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.

Abstract: A computerized system performs automated schedule optimization. In particular, the system solves the problem of determining the range of advertisement placement prices that a seller can select from, while still satisfying all of the constraints on the buyers' orders. The system generates such a range of prices automatically based on a variety of data. As a result, the system helps the seller to maximize the prices of the potential placements that can satisfy a set of orders from buyers.

Abstract: Disclosed example audience measurement apparatus include first estimator means for determining a first audience metric based on set-top box return path tuning data obtained from a plurality of set-top boxes located in a first geographic area, second estimator means for determining a second audience metric from a portion of audience measurement data corresponding to a second geographic area different from the first geographic area, the portion of the audience measurement data associated with monitored sites in the second geographic area having second set-top box characteristics that correspond with first set-top box characteristics of the plurality of set-top boxes located in the first geographic area, and third estimator means for (i) determining a ratio of the first audience metric to the second audience metric, and (ii) processing the audience measurement data based on the ratio to determine an audience exposure metric estimating exposure to media in the first geographic area.

Abstract: A system draws an inference about the audience being targeted by the buyer of an advertisement placement opportunity by correlating the order for the advertisement placement opportunity with characteristics of the audience associated with the television program in which the advertisement placement opportunity is embedded. Sellers of advertisement placement opportunities may use the inferred target audience information to appropriately price their advertising inventories and to fulfill orders more effectively.

Abstract: Example methods, apparatus and articles of manufacture to estimate local market audiences of media are disclosed herein. Disclosed example methods include determining a first audience metric based on set-top box (STB) return path tuning data obtained from STBs located in a first geographic area. The disclosed example methods also include determining a second audience metric from a portion of audience measurement data corresponding to a different second geographic area, the portion of the audience measurement data associated with monitored sites in the second geographic area having second STB characteristics that correspond with first STB characteristics of the STBs located in the first geographic area. The disclosed example methods further include determining a ratio of the first audience metric to the second audience metric, and processing the audience measurement data based on the ratio to determine an audience exposure metric estimating exposure to media in the first geographic area.

Abstract: Methods, apparatus, systems and articles of manufacture to perform identity matching across audience measurement systems are disclosed.

Abstract: Example methods, apparatus and articles of manufacture to estimate local market audiences of media are disclosed herein. Disclosed example methods include determining a first audience metric based on set-top box (STB) return path tuning data obtained from STBs located in a first geographic area. The disclosed example methods also include determining a second audience metric from a portion of audience measurement data corresponding to a different second geographic area, the portion of the audience measurement data associated with monitored sites in the second geographic area having second STB characteristics that correspond with first STB characteristics of the STBs located in the first geographic area. The disclosed example methods further include determining a ratio of the first audience metric to the second audience metric, and processing the audience measurement data based on the ratio to determine an audience exposure metric estimating exposure to media in the first geographic area.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve reach calculation efficiency. An example method includes estimating, with a processor, a sample distribution of marketing data to generate a maximum entropy distribution, generating, with the processor, a geometric distribution based on estimating a minimum cross entropy of (a) the maximum entropy distribution and (b) the sample distribution of marketing data, and improving calculation efficiency of the public reach of the sample distribution of marketing data by generating, with the processor, conserved quantity expressions of the geometric distribution.

Abstract: Example methods, apparatus and articles of manufacture to estimate local market audiences of media are disclosed herein. Disclosed example methods include accessing set-top box return path tuning data obtained from a plurality of set-top boxes located in a first geographic area. Disclosed example methods also include determining a ratio of (1) a first audience metric determined from audience measurement data associated with a second geographic area different from the first geographic area to (2) a second audience metric determined from a portion of the audience measurement data identified to be associated with monitored sites in the second geographic area having second set-top box characteristics that correspond with first set-top box characteristics of the set-top boxes located in the first geographic area. Disclosed example methods further include processing the set-top box return path tuning data based on the ratio to determine an audience exposure metric in the first geographic area.

Abstract: Example methods, apparatus and articles of manufacture to estimate local market audiences of media are disclosed herein. Disclosed example methods include accessing set-top box return path tuning data obtained from a plurality of set-top boxes located in a first geographic area. Disclosed example methods also include determining a ratio of (1) a first audience metric determined from audience measurement data associated with a second geographic area different from the first geographic area to (2) a second audience metric determined from a portion of the audience measurement data identified to be associated with monitored sites in the second geographic area having second set-top box characteristics that correspond with first set-top box characteristics of the set-top boxes located in the first geographic area. Disclosed example methods further include processing the set-top box return path tuning data based on the ratio to determine an audience exposure metric in the first geographic area.

