Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for adjusting a tuning event. Examples disclosed herein include determining an initial adjustment factor for a group of stations and a combination of metadata categories associated with panel data and census data. Examples disclosed herein further include determining a final adjustment factor for the group of stations based on the initial adjustment factor and a bias correction factor. Example disclosed herein further include adjusting weighted minutes of the tuning event based on the final adjustment factor, the tuning event corresponding to the group of stations and the combination of metadata categories, the adjusted tuning event to be credited to media.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for editing tuning data collected via automated content recognition. Examples include determining whether a time conflict exists between first tuning data corresponding to a first tuning event and second tuning data corresponding to a second tuning event. Examples also include that, in response to determining that the time conflict exists, creating a third tuning event based on the first tuning data, the second tuning data, and one or more criteria. Examples also include that modifying at least one of the first tuning event or the second tuning event based on the third tuning event. Examples also include that crediting a media presentation by the presentation device based on edited tuning data, the edited tuning data including the first modified tuning event, the second modified tuning event, and the third tuning event.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to optimize projection of big data beyond its footprint. An example apparatus includes memory, instructions, and processor circuitry to access panel audience sizes corresponding to subscribers of a media provider, a subset of the subscribers of the media provider, and corresponding to a media network determine a relative percent difference between the third panel audience size and the fourth panel audience size, when the relative percent absolute difference satisfies a first threshold, determine percentages of demographic groups represented in the panel audience sizes, determine differences associated with the demographic groups, and when at least one of the differences corresponding to at least one of the demographic groups satisfies a second threshold, determine the subscribers, the subset of the subscribers, and the at least one of the demographic groups as useable to determine an audience size of the media network.

Abstract: Methods, apparatus, systems, and articles of manufacture to determine an additive reach adjustment factor for audience measurement are disclosed. An example apparatus for additive reach adjustment includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to identify a first probability, the first probability associated with a population tuning to a marketing campaign, the tuning including missing data, identify a second probability, the second probability associated with the population not tuning to the marketing campaign or the tuning including missing data, determine an additive reach adjustment based on a compound probability and a no-tuning probability, the compound probability and the no-tuning probability determined using the first probability and the second probability, and credit a population exposed to the marketing campaign to include missing impressions based on the additive reach adjustment.

Abstract: An example apparatus includes an advertisement determiner to identify a first plurality of respondents that received an addressable advertisement and a second plurality of respondents that received a linear advertisement based on combined program tuning data and reference advertisement data; a calculator to calculate a first average commercial minute rating for the addressable advertisement based on first duration weighted impressions associated with the first plurality of respondents and a second average commercial minute rating for the linear advertisement based on second duration weighted impressions associated with the second plurality of respondents; and a communication interface to transmit the first average commercial minute rating and the second average commercial minute rating for crediting the addressable advertisement and the linear advertisement with audience viewership metrics.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate audience metrics. An example apparatus includes online panel factor generator circuitry to generate an online panel adjustment factor based on television panel data and online panel data; over-the-top (OTT) factor generator circuitry to generate an OTT adjustment factor based on the television panel data and a portion of the online panel data, the portion of the online panel data corresponding to OTT panel data; independence probability factor generator circuitry to generate an independence probability adjustment factor; and a convergence circuitry to: generate an aggregated adjustment factor based on the online panel adjustment factor, the OTT adjustment factor, and the independence probability adjustment factor; and generate a deduplicated reach corresponding to a media item based on a comparison of the aggregated adjustment factor to historical data.

Abstract: Methods and apparatus to calibrate media ratings based on return path data are disclosed. An apparatus includes a processor and memory including instructions that, when executed, cause the processor to: determine an initial rating for the media provided in a first geographic area based on return path data (RPD) tuning information obtained from RPD devices in subscriber households in the first geographic area; determine a first panelist rating for the media provided in a second geographic area based on first panel tuning information obtained from first metering devices in a first subset of panelist households in the second geographic area; determine a nonsubscriber calibration factor based on the first panelist rating; and determine a final rating for the media in the first geographic area by modifying the initial rating based on the nonsubscriber calibration factor.

Abstract: Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to deduplicate common devices across multiple data sources are disclosed. An example apparatus includes instructions to identify a first device in a first data source and a second device in a second data source as a possible common device, calculate at least one of a station duration metric, a time match metric or a station path metric, the station duration metric, the time match metric based times of day that the first device tuned to a second set of stations and times of day that the second device tuned to the second set of stations, determine a score based on the at least one of the station duration metric, the time match metric, or the station path metric, and determine when the first device and the second device are a common device based on the score.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for adjusting a tuning event. Examples disclosed herein include determining an initial adjustment factor for a group of stations and a combination of metadata categories associated with panel data and census data. Examples disclosed herein further include determining a final adjustment factor for the group of stations based on the initial adjustment factor and a bias correction factor. Example disclosed herein further include adjusting weighted minutes of the tuning event based on the final adjustment factor, the tuning event corresponding to the group of stations and the combination of metadata categories, the adjusted tuning event to be credited to media.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for a window-metric rule for return path data (RPD). In some examples, a media monitor is to determine a first count of a number of devices in a first household that reported RPD for a first day, and compare the first count to a current window-metric, the current window-metric associated with a window of time for the first household, the window of time associated with a content provider and having N number of days. In some examples, the media monitor is also to exclude the RPD from the first household for the first day when the first count does not satisfy the current window-metric, and generate a media exposure report using the RPD from the first household for the first day when the first count satisfies the current window-metric, independent of if the first household has heartbeat data available.

