a desired result. The steps are those
requiring
physical manipulations of physical quantities. Usually, though not
necessarily, these
quantities take the form of electrical or magnetic signals capable of being
stored, transferred,
combined, compared, and otherwise manipulated. It has proven convenient at
times,
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principally for reasons of common usage, to refer to these signals as bits,
values, elements,
symbols, characters, terms, numbers or the like.
[0048] It should be borne in mind, however, that all of these
and similar terms are to
be associated with the appropriate physical quantities and are merely
convenient labels
applied to these quantities. Unless specifically stated otherwise, as apparent
from the
following discussion, it is appreciated that throughout the description,
discussions utilizing
terms such as "processing- or "computing- or "calculating- or "determining- or
"displaying"
or the like, refer actions and processes of a computer system, or similar
electronic computing
device, that manipulates and transforms data represented as physical
(electronic) quantities
within the computer system's registers and memories into other data similarly
represented as
physical quantities within the computer system memories or registers or other
such
information storage, transmission or display devices.
[0049] The present technology also relates to an apparatus for
performing the
operations described. Parts of this apparatus may be specially constructed for
the required
purposes, or it may comprise general-purpose computing elements that are
selectively
activated or reconfigured by a special computer program stored in the computer
to operate the
specific functionalities described in this application to create a new system.
Such a computer
program may be stored in a computer readable storage medium, such as, but not
limited to,
any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic
disks, read-
only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic
or optical cards, flash memories including USR keys with non-volatile memory
or any type
of media suitable for storing electronic instructions, each coupled to a
computer system bus.
[0050] Portions of the present technology may take the form of
an entirely hardware
embodiment, an entirely software embodiment or an implementation containing
both
hardware and software elements. In some implementations, this technology is
implemented
in software, which includes but is not limited to, firmware, resident
software, microcode, etc.
[0051] Furthermore, this technology may take the form of a
computer program
product accessible from a computer-usable or computer-readable medium
providing program
code for use by or in connection with a computer or any instruction execution
system. For
the purposes of this description, a computer-usable or computer readable
medium can be any
apparatus that can contain, store, communicate, propagate, or transport the
program for use
by or in connection with the instruction execution system, apparatus, or
device.
[0052] A data processing system suitable for storing and/or
executing program code
will include at least one processor coupled directly or indirectly to memory
elements through
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a system bus. The memory elements call include local memory employed during
actual
execution of the program code, bulk storage, and cache memories which provide
temporary
storage of at least some program code in order to reduce the number of times
code must be
retrieved from bulk storage during execution.
[0053] Input/output or I/0 devices (including but not limited to,
keyboards, displays,
pointing devices, etc.) can be coupled to the system either directly or
through intervening I/0
controllers.
[0054] Network adapters may also be coupled to the system to
enable the data
processing system to become coupled to other data processing systems or remote
printers or
storage devices through intervening private or public networks. Modems, cable
modem, and
Ethernet cards are just a few of the currently available types of network
adapters.
[0055] Finally, the algorithms and displays presented here are
not inherently related
to any particular computer or other apparatus. Various general-purpose systems
may be used
in combination with programs in accordance with the teachings herein, or it
may prove
convenient to construct more specialized apparatuses to perform certain
required method
steps. The required structure for a variety of these systems will appear from
the description
below. In addition, the present invention is not described with reference to
any particular
programming language. It will be appreciated that a variety of programming
languages, for
example, high level programming languages such as "C," "Java," or "Pascal," or
"Python" or
other may be used to implement the teachings of the technology as described
herein. The
computers may he specially programmed, and he configured with special purpose
hardware.
Each computer may have a single processor, a multiprocessor or may comprise
multiple
computers, each of which may include a single processor or a multiprocessor,
operably
connected over a computer network. Each compute' may be controlled by one of a
variety of
operating systems including Microsoft Windows, Macintosh, Linux, Unix, or a
Java-based
operating system, to name a few.
[0056] Each computer in the system may include one or more
input and output (I/0)
unit, a memory system, and one or more processing units. The input-output
("I/0") units of
each computer may be connected to various input/output devices, such as a
mouse, keyboard,
video card (video monitor), sound card (with speakers), network card and
printer. The
memory system in a typical general purpose computer system usually includes a
computer
readable and writeable nonvolatile recording medium, of which a magnetic disk,
a flash
memory and tape are examples. The memory system operably holds the operating
system,
utilities, and application programs. It should also be understood the
invention is not limited
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to the particular input devices, output devices, or memory systems used in
combination with
the computer system or to those described herein. Nor should the invention be
limited to any
particular computer platform, processor, or high-level programming language.
System Architecture Overview
[0057] Figure 1 illustrates a block diagram of a CTV, OTT, or
like ad-audience-
responsiveness scoring platform/system in a demand-side platform 101
illustrated in a CTV,
OTT, or like digital advertisement ("ad") placement environment 100 in which
the disclosed
implementation of the CTV, OTT, or like audience responsiveness analytics
index system is
operable. The environment 101 includes: an online advertiser server or website
102
(representing one or more online advertisers), an online content server or
website 104
(representing one or more online content providers), a network 106, and a real-
time bidding
("RTB") market platform 108. The online advertiser server 102 may be a
computing system
(of one or more computers or processors, either linked or distributed) that
submits bids to the
RTB market platform 108 to purchase content-provider inventory and have
advertiser
advertisements shown in the CTV, OTT, or like environment. The advertiser
server 102 is
illustrated as coupled to the RTB market platform via signal line 112 and the
content server is
illustrated as coupled to the RTB market platform via line 114. The content
server 104 may
be a computing system that maintains content for televising that attracts
viewers and contains
placeholders for ads (from the ad inventory) that are submitted to the RTB
market, for sale to
advertisers. The content server 104 has access to data provided by the CTV,
OTT, or like
audience responsiveness analytics index, either directly (not expressly
illustrated in Figure 1)
or otherwise. The RTB 108 may be a computing system that provides a real-time
bidding
market that allows advertisers to bid on provider inventory in real-time.
While only a single
advertiser server 102, a single content server 104 and a single network 106
are shown in
Figure 1, it should be recognized that there may be thousands or even millions
of advertiser
servers 102, content servers 104, or networks 106 that integrate in a
programmatic
advertising environment. Figure 1 is merely provided as one example
illustration of the
systems 102, 104, and 106, which present the environment in which the present
technology
may be implemented.
