U.S. patent application number 14/528495 was filed with the patent office on 2015-07-09 for methods and apparatus to correct misattributions of media impressions.
The applicant listed for this patent is The Nielsen Company (US), LLC. Invention is credited to Albert R. Perez, Antonia Toupet.
Application Number | 20150193816 14/528495 |
Document ID | / |
Family ID | 53493867 |
Filed Date | 2015-07-09 |
United States Patent
Application |
20150193816 |
Kind Code |
A1 |
Toupet; Antonia ; et
al. |
July 9, 2015 |
METHODS AND APPARATUS TO CORRECT MISATTRIBUTIONS OF MEDIA
IMPRESSIONS
Abstract
An example method involves determining an impressions adjustment
factor for a first demographic group based on first impressions
reported by a client device to a first internet domain and second
impressions reported by the client device to a second internet
domain. The first and second impressions correspond to same media
accessed on the client device. The example also involves
determining a misattribution-corrected impressions count for the
first demographic group based on the impressions adjustment factor
and based on a second impressions count determined at the second
internet domain for the first demographic group. The second
impressions count has an error based on some of the second
impressions being misattributed at the second internet domain to
the first demographic group when the some of the second impressions
correspond to a second demographic group.
Inventors: |
Toupet; Antonia; (Sunnyvale,
CA) ; Perez; Albert R.; (San Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Nielsen Company (US), LLC |
Schaumburg |
IL |
US |
|
|
Family ID: |
53493867 |
Appl. No.: |
14/528495 |
Filed: |
October 30, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61923959 |
Jan 6, 2014 |
|
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|
Current U.S.
Class: |
705/14.43 |
Current CPC
Class: |
G06Q 30/0244 20130101;
G06Q 30/0251 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A method, comprising: receiving, at a first internet domain, a
first request from a client device, the first request indicative of
access to media at the client device; sending, from the first
internet domain, a response to the client device, the response to
instruct the client device to send a second request to a second
internet domain, the second request to be indicative of the access
to the media at the client device; determining an impressions
adjustment factor for a first demographic group based on first
impressions reported by the client device to the first internet
domain and second impressions reported by the client device to the
second internet domain, the first and second impressions
corresponding to the same media accessed on the client device; and
determining a misattribution-corrected impressions count for the
first demographic group based on the impressions adjustment factor
and based on a second impressions count determined at the second
internet domain for the first demographic group, the second
impressions count having an error based on some of the second
impressions being misattributed at the second internet domain to
the first demographic group when the some of the second impressions
correspond to a second demographic group.
2. A method as defined in claim 1, wherein determining the
misattribution-corrected impression count comprises shifting an
impression from the second impressions count corresponding to the
first demographic group to a third impressions count corresponding
to the second demographic group based on the impressions adjustment
factor.
3. A method as defined in claim 1, wherein the first impressions
are reported by the client device to an audience measurement entity
at the first internet domain that does not provide the media to the
client device, and a user of the client device is a panel member of
the audience measurement entity.
4. A method as defined in claim 1, wherein the second impressions
are reported by the client device to a social network service at
the second internet domain to which a user of the client device is
subscribed.
5. A method as defined in claim 1, wherein determining the
impressions adjustment factor comprises: subtracting a first unique
audience size determined by an audience measurement entity at the
first internet domain based on the first impressions from a second
unique audience size determined by a database proprietor at the
second internet domain based on the second impressions to generate
a difference; and dividing the difference by a total impressions
count of the first impressions.
6. A method as defined in claim 1, wherein the impressions
adjustment factor is to correct impression quantities having
inaccuracies due to impressions incorrectly attributed to
demographic data not corresponding to persons corresponding to the
impressions.
7. A method as defined in claim 1, wherein the error in the second
impressions count is based on an entity at the second internet
domain incorrectly identifying a user of the client device as
belonging to the first demographic group when the user belongs to
the second demographic group, the misattribution-corrected
impressions count comprising fewer impressions than the second
impression count based on shifting an impression corresponding to
the user from the second impressions count corresponding to the
first demographic group to a third impressions count corresponding
to the second demographic group based on the impressions adjustment
factor.
8. A method as defined in claim 1, wherein the
misattribution-corrected impressions count is determined based on
the impressions adjustment factor without communicating with
individual online users about their online media access activities
and without using survey responses from the online users to
determine the error.
9. A method as defined in claim 8, further comprising conserving
network communication bandwidth by not communicating with
individual online users about their online media access activities
and by not requesting survey responses from the online users to
determine the error.
10. A method as defined in claim 8, further comprising conserving
computer processing resources by not communicating with individual
online users about their online media access activities and by not
requesting survey responses from the online users to determine the
error.
11. An apparatus, comprising: an impression collector to: receive,
at a first internet domain, a first request from a client device,
the first request indicative of access to media at the client
device; and send, from the first internet domain, a response to the
client device, the response to instruct the client device to send a
second request to a second internet domain, the second request to
be indicative of the access to the media at the client device; an
impressions adjustment factor determiner to determine an
impressions adjustment factor for a first demographic group based
on first impressions reported by the client device to the first
internet domain and second impressions reported by the client
device to the second internet domain, the first and second
impressions corresponding to the same media accessed on the client
device; and an impressions corrector to determine, via a processor,
a misattribution-corrected impressions count for the first
demographic group based on the impressions adjustment factor and
based on a second impressions count determined at the second
internet domain for the first demographic group, the second
impressions count having an error based on some of the second
impressions being misattributed at the second internet domain to
the first demographic group when the some of the second impressions
correspond to a second demographic group.
12. An apparatus as defined in claim 11, wherein the impressions
corrector is to determine the misattribution-corrected impressions
count by shifting an impression from the second impressions count
corresponding to the first demographic group to a third impressions
count corresponding to the second demographic group based on the
impressions adjustment factor.
13. An apparatus as defined in claim 11, wherein the first
impressions are reported by the client device to an audience
measurement entity at the first internet domain that does not
provide the media to the client device, and a user of the client
device is a panel member of the audience measurement entity.
14. An apparatus as defined in claim 11, wherein the second
impressions are reported by the client device to a social network
service at the second internet domain to which a user of the client
device is subscribed.
15. An apparatus as defined in claim 11, wherein the impressions
adjustment factor determiner is to: determine the impressions
adjustment factor by subtracting a first unique audience size
determined by an audience measurement entity at the first internet
domain based on the first impressions from a second unique audience
size determined by a database proprietor at the second internet
domain based on the second impressions to generate a difference;
and divide the difference by a total impressions count of the first
impressions.
16. An apparatus as defined in claim 11, wherein the impressions
adjustment factor is to correct impression quantities having
inaccuracies due to impressions incorrectly attributed to
demographic data not corresponding to persons corresponding to the
impressions.
17. An apparatus as defined in claim 11, wherein the error in the
second impressions count is based on an entity at the second
internet domain incorrectly identifying a user of the client device
as belonging to the first demographic group when the user belongs
to the second demographic group, the misattribution-corrected
impressions count comprising fewer impressions than the second
impression count based on shifting an impression corresponding to
the user from the second impressions count corresponding to the
first demographic group to a third impressions count corresponding
to the second demographic group based on the impressions adjustment
factor.
18. An apparatus as defined in claim 11, wherein the impressions
corrector determines the misattribution-corrected impressions based
on the impressions adjustment factor without communicating with
individual online users about their online media access activities
and without using survey responses from the online users to
determine the error.
19. An apparatus as defined in claim 18, wherein the impressions
corrector determining the misattribution-corrected impressions
conserves network communication bandwidth by not communicating with
individual online users about their online media access activities
and by not requesting survey responses from the online users to
determine the error.
20. An apparatus as defined in claim 18, wherein the impressions
corrector determining the misattribution-corrected impressions
conserves computer processing resources by not communicating with
individual online users about their online media access activities
and by not requesting survey responses from the online users to
determine the error.
21-30. (canceled)
31. A method, comprising: receiving, at a first internet domain, a
first request from a client device, the first request indicative of
access to media at the client device; sending, from the first
internet domain, a response to the client device, the response to
instruct the client device to send a second request to a second
internet domain, the second request to be indicative of the access
to the media at the client device; determining an audience
adjustment factor for a demographic group based on first
impressions reported by the client device to the first internet
domain and second impressions reported by the client device to the
second internet domain, the first and second impressions
corresponding to the same media accessed on the client device; and
determining a misattribution-corrected unique audience size for the
demographic group based on the audience adjustment factor and based
on a second unique audience size determined at the second internet
domain for the demographic group, the second unique audience size
having an error based on third impressions misattributed at the
second internet domain to the demographic group when the third
impressions correspond to another demographic group.
32. A method as defined in claim 31, wherein determining the
audience adjustment factor comprises dividing a third unique
audience size corresponding to the first impressions by a fourth
unique audience size corresponding to the second impressions.
33. A method as defined in claim 31, wherein determining the
misattribution-corrected unique audience size for the demographic
group comprises dividing the second unique audience size by the
audience adjustment factor.
34. A method as defined in claim 31, wherein the first impressions
are reported by the client device to an audience measurement entity
at the first internet domain that does not provide the media to the
client device, and a user of the client device is a panel member of
the audience measurement entity.
35. A method as defined in claim 31, wherein the second impressions
are reported by the client device to a social network service at
the second internet domain to which a user of the client device is
subscribed.
36. A method as defined in claim 31, wherein the audience
adjustment factor is to correct unique audience size values having
inaccuracies due to impressions incorrectly attributed to
demographic data not corresponding to persons corresponding to the
impressions.
37. A method as defined in claim 31, wherein the error in the
second unique audience size is based on an entity at the second
internet domain incorrectly identifying a user of the client device
as belonging to the demographic group when the user belongs to the
another demographic group, the misattribution-corrected unique
audience size being different than the second unique audience size
based on dividing the second unique audience size by the audience
adjustment factor.
38. A method as defined in claim 31, wherein the
misattribution-corrected unique audience size is determined based
on the audience adjustment factor without communicating with
individual online users about their online media access habits and
without using survey responses from the online users to determine
the error.
39. A method as defined in claim 38, further comprising conserving
network communication bandwidth by not communicating with
individual online users about their online media access habits and
by not requesting survey responses from the online users to
determine the error.
40. A method as defined in claim 38, further comprising conserving
computer processing resources by not communicating with individual
online users about their online media access habits and by not
requesting survey responses from the online users to determine the
error.
41-60. (canceled)
Description
RELATED APPLICATION
[0001] This application claims priority to U.S. Provisional Patent
Application Ser. No. 61/923,959 filed on Jan. 6, 2014, which is
hereby incorporated herein by reference in its entirety.
FIELD OF THE DISCLOSURE
[0002] The present disclosure relates generally to monitoring media
and, more particularly, to methods and apparatus to correct
misattributions of media impressions.
BACKGROUND
[0003] Traditionally, audience measurement entities determine
audience engagement levels for media based on registered panel
members. That is, an audience measurement entity enrolls people who
consent to being monitored into a panel. The audience measurement
entity then monitors those panel members to determine media (e.g.,
television programs or radio programs, movies, DVDs,
advertisements, streaming media, websites, etc.) exposed to those
panel members. In this manner, the audience measurement entity can
determine exposure metrics for different media based on the
collected media measurement data.
[0004] Techniques for monitoring user access to Internet resources
such as web pages, advertisements and/or other Internet-accessible
media have evolved significantly over the years. Some known systems
perform such monitoring primarily through server logs. In
particular, entities serving media on the Internet can use known
techniques to log the number of requests received for their media
(e.g., content and/or advertisements) at their server.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 illustrates an example client device that reports
audience impressions for media to impression collection entities to
facilitate identifying total impressions and sizes of unique
audiences exposed to different media.
[0006] FIG. 2 is an example communication flow diagram of an
example manner in which an audience measurement entity (AME) and a
database proprietor (DP) can collect impressions and demographic
information based on a client device reporting impressions to the
AME and the DP.
[0007] FIG. 3 illustrates example impressions collected by the AME
and example impressions collected by the DP with a misattribution
error.
[0008] FIG. 4 illustrates example audience adjustment (AA) factors
for unique audience sizes of different demographic groups
determined based on the example impressions of FIG. 3.
[0009] FIG. 5 illustrates example impression adjustment (IA)
factors for total impressions of different demographic groups
determined based on the example impressions of FIG. 3.
[0010] FIG. 6 illustrates example misattribution-corrected unique
audience values and example misattribution-corrected impression
counts determined based on the example AA factors of FIG. 4 and the
example IA factors of FIG. 5 for different demographic groups.
[0011] FIG. 7 illustrates example misattribution-corrected unique
audience values and example misattribution-corrected impression
counts determined based on the example IA factors of FIG. 5 and
example impression frequencies for different demographic
groups.
[0012] FIG. 8 is a flow diagram representative of example machine
readable instructions that may be executed to implement the
misattribution corrector of FIG. 2 to determine
misattribution-corrected unique audience sizes and
misattribution-corrected impression counts.
