Note: Descriptions are shown in the official language in which they were submitted.
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METHODS AND APPARATUS TO CHARACTERIZE
HOUSEHOLDS WITH MEDIA METER DATA
FIELD OF THE DISCLOSURE
[0001] This disclosure relates generally to market research, and, more
particularly, to methods and apparatus to characterize households with media
meter data.
BACKGROUND
[0002] In recent years, panelist research efforts included installing
metering hardware in qualified households that fit one or more demographics of
interest. In some cases, the metering hardware is capable of determining
whether
a media presentation device (such as a television set) is powered on and tuned
to a
particular station via a hardwired connection from the media presentation
device
to the meter. In other cases, the metering hardware is capable of determining
which household member is exposed to a particular portion of media via one or
more button presses on a People Meter by the household member near the
television.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 illustrates an example media distribution environment in
which households may be characterized with media meter data.
[0004] FIG. 2 is a schematic illustration of an example imputation engine
constructed in accordance with the teachings of this disclosure.
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[0005] FIG. 3 is a plot illustrating an example viewing index effect based
on an age of collected data.
[0006] FIG. 4 is an example weighting allocation table to apply a temporal
weight to collected minutes.
[0007] FIG. 5 is an example dimension subset map to illustrate
independent distribution of household dimensions used to characterize
households
with media meter data.
[0008] FIGS. 6-9 are flowcharts representative of example machine
readable instructions that may be executed to implement the example imputation
engine of FIGS. 1 and 2.
[0009] FIG. 10 is an example visitor table to illustrate example visitor
tuning minutes and exposure minutes for a demographic of interest.
[0010] FIG. 11 is a schematic illustration of an example visitor imputation
engine constructed in accordance with the teachings of this disclosure.
[0011] FIG. 12 are example cell parameter calculations including example
demographics of interest and example categories of interest to determine
average
visitor parameters to be used to impute a number of visitors.
[0012] FIG. 13 are example independent parameter calculations to
determine average visitor parameters to be used to impute a number of
visitors.
[0013] FIG. 14 are example probability values and cumulative probability
values generated by the example visitors imputation engine of FIGS. 1 and 11.
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[0014] FIG. 15 is a flowchart representative of example machine readable
instructions that may be executed to implement the example visitor imputation
engine of FIGS. 1 and 11.
[0015] FIG. 16 is a schematic illustration of an example ambient tuning
engine constructed in accordance with the teachings of this disclosure
[0016] FIGS. 17-19 are flowcharts representative of example machine
readable instructions that may be executed to implement the example ambient
tuning engine of FIGS. 1, 10 and 16.
[0017] FIG. 20 is an example crediting chart illustrating example
categories of collected viewing minutes.
[0018] FIG. 21 is a schematic illustration of an example on/off detection
engine constructed in accordance with the teachings of this disclosure.
[0019] FIG. 22 is a flowchart representative of example machine readable
instructions that may be executed to implement the example on/off detection
engine of FIGS. 1 and 21.
[0020] FIG. 23 is a schematic illustration of an example processor
platform that may execute the instructions of FIGS. 6-9, 15, 17-19 and/or 22
to
implement the example ambient tuning engine, the example imputation engine
and the example on/off detection engine of FIGS. 1,2, 10, 16 and/or 21.
DETAILED DESCRIPTION
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[0021] Market researchers seek to understand the audience composition
and size of media, such as radio programming, television programming and/or
Internet media so that advertising prices can be established that are
commensurate
with audience exposure and demographic makeup (referred to herein collectively
as "audience configuration"). As used herein, "media" refers to any sort of
content and/or advertisement which is presented or capable of being presented
by
an information presentation device, such as a television, radio, computer,
smart
phone or tablet. To determine aspects of audience configuration (e.g., which
household member is currently watching a particular portion of media and the
corresponding demographics of that household member), the market researchers
may perform audience measurement by enlisting any number of consumers as
panelists. Panelists are audience members (household members) enlisted to be
monitored, who divulge and/or otherwise share their media exposure habits and
demographic data to facilitate a market research study. An audience
measurement
entity typically monitors media exposure habits (e.g., viewing, listening,
etc.) of
the enlisted audience members via audience measurement system(s), such as a
metering device and a People Meter. Audience measurement typically involves
determining the identity of the media being displayed on a media presentation
device, such as a television.
[0022] Some audience measurement systems physically connect to the
media presentation device, such as the television, to identify which channel
is
currently tuned by capturing a channel number, audio signatures and/or codes
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identifying (directly or indirectly) the programming being displayed. Physical
connections between the media presentation device and the audience
measurement system may be employed via an audio cable coupling the output of
the media presentation device to an audio input of the audience measurement
system. Additionally, audience measurement systems prompt and/or accept
audience member input to reveal which household member is currently exposed
to the media presented by the media presentation device.
[0023] As described above, audience measurement entities may employ
the audience measurement systems to include a device, such as the People Meter
(PM), having a set of inputs (e.g., input buttons) that are each assigned to a
corresponding member of a household. The PM is an electronic device that is
typically disposed in a media exposure (e.g., viewing) area of a monitored
household and is proximate to one or more of the audience members. The PM
captures information about the household audience by prompting the audience
members to indicate that they are present in the media exposure area (e.g., a
living
room in which a television set is present) by, for example, pressing their
assigned
input key on the PM. When a member of the household selects their
corresponding input, the PM identifies which household member is present,
which
includes other demographic information associated with the household member,
such as a name, a gender, an age, an income category, etc. However, in the
event
a visitor is present in the household, the PM includes at least one input
(e.g., an
input button) for the visitor to select. When the visitor input button is
selected,
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the PM prompts the visitor to enter an age and a gender (e.g., via keyboard,
via an
interface on the PM, etc.).
[0024] The PM may be accompanied by a base metering device (e.g., a
base meter) to measure one or more signals associated with the media
presentation device. For example, the base meter may monitor a television set
to
determine an operational status (e.g., whether the television is powered on or
powered off, a media device power sensor), and/or to identify media displayed
and/or otherwise emitted by the media device (e.g., identify a program being
presented by a television set). The PM and the base meter may be separate
devices and/or may be integrated into a single unit. The base meter may
capture
audience measurement data via a cable as described above and/or wirelessly by
monitoring audio and/or video output by the monitored media presentation
device. Audience measurement data captured by the base meter may include
tuning information, signatures, codes (e.g., embedded into or otherwise
broadcast
with broadcast media), and/or a number of and/or identification of
corresponding
household members exposed to the media output by the media presentation device
(e.g., the television).
[0025] Data collected by the PM and/or the base meter may be stored in a
memory and transmitted via one or more networks, such as the Internet, to a
data
store managed by a market research entity such as The Nielsen Company (US),
LLC. Typically, such data is aggregated with data collected from a large
number
of PMs and/or base meters monitoring a large number of panelist households.
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Such collected and/or aggregated data may be further processed to determine
statistics associated with household behavior in one or more geographic
regions
of interest. Household behavior statistics may include, but are not limited
to, a
number of minutes a household media device was tuned to a particular station,
a
number of minutes a household media device was used (e.g., viewed) by a
household panelist member and/or one or more visitors, demographics of an
audience (which may be statistically projected based on the panelist data) and
instances when the media device is on or off. While examples described herein
employ the term "minutes," such as "household tuning minutes," "exposure
minutes," etc., any other time measurement of interest may be employed without
limitation.
[0026] To ensure audience measurement systems are properly installed in
panelist households, field service personnel have traditionally visited each
panelist household, assessed the household media components, physically
installed (e.g., connected) the PM and/or base meter to monitor a media
presentation device(s) of the household (e.g., a television), and trained the
household members how to interact with the PM so that accurate audience
information is captured. In the event one or more aspects of the PM and/or
base
meter installation are inadvertently disrupted (e.g., an audio cable
connection
from the media device to the base meter is disconnected), then subsequent
field
service personnel visit(s) may be necessary. In an effort to allow collected
household data to be used in a reliable manner (e.g., a manner conforming to
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accepted statistical sample sizes), a relatively large number of PMs and/or
base
meters are needed. Each such PM and/or base meter involves one or more
installation efforts and installation costs. As such, efforts to increase
statistical
validity (e.g., by increasing panel size and/or diversity) for a population of
interest
result in a corresponding increase in money spent to implement panelist
households with PMs and/or base meters.
[0027] In an effort to increase a sample size of household behavior data
and/or reduce a cost associated with configuring panelist households with PMs
and/or base meters, example methods, apparatus, systems and/or articles of
manufacture disclosed herein employ a media meter (MM) to collect household
panelist behavior data. Example MMs disclosed herein are distinguished from
traditional PMs and/or base meters that include a physical connection to the
media
presentation device (e.g., a television). In examples disclosed herein, the MM
captures audio without a physical connection to the media device. In some
examples, the MM includes one or more microphones to capture ambient audio in
a room shared by the media device. In some such examples, the MM captures
codes embedded by one or more entities (e.g., final distributor audio codes
(FDAC)), and does not include one or more inputs that are to be selected by
one
or more household panelists to identify which panelist is currently viewing
the
media device. Rather than collecting audience composition data directly from
panelists, example methods, apparatus, systems and/or articles of manufacture
disclosed herein apply one or more models to impute which household members
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are exposed to particular media programming to collected MM data. Such
example imputation techniques are described in further detail below and
referred
to herein as "persons imputation." Additionally, example methods, apparatus
and/or articles of manufacture disclosed herein apply one or more models to
impute a number of visitors in each household and corresponding
age/demographic characteristics of such visitors. In other words, examples
disclosed herein facilitate a manner of determining a probability of household
exposure activity, a number of visitors and/or corresponding visitor ages in a
stochastic manner that avoids the expense of additional PM device installation
in
panelist households.
[0028] In some examples, a household includes two or more media
devices, such as a first television located in a first room and a second
television
located in a second room. In the event the panelist household includes first
and
second meters physically connected to the first and second televisions, then
the
physical connection unambiguously identifies which audio data is originating
from which television in the household, even if such audio from the first
television propagates to the second room having the second television (and/or
vice versa). Circumstances in which media played in one room can be heard
and/or otherwise detected in another room (which may also have a media
presentation device and accompanying meter) are referred to herein as
"spillover." In the event the panelist household includes first and second MMs
located in the first and second rooms, respectively, then spillover audio data
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"heard" (detected) from the first room may erroneously be credited by the
second
MM as media presented in the second room (and/or vice versa). Media tuning
events logged by a MM as occurring in one room, but actually occurring in a
second different room (e.g., due to spillover) are referred to herein as
"ambient
tuning." In other words, because the MM includes microphones to collect audio
emitted from media devices, the possibility exists that the first MM in the
first
room is picking-up and/or otherwise detecting audio from the media device in
an
adjacent (e.g., the second) room. Ambient tuning is distinguished from "real
tuning" in that real tuning occurs when the MM properly credits the media
presentation device (e.g., television) associated with the room in which the
MM is
located with a media exposure for media actually presented on that media
presentation device. Example methods, apparatus, systems and/or articles of
manufacture disclosed herein apply models to identify instances of ambient
tuning
(e.g., due to spillover) as distinguished from real (legitimate) tuning.
Similarly,
example methods, apparatus, systems and/or articles of manufacture disclosed
herein apply models to identify instances of when a media presentation device
is
turned on as distinguished from instances of when the media device is powered
off. This is important in avoiding crediting of media exposure when no such
exposure is occurring. For example, in the event a household member is in a
first
room with an associated media presentation device in a powered-off state, but
the
associated meter in that first room is detecting audio from a second media
device
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in a second room, examples disclosed herein identify the occurrence as
spillover
and do not credit the detection as an actual media exposure.
[0029] Turning to FIG. 1, an example media distribution environment 100
includes a network 102 (e.g., the Internet) communicatively connected to
panelist
households within a region of interest (e.g., a target research geography
104). In
the illustrated example of FIG. 1, some panelist households 106 include People
Meters (PMs) and media meters (MMs) 106 and some other panelist households
108 include only MMs to capture household media exposure information.
Households having both MMs and PMs are referred to herein as MMPM
households 106. Households that do not have a PM, but have a MM are referred
to herein as MMHs (media meter households) 108. Behavior information
collected by the example MMPMs 106 and the example MMHs 108 are sent via
the example network 102 to an example imputation engine 110, an example
visitor imputation engine, an example ambient tuning engine 120, and/or an
example on/off detection engine 130 for analysis. As described above, because
MMHs 108 do not include PMs, they do not include physical button inputs to be
selected by household members to identify which household member is currently
watching particular media, and they do not include physical button inputs to
be
selected by household visitors to identify age and/or gender information.
Therefore, example methods, systems, apparatus and/or articles of manufacture
disclosed herein model household characteristics that predict a likelihood
that a
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particular household member is watching the identified media being accessed in
the MMHs 108.