[00581 The advertiser server 102 is coupled by signal line 112
for communication
with the real-time bidding market 108. Although not explicitly shown in Figure
1, it should
be recognized that any and all the signal lines illustrated in Figure 1 may
route, via the
network 106, as illustrated in Figure 1. The advertiser 102 is coupled to the
real-time bidding
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market 108 to send bids on impressions, and also provides advertisement
content, advertising
target information, price, or any other information related to the impression
or necessary to
serve the ad on streaming content. The RTB market platform 108 is a real-time
bidding
market, which allows advertisers to bid on inventory in real-time.
[0059] The content site 104 is a computing device for providing any type of
video
content for viewing as streamed content on a household or related device. The
signal line
114 provides information to the RTB about which impressions on the content
site are
available for the RTB market. A control line 109 from 104 to 106 indicates
content provision
from the online content servers.
[0060] The network 106 is a conventional type, wired or wireless, and may
have any
number of configurations such as a star configuration, token ring
configuration or other
configurations known to those skilled in the art. Furthermore, the network 106
may comprise
a local area network (LAN), a wide area network (WAN) (e.g., the Internet),
and/or any other
interconnected data path across which multiple devices may communicate. In yet
another
embodiment, the network 106 may be a peer-to-peer network. The network 106 may
also be
coupled to or includes portions of a telecommunications network for sending
data in a variety
of different communication protocols. In yet another embodiment, the network
106 includes
Bluetooth communication networks or a cellular communications network for
sending and
receiving data such as via short messaging service (SMS), multimedia messaging
service
(MMS), hypertext transfer protocol (HTTP), direct data connection, WAP, email,
etc.
[0061] The real-time bidding market platform 108 is coupled by
signal line 118 to an
advertisement server 110, which serves ads, for example video ads. The ad
server 110 is
software that receives requests for ad units, submits, and then fulfills those
requests with
content. The advertisement server 110 is coupled to the network 106 for
communication and
interaction with online advertisers 102 and the content site 104. A viewer
(audience) 125
who is viewing streamed content is a potential consumer of ads. There may be
any number
of viewers (audience) 125a, 125b, through 125n, who are coupled via the
network 106 to
online sites 104 from which content may be streamed. For example, when a
viewer in the
audience (125a -125n) downloads content for viewing on connected household
devices 115a.
115b, through 115n, that is supplied by an online content site 104, requests
are sent to the
content site 104 (the content provider's server) for content. It should be
recognized by those
skilled in the art that the connected household devices 115a, 115b, through
115n may be a
display device or a home audio device. The connected household devices 115a
through 115n
used by consumers include, but are not limited to, one or more of: TVs (
including Smart
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TVs), mobile devices (cell phones, smartphones, media players, tablets,
notebook computers,
laptop computers, and wearables ), desktop computers, networked photo frames,
set-top
boxes, gaming consoles, streaming devices, and devices considered to function
within the"
Internet of Things" such as domestic appliances fridges, etc.), and other
networked "in-
home" monitoring devices such as thermostats and alarm systems . The viewer
(125a -125n)
navigates to content for streaming via a web browser 120. The browser may be
any one of
Chrome, Safari, Firefox, Internet explorer or the like. The viewer may
otherwise be referred
to as a user, consumer, or client. Consumers, viewers or potential viewers of
the advertising
content may have previously purchased the product or service that is being
advertised or may
advantageously to the advertiser be learning of the product or service for the
first time when
they view the advertising content as it is displayed or appears in audio form.
[0062] The content site (provider) serves up the content,
which includes executable
JavaScript tags. Once these tags are loaded in the viewer's content browser
120 (via lines
117a, 117b, through 117n), they are executed (via lines 121 and 107) and
notify the ad server
110 that there is an impression that needs filling in the streaming content.
The impression is
then submitted to the Real-Time Bidding ("RTB") market platform 108, where
advertisers
bid to fill the impression with their video advertisements. The RTB market
platform reads in
the market floors for each of the competing advertisers and uses these market
floors, along
with the advertiser bids, to determine the winner of the auction and their
clearing price. In
the event that all of the received bids are too low, the Auction may not
clear. The operation
of the RTB market platform 108 will be described in more detail below with
reference to
Figure 2.
[0063] Referring also to Figure 1B, which illustrates the
architecture and flow of
event data, the customer device 115a-n may access an advertiser website/app
(e.g.,
www.com) designated by reference numeral 126, at which point, the event and
related data is
generated and recorded as "outcome event data." The outcome event data in some
embodiments of the present invention may include the "User Agent," the "IP
address," the
"Device IP," a "Timestamp," or an "Event Value." The infrastructure or
architecture of the
platform 127 includes an attribution engine 128, to which the outcome event
data is
continuously provided. In some instances, the outcome event data may be
provided at
designated intervals determined by the platform. The attribution engine 128 is
coupled to an
optimization engine 130, which provides a bid decision and bid price to the
bidder 132. As
illustrated, the bidder provides a bid or ad response to the publisher ad
server 134 when a bid
request is received by the bidder 132. Ad Exposure Event data is continuously
tracked and
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recorded as each bid request is generated. Fur example, event data that may be
recorded
includes the "IP," a "Timestamp," a "Device ID," a "Device Type," "Content
Data,"
"Location," or the like compiled at a storage location designated by reference
numeral 135.
A household ("HH") effectiveness graph 136 is coupled to the bidder 132 and
serves to
provide additional data from a 3rd Party Data Enhancement server 138 as
designated by
signal line 146. Examples of the additional data may include, but not limited
to, postal data,
latitude/longitude data, IP type, age, gender, and household income. In
addition, new IDs are
linked to known households and provided to the attribution engine 128, as
designated by
signal line 140. And, the attribution engine 128 adds new results to known or
new
households, as designated by signal line 142. The household effectiveness
graph 136
enhances bid requests with additional data as designated by signal line 144.
The outcome
event data is used by the scoring engine to accord responsiveness scores to
advertising as
further described in Figure 3.
