Methods and apparatus to project ratings for future broadcasts of media

Abstract

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.

What is claimed is: 
     
         1 . A ratings projection method comprising:
 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;   classifying, with the processor, a media asset based on the programming information to determine a media asset classification;   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.   
     
     
         2 . The method as defined in  claim 1 , further including classifying the media asset as a television series when a characteristic of the media asset is indicative of at least one of a premier episode, a repeat episode or a new episode. 
     
     
         3 . The method as defined in  claim 2 , further including, in response to classifying the media asset as a television series, retrieving series historical performance information related to the media asset. 
     
     
         4 . The method as defined in  claim 1 , further including classifying the media asset as special programming when a characteristic of the media asset is indicative of at least one of a movie or a sporting event. 
     
     
         5 . The method as defined in  claim 1 , further including excluding a subset of the first subset of the normalized audience measurement data based on the future quarter. 
     
     
         6 . The method as defined in  claim 5 , further including selecting a second subset of the normalized audience measurement data to train the projection model, the second subset of the normalized audience measurement data included in the first subset of the normalized audience measurement data and not included in the excluded subset of the first subset of the normalized audience measurement data. 
     
     
         7 . The method as defined in  claim 1 , wherein the first subset of the normalized audience measurement data includes historical data related to the media asset and related to a subset of media assets that are (1) included in the normalized audience measurement data and (2) related to the media asset. 
     
     
         8 . A ratings projection comprising:
 a data transformer to normalize 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;   a model builder to:
 classify a media asset based on the programming information to determine a media asset classification; 
 build 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; 
   a ratings projector to apply the programming information to the projection model to project ratings for the media asset.   
     
     
         9 . The apparatus as defined in  claim 8 , wherein the model builder is to classify the media asset as a television series when a characteristic of the media asset is indicative of at least one of a premier episode, a repeat episode or a new episode. 
     
     
         10 . The apparatus as defined in  claim 9 , wherein the model builder retrieves series historical performance information when the media asset is classified as a television series. 
     
     
         11 . The apparatus as defined in  claim 8 , wherein the model builder is to classify the media asset as special programming when a characteristic of the media asset is indicative of at least one of a movie or a sporting event. 
     
     
         12 . The apparatus as defined in  claim 8 , wherein the model builder is to exclude a subset of the first subset of the normalized audience measurement data based on the future quarter. 
     
     
         13 . The apparatus as defined in  claim 12 , wherein the model builder is to select a second subset of the normalized audience measurement data to train the projection model, the second subset of the normalized audience measurement data included in the first subset of the normalized audience measurement data and not included in the excluded subset of the first subset of the normalized audience measurement data. 
     
     
         14 . The apparatus as defined in  claim 8 , wherein the first subset of the normalized audience measurement data includes historical data related to the media asset and related to a subset of media assets that are (1) included in the normalized audience measurement data and (2) related to the media asset. 
     
     
         15 . A tangible computer-readable storage medium comprising instructions that, when executed, cause a processor to at least:
 normalize 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;   classify a media asset based on the programming information to determine a media asset classification;   build 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   apply the programming information to the projection model to project ratings for the media asset.   
     
     
         16 . The tangible machine-readable storage medium as defined in  claim 15 , wherein the instructions further cause the processor to:
 classify the media asset as a television series when a characteristic of the media asset is indicative of at least one of a premier episode, a repeat episode or a new episode; and   retrieve series historical performance information related to the media asset when the media asset is classified as a television series.   
     
     
         17 . The tangible machine-readable storage medium as defined in  claim 15 , wherein the instructions further cause the processor to classify the media asset as special programming when a characteristic of the media asset is indicative of a movie or a sporting event. 
     
     
         18 . The tangible machine-readable storage medium as defined in  claim 15 , wherein the instructions further cause the processor to exclude a subset of the first subset of the normalized audience measurement data based on the future quarter. 
     
     
         19 . The tangible machine-readable storage medium as defined in  claim 18 , wherein the instructions further cause the processor to select a second subset of the normalized audience measurement data to train the projection model, the second subset of the normalized audience measurement data included in the first subset of the normalized audience measurement data and not included in the excluded subset of the first subset of the normalized audience measurement data. 
     
     
         20 . The tangible machine-readable storage medium as defined in  claim 15 , wherein the first subset of the normalized audience measurement data includes historical data related to the media asset and related to a subset of media assets that are (1) included in the normalized audience measurement data and (2) related to the media asset.