[0013] FIG. 9 illustrates an example processor system structured to
execute the example instructions of FIG. 8 to implement the example
AME of FIGS. 1 and/or 2.
DETAILED DESCRIPTION
[0014] Techniques for monitoring user access to Internet-accessible
media such as web pages, advertisements, content and/or other media
have evolved significantly over the years. At one point in the
past, such monitoring was done primarily through server logs. In
particular, entities serving media on the Internet would log the
number of requests received for their media at their server. Basing
Internet usage research on server logs is problematic for several
reasons. For example, server logs can be tampered with either
directly or via zombie programs which repeatedly request media from
the server to increase the server log counts. Secondly, media is
sometimes retrieved once, cached locally and then repeatedly viewed
from the local cache without involving the server in the repeat
viewings. Server logs cannot track these repeat views of cached
media. Thus, server logs are susceptible to both over-counting and
under-counting errors.
[0015] The inventions disclosed in Blumenau, U.S. Pat. No.
6,108,637, fundamentally changed the way Internet monitoring is
performed and overcame the limitations of the server side log
monitoring techniques described above. For example, Blumenau
disclosed a technique wherein Internet media to be tracked is
tagged with beacon instructions. In particular, monitoring
instructions are associated with the hypertext markup language
(HTML) of the media to be tracked. When a client requests the
media, both the media and the beacon instructions are downloaded to
the client. The beacon instructions are, thus, executed whenever
the media is accessed, be it from a server or from a cache.
[0016] The beacon instructions cause monitoring data reflecting
information about the access to the media to be sent from the
client that downloaded the media to a monitoring entity. Typically,
the monitoring entity is an audience measurement entity (AME) that
did not provide the media to the client and who is a trusted (e.g.,
neutral) third party for providing accurate usage statistics (e.g.,
The Nielsen Company, LLC). Advantageously, because the beaconing
instructions are associated with the media and executed by the
client browser whenever the media is accessed, the monitoring
information is provided to the AME irrespective of whether the
client is a panelist of the AME.
[0017] Audience measurement entities and/or other businesses often
desire to link demographics to the monitoring information. To
address this issue, the AME establishes a panel of users who have
agreed to provide their demographic information and to have their
Internet browsing activities monitored. When an individual joins
the panel, they provide detailed information concerning their
identity and demographics (e.g., gender, age, ethnicity, income,
home location, occupation, etc.) to the AME. The audience
measurement entity sets a cookie on the panelist computer that
enables the audience measurement entity to identify the panelist
whenever the panelist accesses tagged media and, thus, sends
monitoring information to the audience measurement entity.
[0018] Since most of the clients providing monitoring information
from the tagged media are not panelists and, thus, are unknown to
the audience measurement entity, it is necessary to use statistical
methods to impute demographic information based on the data
collected for panelists to the larger population of users providing
data for the tagged media. However, panel sizes of audience
measurement entities remain small compared to the general
population of users. Thus, a problem is presented as to how to
increase panel sizes while ensuring the demographics data of the
panel is accurate.
[0019] There are many database proprietors operating on the
Internet. These database proprietors provide services to large
numbers of subscribers. In exchange for the provision of the
service, the subscribers register with the proprietor. As part of
this registration, the subscribers provide detailed demographic
information. Examples of such database proprietors include social
network providers, email providers, etc. such as Facebook, Myspace,
Twitter, Yahoo!, Google, etc. These database proprietors set
cookies or other device/user identifiers on the client devices of
their subscribers to enable the database proprietor to recognize
the user when they visit their website.
[0020] The protocols of the Internet make cookies inaccessible
outside of the domain (e.g., Internet domain, domain name, etc.) on
which they were set. Thus, a cookie set, for example, in the
amazon.com domain is accessible to servers in the amazon.com
domain, but not to servers outside that domain. Therefore, although
an audience measurement entity might find it advantageous to access
the cookies set by the database proprietors, they are unable to do
so.
[0021] The inventions disclosed in Mainak et al., U.S. Pat. No.
8,370,489, which is incorporated by reference herein in its
entirety, enable an audience measurement entity to leverage the
existing databases of database proprietors to collect more
extensive Internet usage and demographic data by extending the
beaconing process to encompass partnered database proprietors and
by using such partners as interim data collectors. The inventions
disclosed in Mainak et al. accomplish this task by structuring the
AME to respond to beacon requests from clients (who may not be a
member of an audience member panel and, thus, may be unknown to the
audience member entity) and redirect the client from the audience
measurement entity to a database proprietor such as a social
network site partnered with the audience member entity. The
redirection initiates a communication session between the client
accessing the tagged media and the database proprietor. The
database proprietor (e.g., Facebook) can access any cookie it has
set on the client to thereby identify the client based on the
internal records of the database proprietor. In the event the
client corresponds to a subscriber of the database proprietor, the
database proprietor logs an impression in association with the
demographics data associated with the client and subsequently
forwards logged impressions to the audience measurement company. In
the event the client does not correspond to a subscriber of the
database proprietor, the database proprietor may redirect the
client to the audience measurement entity and/or another database
proprietor. The audience measurement entity may respond to the
redirection from the first database proprietor by redirecting the
client to a second, different database proprietor that is partnered
with the audience measurement entity. That second database
proprietor may then attempt to identify the client as explained
above. This process of redirecting the client from database
proprietor to database proprietor can be performed any number of
times until the client is identified and the media exposure logged,
or until all database proprietor partners have been contacted
without a successful identification of the client. The redirections
all occur automatically so the user of the client is not involved
in the various communication sessions and may not even know they
are occurring.
[0022] Periodically or aperiodically the partnered database
proprietors provide their logs and demographic information to the
audience measurement entity which then compiles the collected data
into statistical reports accurately identifying the demographics of
persons accessing the tagged media. Because the identification of
clients is done with reference to enormous databases of users far
beyond the quantity of persons present in a conventional audience
measurement panel, the data developed from this process is
extremely accurate, reliable and detailed.
[0023] Significantly, because the audience measurement entity
remains the first leg of the data collection process (e.g.,
receives the request generated by the beacon instructions from the
client), the audience measurement entity is able to obscure the
source of the media access being logged as well as the identity of
the media itself from the database proprietors (thereby protecting
the privacy of the media sources), without compromising the ability
of the database proprietors to log impressions for their
subscribers. Further, when cookies are used as device/user
identifiers, the Internet security cookie protocols are complied
with because the only servers that access a given cookie are
associated with the Internet domain (e.g., Facebook.com) that set
that cookie.
[0024] Examples disclosed in Mainak et al. (U.S. Pat. No.
8,370,489) can be used to determine any type of media impressions
or exposures (e.g., content impressions, advertisement impressions,
content exposure, and/or advertisement exposure) using demographic
information, which is distributed across different databases (e.g.,
different website owners, service providers, etc.) on the Internet.
Not only do such disclosed examples enable more accurate
correlation of Internet advertisement exposure to demographics, but
they also effectively extend panel sizes and compositions beyond
persons participating in the panel of an audience measurement
entity and/or a ratings entity to persons registered in other
Internet databases such as the databases of social media sites such
as Facebook, Twitter, Google, etc. Such extension effectively
leverages the media tagging capabilities of the ratings entity and
the use of databases of non-ratings entities such as social media
and other websites to create an enormous, demographically accurate
panel that results in accurate, reliable measurements of exposures
to Internet media such as advertising and/or programming.
[0025] In illustrated examples disclosed herein, media exposure is
measured in terms of online Gross Rating Points. A Gross Rating
Point (GRP) is a unit of measurement of audience size that has
traditionally been used in the television ratings context. It is
used to measure exposure to one or more media (e.g., programs,
advertisements, etc.) without regard to multiple exposures of the
same media to individuals. In terms of television (TV)
advertisements, one GRP is equal to 1% of TV households. While GRPs
have traditionally been used as a measure of television viewership,
examples disclosed herein may be used in connection with generating
online GRPs for online media to provide a standardized metric that
can be used across the Internet to accurately reflect online
advertisement exposure. Such standardized online GRP measurements
can provide greater certainty to advertisers that their online
advertisement money is well spent. It can also facilitate
cross-medium comparisons such as viewership of TV advertisements
and online advertisements, exposure to radio advertisements and
online media, etc. Because examples disclosed herein may be used to
correct impressions that associate exposure measurements with
corresponding demographics of users, the information processed
using examples disclosed herein may also be used by advertisers to
more accurately identify markets reached by their advertisements
and/or to target particular markets with future advertisements.
[0026] Traditionally, audience measurement entities (also referred
to herein as "ratings entities") determine demographic reach for
advertising and media programming based on registered panel
members. That is, an audience measurement entity enrolls people
that consent to being monitored into a panel. During enrollment,
the audience measurement entity receives demographic information
from the enrolling people so that subsequent correlations may be
made between advertisement/media exposures to those panelists and
different demographic markets. Unlike traditional techniques in
which audience measurement entities rely solely on their own panel
member data to collect demographics-based audience measurements,
example methods, apparatus, and/or articles of manufacture
disclosed herein enable an audience measurement entity to share
demographic information with other entities that operate based on
user registration models. As used herein, a user registration model
is a model in which users subscribe to services of those entities
by creating an account and providing demographic-related
information about themselves. Sharing of demographic information
associated with registered users of database proprietors enables an
audience measurement entity to extend or supplement their panel
data with substantially reliable demographics information from
external sources (e.g., database proprietors), thus extending the
coverage, accuracy, and/or completeness of the AMA's
demographics-based audience measurements. Such access also enables
the audience measurement entity to monitor persons who would not
otherwise have joined an audience measurement panel. Any entity
having a network-accessible database identifying demographics of a
set of individuals may cooperate with the audience measurement
entity. Such entities may be referred to as "database proprietors"
and include entities such as Facebook, Google, Yahoo!, MSN,
Twitter, Apple iTunes, Experian, etc.
[0027] To increase the likelihood that measured viewership is
accurately attributed to the correct demographics, examples
disclosed herein use demographic information located in the
audience measurement entity's records as well as demographic
information located at one or more database proprietors that
maintain records or profiles of users having accounts therewith. In
this manner, examples disclosed herein may be used to supplement
demographic information maintained by a ratings entity (e.g., an
AME such as The Nielsen Company of Schaumburg, Ill., United States
of America, that collects media exposure measurements and/or
demographics) with demographic information from one or more
different database proprietors.
[0028] The use of demographic information from disparate data
sources (e.g., high-quality demographic information from the panels
of an audience measurement company and/or registered user data of
web service providers) results in improved reporting effectiveness
of metrics for both online and offline advertising campaigns.
Example techniques disclosed herein use online registration data to
identify demographics of users and use server impression counts,
tagging (also referred to herein as beaconing), and/or other
techniques to track quantities of impressions attributable to those
users. Online web service providers such as social networking sites
(e.g., Facebook) and multi-service providers (e.g., Yahoo!, Google,
Experian, etc.) (collectively and individually referred to herein
as database proprietors) maintain detailed demographic information
(e.g., age, gender, geographic location, race, income level,
education level, religion, etc.) collected via user registration
processes. As used herein, an impression is defined to be an event
in which a home or individual is exposed to corresponding media
(e.g., content and/or an advertisement). Thus, an impression
represents a home or an individual having been exposed to media
(e.g., an advertisement, content, a group of advertisements, and/or
a collection of content). In Internet advertising, a quantity of
impressions or impression count is the total number of times media
(e.g., content, an advertisement or advertisement campaign) has
been accessed by a web population (e.g., the number of times the
media is accessed). As used herein, a demographic impression is
defined to be an impression that is associated with a
characteristic (e.g., a demographic characteristic) of the person
exposed to the media.
[0029] Although such techniques for collecting media impressions
are based on highly accurate demographic information, in some
instances collected impressions may be misattributed to the wrong
person and, thus, associated with incorrect demographic
information. For example, in a household having multiple people
that use the same client device (e.g., the same computer, tablet,
smart internet appliance, etc.), collected impressions from that
client device may be misattributed to a member of the household
that is not the current user of the client device. That is, when an
online user visits a website and is exposed to an advertisement on
that site that has been tagged with beacon instructions, there is a
redirect to a server of a database proprietor (e.g., Facebook,
Yahoo, Google, etc.). The database proprietor then looks into a
most recent cookie set by the database proprietor in the web
browser of that client device. The database proprietor then
attributes the impression to the user account corresponding to the
cookie value. For example, the cookie value is one that was
previously set in the client device by the database proprietor to
correspond to a particular registered user account of the person
that used the client device to most recently log into the website
of that database proprietor. After collecting and attributing the
impression to the user account associated with the retrieved cookie
value, the database proprietor aggregates the total collected
impressions and the size of the unique audience based on
demographics associated with user accounts for all logged
impressions. When this occurs over time and across many households,
a number of collected impressions are misattributed to the wrong
demographic information because some people use client devices
after another person (e.g., another household member) has logged
into a user account registered with the database proprietor without
logging themselves (e.g., the current audience member) in. As such,
a cookie corresponding to the previous person is still accessed
from the client device while the subsequent user of the client
device (e.g., a user that did not log into a corresponding user
account registered with the database proprietor) accesses media on
the client device which causes impressions to be misattributed to
the previous person associated with the accessed cookie.