[0030] Example households that include a PM collect panelist audience
data. As used herein, "panelist audience data" includes both (a) media
identification data (e.g., code(s) embedded in or otherwise transmitted with
media, signatures, channel tuning data, etc.) and (b) person information
identifying the corresponding household member(s) and/or visitors that are
currently watching/viewing/listening to and/or otherwise accessing the
identified
media. On the other hand, MMH households 108 include only a MM to collect
media data. As used herein, "media data" and/or "media identifier information"
are used interchangeably and refer to information associated with media
identification (e.g., codes, signatures, etc.), but does not include person
information identifying which household member(s) and/or visitors are
currently
watching/viewing/listening to and/or otherwise accessing the identified media.
As described in further detail below, example methods, apparatus, systems
and/or
articles of manufacture disclosed herein impute person identifying data to
media
data collected from MMH household(s) 108.
[0031] Although examples disclosed herein refer to code readers and
collecting codes, techniques disclosed herein could also be applied to systems
that
collect signatures and/or channel tuning data to identify media. Audio
watermarking is a technique used to identify media such as television
broadcasts,
radio broadcasts, advertisements (television and/or radio), downloaded media,
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streaming media, prepackaged media, etc. Existing audio watermarking
techniques identify media by embedding one or more audio codes (e.g., one or
more watermarks), such as media identifying information and/or an identifier
that
may be mapped to media identifying information, into an audio and/or video
component. In some examples, the audio or video component is selected to have
a signal characteristic sufficient to hide the watermark. As used herein, the
terms
"code" or "watermark" are used interchangeably and are defined to mean any
identification information (e.g., an identifier) that may be transmitted with,
inserted in, or embedded in the audio or video of media (e.g., a program or
advertisement) for the purpose of identifying the media or for another purpose
such as tuning (e.g., a packet identifying header). As used herein "media"
refers
to audio and/or visual (still or moving) content and/or advertisements. To
identify
watermarked media, the watermark(s) are extracted and used to access a table
of
reference watermarks that are mapped to media identifying information.
[0032] Unlike media monitoring techniques based on codes and/or
watermarks included with and/or embedded in the monitored media, fingerprint
or
signature-based media monitoring techniques generally use one or more inherent
characteristics of the monitored media during a monitoring time interval to
generate a substantially unique proxy for the media. Such a proxy is referred
to as
a signature or fingerprint, and can take any form (e.g., a series of digital
values, a
waveform, etc.) representative of any aspect(s) of the media signal(s) (e.g.,
the
audio and/or video signals forming the media presentation being monitored). A
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good signature is one that is repeatable when processing the same media
presentation, but that is unique relative to other (e.g., different)
presentations of
other (e.g., different) media. Accordingly, the term "fingerprint" and
"signature"
are used interchangeably herein and are defined herein to mean a proxy for
identifying media that is generated from one or more inherent characteristics
of
the media.
[0033] Signature-based media monitoring generally involves determining
(e.g., generating and/or collecting) signature(s) representative of a media
signal
(e.g., an audio signal and/or a video signal) output by a monitored media
device
and comparing the monitored signature(s) to one or more references signatures
corresponding to known (e.g., reference) media sources. Various comparison
criteria, such as a cross-correlation value, a Hamming distance, etc., can be
evaluated to determine whether a monitored signature matches a particular
reference signature. When a match between the monitored signature and one of
the reference signatures is found, the monitored media can be identified as
corresponding to the particular reference media represented by the reference
signature that with matched the monitored signature. Because attributes, such
as
an identifier of the media, a presentation time, a broadcast channel, etc.,
are
collected for the reference signature, these attributes may then be associated
with
the monitored media whose monitored signature matched the reference signature.
Example systems for identifying media based on codes and/or signatures are
long
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known and were first disclosed in Thomas, US Patent 5,481,294, which is hereby
incorporated by reference in its entirety..
Persons Imputation
[0034] FIG. 2 is a schematic illustration of an example implementation of
the imputation engine 110 of FIG. 1. In the illustrated example of FIG. 2, the
imputation engine 110 includes the visitor imputation engine 112, a People
Meter
(PM) interface 202, a media meter (MM) interface 204, a categorizer 206, a
weighting engine 210 and a probability engine 212. As described in further
detail
below, the example visitor imputation engine 112 employs one or more portions
of the example imputation engine 110. The example probability engine 212 of
FIG. 2 includes an example dimension manager 214, an example cell probability
engine 216 and an example independent distribution engine 218. The example
cell probability engine 216 of FIG. 2 includes an example category fit manager
220, an example minutes aggregator 222 and an example imputation engine 224.
The example independent distribution engine 218 of FIG. 2 includes an example
category qualifier 226, an example proportion manager 228 and an example
distribution engine 230.
[0035] In operation, the example PM interface 202 acquires people meter
data from any and all PMs within the example panelist households 104. In
particular, the example PM interface 202 acquires PM data from the PM devices
located in the example MMPM households 106 (i.e., households that have both
MM devices and PM devices). The PM devices have input (s) (e.g., buttons for
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each household member to select to identify their respective presence in the
audience currently exposed to media). In some examples, the MMPM households
106 are associated with a particular geographic area of focus, such as
nationwide
(sometimes referred to as a "National People Meter" (NPM)), while in other
examples the MMPM households 106 are associated with a subset of a particular
geographic area of focus, such as a localized geography of interest (e.g., a
city
within a nation (e.g., Chicago), and sometimes referred to as "Local People
Meter" (LPM)).
[0036] For example, in the event an analysis of the Charlotte designated
market area (DMA) is desired, then the example PM interface 202 captures data
from LPM households within a time zone corresponding to the desired DMA
(e.g., the Eastern time zone). In some examples, desired data may be streamed
back to one or more storage repositories, from which the example imputation
engine 110, the example ambient tuning engine 120 and/or the example on/off
detection engine 130 may retrieve the data. The example PM interface 202 of
the
illustrated examples collects, acquires and/or otherwise captures PM data
(panelist
audience data) from panelist households 104 (having both PMs and MMs) and
records or aggregates the media exposure minutes to respective persons within
the
household as one or more of the possible audience members (e.g., viewers) of
the
corresponding media. In other words, the captured panelist audience data is at
a
persons-level rather than at a household level, which facilitates an ability
to
generate person probabilities, as described in further detail below.
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[0037] The example categorizer 206 of FIG. 2 categorizes the acquired
panelist audience data in any number of categories, such as by age, by gender,
by
whether a household is of size one (e.g., a single person household) or of
size two
or more (e.g., two or more persons in the household), by a station/affiliate,
by a
genre and/or by daypart. In some examples, categories include those related to
race, ethnicity, geography, language, metro vs. non-metro, etc. In still other
examples, categories include an age of the head of household, a room location
(e.g., a living room, a master bedroom, other bedroom, etc.), and/or the
presence
of children. In the event one or more categories improve results, it may be
used
for analysis, while categories that do not illustrate improvements or cause
negative impacts may be removed during the analysis.
[0038] As used herein, categories refer to classifications associated with
collected exposure minutes (also known as "viewing minutes"). Categories may
include, but are not limited to, a daypart associated with collected exposure
minutes (e.g., Monday through Friday from 5:00 AM to 6:00 AM, Sunday from
10:00 PM to 1:00 AM, etc.), a station associated with collected exposure
minutes
(e.g., WISN, WBBM, etc.), an age/gender associated with collected exposure
minutes (e.g., males age 2-5, females age 35-44, etc.), and a genre (e.g.,
kids
programs, home repair programs, music programs, sports programs, etc.)
associated with collected exposure minutes. In still other examples, the
categorizer 206 categorizes the acquired panelist audience data by education
(e.g.,
8 years or less, 9 years to high school graduate, some college to Bachelor
degree,
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master's degree or higher, etc.), life stage (e.g., pre-family, young family,
older
family, post family, retired, etc.) and/or a number of media presentation
devices
(e.g., television sets in the household. One or more combinations of
station/affiliate/genre/demographic attribute(s) may be categorized in
different
ways based on, for example, variations between data available for one or more
age/gender levels. For example, some local markets have ten stations in which
a
sample size for men age 45-54 may exhibit a data sample size of statistical
significance for seven of those ten stations. In other examples, a local
market
may have relatively fewer stations where the age/gender levels are of
sufficient
size to support statistical significance. In some such examples, the
age/gender
groupings are adjusted (e.g., from males age 40-45 to males age 40-50) to
increase an available sample size to achieve a desired statistical
significance.
[0039] To impute panelist audience data (e.g., exposure minutes, which is
sometimes referred to herein as "viewing minutes") to media data, the example
PM interface 202 identifies Local People Meter (LPM) data that has been
collected within a threshold period of time. On a relative scale, when dealing
with, for example, television exposure, an exposure index, which provides an
indication of how well LPM data accurately imputes exposure minutes, may be
computed in a manner consistent with Equation (1).
No. of imputed LPM exposure min. for ea. cat.
Exposure Index = ______________________________________________
No. of actual LPM exposure min. for ea. cat.
Equation (1)
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In the illustrated example of Equation (1), the exposure index is calculated
as the
ratio of the number of imputed LPM viewing minutes for each category of
interest
and the number of actual LPM viewing minutes for each category of interest.
[0040] The example exposure index of Equation (1) may be calculated on
a manual, automatic, periodic, aperiodic and/or scheduled basis to empirically
validate the success and/or accuracy of imputation efforts disclosed herein.
Index
values closer to one (1) are indicative of a greater degree of accuracy when
compared to index values that deviate from one (1). Depending on the type of
category associated with the collected exposure minutes, corresponding
exposure
index values may be affected to a greater or lesser degree based on the age of
the
collected data. FIG. 3 is an example plot 300 of exposure index values by
daypart. In the illustrated example of FIG. 3, the plot 300 includes an x-axis
of
daypart values 302 and a y-axis of corresponding exposure index values 304.
Index value data points labeled "1-week" appear to generally reside closer to
index values of 1.00, while index value data points labeled "3-weeks" appear
to
generally reside further away from index values of 1.00. In other words,
panelist
audience data that has been collected more recently results in index values
closer
to 1.00 and, thus, reflects an imputation accuracy better than panelist
audience
data that has been collected from longer than 1-week ago.
[0041] As described above, collected data that is more recent exhibits an
imputation accuracy that is better than an imputation accuracy that can be
achieved with relatively older collected data. Nonetheless, some data that is
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relatively older will still be useful, but such older data is weighted less
than data
that is more recent to reflect its lower accuracy. The example weighting
engine
210 applies a temporal weight, and applies corresponding weight values by a
number of days since the date of collection. Relatively greater weight values
are
applied to data that is relatively more recently collected. In some examples,
weight values applied to collected tuning minutes and collected exposure
minutes
are based on a proportion of a timestamp associated therewith. For instance, a
proportionally lower weight may be applied to a portion of collected minutes
(e.g., tuning minutes, exposure minutes) when an associated timestamp is
relatively older than a more recently collection portion of minutes.
[0042] FIG. 4 illustrates an example weighting allocation table 400
generated and/or otherwise configured by the example weighting engine 210. In
the illustrated example of FIG. 4, a MMPM household 106 acquired exposure
minutes (i.e., individualized panelist audience data) via a PM device (row
"A"),
and acquired household tuning minutes (i.e., minutes tuned in a household
without individualizing to a specific person within that household) via a MM
device (row "B"). The example individualized panelist audience and household
tuning minutes are collected over a seven (7) day period. In that way, the
most
recent day (current day 402) is associated with a weight greater than any
individualized panelist audience and/or household tuning minutes from prior
day(s). The example individualized panelist minutes of row "A" may be further
segmented in view of a desired category combination for a given household. As
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described above, categories that characterize a household may include a
particular
age/gender, size of household, viewed station, daypart, number of televisions,
life
stage, education level and/or other demographic attribute(s). For purposes of
illustration, examples described below, the household age/gender category for
the
household is male, age 45-54, the tuned station is associated with a premium
pay
channel (genre) during the daypart associated with Monday through Friday
between 6:00 PM and 7:00 PM.
[0043] In the illustrated example of FIG. 4, the weighting engine 210
applies a unitary weight value to the first six (6) days of individualized
panelist
minutes and household tuning minutes, and applies a weight value of six (6) to
the
most current day. While a value of six (6) is disclosed above, like the other
values used herein, such value is used for example purposes and is not a
limitation. In operation, the example weighting engine 210 of FIG. 2 may
employ
any weighting value in which the most current day value is relatively greater
than
values for one or more days older than the current day. The example weighting
engine 210 may generate a weighted sum of the collected individualized
panelist
audience exposure minutes (hereinafter referred to herein as "exposure
minutes")
in a manner consistent with example Equation (2), and may generate a weighted
sum of the collected household tuning minutes in a manner consistent with
example Equation (3).
Exposure Min.=F14/1(IEMd)1+ [W2EM,]
d=1
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Equation (2)
ni
Tuning Min. =11/1/1(1TMd)1+ [W2TM,1
d=-1
Equation (3)
In the illustrated examples of Equation (2) and Equation (3), WI reflects a
relatively lower weighting value than W2, in which W2 is the weighting value
associated with the current day exposure minutes value. Additionally, d
reflects
one of n days of the collected data prior to the current day, EM(/ reflects
exposure
minutes for corresponding days prior to the current day, TM,/ reflects
household
tuning minutes for corresponding days prior to the current day, EM, reflects
exposure minutes for the current day, and TM, reflects household tuning
minutes
for the current day.