Abstract: Methods, apparatus, systems and articles of manufacture to assign viewers to media meter data are disclosed. An apparatus includes processor circuitry to execute computer readable instructions to at least: identify a candidate household from a plurality of second households to associate with a first household based on an analysis of a first duration of time first media was presented by a first media presentation device and a second duration of time second media was presented by second media presentation devices; match different ones of first panelists of the first household with matching ones of second panelists of the candidate household; and impute respective portions of the first duration of time to the different ones of the first panelists based on portions of the second duration of time for which the matching ones of the second panelists of the candidate household were exposed to the second media.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to rectify false set top box tuning data. Disclosed examples methods include identifying, by executing an instruction with a processor, in the return path data, first tuning data corresponding to a first group of set top boxes, the first group of set top boxes classified as associated with machine events, determining, by executing an instruction with a processor, a ratio between first tuning events in the return path data and second tuning events in the return path data, the first tuning events attributed to the first group of the set top boxes, the second tuning events attributed to a second group of the set top boxes classified at not associated with machine events, and in response to the ratio satisfying a threshold during a time interval, removing second tuning data associated with the time interval from the first tuning data.

Abstract: Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to deduplicate common devices across multiple data sources are disclosed. An example apparatus includes instructions to identify a first device in a first data source and a second device in a second data source as a possible common device, calculate at least one of a station duration metric, a time match metric or a station path metric, the station duration metric, the time match metric based times of day that the first device tuned to a second set of stations and times of day that the second device tuned to the second set of stations, determine a score based on the at least one of the station duration metric, the time match metric, or the station path metric, and determine when the first device and the second device are a common device based on the score.

Abstract: Methods and apparatus to calibrate media ratings based on return path data are disclosed. An apparatus includes programmable circuitry to execute machine readable instructions to: determine a subscriber calibration factor for media based on first panel tuning information obtained from first metering devices in first panelist households that subscribe to a content provider that provides media through return path data (RPD) devices; determine a nonsubscriber calibration factor for the media based on second panel tuning information obtained from second metering devices in second panelist households that do not subscribe to the content provider; and determine a final rating for the media based on an initial rating and at least one of the subscriber calibration factor or the nonsubscriber calibration factor, the initial rating based on RPD tuning information obtained from RPD devices in subscriber households that subscribe to the content provider.

Abstract: An example apparatus to perform media device on/off detection using return path data includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to classify view segments associated with common homes data based on whether first return path data in respective ones of the view segments has matching panel meter data to determine labeled view segments, train a machine learning algorithm, based on a first set of features, to output media device on/off determinations to produce a machine learning algorithm, the first set of features generated from the labeled view segments, and apply second return path data to the machine learning algorithm trained to output a first on/off determination associated with a media device represented in the second return path data.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to rectify false set top box tuning data. Disclosed examples methods include identifying, by executing an instruction with a processor, in the return path data, first tuning data corresponding to a first group of set top boxes, the first group of set top boxes classified as associated with machine events, determining, by executing an instruction with a processor, a ratio between first tuning events in the return path data and second tuning events in the return path data, the first tuning events attributed to the first group of the set top boxes, the second tuning events attributed to a second group of the set top boxes classified at not associated with machine events, and in response to the ratio satisfying a threshold during a time interval, removing second tuning data associated with the time interval from the first tuning data.

Abstract: Methods, apparatus, systems and articles of manufacture to impute media consumption behavior are disclosed. An example system includes one or more media meters to obtain tuning data, one or more people meters to obtain viewing data, and one or more servers to, in response to a determination that a difference satisfies a first threshold, determine that a first subset of the tuning data associated with first panelist households having tuned to first media in a first area exhibits local bias, determine that a second subset of the viewing data associated with second panelist households having viewed the first media in the second area represents heavy viewing, and impute the second subset of the viewing data for the first subset of the tuning data in response to the second subset of the viewing data representing heavy viewing.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate audience metrics. An example apparatus includes online panel factor generator circuitry to generate an online panel adjustment factor based on television panel data and online panel data; over-the-top (OTT) factor generator circuitry to generate an OTT adjustment factor based on the television panel data and a portion of the online panel data, the portion of the online panel data corresponding to OTT panel data; independence probability factor generator circuitry to generate an independence probability adjustment factor; and a convergence circuitry to: generate an aggregated adjustment factor based on the online panel adjustment factor, the OTT adjustment factor, and the independence probability adjustment factor; and generate a deduplicated reach corresponding to a media item based on a comparison of the aggregated adjustment factor to historical data.

Abstract: Example methods and apparatus to onboard return path data providers for audience measurement are disclosed herein. Example apparatus disclosed herein to predict return path data quality include a classification engine to compute a first data set of model features from validation tuning data reported from media metering devices and a second data set of model features from return path data reported from return path data devices. The example apparatus also include a prediction engine to train a machine learning algorithm based on the first data set, apply the trained machine learning algorithm to the second data set to predict quality of the return path data reported from the return path data devices, and determine an onboarding status for a return path data provider based on an aggregate predicted quality of the return path data reported from the return path data devices.

Abstract: Methods, apparatus, systems and articles of manufacture to impute media consumption behavior are disclosed. An example system includes one or more media meters to obtain tuning data, one or more people meters to obtain viewing data, and one or more servers to, in response to a determination that a difference satisfies a first threshold, determine that a first subset of the tuning data associated with first panelist households having tuned to first media in a first area exhibits local bias, determine that a second subset of the viewing data associated with second panelist households having viewed the first media in the second area represents heavy viewing, and impute the second subset of the viewing data for the first subset of the tuning data in response to the second subset of the viewing data representing heavy viewing.