[0030] As used herein, a unique audience measure is based on
audience members distinguishable from one another. That is, a
particular audience member exposed to particular media is measured
as a single unique audience member regardless of how many times
that audience member is exposed to that particular media. If that
particular audience member is exposed multiple times to the same
media, the multiple exposures for the particular audience member to
the same media is counted as only a single unique audience member.
In this manner, impression performance for particular media is not
disproportionately represented when a small subset of one or more
audience members is exposed to the same media an excessively large
number of times while a larger number of audience members is
exposed fewer times or not at all to that same media. By tracking
exposures to unique audience members, a unique audience measure may
be used to determine a reach measure to identify how many unique
audience members are reached by media. In some examples, increasing
unique audience and, thus, reach, is useful for advertisers wishing
to reach a larger audience base.
[0031] As used herein, total impressions refers to the total number
of collected impressions for particular media regardless of whether
multiple ones of those impressions are attributable to the same
audience members. That is, multiple impressions accounted for in
the total impressions may be attributable to a same audience
member.
[0032] Misattribution is a measurement error that typically arises
when impressions are collected from a same client device that is
shared by multiple people in that a media impression caused by one
person that is currently using the client device is incorrectly
attributed (i.e., misattributed) to another person that previously
used the same client device. Sharing of a client device can occur
between two individuals who: (1) live in the same household, and/or
(2) have access to the same client device. Misattribution occurs
when, for a particular media exposure on a client device, a
logged-in-user of a database proprietor service (e.g., Facebook) is
not the same as the current user of the client device that is being
exposed to the media. For example, if person A logs into the
database proprietor's website in the morning on a client device,
but person B uses the same client device in the afternoon without
logging in (e.g., without user A logging out) and is exposed to
media tagged with beacon instructions, the database proprietor
attributes the impression to person A since he/she was the last
person to log into the database proprietor's site from that client
device, while actually it was person B who was using the client
device when the media was presented.
[0033] Examples disclosed herein can be used to correct
misattribution in collected impressions by applying a
misattribution correction to impression data obtained from a
database proprietor (e.g., Facebook, Yahoo, Google, etc.) after a
profile correction (e.g., a Decision Tree (DT) model) has been
applied to the impression data. Examples disclosed herein may be
implemented by an audience measurement entity (e.g., any entity
interested in measuring or tracking audience exposures to
advertisements, content, and/or any other media) in cooperation
with any number of database proprietors such as online web services
providers. Such database proprietors/online web services providers
may be social network sites (e.g., Facebook, Twitter, MySpace,
etc.), multi-service sites (e.g., Yahoo!, Google, Axiom, Catalina,
etc.), online retailer sites (e.g., Amazon.com, Buy.com, etc.),
credit reporting sites (e.g., Experian), and/or any other web
service(s) site that maintains user registration records.
[0034] Example methods and/or articles of manufacture comprising
computer readable instructions disclosed herein may be used to
receive, at a first internet domain, a first request from a client
device, the first request indicative of access to media at the
client device. In such examples, a response is sent from the first
internet domain to the client device. In such examples, the
response instructs the client device to send a second request to a
second internet domain. In such examples, the second request is to
be indicative of the access to the media at the client device. In
such examples, an impressions adjustment factor is determined for a
first demographic group based on first impressions reported by the
client device to the first internet domain and second impressions
reported by the client device to the second internet domain. In
such example, the first and second impressions correspond to the
same media accessed on the client device. In such examples, a
misattribution-corrected impressions count is determined for the
first demographic group based on the impressions adjustment factor
and based on a second impressions count determined at the second
internet domain for the first demographic group. In such examples,
the second impressions count includes an error based on some of the
second impressions being misattributed at the second internet
domain to the first demographic group when the some of the second
impressions correspond to a second demographic group.
[0035] In some examples, determining the misattribution-corrected
impression count involves shifting an impression from the second
impressions count corresponding to the first demographic group to a
third impressions count corresponding to the second demographic
group based on the impressions adjustment factor. In some examples,
the first impressions are reported by the client device to an
audience measurement entity at the first internet domain that does
not provide the media to the client device, and a user of the
client device is a panel member of the audience measurement entity.
In some examples, the second impressions are reported by the client
device to a social network service at the second internet domain to
which a user of the client device is subscribed. In some examples,
the impressions adjustment factor is to correct impression
quantities having inaccuracies due to impressions incorrectly
attributed to demographic data not corresponding to persons
corresponding to the impressions.
[0036] In some examples, determining the impressions adjustment
factor involves subtracting a first unique audience size determined
by an audience measurement entity at the first internet domain
based on the first impressions from a second unique audience size
determined by a database proprietor at the second internet domain
based on the second impressions to generate a difference. In such
examples, the difference is divided by a total impressions count of
the first impressions to determine the impressions adjustment
factor.
[0037] In some examples, the error in the second impressions count
is based on an entity at the second internet domain incorrectly
identifying a user of the client device as belonging to the first
demographic group when the user belongs to the second demographic
group. In such examples, the misattribution-corrected impressions
count comprises fewer impressions than the second impression count
based on shifting an impression corresponding to the user from the
second impressions count corresponding to the first demographic
group to a third impressions count corresponding to the second
demographic group based on the impressions adjustment factor.
[0038] In some examples, the misattribution-corrected impressions
count is determined based on the impressions adjustment factor
without communicating with individual online users about their
online media access activities and without using survey responses
from the online users to determine the error. In some examples,
network communication bandwidth is conserved by not communicating
with individual online users about their online media access
activities and by not requesting survey responses from the online
users to determine the error. In some examples, computer processing
resources are conserved by not communicating with individual online
users about their online media access activities and by not
requesting survey responses from the online users to determine the
error.
[0039] Example disclosed apparatus include an example impression
collector to receive, at a first internet domain, a first request
from a client device, the first request indicative of access to
media at the client device. The example impression collector is
also to send, from the first internet domain, a response to the
client device, the response to instruct the client device to send a
second request to a second internet domain, the second request to
be indicative of the access to the media at the client device. Such
example apparatus also include an impressions adjustment factor
determiner to determine an impressions adjustment factor for a
first demographic group based on first impressions reported by the
client device to the first internet domain and second impressions
reported by the client device to the second internet domain. In
such examples, the first and second impressions correspond to the
same media accessed on the client device. Such example apparatus
also includes an impressions corrector to determine a
misattribution-corrected impressions count for the first
demographic group based on the impressions adjustment factor and
based on a second impressions count determined at the second
internet domain for the first demographic group. In such examples,
the second impressions count includes an error based on some of the
second impressions being misattributed at the second internet
domain to the first demographic group when the some of the second
impressions correspond to a second demographic group.
[0040] In some examples, the impressions corrector is to determine
the misattribution-corrected impressions count by shifting an
impression from the second impressions count corresponding to the
first demographic group to a third impressions count corresponding
to the second demographic group based on the impressions adjustment
factor. In some examples, the first impressions are reported by the
client device to an audience measurement entity at the first
internet domain that does not provide the media to the client
device. In some examples, a user of the client device is a panel
member of the audience measurement entity. In some examples, the
second impressions are reported by the client device to a social
network service at the second internet domain to which a user of
the client device is subscribed.
[0041] In some examples, the impressions adjustment factor
determiner is to determine the impressions adjustment factor by
subtracting a first unique audience size determined by an audience
measurement entity at the first internet domain based on the first
impressions from a second unique audience size determined by a
database proprietor at the second internet domain based on the
second impressions to generate a difference. In such examples, the
difference is divided by a total impressions count of the first
impressions.
[0042] In some examples, the impressions adjustment factor is to
correct impression quantities having inaccuracies due to
impressions incorrectly attributed to demographic data not
corresponding to persons corresponding to the impressions. In some
examples, the error in the second impressions count is based on an
entity at the second internet domain incorrectly identifying a user
of the client device as belonging to the first demographic group
when the user belongs to the second demographic group. In some
examples, the misattribution-corrected impressions count include
fewer impressions than the second impression count based on
shifting an impression corresponding to the user from the second
impressions count corresponding to the first demographic group to a
third impressions count corresponding to the second demographic
group based on the impressions adjustment factor.
[0043] In some examples, the impressions corrector determines the
misattribution-corrected impressions based on the impressions
adjustment factor without communicating with individual online
users about their online media access activities and without using
survey responses from the online users to determine the error. In
some examples, by determining the misattribution-corrected
impressions using the impressions corrector, network communication
bandwidth is conserved by not communicating with individual online
users about their online media access activities and by not
requesting survey responses from the online users to determine the
error. In some examples, by determining the
misattribution-corrected impressions using the impressions
corrector, computer processing resources are conserved by not
communicating with individual online users about their online media
access activities and by not requesting survey responses from the
online users to determine the error.
[0044] Example methods and/or articles of manufacture comprising
computer readable instructions disclosed herein may be used to
receive, at a first internet domain, a first request from a client
device, the first request indicative of access to media at the
client device. In such examples, a response is sent from the first
internet domain to the client device. In such examples, the
response is to instruct the client device to send a second request
to a second internet domain. In such examples, the second request
is to be indicative of the access to the media at the client
device. In such examples, an audience adjustment factor is
determined for a demographic group based on first impressions
reported by the client device to the first internet domain and
second impressions reported by the client device to the second
internet domain. In such examples, the first and second impressions
correspond to the same media accessed on the client device. In such
examples, a misattribution-corrected unique audience size is
determined for the demographic group based on the audience
adjustment factor and based on a second unique audience size
determined at the second internet domain for the demographic group.
In such examples, the second unique audience size includes an error
based on third impressions misattributed at the second internet
domain to the demographic group when the third impressions
correspond to another demographic group.
[0045] In some examples, determining the audience adjustment factor
involves dividing a third unique audience size corresponding to the
first impressions by a fourth unique audience size corresponding to
the second impressions. In some examples, determining the
misattribution-corrected unique audience size for the demographic
group involves dividing the second unique audience size by the
audience adjustment factor. In some examples, the first impressions
are reported by the client device to an audience measurement entity
at the first internet domain that does not provide the media to the
client device, and a user of the client device is a panel member of
the audience measurement entity. In some examples, the second
impressions are reported by the client device to a social network
service at the second internet domain to which a user of the client
device is subscribed. In some examples, the audience adjustment
factor is to correct unique audience size values having
inaccuracies due to impressions incorrectly attributed to
demographic data not corresponding to persons corresponding to the
impressions.
[0046] In some examples, the error in the second unique audience
size is based on an entity at the second internet domain
incorrectly identifying a user of the client device as belonging to
the demographic group when the user belongs to the another
demographic group. In some such examples, the
misattribution-corrected unique audience size is different than the
second unique audience size based on dividing the second unique
audience size by the audience adjustment factor.
[0047] In some examples, the misattribution-corrected unique
audience size is determined based on the audience adjustment factor
without communicating with individual online users about their
online media access habits and without using survey responses from
the online users to determine the error. In some examples, network
communication bandwidth is conserved by not communicating with
individual online users about their online media access habits and
by not requesting survey responses from the online users to
determine the error. In some examples, computer processing
resources are conserved by not communicating with individual online
users about their online media access habits and by not requesting
survey responses from the online users to determine the error.
[0048] Example disclosed apparatus include an example impression
collector to receive, at a first internet domain, a first request
from a client device. In such examples, the first request is
indicative of access to media at the client device. The example
impression collector is also to send, from the first internet
domain, a response to the client device. In such examples, the
response is to instruct the client device to send a second request
to a second internet domain. In such examples, the second request
is to be indicative of the access to the media at the client
device. Such example apparatus also include an audience adjustment
factor determiner to determine an audience adjustment factor for a
demographic group based on first impressions reported by the client
device to the first internet domain and second impressions reported
by the client device to the second internet domain. In such
examples, the first and second impressions correspond to the same
media accessed on the client device. Such example apparatus also
include a unique audience corrector to determine a
misattribution-corrected unique audience size for the demographic
group based on the audience adjustment factor and based on a second
unique audience size determined at the second internet domain for
the demographic group. In such examples, the second unique audience
size includes an error based on third impressions misattributed at
the second internet domain to the demographic group when the third
impressions correspond to another demographic group.
[0049] In some examples, the audience adjustment factor determiner
is to determine the audience adjustment factor by dividing a third
unique audience size corresponding to the first impressions by a
fourth unique audience size corresponding to the second
impressions. In some examples, the unique audience corrector is to
determine the misattribution-corrected unique audience size for the
demographic group by dividing the second unique audience size by
the audience adjustment factor. In some examples, the first
impressions are reported by the client device to an audience
measurement entity at the first internet domain that does not
provide the media to the client device, and a user of the client
device is a panel member of the audience measurement entity. In
some examples, the second impressions are reported by the client
device to a social network service at the second internet domain to
which a user of the client device is subscribed. In some examples,
the audience adjustment factor is to correct unique audience size
values having inaccuracies due to impressions incorrectly
attributed to demographic data not corresponding to persons
corresponding to the impressions.
