Abstract: A system identifies redundancies among a plethora of viewership data of each of several markets. Some embodiments may obtain, from each of a plurality of different devices, a different set of viewership data, each set comprising different subsets that respectively relate to different entities; each of the subsets may indicate several time-based views of content over a period of time. These or other embodiments may determine a set of values by performing a set of deterministic functions using the time-based views of each of the subsets; compare the values, which relate to same entities and to same time intervals, of the determined sets of each distinct pair of the devices, and identify, from among the device pairs, candidate pairs. One exemplary output of this approach may be identifiers of the devices of a first pair of devices that is identified from among the candidate pairs.

Abstract: A system and method for the assignment of person-level viewership. The system receives viewership information describing the viewing of video content at a household. The system additionally receives demographic information for that household, including the numbers of persons associated with the household. For each combination of viewers, the system calculates the probability that the viewers viewed the content based on the demographic attributes of those viewers and the probabilities that individuals sharing those attributes would view the content. The system then attributes the viewing information to one or more persons from the household based on the calculated probabilities. The system additionally updates the probabilities that individuals having different demographic attributes would view the content based on the selection of persons.

Abstract: A system and method for the assignment of person-level viewership. The system receives viewership information describing the viewing of video content at a household. The system additionally receives demographic information for that household, including the numbers of persons associated with the household. For each combination of viewers, the system calculates the probability that the viewers viewed the content based on the demographic attributes of those viewers and the probabilities that individuals sharing those attributes would view the content. The system then attributes the viewing information to one or more persons from the household based on the calculated probabilities. The system additionally updates the probabilities that individuals having different demographic attributes would view the content based on the selection of persons.

Abstract: The various exemplary embodiments provide a method for projecting survey information into a geographic region. The geographic region is divided into a plurality of geographic subregions, each of which are profiled using a plurality of profiling variables to form a selected geographic subregion profile. The profiling variables include both demographic and behavioral variables. A plurality of survey respondents are then randomly assigned into the selected geographic subregion to form a modeled population, with the random assignment weighted based on a representation probability of each of the corresponding plurality of survey respondents for the selected geographic subregion profile, with the representation probabilities having been determined using a sample balancing algorithm. Following such profiling and assignment for all subregions, survey information corresponding to the modeled population is projected into the geographic region.

Abstract: The various exemplary embodiments provide a method for projecting survey information into a geographic region. The geographic region is divided into a plurality of geographic subregions, each of which are profiled using a plurality of profiling variables to form a selected geographic subregion profile. The profiling variables include both demographic and behavioral variables. A plurality of survey respondents are then randomly assigned into the selected geographic subregion to form a modeled population, with the random assignment weighted based on a representation probability of each of the corresponding plurality of survey respondents for the selected geographic subregion profile, with the representation probabilities having been determined using a sample balancing algorithm. Following such profiling and assignment for all subregions, survey information corresponding to the modeled population is projected into the geographic region.

Abstract: A media engagement system determines engagement scores for media vehicles across multiple media channels. The engagements scores for a media vehicle comprise multiple dimension scores indicating various aspects of engagement for that particular media vehicle. The dimension scores may be related to inspirational, trustworthy, life enhancing, social interaction, personal timeout, and ad attention/receptivity engagement dimensions, for example. The dimensions scores associated with respective engagement dimensions are based on responses to specific statements in engagement surveys completed by multiple media consumers. Thus, each statement in the engagement survey is associated with at least one engagement dimension and the engagement dimension score is then calculated based on media consumer responses to the statements associated with the respective engagement dimension.

Abstract: The various exemplary embodiments provide a method for projecting survey information into a geographic region. The geographic region is divided into a plurality of geographic subregions, each of which are profiled using a plurality of profiling variables to form a selected geographic subregion profile. The profiling variables include both demographic and behavioral variables. A plurality of survey respondents are then randomly assigned into the selected geographic subregion to form a modeled population, with the random assignment weighted based on a representation probability of each of the corresponding plurality of survey respondents for the selected geographic subregion profile, with the representation probabilities having been determined using a sample balancing algorithm. Following such profiling and assignment for all subregions, survey information corresponding to the modeled population is projected into the geographic region.

Abstract: A media engagement system determines engagement scores for media vehicles across multiple media channels. The engagements scores for a media vehicle comprise multiple dimension scores indicating various aspects of engagement for that particular media vehicle. The dimension scores may be related to inspirational, trustworthy, life enhancing, social interaction, personal timeout, and ad attention/receptivity engagement dimensions, for example. The dimensions scores associated with respective engagement dimensions are based on responses to specific statements in engagement surveys completed by multiple media consumers. Thus, each statement in the engagement survey is associated with at least one engagement dimension and the engagement dimension score is then calculated based on media consumer responses to the statements associated with the respective engagement dimension.

Abstract: A media engagement system determines engagement scores for media vehicles across multiple media channels. The engagements scores for a media vehicle comprise multiple dimension scores indicating various aspects of engagement for that particular media vehicle. The dimension scores may be related to inspirational, trustworthy, life enhancing, social interaction, personal timeout, and ad attention/receptivity engagement dimensions, for example. The dimensions scores associated with respective engagement dimensions are based on responses to specific statements in engagement surveys completed by multiple media consumers. Thus, each statement in the engagement survey is associated with at least one engagement dimension and the engagement dimension score is then calculated based on media consumer responses to the statements associated with the respective engagement dimension.