[0064] Referring now to Figure 2, the RTB market platform 108
implements a real-
time bidding market. In the implementations described here, the RTB market
platform 108
conducts a market floor auction for ad placement (e.g., video), which is a
specialized auction
that determines an auction winner, auction clearing price based on the bids
submitted by
advertisers, and per-advertiser market floors that are calculated and
distributed by the market
floor system 100. In some implementations, an auction event store 230 may
include a large
collection of computers arranged in a distributed, computational, and storage
grid. The
auction event store 230 may store events from the Advertisement server 110 and
RTB market
platform 108. A market floor engine 220 determines and provides market floor
prices, which
may in some instances be dynamically or selectively set by providers. In some
implementations, the market floor engine 230 may be an analy tics engine that
processes
auction event data in either real-time, near-real-time, or batch mode,
determines market floors
based on this data, and assesses the revenue impact of using these market
floors compared to
provider "static" floors and/or other benchmarks. The provider may determine
market floors
by deriving data from the CTV audience responsiveness analytics index system
224. The
index system 224 may be directly coupled to either market buyer devices 226a,
226, or 226n,
via lines 227a, 227b, through 227n, or an agency 225, via line 223, to
directly provide data
and revenue value to any of these entities.
[0065] During an RBT auction, the advertisement server 110 and
RTB market
platform 108 generate a number of events that include information about the
context in which
the RBT auction is occurring. An "event profile" (with the type of information
available in
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the auction bids that are received) may be generated when all of the bids from
the advertisers
in an RBT auction have been received. An auction event store 230 may store
information
available in the "auction complete" event generated when an auction has
completed. The
auction event store 230 may include a large collection of computers arranged
in a distributed,
computational, and storage grid. The auction event store 230 in some
implementations stores
events from the advertisement server 110 and the RTB market system 108.
[0066] Referring now to Figure 3, an example implementation of
the hardware
structure is illustrated. One or more of the ad request and delivery engine
302, the statistics
data collector 304, the audience tracking module 306, the optimization engine
308, and the
direct request source 310 are software or routines executable on the processor
335. In some
implementations, one or more of the delivery engines 302, the statistics data
collector 304,
the audience tracking module 306, the optimization engine 308, and the direct
request source
310 store data that, when executed by the processor 335, causes the
collectors/modules to
perform the operations described below. In yet other implementations, one or
more of the ad
request and delivery engine 302, the statistics data collector 304, the
audience tracking
module 306, the optimization engine 308, and the direct request source 310
(from publisher)
are instructions executable by the processor 335 to provide the functionality
described in the
flow charts that follow. In still other implementations, one or more of the
delivery engines
302, the statistics data collector 304, the discrete revenue value
determination module 306,
the optimization engine 308, and the direct demand source 210 (publisher) are
stored in the
memory 337 and are accessible and executable by the processor 335. The storage
339 has the
CTV/OTT ad volume stored at 341, CTV/OTT attributes inventory 343, and a
lifetime value
metrics from the advertiser to each household 344. The scoring engine 312
scores
impressions, outcomes, or audience data.
[0067] The scoring engine 312 tracks advertisement exposure and designates
advertisement responsiveness scores to each advertisement. For example, in
some
implementations and scenarios, a particular house "HH 1234" is accorded an
advertisement
responsive score of "98/100" for casual gaming and another house "HH 5678" is
accorded a
score of "45/100," the system would automatically weigh in favor of directing
a new casual
gaming campaign to focus on "HH 1234." The exposure to advertising is critical
to
determine an extent of use and interest by a particular household to different
advertising
campaigns. The exposure data that is compiled translates to
responsiveness/exposure scores,
which are used to automatically direct advertising campaigns to interested
households. This
responsiveness scoring advantageously yields an increase advertising revenue
and benefits
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consumers and advertisers. Consumers are spared from viewing advertising of
little interest
to them and advertisers are spared a wasted effort to exposing advertising to
those who have
no interest. The responsiveness scores may be a presented by ratios as
illustrated here or in
other ways suitable to convey the extent of exposure. Responsiveness scores
may be used in
algorithms to programmatically automate advertising flow in the connected
television or
over-the-top delivery mechanisms of streaming content. The responsiveness
scores may be
stored and used to create an index of responsiveness/exposure scores. This
index serves as
analytics database that may be used to train programmatic models that operate
and execute in
the dynamic advertising environment.
[0068] In some instances, the optimization engine 308 includes a multiplier
(a)
calculation module, a data retrieval module, and a classification module (to
execute any
classification criteria). The CTV/OTT audience responsiveness scoring system
includes data
collection engines. These engines are operated by one or more processors that
comprise an
arithmetic logic unit, a microprocessor, a general-purpose controller or some
other processor
array to perform particular computations as programmed and provide electronic
display
signals to a display device. The processor 335 is coupled to the bus for
communication with
the other components. The processor 335 processes data signals and may
comprise various
computing architectures including a complex instruction set computer (CISC)
architecture, a
reduced instruction set computer (RISC) architecture, or an architecture
implementing a
combination of instruction sets. Although only a single processor 335 is
referenced here,
multiple processors may be included_ It will be obvious to one skilled in the
art that other
processors, operating systems, sensors, displays and physical configurations
are possible.
[0069] The processor 335 is coupled to a memory that stores
instructions and/or data
that may be executed by the processor 335. The memory is coupled to the bus
for
communication with the other components. The instructions and/or data may
comprise code
for performing any and/or all of the techniques described herein. The memory
may be a
dynamic random-access memory (DRAM) device, a static random-access memory
(SRAM)
device, flash memory or some other memory device known in the art.
[0070] In one embodiment, storage stores data, information and
instructions used by
the ad request and delivery engines, data collector engines, optimization
engines, and the
direct request of source (from provider) by User/Agency etc. The storage is a
non-volatile
memory or similar permanent storage device and media such as a hard disk
drive, a floppy
disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device. a DVD-RW
device, a flash memory device, or some other mass storage device known in the
art for
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storing information on a more permanent basis. The data storage is coupled by
the bus for
communication with other components of the analytics index system for
impression
evaluation and allocation.
[00711 One or more of the engines are software or routines
executable on the
processor. In some implementations, one or more of the engines store data
that, when
executed by the processor, causes the collectors/modules to perform the
operations described
below. In yet other implementations, one or more of the engines are
instructions executable
by the processor to provide the functionality described in the flow charts
that follow. In still
other implementations, one or more of the delivery engines are stored in the
memory and are
accessible and executable by the processor. The flow charts illustrated in
Figures 4 through 8
describes the various operations.
[0072] Referring to Figure 4, in some embodiments of the
present invention,
operation flow 400 begins at block 402, with the platform, system, or method,
collectively
referred to as the "Scoring Platform- implemented in a DSP, receives a
combination of
signals continuously and dynamically as events occur. These signals include
for example, a
timestamp, a CTV/OTT identification, a physical location etc. The operation
flow 400
proceeds to the next step of one or more operations designated by block 404.