[0050] In some examples, the error in the second unique audience
size is based on an entity at the second internet domain
incorrectly identifying a user of the client device as belonging to
the demographic group when the user belongs to the another
demographic group. In some such examples, the
misattribution-corrected unique audience size comprising dividing
the second unique audience size by the audience adjustment
factor.
[0051] In some examples, the unique audience corrector is to
determine the misattribution-corrected unique audience size based
on the audience adjustment factor without communicating with
individual online users about their online media access habits and
without using survey responses from the online users to determine
the error. In some examples, by determining the
misattribution-corrected unique audience size, the unique audience
corrector conserves network communication bandwidth by not
communicating with individual online users about their online media
access habits and by not requesting survey responses from the
online users to determine the error. In some examples, by
determining the misattribution-corrected unique audience size, the
unique audience corrector conserves computer processing resources
by not communicating with individual online users about their
online media access habits and by not requesting survey responses
from the online users to determine the error.
[0052] FIG. 1 illustrates an example client device 102 that reports
audience impressions for media to impression collection entities
104 to facilitate identifying total impressions and sizes of unique
audiences exposed to different media. As used herein, the term
impression collection entity refers to any entity that collects
impression data. The client device 102 of the illustrated example
may be any device capable of accessing media over a network. For
example, the client device 102 may be a computer, a tablet, a
mobile device, a smart television, or any other Internet-capable
device or appliance. Examples disclosed herein may be used to
collect impression information for any type of media including
content and/or advertisements. Media may include advertising and/or
content such as web pages, streaming video, streaming audio,
movies, and/or any other type of content and/or advertisement
deliver via satellite, broadcast, cable television, radio frequency
(RF) terrestrial broadcast, Internet (e.g., internet protocol
television (IPTV)), television broadcasts, radio broadcasts and/or
any other vehicle for delivering media. In some examples, media
includes user-generated media that is, for example, uploaded to
media upload sites such as YouTube and subsequently downloaded
and/or streamed by one or more client devices for playback. Media
may also include advertisements. Advertisements are typically
distributed with content (e.g., programming). Traditionally,
content is provided at little or no cost to the audience because it
is subsidized by advertisers that pay to have their advertisements
distributed with the content. As used herein, "media" refers
collectively and/or individually to content and/or advertisement(s)
of any type(s).
[0053] In the illustrated example, the client device 102 employs a
web browser and/or applications (e.g., apps) to access media, some
of which include instructions that cause the client device 102 to
report media monitoring information to one or more of the
impression collection entities 104. That is, when the client device
102 of the illustrated example accesses media, a web browser and/or
application of the client device 102 executes instructions in the
media to send a beacon request or impression request 108 to one or
more of the impression collection entities 104 via, for example,
the Internet 110. The beacon requests 108 of the illustrated
example include information about accesses to media at the client
device 102. Such beacon requests 108 allow monitoring entities,
such as the impression collection entities 104, to collect
impressions for different media accessed via the client device 102.
In this manner, the impression collection entities 104 can generate
large impression quantities for different media (e.g., different
content and/or advertisement campaigns).
[0054] The impression collection entities 104 of the illustrated
example include an example audience measurement entity (AME) 114
and an example database proprietor (DP) 116. In the illustrated
example, the AME 114 does not provide the media to the client
device 102 and is a trusted (e.g., neutral) third party (e.g., The
Nielsen Company, LLC) for providing accurate media access
statistics. In the illustrated example, the database proprietor 116
is one of many database proprietors that operates on the Internet
to provide services to large numbers of subscribers. Such services
may be email services, social networking services, news media
services, cloud storage services, streaming music services,
streaming video services, online retail shopping services, credit
monitoring services, etc. Example database proprietors include
social network sites (e.g., Facebook, Twitter, MySpace, etc.),
multi-service sites (e.g., Yahoo!, Google, etc.), online retailer
sites (e.g., Amazon.com, Buy.com, etc.), credit reporting services
(e.g., Experian) and/or any other web service(s) site that
maintains user registration records. In examples disclosed herein,
the database proprietor 116 maintains user account records
corresponding to users registered for Internet-based services
provided by the database proprietors. That is, in exchange for the
provision of services, subscribers register with the database
proprietor 116. As part of this registration, the subscribers
provide detailed demographic information to the database proprietor
116. Demographic information may include, for example, gender, age,
ethnicity, income, home location, education level, occupation, etc.
In the illustrated example, the database proprietor 116 sets a
device/user identifier (e.g., an identifier described below in
connection with FIG. 2) on a subscriber's client device 102 that
enables the database proprietor 116 to identify the subscriber.
[0055] In the illustrated example, when the database proprietor 116
receives a beacon/impression request 108 from the client device
102, the database proprietor 116 requests the client device 102 to
provide the device/user identifier that the database proprietor 116
had previously set for the client device 102. The database
proprietor 116 uses the device/user identifier corresponding to the
client device 102 to identify demographic information in its user
account records corresponding to the subscriber of the client
device 102. In this manner, the database proprietor 116 can
generate demographic impressions by associating demographic
information with an audience impression for the media accessed at
the client device 102. As explained above, a demographic impression
is an impression that is associated with a characteristic (e.g., a
demographic characteristic) of the person exposed to the media.
[0056] In the illustrated example, the AME 114 establishes an AME
panel of users who have agreed to provide their demographic
information and to have their Internet browsing activities
monitored. When an individual joins the AME panel, the person
provides detailed information concerning the person's identity and
demographics (e.g., gender, age, ethnicity, income, home location,
occupation, etc.) to the AME 114. The AME 114 sets a device/user
identifier (e.g., an identifier described below in connection with
FIG. 2) on the person's client device 102 that enables the AME 114
to identify the panelist. An AME panel may be a cross-platform home
television/computer (TVPC) panel built and maintained by the AME
114. In other examples, the AME panel may be a computer panel or
internet-device panel without corresponding to a television
audience panel. In yet other examples, the AME panel may be a
cross-platform radio/computer panel and/or a panel formed for other
mediums.
[0057] In the illustrated example, when the AME 114 receives a
beacon request 108 from the client device 102, the AME 114 requests
the client device 102 to provide the AME 114 with the device/user
identifier that the AME 114 previously set in the client device
102. The AME 114 uses the device/user identifier corresponding to
the client device 102 to identify demographic information in its
user AME panelist records corresponding to the panelist of the
client device 102. In this manner, the AME 114 can generate
demographic impressions by associating demographic information with
an audience impression for the media accessed at the client device
102.
[0058] In the illustrated example, the client device 102 is used in
an example household 120 in which household members 122 and 124
(identified as subscriber A 122 and subscriber B 124) are
subscribers of an internet-based service offered by the database
proprietor 116. In the illustrated example, subscriber A 122 and
subscriber B 124 share the client device 102 to access the
internet-based service of the database proprietor 116 and to access
other media via the Internet 110. In the illustrated example, when
the database proprietor 116 receives a beacon/impression request
108 for media accessed via the client device 102, the database
proprietor 116 logs an impression for the media access as
corresponding to the subscriber 122, 124 of the household 120 that
most recently logged into the database proprietor 116.
Misattributions of impressions logged by the database proprietor
116 are likely to occur in circumstances similar to the example
household 120 of FIG. 1 in which multiple people in a household
share a client device. For example, if the subscriber A 122 logs
into a service of the database proprietor 116 on the client device
102, and the subscriber B 124 subsequently uses the client device
102 without logging in to the service of the database proprietor
116, the database proprietor 116 attributes logged impression to
the subscriber A 122 even though the use is actually by subscriber
B 124 because the subscriber A 122 was the last person to log into
the database proprietor 116 and, thus, the subscriber A 122 was
most recently identified by the database proprietor 116 as the
subscriber using the client device 102. As such, even though the
subscriber B 124 was subsequently using the client device 102,
impressions logged by the database proprietor 116 during such use
are not attributed to the correct person (i.e., the subscriber B
124) because the most recent login detected by the database
proprietor 116 corresponded to the subscriber A 122. In the
illustrated example, logins are used by the database proprietor 116
to identify subscribers using particular devices by associating
device/user identifiers on the client devices with subscriber
accounts at the database proprietor 116 corresponding to usernames
used during the logins. As such, the database proprietor 116
assumes that the most recent login is indicative of a subscriber
using the client device 102 until another login event is received
at the database proprietor 116 that identifies a different
subscriber. Such assumptions based on the most recent login lead to
the above-described misattributions.
[0059] FIG. 2 illustrates an example communication flow diagram of
an example manner in which the AME 114 and the database proprietor
116 of FIG. 1 can collect impressions and demographic information
based on the client device 102 reporting impressions to the AME 114
and the database proprietor 116. FIG. 2 also shows an example
misattribution corrector 202. The misattribution corrector 202 of
the illustrated example is to correct unique audience sizes and
impression counts that are based on impressions reported by client
devices (e.g., the client device 102) and for which the database
proprietor 116 has misattributed some of those impressions to
incorrect people and, thus, incorrect demographic information. The
example chain of events shown in FIG. 2 occurs when the client
device 102 accesses media for which the client device 102 reports
an impression to the AME 114 and the database proprietor 116. In
some examples, the client device 102 reports impressions for
accessed media based on instructions (e.g., beacon instructions)
embedded in the media that instruct the client device 102 (e.g.,
instruct a web browser or an app in the client device 102) to send
beacon/impression requests (e.g., the beacon/impression requests
108 of FIG. 1) to the AME 114 and/or the database proprietor 116.
In such examples, the media having the beacon instructions is
referred to as tagged media. In other examples, the client device
102 reports impressions for accessed media based on instructions
embedded in apps or web browsers that execute on the client device
102 to send beacon/impression requests (e.g., the beacon/impression
requests 108 of FIG. 1) to the AME 114, and/or the database
proprietor 116 for corresponding media accessed via those apps or
web browsers. In any case, the beacon/impression requests (e.g.,
the beacon/impression requests 108 of FIG. 1) include device/user
identifiers (e.g., AME IDs and/or DP IDs) as described further
below to allow the corresponding AME 114 and/or database proprietor
116 to associate demographic information with resulting logged
impressions.
[0060] In the illustrated example, the client device 102 accesses
media 206 that is tagged with beacon instructions 208. The beacon
instructions 208 cause the client device 102 to send a
beacon/impression request 212 to an AME impressions collector 218
when the client device 102 accesses the media 206. For example, a
web browser and/or app of the client device 102 executes the beacon
instructions 208 in the media 206 which instruct the browser and/or
app to generate and send the beacon/impression request 212. In the
illustrated example, the client device 102 sends the
beacon/impression request 212 to the AME impression collector 218
using an HTTP (hypertext transfer protocol) request addressed to
the URL (uniform resource locator) of the AME impressions collector
218 at, for example, a first internet domain of the AME 114. The
beacon/impression request 212 of the illustrated example includes a
media identifier 213 (e.g., an identifier that can be used to
identify content, an advertisement, and/or any other media)
corresponding to the media 206. In some examples, the
beacon/impression request 212 also includes a site identifier
(e.g., a URL) of the website that served the media 206 to the
client device 102 and/or a host website ID (e.g., www.acme.com) of
the website that displays or presents the media 206. In the
illustrated example, the beacon/impression request 212 includes a
device/user identifier 214. In the illustrated example, the
device/user identifier 214 that the client device 102 provides in
the beacon impression request 212 is an AME ID because it
corresponds to an identifier that the AME 114 uses to identify a
panelist corresponding to the client device 102. In other examples,
the client device 102 may not send the device/user identifier 214
until the client device 102 receives a request for the same from a
server of the AME 114 (e.g., in response to, for example, the AME
impressions collector 218 receiving the beacon/impression request
212).
[0061] In some examples, the device/user identifier 214 may be a
device identifier (e.g., an international mobile equipment identity
(IMEI), a mobile equipment identifier (MEID), a media access
control (MAC) address, etc.), a web browser unique identifier
(e.g., a cookie), a user identifier (e.g., a user name, a login ID,
etc.), an Adobe Flash client identifier, identification information
stored in an HTML5 datastore, and/or any other identifier that the
AME 114 stores in association with demographic information about
users of the client devices 102. When the AME 114 receives the
device/user identifier 214, the AME 114 can obtain demographic
information corresponding to a user of the client device 102 based
on the device/user identifier 214 that the AME 114 receives from
the client device 102. In some examples, the device/user identifier
214 may be encrypted (e.g., hashed) at the client device 102 so
that only an intended final recipient of the device/user identifier
214 can decrypt the hashed identifier 214. For example, if the
device/user identifier 214 is a cookie that is set in the client
device 102 by the AME 114, the device/user identifier 214 can be
hashed so that only the AME 114 can decrypt the device/user
identifier 214. If the device/user identifier 214 is an IMEI
number, the client device 102 can hash the device/user identifier
214 so that only a wireless carrier (e.g., the database proprietor
116) can decrypt the hashed identifier 214 to recover the IMEI for
use in accessing demographic information corresponding to the user
of the client device 102. By hashing the device/user identifier
214, an intermediate party (e.g., an intermediate server or entity
on the Internet) receiving the beacon request cannot directly
identify a user of the client device 102.
