The Nielsen Comapny (us), LLC

Gracenote, Inc.

The Nielson Company (us), LLC

Scott John Sereday

Jingsong Cui

XiaoTing Liang

Peter Campbell Doe

Oana Dan

Shufang Ban

Cathy Zhuang

Methods, apparatus, systems and articles of manufacture are disclosed to project ratings for future broadcasts of media. Disclosed example methods include normalizing, with a processor, audience measurement data corresponding to media exposure data, social media exposure data and programming information associated with a future quarter to determine normalized audience measurement data. Disclosed example methods also include classifying a media asset based on the programming information to determine a media asset classification. Disclosed example methods also include building, with the processor, a projection model based on a first subset of the normalized audience measurement data, the first subset of the normalized audience measurement data associated with a first time frame relative to the future quarter, the first subset of the normalized audience measurement data based on the media asset classification, and applying, with the processor, the programming information to the projection model to project ratings for the media asset.

Methods and apparatus to project ratings for future broadcasts of media

Methods and systems for prediction audience ratings are disclosed. A database of television (TV) viewing data may include program records for a multiplicity of existing TV programs. A system may receive a training plurality of program records from the TV viewing data, and for each program record a most similar TV program based on content characteristics may be identified. A synthetic program record may be constructed by merging features of each record and its most similar record. Audience performance metrics may be omitted from synthetic records. An aggregate of the training plurality of program records and the synthetic program records may be used to train a machine-learning (ML) model to predict audience performance metrics of the new or hypothetical TV programs not yet available for viewing and/or not yet transmitted or streamed.

Machine learning systems and methods for predicting end-user consumption of future multimedia transmissions

Disclosed example apparatus to determine volume of switching (VoS) among television programs examine first viewing data associated with a first time period and second viewing data associated with a second time period to identify a first set of panelists represented in both the first and second viewing data; in response to a size of the first set of panelists satisfying both first and second thresholds, estimate the VoS based on a first subset of the first viewing data and a second subset of the second viewing data associated with the first set of panelists; and in response to the size of the first set of panelists satisfying the first but not the second threshold, estimate the VoS based on the first and second subsets, and a third subset of the first viewing data and a fourth subset of the second viewing data associated with a second set of panelists.

Estimating volume of switching among television programs for an audience measurement panel

Example apparatus disclosed herein are to compare (i) ratios of program ratings to corresponding network ratings with (ii) a threshold to determine adjusted viewing data for respective sites during a monitoring interval, the program ratings and the corresponding network ratings determined for programs tuned on corresponding networks at the respective sites during the monitoring interval, the adjusted viewing data for a combination of a first program and a first site to represent an adjusted amount of time the first program was presented at the first site. Disclosed example apparatus are also to cluster the programs into program clusters based on distances between respective combinations of pairs of the programs, the distances based on the adjusted viewing data. Disclosed example apparatus are further to output information to identify the program clusters.

Clustering television programs based on viewing behavior

Example program clustering systems disclosed herein are to compare (i) ratios of program ratings to corresponding network ratings with (ii) a threshold to determine adjusted viewing data for respective sites during a monitoring interval, the program ratings and the corresponding network ratings determined for programs tuned on corresponding networks at the respective sites during the monitoring interval, the adjusted viewing data for a combination of a first program and a first site to represent an adjusted amount of time the first program was presented at the first site. Disclosed example program clustering systems are also to cluster the programs into program clusters based on distances between respective combinations of pairs of the programs, the distances based on the adjusted viewing data. Disclosed example program clustering systems are further to output information to identify the program clusters.

Examples disclosed herein to estimate volume of switching (VoS) among television programs include determining, based on panelist program viewing data, a first VoS value representing a portion of a decreased amount of tuning by matched panelists to a first program from first to second measurement periods to attribute to an increased amount of tuning by the matched panelists to a second program from the first to second measurement periods; estimating, based on the program viewing data and first VoS value, a second VoS value representing a portion of a decreased amount of tuning by unmatched panelists to the first program from the first to second measurement periods to attribute to an increased amount of tuning by the unmatched panelists to the second program from the first to second measurement periods; and combining the first and second VoS values to determine a third VoS value for a combination of the matched and unmatched panelists.

Example program clustering methods disclosed herein include accessing person-level program viewing data representing lengths of time respective people in an audience have tuned to respective television programs to be clustered. Disclosed example methods also include determining adjusted person-level program viewing data for respective ones of the people having tuned to respective ones of the television programs. For example, first person-level program viewing data for a first person having tuned to a first program is adjusted based on a ratio characterizing a relationship between a first program rating associated with the first person having tuned to the first program and a first network rating associated with the first person having tuned to a first network associated with the first program. Disclosed example methods further include clustering the television programs into clusters based on distances between pairs of the television programs, the distances based on the adjusted person-level program viewing data.

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Rochelle Park, NJ, United States of America

Scott John Sereday