The scoring
platform determines a unique household ("HH") identification ("ID") by
receiving and using
a combination of the signals including the timestamp, CTV ID, IP, Physical
Location. The
method 400 proceeds to the next block of operations designated by block 406.
The
Household ID signals from "Advertiser A" CTV Ad Delivery are collected or
captured via a
combination (determined by the "Scoring Platform") VAST Tag lx1 IMG Tracker
and Ad
Server Logs and recorded to an "Ad Exposure File" creating exposed household
records, for
example, Exposed Household Record 001" and recording data, including but not
limited to,
an IP address, a location, a timestamp, a Device ID, UA, an advertiser, a
category, a product,
a price point, an offer, and a call to action. From that block, the operation
400 flows to the
next block 408, including one or more operations for collecting Household ID
signals from
"Advertiser A" (example) Ad Delivery. From there, the flow 400 proceeds to the
next block
410, where "Via Combination of VAST Tag lx1 IMG Tracker and Ad Server Logs",
the
Household ID signals are recorded to a "Ad Exposure File" to create a Record.
The
"Exposed Household Record 001" by way of example illustrated in block 412 may
include
any one or more of IP address, a timestamp, and a location, illustrated at
block 414. At block
416, one or more operations lead to recording new identifiers including a
device ID. The
process 400 proceeds via connector A to operations 500 described in Figure 5.
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[0073] Referring now to Figure 5, the process 500 proceeds at
block 502, which
includes one or more operations for appending additional household devices to
the "Exposed
Household Record 123." The process 500 continues to block 516, which appends
additional
household devices via a device identification or "Device ID." The Device ID is
anchored by
either an IP address as designated by block 504, a timestamp as designated by
block 506,
location cues as designated by block 508, etc. The process 500 continues for
recording new
identifiers including Device ID as designated by block 510. From block 516,
the process
flows via connector "B" to the operations described in Figure 6. Block 602
illustrates
compiling of "Website Visit Data." The method 600 proceeds to block 604, which
represents
one or more operations for tracking an initial visit, a purchase or other
desirable action on the
advertiser's A website or the like. The method 600 proceeds to block 606,
representing
recording of the website visit data by JavaScript tracking pixel/s2s post back
solution. The
method 600 proceeds to block 608 including one or more operations for storing
the data
compiled on the Advertising Outcome File ("A01--). The method 600 proceeds to
block 610
including one or more operations for creating a visitor record for each
visitor. For example,
the method 600 creates a record "Visitor Record 001," for a first visitor and
subsequently
sequential records for visitors as tracked. The method 600 proceeds to block
612, which
describes one or more operations for recording data, including but not limited
to, the record
IP Address, the location, a time stamp, an event increment, a value of
conversion, and a
conversion type or the like. The website visit data including the initial
visit, purchase or other
desirable action on "Advertiser A's" Website is recorded by a JavaScript
tracking pixel or s2s
post back and stored to the Advertiser Outcome File in each visit instance,
creating a visitor
record, for example, "Record 001" to record the IP address, the location, the
time stamp, the
event increment, the value of conversion, the conversion type, or the like.
[0074] Referring now to Figure 7, the method 700 proceeds to one or more
operations
performed by various engines. Block 702 represents one or more operations for
comparing
data from "AEF" and "AOF" files at regular intervals that are predetermined.
The method
700 proceeds to block 704, which represents one or more operations for
identifying matches
via overlapping combinations. The method 700 proceeds to block 706, which
represents one
or more operations for generating combination options of the appropriate
household in the Ad
Exposure File ("AEF"). The method 700 continues to block 710, which includes
one or more
operations for creating the combinations of data, for example, from the IP
Address/DID at
block 710. Block 712 represents the timestamp (for combination based on
appropriate recent
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instances. Block 714 represents recording a conversion event to the exposed
household
record, and block 716 represents data on the Ad Exposure File ("AEF").
[0075] The method 700 at block 708, takes the records from
both files, Ad Exposure
File and Advertiser Outcome and appends them with the ID from the other file
for future
analysis as needed.
[0076] In operation, at regular intervals, data from the Ad
Exposure File ("AEF") and
Advertiser Outcome File ("AOF') are compared to identify matches via
overlapping
combinations of IF Address/DID, timestamp (for confirmation of appropriate
recent
instances), with recording of a conversion event to the "Exposed Household
Record." of the
appropriate household on the Ad Exposure File.
[0077] For the purpose of this disclosure, it should be
recognized that VAST is a
Video Ad Serving Template for structuring ad tags that serve ads to video
players. Using an
XML schema, VAST transfers important metadata about an ad from the ad server
to a video
player. The VAST tag is a tracking pixel (also called lx1 pixel or pixel tag),
which is a
graphic with dimensions of lx1 pixels that is loaded when a user visits a site
or opens a
communication, Because it is so small, it can hardly be seen by visitors and
the tracking
pixel URI,: is the memory location on the server. The very small size of a
tracking pixel is an
essential part of its functionality. Tracking pixels are intentionally hidden
in the background
of a web page or email so that they are not part of the USet experience. The
intention behind
this is to enable a back-end process that does not distract from the content
of a websi.te or
marketing ernai 1_
[0078] Tracking pixels are embedded in the 1-1TIVIL code of a
website, online ad, or
marketing email and are retrieved from the server every time a user loads that
website, ad, or
email into their web browser. The server then sends the pixel tag to the
user's unique IP
address and logs it. The server thus counts the number of retrievals. Tracking
unique page-
views is the most basic function of a tracking pixel. Web site operators,
email marketers or
advertisers who use a tracking pixel can analyze the server logs and see how
many unique
page-views their content has received.
[0079] The traffic data collected with a tracking pixel can
then be further analyzed,
e.g., for targeting purposes. More accurate analysis of II) addresses can
provide a basic idea
of where users come from geographically and what type of devices and operating
systems,
they use to visit a website. Tracking pixels also work across websites and
servers, giving
website owners and advertisers a clearer view of what users are looking for
and why they are
visiting the site. This data can be used to tailor content and ads to users'
needs through
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targeted marketing campaigns. In operation, tracking pixels may be integrated
into the source
code of a website.
[0080] 'When a visitor accesses a site, the tracking pixel is
loaded from the server
using the <ling> tag. "URL tracking pixel" indicates the location of the
inaage on the server.