Abstract: Example methods, apparatus and articles of manufacture to estimate local market audiences of media content are disclosed herein. An example method disclosed herein includes weighting audience measurement data associated with a first geographic area based on population data associated with a second geographic area different from the first geographic area to form weighted data, determining a factor based on the audience measurement data and tuning data associated with the second geographic area, and processing the weighted data based on the factor to determine an audience exposure metric estimating exposure to media content in the second geographic area.

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.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to predict time-shifted exposure to media. An example method includes normalizing, with a processor, audience measurement data corresponding to media exposure data and social media activity data. The example method also includes building an estimation model based on a relationship between a first subset of the normalized audience measurement data associated with a characteristic of the media asset and historical rating lift measurements associated with the media asset. The example method also includes estimating, with the processor, current ratings for the media asset based on time-period based ratings and broadcast time-periods. The example method also includes applying data related to the media asset and the estimated current ratings to the estimation model to estimate, with the processor, the ratings lift for the media asset.

Abstract: A disclosed example method to determine audience duplication in cross-media campaigns involves determining a first duplicated audience reach for a demographic group between a total television audience and an internet publisher audience. The total television audience is representative of audience members across a plurality of television networks. The internet publisher audience is representative of audience members of one of a plurality of internet publishers. The example method involves determining a second duplicated audience reach between the total television audience and the internet publisher audience for a media campaign based on the first duplicated audience reach. The example method involves determining a third duplicated audience reach between a television network audience and the internet publisher audience for the media campaign based on the second duplicated audience reach.

Abstract: A disclosed example method to determine audience duplication in cross-media campaigns involves determining a first duplicated audience reach between a total television audience and an internet publisher audience for a demographic group. The total television audience is representative of audience members across a plurality of television networks, and the internet publisher audience is representative of audience members across one of a plurality of internet publishers. The example method involves determining a second duplicated audience reach between the total television audience and the internet publisher audience for a media campaign based on the first duplicated audience reach. The example method involves determining a third duplicated audience reach between a television network audience and the internet publisher audience for the media campaign based on the second duplicated audience reach.

Abstract: A disclosed example involves accessing an initial average unique web browser reach corresponding to a first duration, an initial average internet media impressions-per-user frequency corresponding to the first duration, an initial average unique web browser reach corresponding to a second duration, and an initial average internet media impressions-per-user frequency corresponding to the second duration. A probability model is used to determine an adjusted internet media audience reach corresponding to the first duration based on the initial average unique web browser reach corresponding to the first duration, the initial average internet media impressions-per-user frequency corresponding to the first duration, the initial average unique web browser reach corresponding to the second duration, and the initial average impressions-per-user frequency corresponding to the second duration.

Abstract: Example methods, apparatus and articles of manufacture to estimate local market audiences of media content are disclosed herein. An example method disclosed herein includes weighting audience measurement data associated with a first geographic area based on population data associated with a second geographic area different from the first geographic area to form weighted data, determining a factor based on the audience measurement data and tuning data associated with the second geographic area, and processing the weighted data based on the factor to determine an audience exposure metric estimating exposure to media content in the second geographic area.

Abstract: Methods and apparatus to determine board exposure levels are disclosed herein. In a disclosed example method, exposure levels associated with a plurality of media boards are obtained and a plurality of probability distributions indicative of probable exposure levels for the plurality of media boards are determined. The example method also involves selecting one of the plurality of probability distributions corresponding to an expected distribution and smoothing the selected probability distribution. Each of the plurality of media boards is then credited with a respective processed exposure level of the smoothed probability distribution.

Abstract: Methods and apparatus to model set-top box data are disclosed. An example method includes receiving a first set of non-panelist behavior data and receiving a second set of panelist set-top box behavior data, the second set being associated with demographic data. The example method also includes identifying at least one behavior pattern common to the first and second sets of behavior data, and fusing data associated with the at least one behavior pattern from the first set with data associated with the at least one behavior pattern from the second set to impute at least one demographic characteristic from the second set to the first set and generate a quantity of household tuning minutes.

Abstract: Methods and apparatus for crediting media exposure are disclosed. An example method includes deriving a plurality of travel paths traversed by respective ones of a plurality of respondents, determining, via a first technique, exposures of the respective ones of the plurality of respondents to a plurality of media sites based on the derived plurality of travel paths, and modifying the determined exposures to the plurality of media sites to improve a statistical accuracy of the modified determined exposures by constraining the determined exposures based on exposure information for the plurality of media sites obtained via a second technique different than the first technique.