[0062] In response to receiving the beacon/impression request 212,
the AME impressions collector 218 logs an impression for the media
206 by storing the media identifier 213 contained in the
beacon/impression request 212. In the illustrated example of FIG.
2, the AME impressions collector 218 also uses the device/user
identifier 214 in the beacon/impression request 212 to identify AME
panelist demographic information corresponding to a panelist of the
client device 102. That is, the device/user identifier 214 matches
a user ID of a panelist member (e.g., a panelist corresponding to a
panelist profile maintained and/or stored by the AME 114). In this
manner, the AME impressions collector 218 can associate the logged
impression with demographic information of a panelist corresponding
to the client device 102.
[0063] In some examples, the beacon/impression request 212 may not
include the device/user identifier 214 if, for example, the user of
the client device 102 is not an AME panelist. In such examples, the
AME impressions collector 218 logs impressions regardless of
whether the client device 102 provides the device/user identifier
214 in the beacon/impression request 212 (or in response to a
request for the identifier 214). When the client device 102 does
not provide the device/user identifier 214, the AME impressions
collector 218 will still benefit from logging an impression for the
media 206 even though it will not have corresponding demographics.
For example, the AME 114 may still use the logged impression to
generate a total impressions count and/or a frequency of
impressions (e.g., an impressions frequency) for the media 206.
Additionally or alternatively, the AME 114 may obtain demographics
information from the database proprietor 116 for the logged
impression if the client device 102 corresponds to a subscriber of
the database proprietor 116.
[0064] In the illustrated example of FIG. 2, to compare or
supplement panelist demographics (e.g., for accuracy or
completeness) of the AME 114 with demographics from one or more
database proprietors (e.g., the database proprietor 116), the AME
impressions collector 218 returns a beacon response message 222
(e.g., a first beacon response) to the client device 102 including
an HTTP "302 Found" re-direct message and a URL of a participating
database proprietor 116 at, for example, a second internet domain.
In the illustrated example, the HTTP "302 Found" re-direct message
in the beacon response 222 instructs the client device 102 to send
a second beacon request 226 to the database proprietor 116. In
other examples, instead of using an HTTP "302 Found" re-direct
message, redirects may be implemented using, for example, an iframe
source instruction (e.g., <iframe src=" ">) or any other
instruction that can instruct a client device to send a subsequent
beacon request (e.g., the second beacon request 226) to a
participating database proprietor 116. In the illustrated example,
the AME impressions collector 218 determines the database
proprietor 116 specified in the beacon response 222 using a rule
and/or any other suitable type of selection criteria or process. In
some examples, the AME impressions collector 218 determines a
particular database proprietor to which to redirect a beacon
request based on, for example, empirical data indicative of which
database proprietor is most likely to have demographic data for a
user corresponding to the device/user identifier 214. In some
examples, the beacon instructions 208 include a predefined URL of
one or more database proprietors to which the client device 102
should send follow up beacon requests 226. In other examples, the
same database proprietor is always identified in the first redirect
message (e.g., the beacon response 222).
[0065] In the illustrated example of FIG. 2, the beacon/impression
request 226 may include a device/user identifier 227 that is a DP
ID because it is used by the database proprietor 116 to identify a
subscriber of the client device 102 when logging an impression. In
some instances (e.g., in which the database proprietor 116 has not
yet set a DP ID in the client device 102), the beacon/impression
request 226 does not include the device/user identifier 227. In
some examples, the DP ID is not sent until the DP requests the same
(e.g., in response to the beacon/impression request 226). In some
examples, the device/user identifier 227 is a device identifier
(e.g., an international mobile equipment identity (IMEI), a mobile
equipment identifier (MEID), a media access control (MAC) address,
etc.), a web browser unique identifier (e.g., a cookie), a user
identifier (e.g., a user name, a login ID, etc.), an Adobe Flash
client identifier, identification information stored in an HTML5
datastore, and/or any other identifier that the database proprietor
116 stores in association with demographic information about
subscribers corresponding to the client devices 102. When the
database proprietor 116 receives the device/user identifier 227,
the database proprietor 116 can obtain demographic information
corresponding to a user of the client device 102 based on the
device/user identifier 227 that the database proprietor 116
receives from the client device 102. In some examples, the
device/user identifier 227 may be encrypted (e.g., hashed) at the
client device 102 so that only an intended final recipient of the
device/user identifier 227 can decrypt the hashed identifier 227.
For example, if the device/user identifier 227 is a cookie that is
set in the client device 102 by the database proprietor 116, the
device/user identifier 227 can be hashed so that only the database
proprietor 116 can decrypt the device/user identifier 227. If the
device/user identifier 227 is an IMEI number, the client device 102
can hash the device/user identifier 227 so that only a wireless
carrier (e.g., the database proprietor 116) can decrypt the hashed
identifier 227 to recover the IMEI for use in accessing demographic
information corresponding to the user of the client device 102. By
hashing the device/user identifier 227, an intermediate party
(e.g., an intermediate server or entity on the Internet) receiving
the beacon request cannot directly identify a user of the client
device 102. For example, if the intended final recipient of the
device/user identifier 227 is the database proprietor 116, the AME
114 cannot recover identifier information when the device/user
identifier 227 is hashed by the client device 102 for decrypting
only by the intended database proprietor 116.
[0066] In some examples that use cookies as the device/user
identifier 227, when a user deletes a database proprietor cookie
from the client device 102, the database proprietor 116 sets the
same cookie value in the client device 102 the next time the user
logs into a service of the database proprietor 116. In such
examples, the cookies used by the database proprietor 116 are
registration-based cookies, which facilitate setting the same
cookie value after a deletion of the cookie value has occurred on
the client device 102. In this manner, the database proprietor 116
can collect impressions for the client device 102 based on the same
cookie value over time to generate unique audience (UA) sizes while
eliminating or substantially reducing the likelihood that a single
unique person will be counted as two or more separate unique
audience members.
[0067] Although only a single database proprietor 116 is shown in
FIGS. 1 and 2, the impression reporting/collection process of FIGS.
1 and 2 may be implemented using multiple database proprietors. In
some such examples, the beacon instructions 208 cause the client
device 102 to send beacon/impression requests 226 to numerous
database proprietors. For example, the beacon instructions 208 may
cause the client device 102 to send the beacon/impression requests
226 to the numerous database proprietors in parallel or in daisy
chain fashion. In some such examples, the beacon instructions 208
cause the client device 102 to stop sending beacon/impression
requests 226 to database proprietors once a database proprietor has
recognized the client device 102. In other examples, the beacon
instructions 208 cause the client device 102 to send
beacon/impression requests 226 to database proprietors so that
multiple database proprietors can recognize the client device 102
and log a corresponding impression. In any case, multiple database
proprietors are provided the opportunity to log impressions and
provide corresponding demographics information if the user of the
client device 102 is a subscriber of services of those database
proprietors.
[0068] In some examples, prior to sending the beacon response 222
to the client device 102, the AME impressions collector 218
replaces site IDs (e.g., URLs) of media provider(s) that served the
media 206 with modified site IDs (e.g., substitute site IDs) which
are discernible only by the AME 114 to identify the media
provider(s). In some examples, the AME impressions collector 218
may also replace a host website ID (e.g., www.acme.com) with a
modified host site ID (e.g., a substitute host site ID) which is
discernible only by the AME 114 as corresponding to the host
website via which the media 206 is presented. In some examples, the
AME impressions collector 218 also replaces the media identifier
213 with a modified media identifier 213 corresponding to the media
206. In this way, the media provider of the media 206, the host
website that presents the media 206, and/or the media identifier
213 are obscured from the database proprietor 116, but the database
proprietor 116 can still log impressions based on the modified
values which can later be deciphered by the AME 114 after the AME
114 receives logged impressions from the database proprietor 116.
In some examples, the AME impressions collector 218 does not send
site IDs, host site IDS, the media identifier 213 or modified
versions thereof in the beacon response 222. In such examples, the
client device 102 provides the original, non-modified versions of
the media identifier 213, site IDs, host IDs, etc. to the database
proprietor 116.
[0069] In the illustrated example, the AME impression collector 218
maintains a modified ID mapping table 228 that maps original site
IDs with modified (or substitute) site IDs, original host site IDs
with modified host site IDs, and/or maps modified media identifiers
to the media identifiers such as the media identifier 213 to
obfuscate or hide such information from database proprietors such
as the database proprietor 116. Also in the illustrated example,
the AME impressions collector 218 encrypts all of the information
received in the beacon/impression request 212 and the modified
information to prevent any intercepting parties from decoding the
information. The AME impressions collector 218 of the illustrated
example sends the encrypted information in the beacon response 222
to the client device 102 so that the client device 102 can send the
encrypted information to the database proprietor 116 in the
beacon/impression request 226. In the illustrated example, the AME
impressions collector 218 uses an encryption that can be decrypted
by the database proprietor 116 site specified in the HTTP "302
Found" re-direct message.
[0070] Periodically or aperiodically, the impression data collected
by the database proprietor 116 is provided to a DP impressions
collector 230 of the AME 114 as, for example, batch data. As
discussed above, some impressions logged by the client device 102
to the database proprietor 116 are misattributed by the database
proprietor 116 to a wrong subscriber and, thus, to incorrect
demographic information. During a data collecting and merging
process to combine demographic and impression data from the AME 114
and the database proprietor 116, demographics of impressions logged
by the AME 114 for the client device 102 will not correspond to
demographics of impressions logged by the database proprietor 116
because the database proprietor 116 has misattributed some
impressions to the incorrect demographic information. Examples
disclosed herein may be used to determine an impressions adjustment
factor to correct/adjust impression-based data (e.g., total
impressions and unique audience size) provided by the database
proprietor 116.
[0071] Additional examples that may be used to implement the beacon
instruction processes of FIG. 2 are disclosed in Mainak et al.,
U.S. Pat. No. 8,370,489, which is hereby incorporated herein by
reference in its entirety. In addition, other examples that may be
used to implement such beacon instructions are disclosed in
Blumenau, U.S. Pat. No. 6,108,637, which is hereby incorporated
herein by reference in its entirety.
[0072] In the example of FIG. 2, the AME 114 includes the example
misattribution corrector 202 to correct unique audience values and
impression counts that are based on impressions reported by client
devices (e.g., the client device 102) for which the database
proprietor 116 has misattributed some of the impressions to
incorrect demographic information. The misattribution corrector 202
of the illustrated example is provided with an example audience
adjustment factor determiner 232, an example impressions adjustment
factor determiner 234, an example unique audience corrector 236,
and an example impressions corrector 238.
[0073] The example audience adjustment factor determiner 232 of
FIG. 2 is provided to calculate a unique audience (UA) adjustment
factor representative of an inaccurate UA size that is based on
misattributed impressions relative to a UA size that is based on
accurately attributed impressions. As discussed above,
misattribution occurs when the database proprietor 116 identifies
the wrong person as being a current user of the client device 102
when the client device reports an impression for accessed media to
the database proprietor 116. The example impressions adjustment
factor determiner 234 is provided to calculate an impressions
adjustment factor representative of an amount of misattributed
impressions relative to an amount of correctly attributed
impressions.
[0074] The example unique audience corrector 236 of FIG. 2 is
provided to correct unique audience sizes or quantities by applying
the impressions adjustment factor (determined by the impressions
adjustment factor determiner 234) to total unique audience sizes
corresponding to total impressions collected by the AME 114. The
example impressions corrector 238 is provided to correct an
impressions count by applying the impressions adjustment factor
(determined by the impressions adjustment factor determiner 234) to
the total number of impressions collected by the AME 114.
[0075] Although the misattribution corrector 202 is shown in the
illustrated example as being located in the AME 114, the
misattribution corrector 202 may alternatively be located at any
other location such as at the database proprietor 116 or at any
other suitable location (e.g., location(s) separate from the AME
114 and the database proprietor 116). In addition, although the AME
impressions collector 218, the modified ID map 228, and the DP
impressions collector 230 are shown separate from the
misattribution corrector 202, one or more of the AME impressions
collector 218, the modified ID map 228, and/or the DP impressions
collector 230 may be implemented in the misattribution corrector
202.