The style attributes "visibility:hidden" and "display:none" specify that the
image is hidden. or
not displayed. Alternatively, as in the first example, the values for width
and height of the
image may be set to "0" to prevent the tracking pixel from being displayed.
Unlike tracking
pixels, cookies contain code that web servers use to store certain information
in a user's
browser_ This information may then be retrieved at a later time for various
pmposes.
[0081] it should be recognized by those skilled in the art that ad servers,
such as
"DoubleClick," use cookies to set unique user IDs that allow them to identify
the same user
across multiple touchpoints. When an ad server receives an ad request from a
user who does
not yet have such a cookie, the ad server assigns it a new unique m. This ID
is a random
alphanumeric string. For each subsequent request, the cookie returns the same
unique ID so
that the ad server can recognize that it is the same user. Since all requests
are recorded by the
ad server, reports can be generated that provide a record of all touch points
for each user.
This functionality is not available for counting pixels. The advantage of
tracking pixels for
advertisers and website owners lies in the simple implementation of tracking
pixels in
websites, entails, and advertisements. With plugins such as adblockers, users
may also make
tracking pixels visible or block them.
[0082] Referring now to Figure 8, the extent and level of ad
exposure. may he
determined by tracking different events and data. For example, as illustrated
by block 802,
ad exposure is determined, either by sequence of ad displayed as described in
block 804, by
frequency of advertising m described by block 806 or by cross-channel exposure
as described
by Nock 808.
[0083] Refening to Figure 9, the methods here may also
include capabilities to
identify category of an advertising offer, for example, as compared to
categories classified by
the Interactive Advertising Bureau ("JAB") or other such hierarchy in the
advertising
industry. The methods include measuring response rates for each ad in each
household
("HH"), as described by block 904. In some embodiments, the index
responsiveness is based
on frequency determined prior to action, for example total seconds exposed
prior to action, as
described by block 906. The methods include counting actions across distinct
advertisers in
the same category as described by block 908. The methods include counting by
creative type
or outcome, including the length of commercial and other variables, for
example, visual
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components, audio components, elements in the ad creative or ad outcome, for
example,
color scheme, actor gender, etc., as described by block 910.
[00841 Referring now to Figure 10, in some embodiments, an
example
implementation of a CTV audience responsiveness analytics index system 1000 is
illustrated.
This implementation of the analytics index 1000 comprises data collection
engines. These
engines are operated by one or more processors that comprise an arithmetic
logic unit, a
microprocessor, a general-purpose controller or some other processor array to
perform
particular computations as programmed and provide electronic display signals
to a display
device. The processor is coupled to the bus for communication with the other
components.
The Processor processes data signals and may comprise various computing
architectures
including a complex instruction set computer (CISC) architecture, a reduced
instruction set
computer (RISC) architecture, or an architecture implementing a combination of
instruction
sets. Although only a single processor is referenced here, multiple processors
may be
included. It will be obvious to one skilled in the art that other processors,
operating systems,
sensors, displays and physical configurations are possible.
[0085] The processor is coupled to a memory that stores
instructions and/or data that
may be executed by the processor. The memory is coupled to the bus for
communication
with the other components. The instructions and/or data may comprise code for
performing
any and/or all of the techniques described herein. The memory may be a dynamic
random-
access memory (DRAM) device, a static random-access memory (SRAM) device,
flash
memory or some other memory device known in the art.
[00861 In one embodiment, storage stores data, information and
instructions used by
the ad request and delivery engines, data collector engines, optimization
engines, and the
direct request of source (from provider) by User/Agency etc. The storage is a
non-volatile
memory or similar permanent storage device and media such as a hard disk
drive, a floppy
disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW
device, a flash memory device, or some other mass storage device known in the
art for
storing information on a more permanent basis. The data storage is coupled by
the bus for
communication with other components of the analytics index system for
impression
evaluation and allocation.
[00871 One or more of the engines are software or routines
executable on the
processor. In some implementations, one or more of the engines store data
that, when
executed by the processor, causes the collectors/modules to perform the
operations described
below. In yet other implementations, one or more of the engines are
instructions executable
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by the processor to provide the functionality described in the flow charts
that follow. In still
other implementations, one or more of the delivery engines are stored in the
memory and are
accessible and executable by the processor.
[0088] The flow chart illustrated in Figure 10 shows the
analytics index is created by
the following steps including one or more operations for creating and using a
household
("IIII") CTV Responsiveness Index, as described in block 1002. This index is
configured to
collect data on households that are identified as "Exposed and Converting,-
which data is
written or stored to the operating platform's household-responsiveness graph.
It should be
recognized that "Exposed" refers to a household that has access to connected
television and
capability to view CTV/OTT advertising. The flow to create the index flows to
the next block
1004 of operations, including one or more operation for adding increments of
every instance
of exposure to the "Exposed versus Outcome- index, as the "Exposed- household
continues
to be exposed to future or more CTV ads. The process flows to the next block
1010,
including one or more operations for determining values according to the
number, length of
time from ad exposure to action, and category of conversions completed and
assigned to the
homes. The process proceeds to the next block 1012, including one or more
operations for
creating the household advertising responsiveness index to measure households
exposed to
ads, by ad category (CPU, Auto, Travel, commerce, etc.), as described by block
1014. In
some embodiments, as described by block 1016, the advertising responsiveness
may be
measured by households that have either seen the CTV/OTT ad and have responded
to the
CTV/OTT ad, usually via a "second screen response" mechanism (seeing a CTV/OTT
ad and
responding by using their cell phone to go to the advertiser website. Other
examples include
measuring the impact of frequency on conversions as described by block 1018.
For example,
the response rate after seeing one id is X; responsiveness after seeking 2 ads
is Y, etc.
Another example as illustrated by block 1020 includes time from CTV/OTT to
action. For
example, how much time did it take for a house (or group of houses) to respond
to an
advertisement after seeing the advertisement. This may be measured by
advertising vertical
and by a profile of the household (by a demo, etc.). As yet another example
described by
block 1022, advertisement responsiveness by household frequency, including
cross channel
(CTV ad delivery + display + OTT+++) is measured and recorded in the index at
the
household ("HH") level that dictates the optimal frequency and combination of
advertising
formats in support of CTV/OTT ad delivery to drive ad responsiveness.