[0044] In connection with example data shown in the illustrated example
of FIG. 4 (e.g., days one through six having 20, 10, 10, 0,0 and 10 exposure
minutes, respectively, the current day having 40 exposure minutes, days one
through six having 40, 30, 50, 0, 0 and 30 household tuning minutes and the
current day having 50 household tuning minutes), application of example
Equation (2) results in a weighted exposure minutes value of 290 and
application
of example Equation (3) results in a weighted household tuning minutes value
of
450. In some examples, the probability engine 212 calculates an imputation
probability that a MM panelist (e.g., a panelist household with only a MM
device
and no associated PM device) with the aforementioned category combination of
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interest (e.g., male, age 45-54 tuned to a premium pay channel during Monday
through Friday between the daypart of 6:00 PM and 7:00 PM) is actually viewing
this tuning session. The imputation probability is calculated by the example
probability engine 212 by dividing the weighted exposure minutes (e.g., 290
minutes) by the weighted household tuning minutes (e.g., 450 minutes) to yield
a
64.4% chance that the MM panelist with this same household category
combination is associated with this tuning behavior. While examples disclosed
herein refer to probability calculations, in some examples odds may be
calculated
to bound results between values of zero and one. For example, odds may be
calculated as a ratio of a probability value divided by (1-Probability). If
desired,
the odds may be converted back to a probability representation.
[0045] However, while the market researcher may have a particular
category combination of interest, a corresponding probability value accuracy
may
be improved when different probability calculation techniques are applied in
view
of corresponding available sample sizes of households sharing the particular
category combination of interest. As described in further detail below, if
collected LPM data associated with the category combination of interest (e.g.,
male, age 45-54, tuned to premium channel during 6:00 PM to 7:00 PM with three
household members, one television and the head of household have some college
credit or a bachelor's degree) is greater than a threshold value, then a cell
probability technique may yield a probability value with acceptable accuracy.
As
used herein, an acceptable accuracy relates to a sample size that is capable
and/or
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otherwise required to establish results having a statistical significance.
However,
in the event the collected Local People Meter (LPM) data associated with the
category combination of interest falls below the threshold value, then the
cell
probability technique yields unacceptably low probability value accuracy.
Instead, example methods, apparatus, systems and/or articles of manufacture
disclosed herein employ independent distribution probability calculations when
the collected LPM data associated with the category combination of interest is
below a threshold value, such as below a threshold value that is capable of
facilitating one or more calculations to yield results having statistical
significance.
[0046] The example category manager 214 of FIG. 2 identifies categories
and/or a category combinations of interest and determines whether the
particular
category combination of interest has a threshold number of households within a
donor pool. As described above, the donor pool may be a localized geography (a
Local People Meter (LPM), such as the panelist households within the
geographic
region of interest 104). However, as a geographic region of interest decreases
in
size, a corresponding number of qualifying households that match the category
combination of interest also decreases. In some cases, the number of
qualifying
households is below a threshold value, which causes one or more traditional
probability calculation methods (e.g., cell probability) to exhibit poor
predictive
abilities and/or results that fail to yield statistical significance. On the
other hand,
in the event the donor pool of households exceeds a threshold value count,
then
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such traditional probability calculation methods (e.g., cell probability)
exhibit
satisfactory predictive capabilities under industry standard(s).
[0047] In operation, the example category manager 214 of FIG. 2
generates a logical "AND" condition test for a set of categories of interest.
For
example, if the categories of interest include (1) a particular station, (2) a
particular daypart, (3) a particular number of household members, (4) a
particular
age, (5) a particular gender, (6) a particular number of television sets in
the
household, (7) a particular education level of the head of household, and (8)
a
particular life stage, then the category manager 214 determines whether the
combination of all eight categories of interest are represented by a threshold
number of households within the donor pool. If so, then the example category
manager 214 invokes the example cell probability engine 216 to calculate a
probability value of the category combination occurring within MMH households
108. Generally speaking, when a number of households sharing the combination
of categories of interest (e.g., items (1) through (8) above) are greater than
the
threshold value, a corresponding level of confidence in probability
calculation via
the cell probability technique is deemed satisfactory.
[0048] In the event a market researcher seeks probability information for a
male aged 50 watching a premium pay channel between the hours of 6:00 PM and
6:30 PM, the example category fit manager 220 of the illustrated example
identifies which previously established category groups already exist that
would
best fit this desired task. In other words, the specific and/or otherwise
unique
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research desires of the market researcher may not align exactly with existing
categorical groups collected by LPM and/or NPM devices. Instead, the example
category fit manager 220 identifies that the closest categorical combination
of
industry standard and/or otherwise expected data is with males age 45-54
between
the hours of 6:00 PM and 7:00 PM. The example minutes aggregator 222 of the
illustrated example identifies a total number of household tuning minutes in
all
households associated with the identified closest categorical combination, and
also identifies a total number of exposure minutes associated with the males
age
45-54 in such households. For example, the minutes aggregator 222 may identify
forty-five (45) qualifying households that have males 45-54 (e.g., the
household
could have more than just the males 45-54) in which a premium pay genre
station
was tuned between the hours of 6:00 PM to 7:00 PM, three household members
with one television set and a head of household having some college credit or
bachelor's degree.
[0049] Within these forty-five (45) qualifying households, the tuning
minutes aggregator 222 may identify two-hundred (200) household tuning
minutes total, but only one hundred and two (102) of those minutes were
associated with the males 45-54. The example imputation engine 224 of the
illustrated example calculates a probability for imputation as the ratio of
exposure
minutes for the males 45-54 and the total household tuning minutes for all
qualifying households in a manner consistent with example Equation (4).
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Probability of Imputation
Exposure Minutes by Persons of Interest
Tuning Minutes of Qualifying Households
Equation (4)
In the illustrated example of Equation (4), the probability of imputation
using the
examples disclosed above is 0.51 (i.e., 102 exposure minutes divided by 200
tuning minutes, in this example). In some examples, the probability value
calculated by the example cell probability engine 216 is retained and/or
otherwise
imputed to MMH households 108 based on a normal distribution, such as a
comparison of the calculated probability value to a random or pseudo-random
number. In the event the calculated probability value is greater than the
random
number, then the household member having the categorical combination of
interest is credited as viewing a tuning segment. In other words, the
household
tuning data is imputed to the MMH household 108 as exposure data for the
categorical combination of interest. On the other hand, in the event the
calculated
probability value is less than the random or pseudo-random number, then the
household member having the categorical combination of interest is not
credited
as viewing the tuning segment. In other words, the household tuning data is
not
imputed to the MMH household 108.
[0050] As discussed above, when the combinations of all categories of
interest are represented by a number of households less than a threshold value
within the donor pool, the cell probability calculation approach may not
exhibit a
level of confidence deemed suitable for statistical research. Generally
speaking, a
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number of households in a research geography of interest matching a single one
of the categories of interest may be relatively high. However, as additional
categories of interest are added, the number of households having an inclusive
match for all such categories decreases. In some circumstances, the number of
matching households available in the donor pool after performing a logical
"AND" of all categories of interest eventually results in a donor pool having
a
population lower than a threshold value, which may not exhibit statistical
confidence when applying the cell probability technique described above. In
such
examples, the probability engine 212 prevents a traditional cell probability
technique from being employed to calculate a probability of whether a
household
of interest should be credited with exposure behavior for the categorical
combination of interest (e.g., whether the male age 45-54 of the household
should
be credited with captured exposure (tuning) behavior of the household).
Instead,
the example probability engine 212 invokes the example independent
distribution
engine 218 when the number of households having the desired combination of
categories of interest is below a threshold value. As described in further
detail
below, instead of using a pool of households that match all categories of
interest,
households are employed that match some of the categories of interest are used
when calculating a probability of viewing.
[0051] In operation, the example category qualifier 226 of FIG. 2
identifies all households within the donor pool (e.g., within the LPM
collection
geography, such as the Charlotte DMA) that have the same set of key predictors
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(i.e., particular categories within the set of categories of interest). In
some
examples, key predictors reflect a set of categories that exhibit a relatively
greater
degree of success than other combinations of categories. For instance, a first
set
of key predictors may include a first set of categories related to a geography
of
interest, such as sunscreen products in geographic vicinity to ocean vacation
areas, or skiing products in geographic vicinity to mountain ranges. While
examples disclosed herein refer to a Local People Meter (LPM), such examples
are not limited thereto. In some examples, a National People Meter (NPM) may
be employed as a collection geography that reflects a relatively larger area,
such
as a nation. In particular, a subset of the example eight (8) original
categories of
interest may include (1) households matching a household size category, (2)
households matching a same member gender category, and (3) households
matching a same member age category. In other words, while the original eight
example categories of interest included the aforementioned three categories,
the
remaining categories are removed from consideration when identifying
households from the available data pool. For example, the remaining categories
are removed that are related to (4) households matching a same tuned station
category, (5) households matching a same education category, (6) households
matching a same number of television sets category, (7) households matching a
same daypart category, and (8) households matching a same life stage/household
size category.
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[0052] Because, in the illustrated example, the donor pool is constructed
with only MMPM households 106, the example category qualifier 226 retrieves
and/or otherwise obtains a total household tuning minutes value and a total
exposure minutes value for the available households meeting the
size/gender/age
criteria of interest (e.g., dimensions (1), (2) and (3) from above). For
example, if
the size/gender/age criteria of interest is for a household size of two or
more
people having a male age 45-54, then the example category qualifier 226
identifies a number of households from that size/gender/age subset.
[0053] FIG. 5 illustrates an example category subset map 500 created by
the independent distribution engine 226 of the example of FIG. 2. The example
independent distribution engine assembles household tuning minutes and
exposure minutes from subsets of the categories of interest. In the
illustrated
example of FIG. 5, the map 500 includes a total household tuning minutes count
and a total exposure minutes count associated with the key predictor
categories
502 of size/age/gender. In this example, the category qualifier 226 identified
a
total of two-hundred (200) households matching the size/gender/age criteria.
The
two-hundred households include a total of 4500 tuning minutes (i.e., minutes
that
identify a tuned station but do not identify a corresponding household member)
and a total of 3600 exposure minutes (e.g., minutes for an identified station
and
also identified individuals who were present in the audience).
[0054] The example proportion manager 228 of FIG. 2 selects one or
more remaining categories of interest that fall outside the key predictor
categories
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to determine corresponding available matching households, household tuning
minutes and exposure minutes. The example remaining categories may be
referred to as secondary predictors or secondary categories that affect the
probability of media exposure. While example key predictor categories
disclosed
herein include household size, gender and age, example methods, apparatus,
systems and/or articles of manufacture may include any other, additional
and/or
alternate type(s) of categories for the key predictors. Additionally, while
example
secondary categories disclosed herein include tuned station, education, number
of
media presentation devices (e.g., TV sets), daypart and lifestage, example
methods, apparatus, systems and/or articles of manufacture may additionally
and/or alternatively include any other type of categories as the secondary
categories.
[0055] For example, the proportion manager 228 of the illustrated
example selects one or more secondary categories to determine a corresponding
number of matching households, household tuning minutes and exposure minutes.
Again, and as described above, the temporal units of "minutes" are employed
herein as a convenience when discussing example methods, apparatus, systems
and/or articles of manufacture disclosed herein, such that one or more
additional
and/or alternative temporal units (e.g., seconds, days, hours, weeks, etc.)
may be
considered, without limitation. In the illustrated example of FIG. 5, a tuned
station category 504 (e.g., one of the secondary categories of interest) is
identified
by the proportion manager 228 to have eighty (80) households, which match the
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desired station of interest (e.g., station "WAAA"), in which those households
collected 1800 household tuning minutes and 1320 exposure minutes.
Additionally, the example proportion manager 228 of FIG. 2 selects an
education
category 506 (e.g., one of the secondary categories of interest) and
determines
that one-hundred and ten (110) households match the desired education level of
interest (e.g., households in which the head of household has 9 years of
school to
high school graduation), in which those households collected 1755 household
tuning minutes and 1200 exposure minutes. Further, the example proportion
manager 228 of FIG. 2 selects a number of television sets category 508 (e.g.,
one
of the secondary categories of interest) and determines that one-hundred (100)
households match the desired number of TV sets within a household value, in
which those households collected 2100 household tuning minutes and 1950
exposure minutes. Other example categories considered by the example
proportion manager 228 of FIG. 2 include a daypart category 510 (e.g., one of
the
secondary categories of interest), in which the proportion manager 228 of FIG.