[0076] While an example manner of implementing the example
misattribution corrector 202, the example impressions collector
218, the example modified ID map 228, the example DP impressions
collector 230, the example audience adjustment factor determiner
232, the example impressions adjustment factor determiner 234, the
example unique audience corrector 236, and the example impressions
corrector 238 is illustrated in FIG. 2, one or more of the
elements, processes and/or devices illustrated in FIG. 2 may be
combined, divided, re-arranged, omitted, eliminated and/or
implemented in any other way. Further, the example misattribution
corrector 202, the example AME impressions collector 218, the
example modified ID map 228, the example DP impressions collector
230, the example audience adjustment factor determiner 232, the
example impressions adjustment factor determiner 234, the example
unique audience corrector 236, and/or the example impressions
corrector 238 of FIG. 2 may be implemented by hardware, software,
firmware and/or any combination of hardware, software, and/or
firmware. Thus, for example, any of the example misattribution
corrector 202, the example AME impressions collector 218, the
example modified ID map 228, the example DP impressions collector
230, the example audience adjustment factor determiner 232, the
example impressions adjustment factor determiner 234, the example
unique audience corrector 236, and/or the example impressions
corrector 238 could be implemented by one or more analog or digital
circuit(s), logic circuits, programmable processor(s), application
specific integrated circuit(s) (ASIC(s)), programmable logic
device(s) (PLD(s)), and/or field programmable logic device(s)
(FPLD(s)). When reading any of the apparatus or system claims of
this patent to cover a purely software and/or firmware
implementation, at least one of the example misattribution
corrector 202, the example AME impressions collector 218, the
example modified ID map 228, the example DP impressions collector
230, the example audience adjustment factor determiner 232, the
example impressions adjustment factor determiner 234, the example
unique audience corrector 236, and/or the example impressions
corrector 238 is/are hereby expressly defined to include a tangible
computer readable storage device or storage disk such as a memory,
a digital versatile disk (DVD), a compact disk (CD), a Blu-ray
disk, etc. storing the software and/or firmware. Further still, the
example misattribution corrector 202, the example impressions
collector 218, the example modified ID map 228, the example DP
impressions collector 230, the example audience adjustment factor
determiner 232, the example impressions adjustment factor
determiner 234, the example unique audience corrector 236, and/or
the example impressions corrector 238 of FIG. 2 may include one or
more elements, processes and/or devices in addition to, or instead
of, those illustrated in FIG. 2, and/or may include more than one
of any or all of the illustrated elements, processes and
devices.
[0077] Examples disclosed herein to correct impression-based data
(e.g., total impressions and unique audience size) provided by the
database proprietor 116 involve generating an adjustment factor
based on impressions collected by the AME 116 and correctly
attributed to demographic information for corresponding AME
panelists. The misattribution corrector 202 of FIG. 2 may be
implemented using the example techniques below to correct
impression-based data that is based on impressions of which some
are misattributed to the wrong demographic information by the
database proprietor 116.
[0078] Examples disclosed herein involve using impressions logged
by the AME 114 in association with demographic data collected from
AME panel members to calculate an audience adjustment factor using
example Equation 1 below and an impression adjustment factor using
example Equation 2 below. Audience adjustment factors determined
using example Equation 1 can be used to correct unique audience
size values having inaccuracies due to misattributions of
impressions by database proprietors. Impression adjustment factors
determined using example Equation 2 below can be used to correct
impression quantities having inaccuracies due to misattributions of
impressions by database proprietors.
[0079] In the illustrated example of FIG. 2, the audience
adjustment factor determiner 232 can use example Equation 1 below
to determine an audience adjustment factor (f.sub.i,j) for persons
in a demographic group (j) that accessed media (i).
f i , j = i , j F i , j i , j T i , j Equation 1 ##EQU00001##
[0080] In example Equation 1 above, f.sub.i,j is the adjustment
factor for a unique audience (UA) size of a particular demographic
group (j) that accessed media (i), F.sub.i,j is a database
proprietor (DP) UA count of the number of AME panelists of the AME
114 that the database proprietor 116 observes (e.g., recognizes,
identifies, logs impressions for, etc.) in the demographic group
(j) as accessing the media (i), and T.sub.i,j is an AME UA count of
AME panelists that the AME 114 observes in the demographic group
(j) as accessing the media (i).
[0081] In the illustrated example of FIG. 2, the impressions
adjustment factor determiner 234 employs example Equation 2 below
to determine an impressions adjustment factor (k.sub.i,j) for
persons in a demographic group (j) that accessed media (i).
k i , j = i , j Q i , j i S i - i , j R i , j i S i = i , j Q i , j
- i , j R i , j i S i Equation 2 ##EQU00002##
[0082] In example Equation 2 above, k.sub.i,j is the impressions
adjustment factor for impressions logged for a particular
demographic group (j) that accessed media (i), R.sub.i,j is a DP UA
count of the number of AME panelists of the AME 114 that the
database proprietor 116 observes (e.g., recognizes, identifies,
logs impressions for, etc.) in the demographic group (j) as
accessing the media (i), Q.sub.i,j is an AME UA count of AME
panelists that the AME 114 observes in the demographic group (j) as
accessing the media (i), and S.sub.i is the total AME impressions
of AME panelists (summed across all demographic groups) that
accessed media (i).
[0083] FIG. 3 illustrates an example table 300 with example AME
impressions 302 collected by the AME 114 and example DP impressions
304 collected by the database proprietor 116 for different
demographic groups (e.g., females younger than 50 years (F<50),
females 50 years old and older (F>=50), males younger than 50
years (M<50), and males 50 years old and older (M>=50)). The
example AME impressions 304 and the example DP impressions 304
shown in the example table 300 are development or test impressions
that are collected by the AME 114 and the DP 116 during an
adjustment factors development phase (e.g., an adjustment factors
development phase 802 of FIG. 8) with the purpose of calculating
adjustment factors (e.g., audience adjustment (AA) factors 402 of
FIGS. 4 and 6 and impression adjustment (IA) factors 502 of FIGS. 5
and 6) that can be subsequently used on large logs of real
impressions collected by the database proprietor 116 to correct for
impression misattributions that affect unique audience sizes and
impression counts that are generated using the database
proprietor's logged impressions.
[0084] In the illustrated example of FIG. 3, the DP impressions 304
have an example misattribution error 308 for impression #9 (IMP
#9). That is, the impressions 302, 304 collected by both the AME
114 and the database proprietor 116 are based on client devices
(e.g., the client device 102) having users that are both (1) AME
panelists of the AME 114 and (2) registered subscribers of the
database proprietor 116. When the AME 114 logs an impression based
on, for example, the beacon/impression request 212 of FIG. 2 from a
panelist of the AME 114, the AME 114 logs an accurate impression.
In the illustrated example of FIG. 3, such AME impressions 302 are
also referred to as truth impressions 302 because the AME 114
regards them as correctly associated with corresponding demographic
information of the current user of the client device 102. In some
examples, to assure the accuracy of the AME impressions 302, the
AME 114 incentivizes (e.g., through cash or other rewards) AME
panel members to login to an AME website whenever the AME panel
members begin using a client device 102. In this manner, the AME
114 can accurately set and/or associate an AME ID (e.g., the
device/user identifier 214 of FIG. 2) with an AME panelist that is
currently using the client device 102.
[0085] Unlike the known accuracy, or truth, of the AME impressions
302, there are no assurances that the DP impressions 304 are
accurately associated with correct demographic information. That
is, subscribers of the database proprietor 116 may not be
incentivized to login to a website or service of the database
proprietor 116 when the subscribers begin using a client device
102. As such, the database proprietor 116 is sometimes unable to
accurately set and/or associate a DP ID (e.g., the device/user
identifier 227 of FIG. 2) with a person that is currently using the
client device 102. The misattributions present in the development
or test impressions of the table 300 of FIG. 3 are representative
of the types of misattributions that the database proprietor 116 is
likely to make when logging impressions for persons that may or may
not be AME panelists and/or may or may not be known to the database
proprietor 116. Therefore, calculating adjustment factors based on
the development impressions of the table 300 of FIG. 3 results in
adjustment factors that can be used to correct for misattributions
in impressions subsequently collected by the database proprietor
116 for other users.
[0086] In the illustrated example of FIG. 3, the example
misattribution error 308 at impression #9 is created when the
database proprietor 116 mis-recognizes an impression reported by
the client device 102 (e.g., via the beacon/impression request 226
of FIG. 2) as being associated with demographic information for a
male (M) of age 30. In the example table 300 of FIG. 3, a correct
demographic impression at IMP #9 logged by the AME 114 for the same
person (e.g., via the beacon/impression request 212 of FIG. 2)
shows that the correct demographics indicate that the actual person
corresponding to the impression is a female (F) of age 29. Example
Equations 1 and 2 above may be used to correct unique audience
sizes and total impression counts that are affected by
misattribution errors such as the misattribution error 308 of FIG.
3.
[0087] FIG. 4 illustrates an example table 400 with example
audience adjustment (AA) factors 402 (e.g., the audience adjustment
factor (f.sub.i,j) of Equation 1 above) for unique audience sizes
of different demographic (DEMO) groups. Based on the AME
impressions 302 of FIG. 3, the audience adjustment factor
determiner 232 of FIG. 2 can use Equation 1 above to calculate the
example audience adjustment factors 402 for different demographic
groups (j) that access particular media (i). The example table 400
shows AME UA sizes 404 and example DP UA sizes 406 for different
demographic groups. The example AME UA sizes 404 correspond to the
term (T.sub.i,j) of Equation 1 above, and the DP UA sizes 406
correspond to the term (F.sub.i,j) of Equation 1 above. In the
illustrated example of FIG. 4, the AME UA sizes 404 show that the
AME impressions 302 of FIG. 3 include three (3) unique audience
members of the F<50 demo group, one (1) unique audience member
of the F>=50 demo group, two (2) unique audience members of the
M<50 demo group, and one (1) unique audience member of the
M>=50 demo group. The example DP UA sizes 406 show that the DP
impressions 304 of FIG. 3 include two (2) unique audience members
of the F<50 demo group, one (1) unique audience member of the
F>=50 demo group, two (2) unique audience members of the M<50
demo group, and one (1) unique audience member of the M>=50 demo
group.
[0088] The example DP UA sizes 406 have a misattribution-based
error 410 for the F<50 demo group which results from the
misattribution error 308 of FIG. 3. That is, the misattribution
error 308 of FIG. 3 mistakenly identifies impression 9 (IMP #9) as
corresponding to a male (M) of age 30 rather than the correct
demographic of female (F) of age 29, as noted in the AME impression
302. Because of the misattribution error 308 of FIG. 3, impression
9 (IMP #9) for the DP impressions 304 is not counted for a female
(F) of age 29. Therefore, the DP UA 406 of FIG. 4 for the F<50
demo group is only two (2), which is less than the correct (truth)
unique audience for the F<50 demo group of three (3) as shown by
the corresponding AME UA size 404. Because there were no other
misattribution errors in the example impressions of FIG. 3, the DP
UA sizes 406 match corresponding AME UA sizes 404 for the other
demo groups.
[0089] In the illustrated example, the audience adjustment factor
determiner 232 of FIG. 2 uses Equation 1 above to determine the AA
factors 402. For example, for each of the demo groups F<50,
F>=50, M<50, and M>=50, the audience adjustment factor
determiner 232 divides the corresponding AME UA 404 (T.sub.i,j) by
the corresponding DP UA 406 (F.sub.i,j) to determine the
corresponding AA factor 402 for that demo group. As shown in the
example table 400, the AA factor 402 corresponding to the DP UA 406
having the misattribution-based error 410, the corresponding AA
factor 402 is 0.67 (e.g., T.sub.i,j/F.sub.i,j=>3/2=0.67).
[0090] FIG. 5 illustrates an example table 500 with example
impression adjustment (IA) factors 502 (e.g., the impressions
adjustment factor (k.sub.i,j) of Equation 2 above) for total AME
impression counts 504 and total DP impression counts 506 of
different demographic groups (j) determined based on the example
AME impressions 302 and the example DP impressions 304 of FIG. 3.
In the illustrated example of FIG. 5, a misattribution-based error
508 occurs in association with the F<50 demographic group, and a
misattribution-based error 510 occurs in association with the
M<50 demographic group. The misattribution-based errors 508, 510
occur because of the misattribution error 308 of FIG. 3. That is,
since the misattribution error 308 incorrectly indicates a male (M)
of age 30 instead of the correct female (F) of age 29, the DP
impressions count 506 for the F<50 demographic group has one
fewer impression than the correct (truth) AME impressions count 504
for the F<50 demographic group. In addition, the DP impressions
count 506 for the M<50 demographic group has one more impression
than the correct (truth) AME impressions count for the M<50
demographic group.