[0089] As yet another example, described by block 1024, the
index provides a
measure of second-screen audience response to television advertising. It
should be recognized
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that the second screen response encompasses instances when
users/viewers/audience are
exposed to a CTV/OTT advertising and then undertake one of the following
actions. These
examples are provided for illustration purposes and are not exhaustive. The
description
should not be limited to these specific examples. A second screen response may
cause a
viewer to pick up a cell phone and go directly or indirectly to the
advertiser's site. Yet
another example may cause a viewer to pick up a laptop computer and go
directly to
the CTV/OTT advertiser's site. Yet another example causes a viewer to pick up
a tablet and
navigate directly or indirectly to the CTV/OTT advertiser's site. Yet another
example causes
viewers to go to their desktop computer and navigate directly or indirectly to
the CTV
advertiser's site. Yet another illustrative example causes viewers to pick up
their cell phones
and engage with a Quick Response ("QR") code in the CTV/OTT advertising,
thereby taking
them to the advertiser's offer page. Yet another example is for viewers to
pick up their cell
phone and dial a phone number to engage with the CTV/OTT advertiser.
[0090] In some embodiments, the application of an ad
responsiveness graph serves to
optimize advertising. The graph may comprise household identification rows
(rows of "HH
IDs" and columns with various values accorded, including but not limited to,
impressions by
advertising category, frequency, by device type, sequence of cross-channel
impressions,
initial response rates, secondary response rates, post-conversion data (e.g.,
viewing to
purchase action), such as average purchase price, order value, lifetime value
etc. The graph
may also illustrate a time decay from exposure to outcome.
[0091] Referring now to Figure 11, an example household graph
or advertising
responsiveness index is illustrated. The graph and index are created by a
processor 1102
coupled to a memory 1104 with executable code execute all tasks to track and
compile data
and metrics for the index. This advertising responsiveness graph is used to
optimize
advertising delivery to CTV and OTT streaming content. The graph 1106 is an
example for
purposes of illustration. Household identifiers or identification are recorded
in rows and the
columns record values for impressions. For example, impressions by be
classified by
advertising category, frequency, by device type, sequence of cross-channel
impressions,
initial response rates, secondary response rates, post-conversion data, such
as average
purchase price, order value, lifetime value. Time decay from exposure to
outcome is another
critical data point. Measures for the Index may include frequency, time to
action from first
and last exposure, frequency prior to action, sequence of device exposure,
presence of other
ad types in the journey, etc. Households may be assigned scores for multiple
categories of
advertising, for example, "10" for Gaming, "8" for Food and Beverage, and "1"
for
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Automotive. Ad categories may include commerce, travel etc. The categories
described here
are only by way of example.
[0092] In some embodiments, measures for the index, include
but are not limited to,
frequency, time to action from first to last exposure, frequency prior to
action, sequence of
device exposure, presence of other types of advertising in the journey.
Households may be
accorded composite or lifetime scores, for example, some form of cumulative
score accorded
based on scores and metrics tracked for multiple categories of advertising,
for example, a
"10" for gaming, a "8" for Food and Beverage, and a "1" for automotive.
[0093] In some embodiments, the household ("HH") advertising responsiveness
index
measures households exposed to advertising, by advertising category (CPG,
Auto, Travel,
Commerce etc.). Advertising responsiveness or exposure measures households
that have
seen an advertisement in streaming content, on one or more of connected
television
(CTV/OTT), tablet, mobile phone, desktop, or laptop, responded to the CTV
advertising
usually via a second screen response mechanism. As recognized by those skilled
in the art,
second screen viewing refers to seeing a CTV/OTT add and responding by using a
cell phone
to the advertiser website.
[0094] Referring now to Figure 12, the Scientific DSP approach
designated by
reference numeral 1200 here, includes the Scientific DSP configured for
optimization and
designated by reference numeral 101 (as in Figure 1A). It comprises an Al
engine with
intelligent engines or modules configured and programmed to identify the
common attributes
that drive performance on the ads displayed during social media interactions.
In accordance
with some embodiments of the present invention, the Scientific DSP's Al Engine
includes an
"Initial-Feedback" engine 1202 that develops and refines creatives using
artificial intelligence
with machine learning and social media platforms, such as Facebook (see 1210,
connected by
a network 106), as an initial feedback loop. It also includes an "Ad-
Selection" engine 1204
that considers and obtains the highest performing social media ("SM") or
Facebook ad and
moves it to CTV. The Scientific DSP also comprises a "Performance" engine 1206
configured to
optimize for performance on CTV and then moves the winning combination of
creative or
outcome + app inventory + audience segment + day part + frequency to linear TV
buys.
[0095] Referring also to Figure 13, an example implementation
of the Scientific DSP
is illustrated. This implementation of Scientific DSP and its scientific
approach designated
generally by reference numeral 100 (Figure 1A) comprises data collection and
execution
engines. These engines are operated by one or more processors 1212 (Figure 12)
that
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comprise an arithmetic logic unit, a microprocessor, a general-purpose
controller or some
other processor array to perform particular computations as programmed and
provide
electronic display signals to a display device. The processor 1212 is coupled
to a bus for
communication with the other components. The processor 1212 processes data
signals and
may comprise various computing architectures including a complex instruction
set computer
("CISC") architecture, a reduced instruction set computer ("RISC")
architecture, or an
architecture implementing a combination of instruction sets. Although only a
single
processor 1212 is referenced here, multiple processors 1212 may be included.
It will be
obvious to one skilled in the art that other processors, operating systems,
sensors, displays
and physical configurations are possible.
[0096] The processor 1212 is coupled to a memory 1208 that
stores instructions
and/or data that may be executed by the processor 1212. The memory 1208 is
coupled to the
bus for communication with the other components. The instructions and/or data
may
comprise code for performing any and/or all of the techniques described
herein. The memory
1208 may be a dynamic random-access memory (DRAM) device, a static random-
access
memory (SRAM) device, flash memory or some other memory device known in the
art.
[0097] In one embodiment, storage 1214 stores data,
information and instructions
used by ad request and delivery engines, data collector engines, optimization
engines, and the
direct request of source (from provider) by User/Viewer/Agency etc. The
storage 1214 is a
non-volatile memory or similar permanent storage device and media such as a
hard disk
drive, a floppy disk drive, a CD-ROM device, a DVD-ROM device, a DVD-R AM
device, a
DVD-RW device, a flash memory device, or some other mass storage device known
in the art
for storing information on a more permanent basis. The data storage 1214 is
coupled by the
bus for communication with other components of the system for impression or
outcome
evaluation and allocation.