2
determines that one-hundred (100) households match the desired daypart
category, in which those households collected 1365 household tuning minutes
and
825 exposure minutes. The example proportion manager 228 of FIG. 2 also
selects a life stage/household size category 512 (e.g., one of the secondary
categories of interest) and determines that seventy (70) households match the
desired type of life stage/household size value, in which those households
collected 1530 household tuning minutes and 1140 exposure minutes.
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[0056] Generally speaking, the proportion manager 228 of the illustrated
example identifies secondary category contributions of household tuning
minutes
and exposure minutes independently from the household tuning and exposure
minutes that may occur for only such households that match all of the desired
target combination of categories of interest. After each individual secondary
category contribution household tuning minute value and exposure minute value
is identified, the example distribution engine 230 calculates a corresponding
household tuning proportion and exposure proportion that is based on the key
predictor household tuning and exposure minute values. As described in further
detail below, the example distribution engine 230 calculates a household
tuning
proportion and an exposure proportion associated with each of the secondary
categories of interest (e.g., the tuned station cagegory 504, the education
category
506, the number of sets category 508, the daypart category 510 and the life
stage/size category 512). In other words, examples disclosed herein capture,
calculate and/or otherwise identify contributory effects of one or more
secondary
categories of interest by calculating and/or otherwise identifying a separate
corresponding tuning proportion and separate corresponding exposure proportion
for each one of the secondary categories. As described in further detail
below,
separate contributory effects of the one or more secondary categories are
aggregated to calculate expected tuning minutes and expected exposure minutes.
[0057] In the illustrated example of FIG. 5, the distribution engine 230
divides the household tuning minutes associated with the tuned station
category
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504 (e.g., 1800 household tuning minutes) by the total household tuning
minutes
associated with the key predictor categories 502 (e.g., 4500 household tuning
minutes) to calculate a corresponding tuned station category tuning proportion
514. Additionally, the distribution engine 230 of the illustrated example
divides
the exposure minutes associated with the tuned station category 504 (e.g.,
1320
exposure minutes) by the total exposure minutes associated with the key
predictor
categories 502 (e.g., 3600 household viewing minutes) to calculate a
corresponding tuned station category viewing proportion 516. For the sake of
example, the calculated tuned station category tuning proportion 514 is 0.40
(e.g.,
1800 household tuning minutes divided by 4500 total exposure minutes) and the
calculated tuned station category viewing proportion 516 is 0.37 (e.g., 1320
exposure minutes divided by 3600 total exposure minutes).
[0058] The example distribution engine 230 of FIG. 2 also calculates a
household tuning proportion and exposure proportion in connection with the
example education category 506. In the illustrated example of FIG. 5, the
distribution engine 230 divides the household tuning minutes associated with
the
education category 504 (e.g., 1755 household tuning minutes) by the total
household tuning minutes associated with the key predictor categories 502
(e.g.,
4500 household tuning minutes) to calculate a corresponding education category
household tuning proportion 518. Additionally, the example distribution engine
230 of the illustrated example divides the exposure minutes associated with
the
education category 506 (e.g., 1200 exposure minutes) by the total exposure
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minutes associated with the key predictor categories 502 (e.g., 3600 exposure
minutes) to calculate a corresponding education category exposure proportion
520. For the sake of example, the calculated education category household
tuning
proportion 518 is 0.39 (e.g., 1755 household tuning minutes divided by 4500
total
household tuning minutes) and the calculated education category exposure
proportion 520 is 0.33 (e.g., 1200 exposure minutes divided by 3600 total
exposure minutes).
[0059] The example distribution engine 230 of FIG. 2 also calculates a
household tuning proportion and exposure proportion in connection with the
example household sets category 508. In the illustrated example of FIG. 5, the
distribution engine 230 divides the household tuning minutes associated with
the
household sets category 508 (e.g. 2100 household tuning minutes) by the total
household tuning minutes associated with the key predictor categories 502
(e.g.,
4500 household tuning minutes) to calculate a corresponding household sets
category household tuning proportion 522. Additionally, the example
distribution
engine 230 of the illustrated example divides the exposure minutes associated
with the household sets category 508 (e.g., 1950 exposure minutes) by the
total
exposure minutes associated with the key predictor categories 502 (e.g., 3600
exposure minutes) to calculate a corresponding household sets category
exposure
proportion 524. For the sake of example, the calculated household sets
category
household tuning proportion 522 is 0.47 (e.g., 2100 household tuning minutes
divided by 4500 total household tuning minutes) and the calculated household
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sets category exposure proportion 524 is 0.54 (e.g., 1950 exposure minutes
divided by 3600 total exposure minutes).
[0060] The example distribution engine 230 of FIG. 2 also calculates a
household tuning proportion and exposure proportion in connection with the
example daypart category 510. In the illustrated example of FIG. 5, the
distribution engine 230 divides the household tuning minutes associated with
the
daypart category 510 (e.g., 1365 household tuning minutes) by the total
household
tuning minutes associated with the key predictor categories 502 (e.g., 4500
household tuning minutes) to calculate a corresponding daypart category
household tuning proportion 526. Additionally, the example distribution engine
230 of FIG. 2 divides the exposure minutes associated with the daypart
category
510 (e.g., 825 exposure minutes) by the total exposure minutes associated with
the key predictor categories 502 (e.g., 3600 exposure minutes) to calculate a
corresponding daypart category exposure proportion 528. For the sake of
example, the calculated daypart category household tuning proportion 526 is
0.30
(e.g., 1365 household tuning minutes divided by 4500 total household tuning
minutes) and the calculated daypart category exposure proportion 528 is 0.23
(e.g., 825 exposure minutes divided by 3600 total exposure minutes).
[0061] The example distribution engine 230 of FIG. 2 also calculates a
household tuning proportion and exposure proportion in connection with the
example life stage/size category 512. In the illustrated example of FIG. 5,
the
distribution engine 230 divides the household tuning minutes associated with
the
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life stage/size category 512 (e.g. 1530 household tuning minutes) by the total
household tuning minutes associated with the key predictor categories 502
(e.g.,
4500 household tuning minutes) to calculate a corresponding life stage/size
category household tuning proportion 530. Additionally, the example
distribution
engine 230 of FIG. 2 divides the exposure minutes associated with the life
stage/size category 512 (e.g., 1140 exposure minutes) by the total exposure
minutes associated with the key predictor categories 502 (e.g., 3600 exposure
minutes) to calculate a corresponding life stage/size category exposure
proportion
532. In this example, the calculated life stage/size category tuning
proportion 530
is 0.34 (e.g., 1530 household tuning minutes divided by 4500 total household
tuning minutes) and the calculated life stage/size category exposure
proportion
532 is 0.32 (e.g., 1140 exposure minutes divided by 3600 total exposure
minutes).
[0062] As described above, each of the target combinations of categories
of interest has an independently calculated household tuning proportion value
and
an independently calculated exposure proportion value. The example
distribution
engine 230 of FIG. 2 calculates the product of all household tuning proportion
values (e.g., the tuned station category household tuning proportion 514, the
education category household tuning proportion 518, the household sets
category
household tuning proportion 522, the daypart category household tuning
proportion 526, and the life stage/size category household tuning proportion
530)
to determine total expected household tuning minutes 534. Additionally, the
example distribution engine 230 of FIG. 2 calculates the product of all
household
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exposure proportion values (e.g., the tuned station category exposure
proportion
516, the education category exposure proportion 520, the household sets
category
exposure proportion 524, the daypart category exposure proportion 528, and the
life stage/size category exposure proportion 532) to determine total expected
exposure minutes 536. A final independent distribution is calculated by the
example distribution engine 230 in a manner consistent with example Equation
(5), and reflects a panelist behavior probability associated with the target
combination of categories of interest.
Independent Distribution Probability
Expected Exposure Minutes
Expected Household Tuning Minutes
Equation (5)
[0063] In the example exposure and household tuning minutes discussed
above, the resulting independent distribution probability is 0.52. In effect,
the
resulting independent distribution probability is interpreted as a male 45-54
who
lives in a three (3) person household, classified as an older family, with a
head of
house education of nine (9) years to high school graduate, with two (2)
television
sets in the household, has a 52% likelihood of watching station WAAA during
the
daypart of Monday through Friday from 9:00 AM to 12:00 PM.
[0064] While an example manner of implementing the imputation engine
110 of FIG. 1 is illustrated in FIGS. 2-5, 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. Additionally, while
an
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example manner of implementing the visitor imputation engine 112 of FIGS. 1, 2
and 11, and described in further detail below, one or more of the elements,
processes and/or devices illustrated in FIG. 11 may be combined, divided,
rearranged, omitted, eliminated and/or implemented in any other way.
Additionally, while an example manner of implementing the ambient tuning
engine 120 and the example on/off detection engine 130 of FIG. 1 is
illustrated in
FIGS. 10 and 15, respectively, and as described in further detail below, one
or
more of the elements, processes and/or devices illustrated in FIGS. 10 and 15
may
be combined, divided, rearranged, omitted, eliminated and/or implemented in
any
other way. Further, the example people meter interface 202, the example
categorizer 206, the example weighting engine 210, the example media meter
interface 204, the example probability engine 212, the example category
manager
214, the example cell probability engine 216, the example category fit manager
220, the example minutes aggregator 222, the example imputation engine 224,
the
example independent distribution engine 218, the example category qualifier
226,
the example proportion manager 228, the example distribution engine 230
and/or,
more generally, the example imputation engine 110 and/or the example visitor
imputation engine 112 of FIG. 1 may be implemented by hardware, software,
firmware and/or any combination of hardware, software and/or
firmware. Additionally, an example average visitor parameter (AVP) calculator
1102, an example distribution engine 1104, an example random number generator
1106, an example visitor assignor 1108, an example simultaneous tuning monitor
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1602, an example crediting manager 1604, an example station comparator 1606,
an example tuning type assignor 1608, an example automatic gain control
monitor
1610, an example code presence manager 1612, an example modeling engine
1614, an example code stacking manager 1616 and/or, more generally, the
example ambient tuning engine 120 of FIGS. 1 and 16 may be implemented by
hardware, software, firmware and/or any combination of hardware, software
and/or firmware. Thus, for example, any of the example people meter interface
202, the example categorizer 206, the example weighting engine 210, the
example
media meter interface 204, the example probability engine 212, the example
category manager 214, the example cell probability engine 216, the example
category fit manager 220, the example minutes aggregator 222, the example
imputation engine 224, the example independent distribution engine 218, the
example category qualifier 226, the example proportion manager 228, the
example distribution engine 230, the example average visitor parameter (AVP)
calculator 1102, an example distribution engine 1104, an example random number
generator 1106, an example visitor assignor 1108, the example simultaneous
tuning monitor 1602, the example crediting manager 1604, the example station
comparator 1606, the example tuning type assignor 1608, the example automatic
gain control monitor 1610, the example code presence manager 1612, the
example modeling engine 1614, the example code stacking manager 1616 and/or,
more generally, the example imputation engine 110, the example visitor
imputation engine 112, the example ambient tuning engine 120, and/or the
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example on/off detection engine 130 of FIG. 1 could be implemented by one or
more analog or digital circuit(s), logic circuits, programmable processor(s),
application specific integrated circuit(s) (AS IC(s)), programmable logic
device(s)
(PLD(s)) and/or field programmable logic device(s) (FPLD(s)).
[0065] 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 people meter interface 202, the example categorizer 206, the example
weighting engine 210, the example media meter interface 204, the example
probability engine 212, the example category manager 214, the example cell
probability engine 216, the example category fit manager 220, the example
minutes aggregator 222, the example imputation engine 224, the example
independent distribution engine 218, the example category qualifier 226, the
example proportion manager 228, the example distribution engine 230, the
example average visitor parameter (AVP) calculator 1102, an example
distribution engine 1104, an example random number generator 1106, an example
visitor assignor 1108, the example simultaneous tuning monitor 1602, the
example crediting manager 1604, the example station comparator 1606, the
example tuning type assignor 1608, the example automatic gain control monitor
1610, the example code presence manager 1612, the example modeling engine
1614, the example code stacking manager 1616 and/or, more generally, the
example imputation engine 110, the example visitor imputation engine 112, the
example ambient tuning engine 120, and/or the example on/off detection engine
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130 of FIG. 1 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 imputation engine 110, the example
visitor
imputation engine 112, the example ambient tuning engine 120, and/or the
example on/off detection engine 130 of FIGS. 1,2, 11, 16 and/or 21 may include
one or more elements, processes and/or devices in addition to, or instead of,
those
illustrated in FIGS. 2, 11, 16 and/or 21 and/or may include more than one of
any
or all of the illustrated elements, processes and devices.
[0066] Flowcharts representative of example machine readable
instructions for implementing the imputation engine 110, the visitor
imputation
engine 112, the ambient tuning engine 120 and the on/off detection engine 130
of
FIGS. 1, 2, 11, 16 and 21 are shown in FIGS. 6-9, 15, 17-19 and 22. In these
examples, the machine readable instructions comprise program(s) for execution
by a processor such as the processor 2312 shown in the example processor
platform 2300 discussed below in connection with FIG. 23. 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 2312, but the entire
program(s) and/or parts thereof could alternatively be executed by a device
other
than the processor 2312 and/or embodied in firmware or dedicated hardware.