[0091] In the illustrated example, the impressions adjustment
factor determiner 234 of FIG. 2 uses Equation 2 above to determine
the IA factors 502. For example, for each of the demo groups
F<50, F>=50, M<50, and M>=50, the impressions
adjustment factor determiner 234 processes Equation 2 using the
corresponding AME impressions 504 (Q.sub.i,j) of FIG. 5, the
corresponding DP impressions 506 (R.sub.i,j) of FIG. 5, and the
total AME impressions count (S.sub.i) summed across all demographic
groups, to determine the corresponding IA factor 502 for that demo
group. For example, using Equation 2 above, the impressions
adjustment factor determiner 234 subtracts the DP impressions 506
(R.sub.i,j) of a particular demographic group from the AME
impressions 504 (Q.sub.i,j) of the same demographic group, and
divides the resulting difference by the total AME impressions count
(S.sub.i) summed across all demographic groups (e.g., IA factor
502=((AME impressions 504 (Q.sub.i,j))-(DP impressions 506
(R.sub.i,j)))/(total AME impressions count (S.sub.i))).
[0092] As shown in the example table 500, the IA factor 502
corresponding to the F<50 demographic group having the
misattribution-based error 508 is 11.11%, and the IA factor 502
corresponding to the M<50 demographic group having the
misattribution-based error 510 is -11.11%. In the illustrated
example, the IA factors 502 are 0.0% for the demographic groups not
having misattribution-based errors. In the illustrated example, the
impressions adjustment factor determiner 234 determines the
misattribution-based error 508 of 11.11% for the F<50
demographic group based on Equation 2 above by subtracting the DP
impressions 506 (R.sub.i,j) of 3 for the F<50 demographic group
shown in FIG. 5 from the AME impressions 504 (Q.sub.i,j) of 4 for
the F<50 demographic group shown in FIG. 5, and divides the
resulting difference of one (1) by the total AME impressions count
(S.sub.i) of nine (9). Also in the illustrated example, the
impressions adjustment factor determiner 234 determines the
misattribution-based error 508 of -11.11% for the M<50
demographic group based on Equation 2 above by subtracting the DP
impressions 506 (R.sub.i,j) of 4 for the M<50 demographic group
shown in FIG. 5 from the AME impressions 504 (Q.sub.i,j) of 3 for
the M<50 demographic group shown in FIG. 5, and divides the
resulting difference of negative one (-1) by the total AME
impressions count (S.sub.i) of nine (9).
[0093] The IA factors 502 of the illustrated example are
percentages of the total AME impressions count 504 summed across
all demographic groups. Thus, the IA factor 502 of 11.11%
corresponding to the F<50 demographic group means that 11.11% of
9 total AME impressions (S.sub.i) (i.e., the sum of all of the AME
impressions 504 logged across all of the demographic groups shown
in FIG. 5) need to be added to the DP impressions count 506 for the
F<50 demographic group. For example, 11.11% of nine (9) total
AME impressions is one (1), which can be added to the three (3) DP
impressions count 506 for the F<50 demographic group to make the
DP impressions count 506 equal to the AME impressions count 504 for
the F<50 demographic group. In addition, the IA factor 502 of
-11.11% corresponding to the M<50 demographic group means that
-11.11% of the nine (9) total AME impressions (i.e., the sum of all
of the AME impressions 504 logged across all of the demographic
groups shown in FIG. 5) need to be added (or 11.11% need to be
subtracted) from the DP impressions count 506 for the M<50
demographic group. For example, -11.11% of nine (9) total AME
impressions is negative one (-1), which can be added to the four
(4) DP impressions count 506 for the M<50 demographic group to
make the DP impressions count 506 equal to the AME impressions
count 504 for the M<50 demographic group. Thus, the effect of
the 11.11% IA factor 502 for the F<50 demographic group and the
-11.11% IA factor 502 for the M<50 demographic group is that one
(1) DP impression 506 is shifted away from the M<50 demographic
group to the F<50 demographic group. In this manner, the total
DP impressions 506 summed across all of the demographic groups
remains the same after applying the IA factors 502.
[0094] FIG. 6 illustrates an example table 600 with
misattribution-corrected UA size values 602 and
misattribution-corrected impression counts 604 based on the AA
factors 402 of FIG. 4 and the IA factors 502 of FIG. 5 for
different demographic groups. The data of example table 600
illustrates how UA size values and impression counts received by
the AME 114 in the aggregate (e.g., not individual impression
records) from the database proprietor 116 can be adjusted to
correct for misattribution-based errors. The aggregate DP UA size
values are shown in the example table 600 as DP decision tree
(DT)-corrected UA size values 606. The aggregate DP impression
count values are shown in the example table 600 as DP DT-corrected
impression counts 608. To generate the DP DT-corrected UA size
values 606 and the DP DT-corrected impression counts 608, the
database proprietor 116 performs a profile correction by applying a
DT model on demographic data used to log impressions. That is,
during initial registration with the database subscriber 116, some
subscribers may provide inaccurate demographic information and/or
may omit certain demographic information. To fill in some of the
missing demographic information in subscriber accounts, the
database proprietor 116 processes the demographic data in
subscriber accounts using a DT model that produces the most likely
outcomes for the missing demographic data. Any suitable DT model
can be used by the database proprietor 116 to correct profile data
for subscribers of the database proprietor 116.
[0095] In the illustrated example of FIG. 6, the example unique
audience corrector 236 of FIG. 2 applies the AA factors 402 to the
DT-corrected UA size values 606 to determine the
misattribution-corrected UA size values 602. That is, the unique
audience corrector 236 divides a DT-corrected UA size value 606 for
a demographic group by a corresponding AA factor 402 for the same
demographic group to calculate a corresponding
misattribution-corrected UA size value 602 (e.g.,
(misattribution-corrected UA size)=(DT-corrected UA size)/(AA
factor)). For example, for the F<50 demographic group, the
unique audience corrector 236 divides the DT-corrected UA size
value 606 of 63,000 by the corresponding AA factor 402 of 0.67 to
calculate the misattribution-corrected UA size value 602 of 94,500
(e.g., 94,500=63,000/0.67). Thus, using the AA factors 402 in this
manner to calculate the misattribution-corrected UA size values 602
substantially reduces or eliminates the effects that misattributed
impressions logged by the database proprietor 116 have on the
DT-corrected UA size values 606.
[0096] In the illustrated example of FIG. 6, the example
impressions corrector 238 of FIG. 2 applies the IA factors 502 to
the DT-corrected impression counts 608 to determine the
misattribution-corrected impression counts 604. That is, the
example impressions corrector 238 increases a DT-corrected
impression count 608 for a demographic group based on a
corresponding IA factor 502 for the same demographic group to
calculate a corresponding misattribution-corrected impressions
count 604. In particular, the example impressions corrector 238
multiples an IA factor 502 for a demographic group by the total DP
DT-corrected impressions count 612 summed across all of the
demographic groups to determine a number of adjustment impressions
by which to adjust the DP DT-corrected impressions count 608 for
the same demographic group corresponding to the selected IA factor
502 (e.g., (adjustment impressions)=(IA factor).times.(total
cross-demographic DP DT-corrected impressions)). The example
impressions corrector 238 then adds the calculated adjustment
impressions to the corresponding DP DT-corrected impressions count
608 for the same demographic group to determine a corresponding
misattribution-corrected impressions count 604 (e.g.,
(misattribution-corrected impressions count)=(DP DT-corrected
impressions count 608)+(adjustment impressions)).
[0097] For example, to determine the misattribution-corrected
impressions 604 corresponding to the F<50 demographic group, the
example impressions corrector 238 of FIG. 2 multiples the IA factor
502 of 11.11% for the F<50 demographic group by the total DP
DT-corrected impressions count 612 of 710,000 to calculate the
adjustment impressions of 78,888. The example impressions corrector
238 then adds the 78,888 adjustment impressions to the
corresponding DP DT-corrected impressions count 608 of 210,000 for
the F<50 demographic group to calculate the
misattribution-corrected impressions count 604 of 288,889 for the
F<50 demographic group.
[0098] To determine the misattribution-corrected impressions 604
for the M<50 demographic group, the example impressions
corrector 238 of FIG. 2 multiples the IA factor 502 of -11.11% for
the M<50 demographic group by the total DP DT-corrected
impressions count 612 of 710,000 to calculate the adjustment
impressions of -78,888. The example impressions corrector 238 then
adds the -78,888 adjustment impressions to (or subtracts 78,888
from) the corresponding DP DT-corrected impressions count 608 of
165,000 for the M<50 demographic group to calculate the
misattribution-corrected impressions count 604 of 86,111 for the
M<50 demographic group.
[0099] An alternative technique to determine the
misattribution-corrected unique audience sizes involves using
impressions frequency values as described in connection with FIG.
7. FIG. 7 illustrates an example table 700 with
misattribution-corrected unique audience values 702 and
misattribution-corrected impression counts 604 determined based on
the IA factors 502 of FIG. 5 and impression frequencies 706 for
different demographic groups. As used herein, impressions frequency
is a number of total impressions (e.g., a DP DT-corrected
impression count 608 of FIG. 6) divided by a quantity of unique
audience members (e.g., a DT-corrected UA size value 606 of FIG. 6)
(e.g., frequency=impressions count/UA). For example, for the
F<50 demographic group, the database proprietor impressions
frequency 706 is 3.33, which is calculated by dividing 210,000 DP
DT-corrected impressions by 63,000 DP DT-corrected UA. In the
illustrated example of FIG. 7, after the example impressions
corrector 238 determines the misattributions-corrected impressions
604 based on the IA factors 502 as described above in connection
with FIG. 6, the example unique audience corrector 236 divides the
misattribution-corrected impressions 604 of 288,889 for the F<50
demographic group by the DP frequency of 3.33 (for the F<50
demographic group) to calculate a misattribution-corrected UA size
702 of 86,667. The frequency-based approach to determining
misattribution-corrected impressions 704 preserves the impressions
frequencies for the demographic groups.
[0100] As shown in FIGS. 6 and 7, the misattribution-corrected UA
sizes 602 of FIG. 6 are different from the misattribution-corrected
UA sizes 702 of FIG. 7. In determining whether to use the AA factor
approach described above in connection with FIG. 6 or the
impressions frequency approach described in connection with FIG. 7
to determine misattribution-corrected UA sizes, both approaches can
be applied over multiple iterations on test data for which true UA
sizes are known. The approach that produces the most accurate
misattribution-corrected UA sizes relative to the true UA sizes can
then be selected for use on real impression data. Alternatively,
the impression frequency approach may be selected if a party wishes
to preserve impression frequency even if the accuracies of
resulting misattribution-corrected UA sizes are not optimal.
[0101] An example advantage of example misattribution adjustment
techniques disclosed herein is that the total DP DT-corrected
impressions count 612 (e.g., 710,000 impressions in FIGS. 6 and 7)
remains the same after correcting the data for misattribution
errors. That is, impressions are not changed, but are instead
redistributed. For example, as shown in FIGS. 6 and 7, a total
misattribution-corrected impressions count 614 across all
demographic groups is 710,000, which is equal to the DP
DT-corrected impressions count 612 of 710,000.
[0102] FIG. 8 is a flow diagram representative of machine readable
instructions that may be executed to implement the misattribution
corrector 202 of FIG. 2 to determine the AA factors 402 of FIGS. 4
and 6, the IA factors 502 of FIGS. 5, 6, and 7, the
misattribution-corrected unique audience sizes 602 of FIG. 6, the
misattribution-corrected unique audience sizes 702 of FIG. 7, and
the misattribution-corrected impression counts 604 of FIGS. 6 and
7. In this example, the machine readable instructions comprise one
or more programs for execution by a processor such as the processor
912 shown in the example processor platform 900 discussed below in
connection with FIG. 9. The program(s) may be embodied in software
stored on a tangible computer readable storage medium such as a
CD-ROM, a floppy disk, a hard drive, a digital versatile disk
(DVD), a Blu-ray disk, or a memory associated with the processor
912, but the entire program and/or parts thereof could
alternatively be executed by a device other than the processor 912
and/or embodied in firmware or dedicated hardware. Further,
although the example program(s) is/are described with reference to
the flowchart illustrated in FIG. 8, many other methods of
implementing the example misattribution corrector 202 may
alternatively be used. For example, the order of execution of the
blocks may be changed, and/or some of the blocks described may be
changed, eliminated, or combined.
[0103] As mentioned above, the example process(es) of FIG. 8 may be
implemented using coded instructions (e.g., computer and/or machine
readable instructions) stored on a tangible computer readable
storage medium such as a hard disk drive, a flash memory, a
read-only memory (ROM), a compact disk (CD), a digital versatile
disk (DVD), a cache, a random-access memory (RAM) and/or any other
storage device or storage disk in which information is stored for
any duration (e.g., for extended time periods, permanently, for
brief instances, for temporarily buffering, and/or for caching of
the information). As used herein, the term tangible computer
readable storage medium is expressly defined to include any type of
computer readable storage device and/or storage disk and to exclude
propagating signals and to exclude transmission media. As used
herein, "tangible computer readable storage medium" and "tangible
machine readable storage medium" are used interchangeably.