[0098] One or more of the engines are software or routines
executable on the
processor. In some implementations, one or more of the engines store data
that, when
executed by the processor, causes the collectors/modules to perform the
operations described
below. In yet other implementations, one or more of the engines are
instructions executable
by the processor to provide the functionality described in the flow charts
that follow. In still
other implementations, one or more of the delivery engines are stored in the
memory and are
accessible and executable by the processor. The flow chart illustrated in
Figure 13 shows the
execution protocol by the Scientific demand-side platform.
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[0099] The Scientific demand-side platform comprises an
artificial engine with
intelligent engines or modules created, configured and programmed to identify
and track the
common attributes that drive performance on the ads displayed on social media
platforms
during social media interactions.
[00100] In accordance with some embodiments of the present invention, the
Scientific
demand-side platform's artificial engine includes an "initial-feedback" engine
(1202 in
Figure 12) that develops and refines creatives or outcomes using artificial
intelligence with
machine learning and social media platforms, such as Facebook, as an initial
feedback loop.
It also includes an "ad-selection" engine (1204 in Figure 12) that considers
and obtains the
highest performing social media ("SM") or Facebook ad and moves it to the
connected
television ("CTV") or OTT delivery mechanisms. The Scientific demand-side
platform also
comprises a "performance engine" (1206 in Figure 12) configured to optimize
for
performance on CTV and then moves the winning combination of creative or
outcome + app
inventory + audience segment + day part + frequency to linear TV buys.
[00101] It should be recognized that these intelligent artificial engines
have modules
that gather the relevant data (structured and unstructured), prepare and
organize the data, train
models (using neural networks), test the data, and improve it for application.
[00102] The common attributes that are analyzed for performance
include, but are not
limited to, the "frame rate," "aspect ratio," "ethnicity of actor," "color
scheme," when the
brand is introduced, live action vs animation, or the like.
[00103] Figure 13 illustrates the flow of the process for
developing multiple TV
creatives or outcomes into creative or outcome attributes that drive social
media (e.g.,
Facebook) performance by block 1302.
[00104] The Scientific demand-side platform executes the
scientific approach through
a second measurement phase, which measures results, illustrated by block 1304.
Once
multiple creatives or outcomes are produced, the Scientific demand-side
platform tests each
creative or outcome on the social media platform (e.g., Facebook) and measures
the results.
The Scientific demand-side platform executes the third step in the scientific
process, which is
the iteration step, illustrated by block 1306. As the Scientific demand-side
platform learns
which attributes or combinations or attributes drive performance, new ad
creatives or
outcomes are developed based on feedback that considers the factors that drove
the best
performance on the social media platform (e.g., Facebook).
[00105] The Scientific demand-side platform executes the fourth
step including one or
more operations, which involve moving or directing the winning creatives or
outcomes of the
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social media (e.g., Facebook) ads to CTV and measure performance against
audience
segments, apps, day parts, etc. These operations are illustrated by block
1308. The Scientific
demand-side platform executes the fifth step again by executing measures,
including by
refining the CTV buy based on, the "best performing" creative or outcome + ad
execution.
By "best perfonning," the Scientific demand-side platform focuses on
advertiser outcomes
(website visits, conversions, app downloads, form fills, etc.). The operations
at this stage are
illustrated by block 1310. The sixth step executes when the CTV/OTT buys are
refined or
negotiated, at which point, the Scientific demand-side platform uses the
highest performing
attributes from CTV/OTT to inform linear TV buys at a much larger scale. The
operations at
this stage are illustrated by block 1312. It should be recognized that this
step includes
negotiations on pricing.
[00106] The system and methods disclosed below may be
advantageous in a number of
respects. They provide a novel approach to optimizing TV advertising. As
illustrated in
Figure 2, the storage 214 in some embodiments of the present invention
compiles lifetime
value metrics from advertisers to each household record that is created. In
addition, the
extent and level of ad exposure may be determined by tracking different events
and data. For
example, ad exposure is determined, either by sequence of ad displayed, by
frequency of
advertising or by cross-channel exposure. In some embodiments, advertising
campaigns may
be adjusted based on many variables. For example, in some instances
advertising campaigns
may be adjusted based on the application, the creative formats, sizes, and
elements. In other
instances, advertising campaigns may he adjusted based on the advertisement
length, the
frequency, and cross-screen frequency and sequencing. For example, connected
television
before a tablet or a tablet before connected television. In yet other
instances, advertising
campaigns may be adjusted based on creative sequencing or first-party data
targeting. In
some instances, advertising campaigns may be adjusted based on "lookalike" or
similar
audience building or third-party data segments, context, and Automatic Content
Recognition
("ACR") data.
[00107] Referring now to Figure 14, a television advertisement
creative variant testing
and optimization process is described. It should be understood that a
television advertisement
("TV AD") refers to a television creative object, for example a :15, :30, or
:60 second unit
comprising multiple advertisement elements. As should be recognized by those
skilled in the
art, an advertisement element is a component of the television advertisement
that is being
tested, for example, an actor's sex, a voiceover style, an offer type, a quick
response code
("QR"), a video style or the like. It should also be recognized by those
skilled in the art that a
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television advertisement variant refers to a versioning of a television
advertisement that is
composed of different versions of the advertisement elements.