Further, although the example program(s) is/are described with reference to
the
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flowcharts illustrated in FIGS. 6-9, 15, 17-19 and 22, many other methods of
implementing the example imputation engine 110, the example visitor imputation
engine 112, the example ambient tuning engine 120 and/or the example on/off
detection engine 130 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.
[0067] As mentioned above, the example processes of FIGS. 6-9, 15, 17-
19 and 22 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 processes of FIGS.
6-
9, 15, 17-19 and 22 may be implemented using coded instructions (e.g.,
computer
and/or machine readable instructions) stored on a non-transitory computer
and/or
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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.
[0068] The program 600 of FIG. 6 begins at block 602 where the example
people meter interface 202 acquires PM data associated with household members
from the PM devices located in the example MMPM households 106 that have
both MM devices and PM devices. As described above, the PM devices have
input (s) (e.g., buttons for each household member and a visitor button to
identify
their respective presence in the audience currently exposed to media). The
example PM interface 202 identifies collected data that is within a threshold
period of time from a current day in an effort to weight such data according
to its
relative age. As described above in connection with example Equation (1), an
accuracy of the viewing index is better when the corresponding collected data
is
more recent. The example categorizer 206 categorizes the acquired PM data
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based on one or more categories of interest (block 604). In some examples, the
categorizer 206 categorizes and/or otherwise identifies particular households
associated with one or more categories, such as an age/gender combination of
interest, a particular household size of interest, a particular life stage of
interest, a
particular viewed station/affiliate/genre of interest, a particular daypart of
interest,
a number of television sets of interest within the household (e.g., households
with
one television set, households with 2-3 television sets, households with three
or
more television sets, etc.), and/or an education level of the head of
household.
While a relatively large number of MMPM households 106 will have at least one
of the aforementioned categories, a substantially smaller number of MMPM
households 106 will represent all of the target combination of categories of
interest to a market researching during a market study.
[0069] As described above in connection with FIG. 4, the example
weighting engine 210 applies weights in proportions that are based on a number
of days since the date of collection of the donor data (block 606). The
example
media meter interface 204 also acquires household tuning data from media
meters
in the MMH households 108 (block 608). Depending on whether a threshold
number of households exist in the donor pool (e.g., the donor pool of MMPM
households in the region of interest 104) that match all of the categories of
interest, the example probability engine 212 will invoke a corresponding
probability calculation technique (block 610) as described in further detail
below
in connection with FIG. 7.
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[0070] FIG. 7 includes additional detail from the illustrated example of
FIG. 6. When generating probabilities, the example category manager identifies
categories of interest to use. Generally speaking, example methods, apparatus,
systems and/or articles of manufacture disclosed herein generate probabilities
based on a target combination of categories of interest such as, for example,
determining the likelihood of viewing for (1) a male age 45-54 (2) who lives
in a
three-person household, (3) classified as an older family (4) with the head of
the
household having an education of nine (9) years of school to high-school
graduate, (5) with two television sets in the household and (6) is watching
station
WAAA (7) between the daypart of 9:00 AM to 12:00 PM. The example category
manager 214 identifies categories of interest for which a probability of
viewing
(exposure) is desired (block 702), such as the example seven categories
referred-
to above. Based on the identified target combination of categories of
interest,
such as the example above having the male age 45-54 et al., the example
category
manager 214 determines whether the available pool of data, previously weighted
by the example weighting engine 210, includes a threshold number of households
that match all (e.g., all seven) of the target combination of categories of
interest
(block 704).
[0071] Assuming, for the sake of example, the threshold number of
households to match all of the categories of interest is thirty (30), and the
pool of
data includes that threshold amount of available households (block 704), the
example cell probability engine 216 is invoked by the probability engine 212
to
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calculate a probability value via a cell probability technique (block 706). On
the
other hand, if the pool of data does not satisfy the threshold amount of
thirty
households (e.g., has less than 30 households) (block 704), then the example
probability engine 212 invokes the example independent distribution engine 218
to calculate a probability value via an independent distribution technique
(block
708).
[0072] FIG. 8 illustrates an example manner of implementing the cell
probability calculation (block 706) of FIG. 7. In the illustrated example of
FIG. 8,
the category fit manager 220 culls and/or otherwise limits tuning and viewing
data
to fit previously established categories (block 802). As described above, in
the
event a market researcher has an interest for a male age 50, industry standard
panelist data acquisition techniques may not exactly fit the desired
demographic
category. Instead, the industry standard available data may be categorized in
terms of males between an age range of 45-54. Because the desired category of
interest is for a male age 50, the example category fit manager 220 identifies
the
closest relevant category grouping that will satisfy the market researcher,
which in
this example, includes the group of men between the ages of 45-54. The example
minutes aggregator 222 identifies a total number of household tuning minutes
from the selected category (block 804) and identifies a total number of
exposure
minutes from the selected category (block 806). In other words, of all the
households that match the categories of men age 45-54, the total number of
household tuning minutes and exposure minutes are identified.
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[0073] The example imputation engine 224 of FIG. 2 calculates a
probability for imputation based on the aforementioned totals (block 808). As
described above, the probability of imputation may be calculated by the
example
imputation engine 224 in a manner consistent with example Equation (4). The
example imputation engine 224 invokes a random number generator to generate a
random or pseudo-random number (block 810) and, if the resulting random or
pseudo-random number is less than or equal to the probability value (block
812),
a household member within a household having a media meter 108 is assigned as
a viewer of the tuning segment (block 814). On the other hand, in the event
the
resulting random or pseudo-random number is not less than or equal to the
probability value, then the household member within the household having the
media meter 108 is not assigned as a viewer of the tuning segment (block 81
6).
[0074] Returning to block 704 of FIG. 7, and continuing with the
assumption that the threshold number of households to match all of the
categories
of interest is thirty (30), and the pool of data fails to include that
threshold number
of qualifying households (block 704), then the example independent
distribution
engine 218 is invoked by the probability engine 212 to calculate a probability
value via an independent distribution technique (block 710).
[0075] FIG. 9 illustrates an example implementation of the independent
distribution probability calculation (block 708) of FIG. 7. In the illustrated
example of FIG. 9, the category qualifier 226 identifies all panelist
households
(e.g., LPM, NPM, etc.) within the donor pool that have the same set of key
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predictors (block 902). Additionally, the example category qualifier 226
identifies a corresponding number of total tuning minutes associated with the
key
predictors, and a corresponding number of total household exposure minutes
associated with the key predictors. As described above, key predictors may
refer
to a particular combination of a household size, a gender of interest within
the
household, and/or an age of interest within the household. For example, the
category qualifier 226 may identify all households within the donor pool that
have
two or more household members, in which one of them is a male age 45-54. For
illustration purposes, assume the example category qualifier identified two-
hundred (200) households that have two or more members therein, in which one
of them is a male age 45-54. Also assume that the combined number of
identified
households (200) reflect 4500 total household tuning minutes and 3600 total
exposure minutes.
[0076] In addition to key predictors having an influence on the probability
of viewing, one or more additional secondary predictors may also influence the
probability of viewing. As described above, the market researcher may have a
combined set or target combination of categories of interest, but a number of
households having all of those combined set of categories of interest does not
exceed a threshold value (e.g., thirty (30) households). However, while the
combined set of categories of interest may not be represented en masse from
the
donor pool, sub portions of the combined set or target combination of
categories
may include a relatively large representation within the donor pool. Example
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methods, apparatus, systems and/or articles of manufacture disclosed herein
identify independent sub portions (subgroups) of the combined set of
categories
of interest and corresponding households associated with each subgroup of
interest, which are applied independently to calculate a household exposure
probability.
[0077] The example proportion manager 228 identifies a number of
households from the key predictors group (e.g., 200 households having a size
2+
and a male age 45-54) that match a subgroup of interest (block 904). From the
subgroup of interest, the example proportion manager 228 identifies a number
of
household tuning minutes and divides that value by the total household tuning
minutes to calculate a household tuning proportion associated with the
subgroup
of interest (block 906). For example, if the subgroup of interest is all
households
tuned to the same station (e.g., WAAA) (e.g., the tuned station category) and
such
households reflect 1800 tuning minutes, then the example proportion manager
228 divides 1800 by the total household tuning minutes of 4500 to calculate a
tuned station category household tuning proportion of 0.40 (block 906). The
example proportion manager 228 also identifies a number of exposure minutes
and divides that value by the total exposure minutes to calculate an exposure
proportion associated with the subgroup of interest (e.g., the example tuned
station category) (block 908). For example, if the subgroup of interest is all
households tuned to the same station (e.g., WAAA) (e.g., the household tuned
station dimension) and such households reflect 1320 exposure minutes, then the
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example proportion manager 228 divides 1320 by the total exposure minutes of
3600 to calculate a tuned station category exposure proportion of 0.37 (block
908). If more subgroups of interest from the donor pool are available (block
910),
then the example proportion manager 228 selects the next subgroup of interest
(block 912) and control returns to block 904.
[0078] After category household tuning proportion values and exposure
proportion values have been calculated for each subgroup of interest, the
example
distribution engine 230 calculates the product of all household tuning
proportion
values and the total household tuning minutes (e.g., 4500 in this example)
from
the categories of interest (block 914), and calculates the product of all
exposure
proportion values and the total exposure minutes (e.g., 3600 in this example)
from
the categories of interest (block 916). A final independent distribution
probability
may then be calculated as the ratio of the exposure minutes and the household
tuning minutes in a manner consistent with example Equation (5). For example,
and as described above in connection with FIG. 5, the resulting ratio of
expected
exposure minutes (17.47) and expected household tuning minutes (33.65) may be
a value of 0.52. This resulting ratio indicates a 52% likelihood that the
panelist
member is a male age 45-54 that lives in a three person household, classified
as an
older family, with the head of household education of 9 years to high school
graduate, with two television sets in the household, and watching station WAAA
on Mondays through Fridays between 9:00 AM to 12:00 PM.
Visitor Imputation
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[0079] As disclosed above, persons imputation utilizes who is in the
household and what the household viewed such that for a given tuning segment,
one or more household members may be assigned and/or otherwise associated
with exposure. However, panelist households may have visitors that are exposed
to media within the household, in which the available visitor information is
limited to an age and a gender. As described above, the example PM includes
inputs (e.g., buttons) for each household member as well as button(s) for
entering
age and gender information for any visitors interacting with the media device
(e.g., a television). Example methods, apparatus, systems and/or articles of
manufacture disclosed herein apply a model to, in view of collected panelist
household visitor information, determine a number and corresponding age/gender
of visitors for households that do not employ a PM.
[0080] Visitor imputation disclosed herein exhibits some similarities to
persons imputation, and aspects of FIGS. 1-9 will be referred to in the
following
disclosure, as necessary. For example, both the persons imputation disclosed
above and the visitor imputation disclosed below utilize tuning and exposure
information to assign tuning segments and calculate ratios of exposure to
tuning
minutes. However, the visitor imputation viewing/tuning ratio, being the ratio
of
total visitor exposure to total household-level tuning exposure, reflects an
average
count of visitor exposure and not a probability. FIG. 10 further illustrates a
manner in which visitor information is processed as compared to household
member exposure information.
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[0081] In the illustrated example of FIG. 10, information for a first
household 1002, a second household 1004 and a third household 1006 exhibit
twelve, fifteen and eight minutes, respectively, of time tuned by a particular
station of interest (as determined by each household with both MM devices and
PM devices). While the illustrated example of FIG. 10 only includes three
households, such example is for illustrative purposes only and any number of
household may be considered. One member of the first household 1002 was
exposed to seven minutes out of twelve total tuning minutes, which results in
a
probability of viewing of 7/12(58.3%). In the second household 1004, a first
member was exposed to the full fifteen minutes, while a second member was
exposed to five minutes of the tuned duration, resulting in a probability of
viewing of (15+5)/(15+15) (66.7%). In the third household 1006, a first member
of that household was exposed to the full eight minutes, resulting in a
probability
of viewing of 8/8 (100%). An overall viewing probability for the example
households is determined in a manner consistent with example Equation 6.
HH Member Exposure Minutes
Probability Viewed = _____________________________________
HH Tuning Minutes (by Person)
Equation 6.
In the illustrated example of Equation 6, HH refers to household, and applying
the
example data from FIG. 10 to Equation 6 is shown in example Equation 7.
(7) + (15 + 5) + (8) 35
(12)+(15+15)+(8)50 = = .70
Equation 7.
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In the illustrated example of Equation 7, the households of interest for the
example demographic group of males age 25-34 have a viewing probability of
0.70. However, the following analysis of visitors in the same households of
interest calculates an average visitor viewing ratio in a manner consistent
with
example Equation 8.
E Visitor Exposure Minutes for Each Person
Ave. Visitor Viewing = _______________________________________
E Household Tuning Minutes
Equation 8.