Additionally or alternatively, the example process(es) of FIG. 8
may be implemented using coded instructions (e.g., computer and/or
machine readable instructions) stored on a non-transitory computer
and/or machine readable medium such as a hard disk drive, a flash
memory, a read-only memory, a compact disk, a digital versatile
disk, a cache, a random-access memory and/or any other storage
device or storage disk in which information is stored for any
duration (e.g., for extended time periods, permanently, for brief
instances, for temporarily buffering, and/or for caching of the
information). As used herein, the term non-transitory computer
readable medium is expressly defined to include any type of
computer readable storage device and/or storage disk and to exclude
propagating signals and to exclude transmission media. As used
herein, when the phrase "at least" is used as the transition term
in a preamble of a claim, it is open-ended in the same manner as
the term "comprising" is open ended.
[0104] The example flow diagram of FIG. 8 is shown as two phases
including an example adjustment factors development phase 802 and
an example misattribution correction phase 804. During the
adjustment factors development phase 802, the misattribution
corrector 202 (FIG. 2) determines the AA factors 402 (FIGS. 4 and
6) and the IA factors 502 (FIGS. 5 and 6) for different demographic
groups based on development or test impressions such as the
impressions shown in table 300 of FIG. 3. During the misattribution
correction phase 804, the misattribution corrector 202 corrects
aggregate impression data (e.g., unique audience measures and total
impression counts) generated based on impressions collected by the
database proprietor 116 (and/or one or more other database
proprietors). For example, the misattribution corrector 202 uses
the AA factors 402 and/or the IA factors 502 to determine the
misattribution corrected UA size values 602 of FIG. 6, the
misattribution-corrected UA size values 702 of FIG. 7, and/or the
misattribution-corrected impression counts 604 of FIGS. 6 and 7 for
different demographic groups. In some examples, the misattribution
correction phase 804 may begin immediately after the adjustment
factors development phase 802. In other examples, the
misattribution correction phase 804 may begin after a significant
amount of time (e.g., hours, days, weeks, etc.) has passed
following the completion of the adjustment factors development
phase 802. In some examples, the adjustment factors development
phase 802 and the misattribution correction phase 804 may be
implemented as part of a same program. In other examples, the
adjustment factors development phase 802 and the misattribution
correction phase 804 may be implemented as two separate
programs.
[0105] The example adjustment factors development phase 802 of FIG.
8 begins at block 806 at which the AME impressions collector 218
collects impressions from the client device 102. For example, the
AME impressions collector 218 collects impressions using the
techniques described above in connection with FIG. 2. The DP
impressions collector 230 obtains development impression records
from the database proprietor 116 that correspond to AME panelists
that are also subscribers of the database proprietor 116 (block
808). The misattribution corrector 202 selects a demographic group
(block 810). For example, the misattribution corrector 202 selects
one of the demographic groups of FIGS. 4-7. The example impressions
adjustment factor determiner 234 (FIG. 2) determines an IA factor
502 for the selected demographic group (block 812). For example,
the impressions adjustment factor determiner 234 determines the IA
factor 502 using Equation 2 above and/or the technique described
above in connection with FIG. 5.
[0106] The example unique audience adjustment factor determiner 232
(FIG. 2) determines an AA factor 402 for the selected demographic
group (block 814). For example, the unique audience adjustment
factor determiner 232 determines the AA factor 402 using Equation 1
above and/or the technique described above in connection with FIG.
4. The misattribution corrector 202 determines whether there is
another demographic group for which to determine adjustment factors
(block 816). If there is another demographic group, control returns
to block 810. If there is not another demographic group, the
adjustment factors development phase 802 ends. In the illustrated
example, after the adjustment factors development phase 802 ends,
the misattribution correction phase 804 begins based on the IA
factors 502 and the AA factors 402 determined during the adjustment
factors development phase 802. In some examples, the adjustment
factors development phase 802 is repeated from time to time (e.g.,
after a number of days, weeks, months, etc.) to update the IA
factors 502 and/or the AA factors 402. For example, the ability of
the database proprietor 116 to identify subscribers may change
(e.g., increased or decreased accuracy) from time to time. As such,
to increase the likelihood that the IA factors 502 and the AA
factors 402 reflect such changes, the adjustment factors
development phase 802 can be repeated from time to time.
[0107] In the misattribution correction phase 804, the DP
impressions collector 230 obtains the DP DT-corrected unique
audience sizes 606 (FIG. 6) and DP DT-corrected impression counts
608 (FIG. 6) from the database proprietor 116 (block 818). The
misattribution corrector 202 selects a demographic group (block
820). For example, the misattribution corrector 202 selects one of
the demographic groups of FIGS. 4-7. The example impressions
corrector 238 (FIG. 2) determines a misattribution-corrected
impressions count 604 (FIGS. 6 and 7) based on the IA factor 502
for the selected demographic group (block 822). For example, the
impressions corrector 238 can determine the
misattribution-corrected impressions count 604 as described above
in connection with FIG. 6.
[0108] The misattribution corrector 202 determines whether to use
impressions frequency to determine a misattribution-corrected
unique audience size (block 824). For example, the misattribution
corrector 202 may check a configuration setting in a file, a
program, and/or a hardware setting indicating whether to determine
a misattribution-corrected unique audience size based on an
impressions frequency 706 (FIG. 7). If the misattribution corrector
202 determines that it should determine a misattribution-corrected
unique audience size based on an impressions frequency 706, the
misattribution corrector 202 determines an impressions frequency
706 for the selected demographic profile (block 826). For example,
the misattribution corrector 202 may determine the impressions
frequency 706 for the selected demographic profile as described
above in connection with FIG. 7. If the misattribution corrector
202 determines that it should not use an impressions frequency 706
to determine a misattribution-corrected unique audience size,
control advances to block 828 without determining an impressions
frequency 706. In some examples, the impressions frequency 706 is
determined by the database proprietor 116 and provided by the
database proprietor 116 to the misattribution corrector 202 via the
DP impressions collector 230. In such examples, the misattribution
corrector 202 does not need to determine the impressions frequency
706.
[0109] At block 828, the example unique audience corrector 236
(FIG. 2) determines the misattribution-corrected UA size for the
selected demographic group (block 828). For example, if the
misattribution corrector 202 determined at block 824 that the
impressions frequency 706 is to be used to determine the
misattribution-corrected UA size 702 (FIG. 7) for the selected
demographic group, the unique audience corrector 236 determines the
misattribution-corrected UA size 702 based on the impressions
frequency 706 of block 826 as described above in connection with
FIG. 7. Alternatively at block 828, if the misattribution corrector
202 determined at block 824 that the impressions frequency 706 is
not to be used to determine the misattribution-corrected UA size
602 (FIG. 6) for the selected demographic group, the unique
audience corrector 236 determines the misattribution-corrected UA
size 602 based on the AA factor 402 (FIG. 4) of the selected
demographic group as described above in connection with FIG. 6.
[0110] The misattribution corrector 202 then determines whether
there is another demographic group for which
misattribution-adjusted impression counts or
misattribution-adjusted UA sizes are to be determined (block 830).
If there is another demographic group, control returns to block
820. Otherwise, the example program of FIG. 8 ends.
[0111] FIG. 9 is a block diagram of an example processor platform
900 capable of executing the instructions of FIG. 9 to implement
the misattribution corrector 202 of FIG. 2. The processor platform
900 can be, for example, a server, a personal computer, or any
other type of computing device.
[0112] The processor platform 900 of the illustrated example
includes a processor 912. The processor 912 of the illustrated
example is hardware. For example, the processor 912 can be
implemented by one or more integrated circuits, logic circuits,
microprocessors or controllers from any desired family or
manufacturer.
[0113] In the illustrated example, the processor 912 implements the
example misattribution corrector 202, the example AME impressions
collector 218, the example DP impressions collector 230, the
example audience adjustment factor determiner 232, the example
impressions adjustment factor determiner 234, the example unique
audience corrector 236, and/or the example impressions corrector
238 described above in connection with FIG. 2.
[0114] The processor 912 of the illustrated example includes a
local memory 913 (e.g., a cache). The processor 912 of the
illustrated example is in communication with a main memory
including a volatile memory 914 and a non-volatile memory 916 via a
bus 918. The volatile memory 914 may be implemented by Synchronous
Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory
(DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any
other type of random access memory device. The non-volatile memory
816 may be implemented by flash memory and/or any other desired
type of memory device. Access to the main memory 914, 916 is
controlled by a memory controller.
[0115] In the illustrated example, the local memory 913 stores the
example modified ID map 228 described above in connection with FIG.
2. In other examples any one or more of the local memory 913, the
random access memory 914, the read only memory 916, and/or a mass
storage device 928 may store the example modified ID map 228.
[0116] The processor platform 900 of the illustrated example also
includes an interface circuit 920. The interface circuit 920 may be
implemented by any type of interface standard, such as an Ethernet
interface, a universal serial bus (USB), and/or a PCI express
interface.
[0117] In the illustrated example, one or more input devices 922
are connected to the interface circuit 920. The input device(s) 922
permit(s) a user to enter data and commands into the processor 912.
The input device(s) can be implemented by, for example, an audio
sensor, a microphone, a camera (still or video), a keyboard, a
button, a mouse, a touchscreen, a track-pad, a trackball, isopoint
and/or a voice recognition system.
[0118] One or more output devices 924 are also connected to the
interface circuit 920 of the illustrated example. The output
devices 924 can be implemented, for example, by display devices
(e.g., a light emitting diode (LED), an organic light emitting
diode (OLED), a liquid crystal display, a cathode ray tube display
(CRT), a touchscreen, a tactile output device, a light emitting
diode (LED), a printer and/or speakers). The interface circuit 920
of the illustrated example, thus, typically includes a graphics
driver card, a graphics driver chip or a graphics driver
processor.
[0119] The interface circuit 920 of the illustrated example also
includes a communication device such as a transmitter, a receiver,
a transceiver, a modem and/or network interface card to facilitate
exchange of data with external machines (e.g., computing devices of
any kind) via a network 926 (e.g., an Ethernet connection, a
digital subscriber line (DSL), a telephone line, coaxial cable, a
cellular telephone system, etc.).
[0120] The processor platform 900 of the illustrated example also
includes one or more mass storage devices 928 for storing software
and/or data. Examples of such mass storage devices 928 include
floppy disk drives, hard drive disks, compact disk drives, Blu-ray
disk drives, RAID systems, and digital versatile disk (DVD)
drives.
[0121] Coded instructions 932 include the machine readable
instructions of FIG. 8 and may be stored in the mass storage device
928, in the volatile memory 914, in the non-volatile memory 916,
and/or on a removable tangible computer readable storage medium
such as a CD or DVD.
[0122] From the foregoing, it will be appreciate that methods,
apparatus and articles of manufacture have been disclosed which
enhance the operations of a computer to improve the accuracy of
impression-based data such as unique audience and impression counts
so that computers and processing systems therein can be relied upon
to produce audience analysis information with higher accuracies. In
some examples, computer operations can be made more efficient based
on the above equations and techniques for determining IA factors,
AA factors, misattribution-corrected unique audience sizes, and
misattribution-corrected impression counts. That is, through the
use of these processes, computers can operate more efficiently by
relatively quickly determining parameters and applying those
parameters through the above disclosed techniques to determine the
misattribution-corrected data. For example, using example processes
disclosed herein, a computer can more efficiently and effectively
identify misattribution errors (e.g., the misattribution error 308
of FIG. 3) in development or test data logged by the AME 114 and
the database proprietor 116 without using large amounts of network
communication bandwidth (e.g., conserving network communication
bandwidth) and without using large amounts of computer processing
resources (e.g., conserving processing resources) to communicate
with individual online users to request survey responses about
their online media access habits and without needing to rely on
such survey responses from such online users. Survey responses from
online users can be inaccurate due to inabilities or unwillingness
of users to recollect online media accesses. Survey responses can
also be incomplete, which could require additional processor
resources to identify and supplement incomplete survey responses.
As such, examples disclosed herein more efficiently and effectively
determine misattribution-corrected data. Such
misattribution-corrected data is useful in subsequent processing
for identifying exposure performances of different media so that
media providers, advertisers, product manufacturers, and/or service
providers can make more informed decisions on how to spend
advertising dollars and/or media production and distribution
dollars.
[0123] Furthermore, example methods, apparatus, and/or articles of
manufacture disclosed herein identify and overcome inaccuracies in
impressions and/or aggregate impression-based data provided by
database proprietors. For example, example methods, apparatus,
and/or articles of manufacture disclosed herein overcome the
technical problem of counting impressions and determining unique
audiences of media on media devices that are shared by multiple
people. Example methods, apparatus, and/or articles of manufacture
disclosed herein solve this problem without forcing such media
devices to be used by only a single person and without forcing
people to always login to their subscriber accounts of database
proprietors. By not forcing logins into database proprietor
accounts, examples disclosed herein do not force additional network
communications to be employed, thus, reducing network traffic.
[0124] Although certain example methods, apparatus and articles of
manufacture have been disclosed herein, the scope of coverage of
this patent is not limited thereto. On the contrary, this patent
covers all methods, apparatus and articles of manufacture fairly
falling within the scope of the claims of this patent.
* * * * *
References