[00108] The method 1400 illustrated in Figure 14 begins and
proceeds to block 1402
including one or more operations for creating a certain number of advertising
("ad") element
variations for a particular television advertising campaign. It should be
recognized that the
variants represent differing treatments of the same components of an
advertisement, for
example, an actor's sex, a voiceover type, a presence of an offer, placement
and/or treatment
of a quick response ("QR") code, or other variation in the advertisement. The
method 1400
proceeds to the next block 1404, including one or more operations for creating
multiple
advertisement variants utilizing combinations of the same advertising element
variations
across multiple advertisements. The process 1400 proceeds to the next block of
operations
1406, including one or more operations for tagging advertising element
versions for tracking
exposure to "outcome" when tracked across multiple television advertising. In
some
instances, for example, a particular television advertisement - All 1" has
"Sexi"-
"VoiceOven," and "0ffer3." As yet another example, another TV AD 2 has "Sex7,"
¨
"VoiceOveri," and "Offer2" and so on across nine television advertisements
that shuffle three
versions of each advertising element. The method 1400 continues to the next
block of
operations 1408, including one or more operations for fighting television
advertising variants
with tagged elements into a television advertising campaign. As recognized by
those skilled
in the art advertising flight describes the time when commercials or
advertising is aired. The
method 1400 proceeds to the next block of operations designated by reference
numeral 1410,
including one or more operations for tracking advertising exposure to a
particular outcome
using the attribution method in accordance with the present invention to
understand
conversion/impression for each advertisement in rotation. As recognized by
those skilled in
the art, conversion refers to an action that is counted when someone interacts
with advertising
or a free product listing (for example, clicks a text ad or views a video
advertisement) and
then takes an action that is defined as valuable to the advertiser's business,
such as an online
purchase or a call to the advertiser's business from a mobile phone. The
method 1400
proceeds to the. next block 1412, including one or more operations for
evaluating the
effectiveness of each advertisement element in driving exposure to outcome by
applying a
statistical approach to determine incremental increase in outcome rate for the
presence of
each element. In some embodiments, a "Shapley Value Weighting" approach is
used to fairly
attribute contribution to the end result of an outcome. As is recognized by
those skilled in the
art, the "Shapley" value is one of the most widely used measures of feature
importance partly
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as it measures a feature's average effect on a model's prediction. By using
joint Shapley
values, which directly extend Shapley's axioms and intuitions, a set of
features' average
contribution to a model's prediction are measured by the joint Shapley values.
Joint Shapley
values present different insights from existing interaction indices, which
assess the effect of a
feature outcome within a set of features. The joint Shapley values provide
intuitive results in
machine learning attribution problems.
[00109] The method 1400 proceeds to the next block of
operations 1414, including one
or more operations for applying a statistical approach to weighting future
advertisement
delivery to increase the likelihood of an outcome based on the data analysis
in the preceding
operations.
[00110] Referring now to Figure 15, a flow chart at 1500,
represents the flow of the
following operations. It should be recognized by those skilled in the art that
these operations
are specific to the Video Gaming Industry, that is, in some implementations
specifically, to
use of console games. It should be recognized that some may participate by
phone-based
access to the games. The flow 1500 begins and proceeds to block 1502,
including one or
more operations for media activation for promoting a game. In this operation,
the platform
101 executes CTV/OTT media based on multiple targeting criteria that may be
used. The
flow 1500 proceeds to the next block 1504, including one or more operations
for performing
measurements to determine the exposure to outcomes. The flow 1500 proceeds to
the next
block 1506, including one or more operations for determining exposure, by
executing
measurements. The process 1500 proceeds to the next block 1508, including one
or more
operations for determining the "outcome," on the instrument of the game by a
"post back"
solution. The process 1500 proceeds to the next block 1510, including one or
more
operations for generating and providing a signal linking the platform 101 when
defined
events occur. The defined events may be any of several. For example, as
described in block
1512, a user arriving at the landing page of a website. As another example, a
user
downloading an application or game, as described in block 1514. As yet another
example, as
described in block 1516, a user activating an application and linking platform
101 to initiate a
"post back" call with an IP address. Another example may be a "post back" may
be set to
trigger against multiple events (levels occurring during in-game pursuits), as
described by
block 1518.
[00111] Referring now to Figure 16, the flow 1600 begins and
proceeds to block 1602,
to determine exposure and execute measurements for advertisement exposure. The
process
flow 1600 continues to the next block 1604, including one or more operations
for collecting
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IP addresses. The process flow 1600 continues to the next block 1606,
including one or more
operations for mapping to all devices tethered to the same IP address. The
process flow 1600
proceeds to the next block 1608, including one or more operations for creating
an "Exposure
File" in which exposure data is recorded. The process flow 1600 proceeds to
the next block
1610, including one or more operations for recording signals for IP address,
timestamp,
location cues etc. The process flow proceeds to the next block 1612, including
one or more
operations for recording new identifiers including a device identification.
The "Exposure
files- that are created may record all of these signals and more.
[00112] Referring now to Figure 17, in some implementations,
advertising exposure as
described by block 1702 is determined by sequence, as described in block 1704.
Alternatively, advertising exposure may be determined by frequency, as
described in block
1706. In some instances, advertising exposure may be determined by cross-
channel
exposure, as described by block 1708.
[00113] Referring now to Figure 18, at block 1802, CTV/OTT Ad
Volume is tracked.
At block 1804, value metrics of CTV/OTT advertising from each Advertiser to
each
household ("HIA-) is determined and tracked. At block 1806, the CTV/OTT
attributes
inventory is tracked. The next block 1808 describes how the value metrics of
the CTV/OTT
advertising attributes from each advertiser to each household ("HH") is
tracked. At block
1810, the lifetime value metrics from an advertiser to each household record
is tracked.
[00114] Reference in the specification to "one implementation or embodiment-
or "an
implementation or embodiment" simply means that a particular feature,
structure, or
characteristic described in connection with the implementation or embodiment
is included in
at least one implementation or embodiment of the technology described. The
appearances of
the phrase "in one implementation or embodiment" in various places in the
specification are
not necessarily all referring to the same implementation or embodiment.
[00115] The foregoing description of the embodiments of the
present invention has
been presented for the purposes of illustration and description. It is not
intended to be
exhaustive or to limit the present invention to the precise form disclosed.
Many
modifications and variations are possible in light of the above teaching. It
is intended that the
scope of the present inventive technology be limited not by this detailed
description, but
rather by the claims of this application. As will be understood by those
familiar with the art,
the present inventive technology may be embodied in other specific forms
without departing
from the spirit or essential characteristics thereof. Likewise, the particular
naming and
division of the modules, routines, features, attributes, methodologies and
other aspects are not
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mandatory or significant, and the mechanisms that implement the present
inventive
technology or its features may have different names, divisions and/or formats.
Furthermore,
as will be apparent to one of ordinary skill in the relevant art, the modules,
routines, features,
attributes, methodologies and other aspects of the present inventive
technology can be
implemented as software, hardware, firmware or any combination of the three.
Also,
wherever a component, an example of which is a module, of the present
inventive technology
is implemented as software, the component can be implemented as a standalone
program, as
part of a larger program, as a plurality of separate programs, as a statically
or dynamically
linked library, as a kernel loadable module, as a device driver, and/or in
every and any other
way known now or in the future to those of ordinary skill in the art of
computer
programming. Additionally, the present invention is in no way limited to
implementation in
any specific programming language, or for any specific operating system or
environment.
Accordingly, the disclosure of the present inventive technology is intended to
be illustrative.
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