Applying the example data from FIG. 10 to Equation 8 is shown in example
Equation 9.
(12 + 10) + (15) + (5) 42
=5 = 1.20
(12) + (15) + (8)
Equation 9.
In the illustrated example of Equation 9, the households of interest for the
visitors
that are reporting male age 25-34 exhibit an average of 1.20 minutes of
viewing
time for each tuned minute.
[0082] FIG. 11 is a schematic illustration of an example implementation
of the example visitor imputation engine 112 of FIG. 1. The example visitor
imputation engine 112 of FIG. 1 is constructed in accordance with the
teachings
of this disclosure, and includes an average visitor parameter (AVP) calculator
1102, a distribution engine 1104, a random number generator 1106, and a
visitor
assignor 1108. As described above, operation of the example visitor imputation
engine 112 may occur in conjunction with one or more portions of the example
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imputation engine 110 of FIGS. I and 2. In operation, the example people meter
interface 202 acquires PM data associated with visitors, in which the PM data
is
from the PM devices located in the example MMPM households 106 that have
both MM devices and PM devices. The example PM interface 202 identifies
collected visitor data that is within a threshold period of time from a
current day
in an effort to weight such data according to its relative age, as described
above in
connection with example Equation (1).
[0083] The example visitor imputation engine 112 invokes the example
categorizer 206 and/or example category qualifier 226 to categorize the
acquired
PM visitor data based on one or more categories of interest. As described
above,
for a given category or categories of interest, particular households
associated
with such categories are identified. Depending on whether a threshold number
of
households exist in the donor pool of visitor data that match all of the
desired
categories of interest, the example AVP calculator 1102 will invoke a
corresponding AVP calculation technique. For example, if more than a threshold
number of households exist that have the desired categories of interest (e.g.,
30
households), then the cell category approach may be used to calculate AVP,
while
the independent category approach may be used to calculate AVP, such as the
independent category approach described in connection with example FIG. 5.
[0084] In the event the threshold number of households exist for a given
set of categories of interest, the example AVP calculator 1102 calculates the
AVP
in a manner consistent with example Equation 8, and shown in FIG. 12. In the
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illustrated example of FIG. 12, categories of interest include particular
tuning
characteristics 1202 (e.g., households that watch Disney station between 12:30
and 5:00 PM on Mondays through Fridays) and particular household
characteristics 1204 (e.g., households in an Older Family Life Stage with 2
television sets). Additionally, the example analysis of FIG. 12 is performed
for
two types of visitors; one associated with females age 6-11 (column 1206) and
one associated with males age 55-64 (column 1208). Among households that
matched the desired set of characteristics of interest exhibited (as
determined by
collected PM visitor data), there were 3,892 minutes of visitor female age 6-
11
exposure (cell 1210) and 3,109 total household tuning minutes (cell 1212).
Application of example Equation 8 yields an AVP of 1.252 (cell 1214) for such
visitors that are female age 6-11. Additionally, households that matched the
desired set of characteristics of interest exhibited 1,081 minutes of visitor
male
age 55-64 exposure (cell 1216), and the total household tuning minutes (cell
1218) remains the same at 3,109. Application of example Equation 8 yields an
AVP of 0.348 (cell 1220) for such visitors that are male age 55-64.
[0085] On the other hand, in the event a threshold number of households
are not available for the desired categories of interest (e.g., less than 30
households), then the example AVP calculator 1102 calculates the AVP in a
manner consistent with Equation 8 after determining expected exposure minutes
and expected tuning minutes as category proportions, as described above in
connection with FIG. 5. FIG. 13 illustrates example tuning data and exposure
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data for target demographic of females age 6-11, where the threshold number of
households meeting the category combination of interest (e.g., life-stage =
older
family plus TV sets = 2) were not available. In the illustrated example of
FIG. 13,
households reflecting the category "Life Stage = Older Family" exhibited
443,940
female age 6-11 visitor exposure minutes (cell 1302) and 733,317 tuning
minutes
(cell 1304), and households reflecting the category "TV Sets = 2" exhibited
150,844 female age 6-11 visitor exposure minutes (cell 1306) and 285,877
tuning
minutes (cell 1308). Additionally, a total amount of female age 6-11 visitor
exposure minutes exhibited 1,741,474 minutes (cell 1310), and a total amount
of
household tuning minutes exhibited 8,200,347 minutes (cell 1312).
[0086] The example AVP calculator 1102 and/or the example distribution
engine 230 calculates an exposure proportion for each category of interest
1314
and a tuning proportion for each category of interest 1316. Continuing with
the
illustrated example of FIG. 13, the exposure proportion associated with the
life
stage category is the ratio of visitor exposure minutes to total viewing
minutes to
yield a proportion factor of 0.255 (result 1318). Additionally, the exposure
proportion associated with the TV sets category is 0.087 (result 1320). The
example tuning proportion associated with the life stage category is the ratio
of
household tuning minutes to total tuning minutes to yield a tuning proportion
of
0.089 (result 1322), and a tuning proportion of 0.035 associated with the TV
sets
category (result 1324). While the illustrated example of FIG. 13 includes two
(2)
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categories of interest, example methods, apparatus, systems and/or articles of
manufacture may include any number of categories of interest.
[0087] The example AVP calculator 1102 calculates an expected exposure
minutes value (cell 1326) as the product of the total exposure minutes (cell
1310)
and any number of calculated exposure proportion values that occur based on
the
number of categories of interest (e.g., result 1318 and result 1320). The
example
AVP calculator 1102 also calculates an expected tuning minutes value (cell
1328)
as the product of the total tuning minutes (cell 1312) and any number of
calculated tuning proportion values that occur based on the number of
categories
of interest (e.g., result 1322 and result 1324). In a manner consistent with
example Equation 8, the example AVP calculator 1102 calculates the AVP value
(cell 1330), which is used to determine a number of visitors and associated
ages,
as described in further detail below.
[0088] To determine a number of visitors and corresponding ages,
example methods, apparatus, systems and/or articles of manufacture disclosed
herein employ a distribution model. While the type of distribution model
described below is a Poisson distribution, this distribution is used for
example
purposes and not limitation. The Poisson distribution is a discrete
probability
distribution to express the probabilities of given numbers of events when
their
average rate is known, and applied herein to assign a number of visitors
watching
a given tuning segment (the previously calculated AVP being the known average
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rate). Probabilities for the Poisson distribution are defined in a manner
consistent
with example Equation 10.
* Ad)
p(i) = __________________________________
v!
Equation 10.
In the illustrated example of Equation 10, v reflects a number of visitors,
p(v)
reflects a probability calculated for "v" visitors, and )(1 reflects the AVP
for a
given demographic group of interest (e.g., female age 6-11). The example
distribution engine 1104 defines the distribution, such as the example Poisson
distribution above, and calculates probability values for a candidate number
of
visitors of interest, as shown in further detail in FIG. 14.
[0089] In the illustrated example of FIG. 14, eleven (11) different number
of visitor values 1402 are selected by the example distribution engine 1104
for a
first demographic group of interest 1404 (e.g., female age 6-11), and eleven
(11)
different number of visitor values 1406 are selected by the example
distribution
engine 1104 for a second demographic group of interest 1408 (e.g., male age 55-
64). For each discrete number of visitor value, the example distribution
engine
1104 calculates a corresponding probability value (see row 1410 associated
with
females age 6-11, and see row 1412 associated with males age 55-64). The
example distribution engine 1104 also calculates the corresponding cumulative
probabilities c(v) within each demographic group of interest (see row 1414
associated with females age 6-11, and see row 1416 associated with males age
55-
64). The example cumulative distribution of FIG. 14 allows arrangement of the
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probabilities between boundaries of zero and one as a matter of convenience
such
that the example random number generator 1106 can identify a lookup value.
[0090] For each demographic group of interest, the example visitor
assignor 1108 invokes the random number generator 1106 to generate a random
number that, when referenced against the cumulative distribution values,
reveals a
number of visitors to attribute to that demographic group of interest. For
example, if the random number generator produces a value of 0.757000 for the
first group 1404 associated with females age 6-11, then this value is
associated by
the example visitor assignor 1108 to fall within a visitor (v) value of 2.
Additionally, if the random number generator produces a value of 0.52700 for
the
second group 1408 associated with males age 55-64, then this value is
associated
by the example visitor assignor 1108 to fall within a visitor (v) value of 1.
As a
result, the first group 1404 is deemed to have two visitors, each having an
age
somewhere between 6-11, and the second group 1408 is deemed to have one
visitor having an age somewhere between the ages of 55-64. The example
random number generator 1106 is again employed to randomly assign
corresponding ages for each of the two visitors from the first group 1404
between
the ages of 6-11, and to randomly assign an age for the visitor from the
second
group 1408 between the ages of 55-64. While the aforementioned example was
performed for a target demographic group of interest of females between the
ages
of 6-11 and males between the ages of 55-64, the same process may be repeated
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for all demographic groups of interest to possibly assign other visitors to a
given
tuning segment.
[0091] The program 1500 of FIG. 15 begins at block 1502 where the
example PM interface 202 acquires and identifies data associated with visitors
that have selected visitor button(s) of panelist households within a region of
interest (e.g., a DMA). The example weighting engine 210 applies weights to
the
collected visitor data in proportions that are based on an amount of time
since the
date of collection of the donor data (block 1504). As described above, index
value data points that are more recent in time generally reside closer to an
index
value of 1.00 (see FIG. 3). In other words, an accuracy of the viewing index
is
better when the corresponding collected data is more recent.
[0092] When performing an analysis of a market of interest, the example
categorizer 206 categorizes the acquired PM data based on one or more
categories
of interest (block 1506). As described above, categories of interest may
include,
but are not limited to an age/gender combination of interest, a particular
household size of interest, a particular life stage of interest, a particular
viewed
station/affiliate/genre of interest, a particular daypart of interest, a
number of
television sets of interest within the household (e.g., households with one
television set, households with 2-3 television sets, households with three or
more
television sets, etc.), and/or an education level of the head of household.
While a
relatively large number of MMPM households 106 will have at least one of the
aforementioned categories, a substantially smaller number of MMPM households
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106 will represent all of the target combination of categories of interest to
a
market researching during a market study.
[0093] If the example visitor imputation engine 112 determines that a
threshold number of households associated with a preferred and/or otherwise
desired set of characteristics is satisfied (e.g., a threshold of at least 30
households) (block 1508), then the AVP value(s) are calculated by the example
AVP calculator 1102 in a manner consistent with FIG. 12 (block 1510). On the
other hand, in the event the example visitor imputation engine 112 determines
that
a threshold number of households is not satisfied (block 1508), then the AVP
value(s) are calculated by the example AVP calculator 1102 in a manner
consistent with FIG. 13 (block 1512). In particular, the example AVP
calculator
1102 and/or the example distribution engine 230 calculates an exposure
proportion for each category of interest, and calculates a tuning proportion
for
each category of interest. The product of each calculated category-specific
exposure proportion and total exposure minutes yields expected exposure
minutes, and the product of each calculated category-specific tuning
proportion
and total tuning minutes yields expected tuning minutes. The resulting
expected
exposure minutes and expected tuning minutes are applied to example Equation 8
to generate corresponding AVP values.
[0094] The example distribution engine 1104 defines a distribution model
to apply, such as the Poisson distribution (block 1514), and calculates
probabilities for any number of visitors (v) of interest in a manner
consistent with
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example Equation 10 (block 1516). For example, FIG. 14 illustrates eleven (11)
different number of visitor values 1402 from zero (0) to ten (10). The example
distribution engine 1104 also calculates cumulative probabilities so that
selections
from the distribution can be selected from values bounded between zero (0) and
one (1) (block 1518). The example distribution engine 1104 invokes the random
number generator 1106 to select a corresponding number of visitors (v) from
the
cumulative probabilities set for each demographic set of interest (block
1520).
Once each demographic set of interest has a determined number of visitors,
bounded age values are randomly selected for each visitor to be associated
with
tuning minutes (block 1522).
Ambient Tuning
[0095] As described above, employing a MM without a PM to
characterize household media exposure behavior facilitates substantial cost
savings when compared to employing PM devices, which may be physically
connected to media devices (e.g., televisions) and require professional
installation. For example, a MM may be mailed to a panelist, plugged in to
power
and function without professional installation and/or without connection to
the
panelist's electronics (e.g., media electronics such as DVD players, set top
boxes,
televisions, etc.). Although using MM devices without PMs result in
substantial
panelist household cost savings, some households have two or more media
devices located in rooms in a relative proximity to where sound from a first
media
device reaches the room in which the second media device is located, and vice
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versa. In such circumstances, a MM device from the first room may incorrectly
credit exposure minutes based on audio spillover associated with the second
media device in the second room (and vice versa). When MM devices incorrectly
credit exposure minutes, one or more household tuning estimates and/or
projections may be overreported/inflated. Example methods, apparatus, systems
and/or articles of manufacture disclosed herein distinguish instances of
ambient
tuning (e.g., due to spillover) from instances of real tuning.
[0096] FIG. 16 is a schematic illustration of an example implementation
of the example ambient tuning engine 120 of FIG. 1. The example ambient
tuning engine 120 of FIG. 1 is constructed in accordance with the teachings of
this disclosure. In the illustrated example of FIG. 16, the ambient tuning
engine
120 includes the PM interface 202 and the MM interface 204 as disclosed above
in connection with FIG. 2. Additionally, the illustrated example of FIG. 16
includes a simultaneous tuning monitor 1602, a crediting manager 1604, a
station
comparator 1606, a tuning type assignor 1608, a modeling engine 1614, a code
stacking manager 1616, an automatic gain control (AGC) monitor 1610 and a
code presence manager 1612.
[0097] In operation, the example PM interface 202 and the example MM
interface 204 collect household tuning data from MMPM households 106 and
MMH households 108 within a region of interest 104 (e.g., panelist households
within a direct marketing area (DMA)) that comprise an available data pool
(e.g.,
LPM households, NPM households, etc.). The example ambient tuning engine
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120 invokes the example simultaneous tuning monitor 1602 to identify whether
instances of simultaneous tuning minutes from collected household data are
either
ambient or real. As used herein, "simultaneous tuning" refers to instances
where
two or more meters within a household are detecting the same media (e.g., the
same television station). To illustrate, assume a first MM proximate to a
first
television in a first room detects station WAAA, and a second MM proximate to
a
second television in a second room also detects station WAAA. One possibility
that may be true is that both media devices (e.g., televisions) are powered on
and
tuned to station WAAA. However, another possibility is that the first
television is
on and tuned to station WAAA while the second television is tuned to another
station while muted. Yet another possibility is that the first television is
on and
tuned to station WAAA while the second television is not powered on. In such
circumstances, the second MM device may be detecting audio (e.g., spillover)
from the first television and, thus, improperly inflating media exposure
(e.g.,
consumption) metrics associated with the second television and/or household
members.
[0098] In some examples, the crediting manager 1604 identifies quantities
of time (e.g., minutes) where the MM device credited a station, and the
example
station comparator 1606 determines whether an AP device paired with the MM
device is also crediting the same station. If so, then the example tuning type
assignor 1608 assigns the corresponding tuning minute as real. On the other
hand, if the example crediting manager 1604 identifies minutes where the MM
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devices credited a station (e.g., embedded codes detected by the MM device,
embedded codes passed-on by the MM device and detected by the ambient tuning
engine 120 during post-processing, signature post processing, etc.), and the
example station comparator 1606 determines that the paired AP device is not
tuned to the same station, then the example station comparator 1606 determines
whether a separate metering device within the household is tuned to the same
station, such as another AP and/or MM device associated with a second
television
in a second room of the household. If so, then that household tuning minute is
deemed and/or otherwise labeled as ambient tuning/spillover, which should be
ignored to prevent improper overrepresentation. On the other hand, in the
event
the example station comparator 1606 determines that no other metering device
in
the household is also tuned to the same station, then the example tuning type
assignor 1608 assigns the minute as non-tuning. The example simultaneous
tuning monitor 1602 continues to evaluate each received tuning minute within
the
pool of data collected from the example panelist households 104.
[0099] To develop a stochastic approach to determine the occurrence of
spillover in which derived model coefficients are derived for use in MMH
households 108, the example ambient tuning engine 120 collects additional
predictive variables indicative of the occurrence or non-occurrence of
spillover.
The predictive variables are applied to a model, such as a regression model,
to
generate coefficients/parameters that facilitate calculation of a probability
that
spillover is occurring or not occurring within the MMH households 108. At
least
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three predictive variables indicative of the occurrence or non-occurrence of
spillover include automatic gain control (AGC) values, the presence of
embedded
codes, such as final distributor audio codes (FDACs), and the duration of the
collected segment.
[00100] Generally speaking, by comparing AGC values between
two separate MM devices within a household (e.g., calculating a difference
therebetween), an indication of spillover may be evaluated. A MM device placed
relatively close to a first television, for example, is more likely to have a
low
AGC value because of a higher relative volume when compared to an AGC value
associated with sound from a television relatively farther away. AGC values
are
typically established by acoustic gain circuits that apply greater gain (e.g.,
amplification) when attempting to discern and/or otherwise detect sound energy
that has a relatively low volume than when attempting to detect sound energy
of a
higher volume. Volume may be lower, for example, due to a greater distance
from a source of the originating sound energy. Additionally, quantities and/or
densities of detected codes per unit of time are additional example predictive
variable(s) that may be applied to the model to derive an indication of the
likelihood of the occurrence or non-occurrence of spillover. Without
limitation,
segment duration is another predictive variable useful in the indication of
spillover, as described in further detail below.
[00101] The example AGC monitor 1610 of FIG. 16 assigns each
collected minute to a corresponding AGC value. The example code presence
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manager 1612 of FIG. 16 assigns each collected minute an indicator
corresponding to the presence or absence of an embedded code. In some
examples, code detection activit(ies) may occur during post processing of raw
audio information collected by meter(s). In other examples, the codes are
detected in real time or near real time. The example ambient tuning engine 120
of
FIG. 16 segregates instances of simultaneous tuning minutes based on whether
embedded codes have been detected. For example, the modeling engine 1614
prepares a regression model with dependent variables reflecting the previously
determined real or ambient status occurrence(s). The example AGC monitor 1610
determines a minimum (e.g., lowest) AGC for the household devices for a
particular monitored time period and/or collected set of audio data. For each
device and minute, the example AGC monitor 1610 determines an AGC
difference value with respect to the minimum AGC value and each collected
minute.
[00102] The example code presence manager 1612 of FIG. 16
identifies one of three possible scenarios for the type and presence of codes
in
collected MM data for devices (e.g., TV sets, radio, etc.) within a household.
A
first possible scenario is that no codes are present in the collected MM data
for
any of the devices of the household of interest. A second possible scenario is
that
the collected MM data has some codes for some of the devices within the
household, but not all of the devices have associated codes detected in the
collected minutes. A third possible scenario is that the collected MM data for
the
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household has codes in all of the data collected. In other words, each
collected
minute of tuning data has corresponding codes in all devices within that
household.
[00103] If none of the meters within the household have collected
codes in the collected minutes, then the example simultaneous tuning monitor
1602 of FIG. 16 places a greater weight on a type of segment duration for the
household. For instance, if a television is tuned to station WAAA, then the MM
device closest to that television will have a relatively longer collected
segment
duration than a MM device located further away from the television. The sound
emanating from a television located further away from that same MM device may
fluctuate in intensity such that the MM device may not capture full segment
durations. The example simultaneous tuning monitor 1602 of FIG. 16 identifies,
for each household, a longest (e.g., maximum) segment duration and calculates
a
duration difference to be applied to the logistic regression fit of the
collected data
in a manner consistent with example Equation (11).
L [Probability (Simultaneous Minute = Ambient)
og ]
Probability (Simultaneous Minute = Real)
= Bo + + ===+ BkXk
Equation (II)
In the illustrated example of Equation (11), the model has the response
(dependent) dependent variable as the ambient or real value to which each
simultaneous tuning minute is assigned. Independent variables X1, Xk may be
coded and/or otherwise categorized with model coefficients B/, ..., Bk.
Categories
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may be represented by any scale, such as AGC values ranging from zero to one
hundred having sub-groups therein.
[00104] If some of the
meters within the household have collected
codes in the collected minutes (e.g., collected raw audio with codes embedded
therein and subsequently identified during audio data post processing), but
others
do not, then the example modeling engine 1614 of FIG. 16 builds a model in a
manner consistent with example Equation (11) using data associated with the
AGC difference values. If all of the meters within the household have
collected
codes in the collected minutes, then the example code stacking manager 1616
determines a maximum unstacked count and a maximum stacked count for the
household devices. As used herein, a stacked code refers to an instance of
code
repair and/or imputation when part of a code is detected. In such cases where
the
entire code content is not correctly collected by the MM device, a stacking
procedure fills-in portions of the code that were not detected. Generally
speaking,
meter devices (e.g., MMs) that are relatively closer to the media device
(e.g.,
television) will have a better ability to collect unstacked codes that are not
in need
of repair or padding due to, for example, a relatively closer proximity to the
meter
device(s). However, when the meter devices operate at a distance relatively
farther away from the monitored device, the ability for the meter devices to
accurately collect the entire code becomes more difficult and erroneous. The
code stacking manager 1616 of the illustrated example determines a difference
between meter devices within the household of the stacked and unstacked count
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values, which is applied to the model. Additionally, the simultaneous tuning
monitor 1602 of the illustrated example identifies a maximum average of
seconds
of collected code for all meter devices within the household, and calculates a
difference between those household devices. The difference of seconds of
collected code, the stacked and unstacked code count difference values, and
the
AGC difference values are applied to the example model of Equation (11) to
derive the corresponding model coefficients (e.g., Bl, Bk).
[00105] As described above, the example code presence manager
1612 of FIG. 16 identifies one of the three possible scenarios for the type
and
presence of codes in the household and, based on the detected scenario,
applies a
different combination of predictive variables (e.g., AGC values, segment
duration,
count of stacked versus unstacked codes). Each of these scenarios applies the
corresponding predictive variables to the example model of Equation (11), and
the
example modeling engine 1614 of FIG. 16 calculates a probability of spillover
in
a manner consistent with example Equation (12).
1
Probability (Minute = Ambient) = ___________________________
[1 + e-(Bo+B,x,+===+Bkxid
Equation (12)
Each simultaneous tuning minute may be identified as either ambient tuning or
real tuning based on the resulting probability value and a threshold
established by,
for example, a market researcher. For example, if the probability value is
greater
than or equal to 0.50, then the minute may be designated as ambient tuning. On
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the other hand, the minute may be designated as real tuning for probability
values
less than 0.50.
[00106] The program 1700 of FIG. 17 begins at block 1702 where
the example PM interface 202 and the example MM interface 204 of the
illustrated example collect tuning data from MMPM households 106 and MMH
households 108 within panelist households 104. The example simultaneous
tuning monitor 1602 of FIG. 16 identifies whether simultaneous tuning minutes
within such households are either ambient or real (block 1704), as described
in
further detail below in connection with FIG. 18.
[00107] FIG. 18 includes additional detail from the illustrated
example of FIG. 17. When identifying whether simultaneous tuning minutes are
ambient or real, the example crediting manager 1604 of FIG. 16 identifies
minutes
where a MM device (e.g., a MM device in the MMPM household 106) within the
household of interest credited a station (block 1802). The station comparator
1606 of FIG. 16 determines whether an AP device in the household of interest
is
also crediting the same station as the MM device at the same time (block
1804).
In some examples, the crediting manager 1604 compares a timestamp associated
with minutes collected from the MM device with a timestamp associated with
minutes collected from the PM device of the same household. If the timestamps
match and the detected stations are the same, then the example tuning type
assignor 1608 of FIG. 16 assigns that corresponding minute as real tuning
(block
1806). The example simultaneous tuning monitor 1602 determines if there are
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additional minutes from the household of interest to analyze (block 1808). If
so,
then the example simultaneous tuning monitor 1602 selects the next minute for
analysis (block 1810) and control returns to block 1804.
[00108] If the example station comparator 1606 of FIG. 16
determines that the AP device is not crediting the same station as the MM
device
within the household (block 1804), which could be due to multiple media
presentation devices (e.g., TV sets) within the household being tuned to
different
stations or turned off, then the example station comparator 1606 of the
illustrated
example determines whether the other device is tuned to the same station
(block
1812). As described above, example methods, apparatus, systems and/or articles
of manufacture disclosed herein employ MMPM households 106 that have both
MM devices and PM devices so that ambiguity of actual device behavior is
eliminated. Once model coefficients are generated based on such observed
behaviors in the MMPM households 106, the data collected from the MMH
households 108 may be imputed with the coefficients to allow an indication of
spillover to be calculated. As such, panelist households without PMs can be
effectively utilized. As a result, a greater number of panelist households may
be
implemented in the example region of interest 104 without the added capital
expense of PM devices that require professional installation, relatively
greater
training, and/or more routine maintenance than MM devices.
[00109] If the example station comparator 1606 determines that the
other device in the household is tuned to the same station (block 1812) (e.g.,
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based on the detection of the same codes), then the example tuning type
assignor
1608 assigns the corresponding minute as ambient tuning (also referred to
herein
as spillover) (block 1814). On the other hand, if the example station
comparator
1606 determines that the other device in the household is not tuned to the
same
station (block 1812), then the example tuning type assignor 1608 of the
illustrated
example assigns the corresponding minute as a non-tuning minute (block 1816).
[00110] Returning to FIG. 17, the example AGC monitor 1610 of
the illustrated example assigns each minute to a corresponding AGC value
(block
1706). As described above, the AGC value associated with a collected minute in
some example predictive variables assist in calculating a probability of
ambient
tuning occurrences. Additionally, another example predictive variable
discussed
above includes the presence or absence of embedded codes within the collected
minute of media. The example code presence manager 1612 of the illustrated
example assigns each minute an indicator regarding the presence or absence of
embedded codes (block 1708). The example ambient tuning engine 120
segregates instances of simultaneous tuning minutes based on whether such
embedded codes have been detected (block 1710), as described further below in
connection with FIG. 19.
[00111] In the illustrated example of FIG. 19 (block 1710), the
modeling engine 1614 of the illustrated example prepares a regression model
with
dependent variables reflecting corresponding real or ambient status indicators
(block 1902). For each household device of interest, the example AGC monitor
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1610 determines a minimum AGC value across two or more MM devices (block
1904) and determines a difference value therebetween (block 1906). In view of
the possibility that the MM devices within the household of interest may
either
collect no codes, collect some codes for some of the minutes and not others,
or
collect codes for all of the minutes, the example code presence manager 1612
of
the illustrated example identifies which circumstance applies (block 1908).
[00112] If the example code presence manager 1612 of the
illustrated example identifies a first category in which no codes are
detected, the
example simultaneous tuning monitor 1602 determines a maximum segment
duration associated with the MM devices (block 1910), and calculates a
difference
therebetween (block 1912). The example modeling engine 1614 of FIG. 16
applies a logistic regression fit to the collected data in a manner consistent
with
example Equation (11) (block 1914), as described above. In particular, when
the
household does not detect any codes in the collected minutes, the example
model
of Equation (11) is tailored to consider (1) the AGC values and (2)
differences in
collected segment durations (block 1914).
[00113] If the example code presence manager 1612 of the
illustrated example identifies a second category in which some codes are
detected
in some minutes, while no codes are detected in other minutes (block 1908),
then
the example modeling engine 1614 of FIG. 16 applies a logistic regression fit
to
the collected data in a manner consistent with example Equation (11) (block
1916). However, in this application of example Equation (11), the model
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employs (1) the AGC values and (2) the presence or absence of codes in
corresponding collected minutes (block 1916).
[00114] If the example code presence manager 1612 identifies a
third category in which all codes are detected in all collected minutes (block
1908), then the example code stacking manager 1616 of this example determines
whether the detected codes are, themselves, complete (block 1918). As
described
above, while the example MM devices may detect and/or otherwise capture codes
that may have been embedded in media from a media device (e.g., a television),
the quality of the detected codes may differ. Such differences may be due to,
for
example, the MM device collecting audio from a television that is relatively
far
away from where the MM device is located. In such situations, one or more
stacking operations may supplement missing portions of the detected code with
accurate code data. The example code stacking manager 1616 of this example
identifies a difference between MM devices in the household regarding the
number of stacked codes versus unstacked codes detected (block 1920).
Additionally, the example simultaneous tuning monitor 1602 calculates an
average (e.g., a maximum average) seconds of code per metering device in the
household, and a corresponding difference value between each metering device
(block 1922). The example modeling engine 1614 of the illustrated example
applies the logistic regression fit to the collected data in a manner
consistent with
example Equation (11) (block 1924). However, in this application of example
Equation (11), the model employs (1) the AGC values, (2) the differences
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between stacked/unstacked embedded codes and (3) the differences between the
average number of seconds of code between metering devices (block 1924).
[00115] Returning to
FIG. 17, the example modeling engine 1614
of this example applies calculated coefficients from the model (e.g., Equation
(11)) to a probability calculation in a manner consistent with example
Equation
(12) to determine a probability that tuning for a given minute should be
categorized as spillover (ambient tuning) (block 1712).
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On/Off Detection
[00116] As described above, employing a MM to characterize
household media viewing behavior may be performed in a stochastic manner
rather than by employing a PM to save money that would otherwise be spent on
the relatively expensive PM devices. When a MM device is employed to collect
audio signal (tuning) data from a household, some of the collected minutes may
include codes (e.g., embedded codes collected in the raw audio and passed on
to
the on/off detection engine 130 for post processing), some of the collected
minutes may be analyzed via signature analysis (e.g., analysis of the raw
audio
collected by the MM device and passed on to the on/off detection engine 130
for
audio signature comparison against one or more signature database(s)), and
some
of the collected minutes may have neither codes nor have corresponding
signature
matches for media identification.
[00117] FIG. 20 illustrates an example crediting chart 2000 having
a block of twenty-four (24) hours of tuning data collected from an example MM
device in an example household. In the illustrated example of FIG. 20, some
portions of collected minutes from the household are associated with codes
2002,
which also indicates that a media device (e.g., a television) within the
household
is turned on. Additionally, some portions of collected minutes from the
household are associated with signatures of the detected media 2004 that, when
compared to a reference database, allow identification of media. However,
still
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other portions of collected minutes from the household have neither codes nor
signatures that match known media in a reference database 2006.
[00118] Minutes that have neither codes nor corresponding
signatures that may be used with a reference database are referred to herein
as all
other tuning (AOT) minutes. In such circumstances with a PM device, the media
device (e.g., television) will be detected in an on state (e.g., power status
ON
based on a power status detector of the PM device), but no station and/or
media
can be credited with tuning. In other circumstances, a media device may be in
a
muted state or an off state (e.g., power status OFF), thus no audio is emitted
that
can be used for crediting. Example methods, systems, apparatus and/or articles
of
manufacture disclosed herein apply a stochastic manner of determining whether
AOT minutes are associated with an off state or an on state, which may be
associated with other media device usage separate from media programming
(e.g.,
video game usage, video conferencing, etc.).
[00119] FIG. 21 is a schematic illustration of the example on/off
detection engine 130 of FIG. 1 and constructed in accordance with the
teachings
of this disclosure. In the illustrated example of FIG. 21, the on/off
detection
engine 130 includes the PM interface 202, the MM interface 204, the AGC
monitor 1610 and the modeling engine 1614 as disclosed above in connection
with FIGS. 2 and 16.
[00120] In operation, the example PM interface 202 collects
minutes from a PM device (e.g., an active/passive people-meter) within the
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household related to three categories of media device usage. A first category
of
media device usage associated with some collected minutes is related to a
particular station or media, such as media identified by way of codes or
signature
matching. A second category of media device usage associated with other
collected minutes is related to instances of non-programming related usage,
such
as video game play, video conferencing activity, home picture viewing, etc. A
third category of media device usage associated with still other collected
minutes
is related to instances where the media device is powered off.
[00121] The example
MM interface 204 also collects minutes from
a MM device within the household. As described above, because the MM
interface 204 is not physically connected to the media device, it cannot
directly
verify whether the media device is powered on and, instead, collects only
audio-
based information via one or more built-in microphones. The example MM
interface 204 may collect minutes data that either credits a station or media,
or
designates the collected minutes as AOU. The example AGC monitor 1610
collects AGC values from each of the example MM interface 204 and the
example PM interface 202 for each corresponding minute, and the example
modeling engine 1614 prepares a regression model to fit the collected data in
a
manner consistent with example Equation (13).
[Probability (Given Minute = HUT)]
Log = Bo + Bi + = == + BkXk
Probability (Given Minute = OFF)]
Equation (13)
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In the illustrated example of Equation (13), HUT is indicative of a "household
using television" on (e.g., an ON power status), off is indicative of an OFF
power
status, and the independent variables (X) include AGC values, daypart
information and/or a number of minutes since a code reader credit occurred.
[00122] The example modeling engine 1614 uses derived
coefficients (B) to calculate a probability for each minute as either on (HUT)
or
off in a manner consistent with example Equation (14).
1
Probability (AOU Minute = HUT) = ____________________________
[1+ e-(Bo+B1x1+=..+Bkxk)]
Equation (14)
[00123] The program 2200 of FIG. 22 begins at block 2202 where
the example PM interface 202 collects minutes from the PM device related to
minutes where a station was credited, minutes where the television was in use,
but
had no crediting, and where the television was powered off. The example MM
interface 204 collects minutes from the MM device in the dual panel household
related to minutes where a station was credited, and minutes of AOU (block
2204). The example AGC monitor 1610 collects AGC values associated with
each minute collected by the example PM interface 202 and MM interface 204
(block 2206).
[00124] The example modeling engine 1614 prepares a model based
on AGC values, day parts and a number of minutes since a last MM device credit
in a manner consistent with example Equation (13) (block 2208). The model may
include, but is not limited to, a regression model, in which coefficients may
be
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derived after fitting the collected data. The derived model coefficients are
used
by the example modeling engine 1614 to calculate a probability that any
particular
minute of interest was associated with either an on state or an off state of
the
household media device. These derived coefficients may be associated with
panelist households within the region of interest 104 having only MM devices
108
(block 2210).
[00125] FIG. 23 is a block diagram of an example processor
platform 2300 capable of executing the instructions of FIGS. 6-9, 15, 17-19
and
22 to implement the ambient tuning engine 120, the imputation engine 110, the
visitor imputation engine 112, and the on/off detection engine 130 of FIGS. 1,
2,
11, 16 and 21. The processor platform 2300 can be, for example, a server, a
personal computer, an Internet appliance, a digital video recorder, a personal
video recorder, a set top box, or any other type of computing device.
[00126] The processor platform 2300 of the illustrated example
includes a processor 2312. The processor 2312 of the illustrated example is
hardware. For example, the processor 2312 can be implemented by one or more
integrated circuits, logic circuits, microprocessors or controllers from any
desired
family or manufacturer.
[00127] The processor 2312 of the illustrated example includes a
local memory 2313 (e.g., a cache). The processor 2312 of the illustrated
example
is in communication with a main memory including a volatile memory 2314 and a
non-volatile memory 2316 via a bus 2318. The volatile memory 2314 may be
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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 2316 may be implemented by flash memory
and/or any other desired type of memory device. Access to the main memory
2314, 2316 is controlled by a memory controller.
[00128] The processor platform 2300 of the illustrated example also
includes an interface circuit 2320. The interface circuit 2320 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.
[00129] In the illustrated example, one or more input devices 2322
are connected to the interface circuit 2320. The input device(s) 2322
permit(s) a
user to enter data and commands into the processor 2312. 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.
[00130] One or more output devices 2324 are also connected to the
interface circuit 2320 of the illustrated example. The output devices 2324 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 light emitting diode (LED), a printer
and/or
speakers). The interface circuit 2320 of the illustrated example, thus,
typically
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includes a graphics driver card, a graphics driver chip or a graphics driver
processor.
[00131] The interface circuit 2320 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 2326 (e.g., an
Ethernet connection, a digital subscriber line (DSL), a telephone line,
coaxial
cable, a cellular telephone system, etc.).
[00132] The processor platform 2300 of the illustrated example also
includes one or more mass storage devices 2328 for storing software and/or
data.
Examples of such mass storage devices 2328 include floppy disk drives, hard
drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and
digital
versatile disk (DVD) drives.
[00133] The coded instructions 2332 of FIGS. 6-9, 15, 17-19 and 22
may be stored in the mass storage device 2328, in the volatile memory 2314, in
the non-volatile memory 2316, and/or on a removable tangible computer readable
storage medium such as a CD or DVD.
[00134] From the foregoing, it will be appreciated that the above
disclosed methods, apparatus and articles of manufacture allow audience
measurement techniques to occur with a substantially larger quantity of
households, in which each household has a substantially lower metering
equipment cost by employing audio-based code reader devices instead of
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relatively more expensive people meter devices. Examples disclosed herein
permit a determination of behavior probability that can be applied to
households
that do not have a People Meter device and, instead, employ a media meter that
captures audio without a physical connection to a media device (e.g., a
television).
Such examples allow behavior probability calculations based on utilization of
other households that include the People Meter device, in which the
calculations
reveal behavior probabilities in a stochastic manner that adheres to
expectations
of statistical significance.
[00135] Example methods, systems, apparatus and/or articles of
manufacture disclosed herein also facilitate a stochastic manner of
determining a
probability of ambient tuning within households that do not employ a People
Meter device. In some examples disclosed herein, both a panelist audience
meter
(e.g., a People Meter) and a media meter (e.g., captures audio without a
physical
connection to a media device) are employed to obtain data associated with
media
code status and one or more automatic gain control (AGC) values. Based on the
obtained code status and AGC values, examples disclosed herein create model
coefficients that may be applied to households with only media meters in a
manner that determines a probability of ambient tuning that upholds
expectations
of statistical significance. Additionally, data obtained related to AGC values
are
disclosed herein to be used with daypart information to calculate model
coefficients indicative of whether a media device (e.g., a television) is
powered on
or powered off.
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[00136] Additional example methods, systems, apparatus and/or
articles of manufacture disclosed herein identify probabilities of a number of
visitors in a household and their corresponding ages. In particular, examples
disclosed herein calculate an average visitor parameter (AVP) based on
exposure
minutes and tuning minutes, which are further applied to a Poisson
distribution to
determine a probability of having a certain number of visitors in a household.
Such probabilities are in view of a target demographic of interest having a
particular age range, which may be selected based on inputs from a random
number generator.
[00137] 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.
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