Abstract: Systems and methods for analyzing viewing behavior of users viewing video content and advertisements are provided. A system may periodically or continuously receive tune data reflecting the viewing behavior of users, analyze the viewing behavior to determine a location and time period of advertisement blocks in a viewed segment, and determine parameters reflecting a comparison between a number of viewers that viewed the advertisement blocks and a number of viewers that viewed non-advertised content. The system may also deliver a report of the analysis to advertisers, agencies, media sellers, or other parties that are interested in measuring the effectiveness of advertisements on users. The analysis by the system can be done over multiple different types of content-distribution platforms.

Abstract: Systems and methods for analyzing viewing behavior of users viewing video content and advertisements are provided. A system may periodically or continuously receive tune data reflecting the viewing behavior of users, analyze the viewing behavior to determine a location and time period of advertisement blocks in a viewed segment, and determine parameters reflecting a comparison between a number of viewers that viewed the advertisement blocks and a number of viewers that viewed non-advertised content. The system may also deliver a report of the analysis to advertisers, agencies, media sellers, or other parties that are interested in measuring the effectiveness of advertisements on users. The analysis by the system can be done over multiple different types of content-distribution platforms.

Abstract: A system and method for analyzing the viewing behavior of end users that are viewing video content to determine the likely presence of one or more advertisements being viewed and to assess the viewers who stop viewing during the course of such advertisements. The system periodically or continuously receives tune data reflecting the viewing behavior of end users, analyzes the cumulative viewing behavior reflected by the received tune data, and delivers a report of the analysis to advertisers, agencies, media sellers, or other parties that are interested in measuring the effectiveness of advertisements on end users. The analysis by the system can be done over multiple different types of content-distribution platforms.

Abstract: A system and methods for estimating network subscription coverage within or across various markets. The system generates estimates of viewers that subscribe to certain network subscription packages in various geographic markets. The estimates may be organized in different fashions, such as organized by households, by income or other demographic, etc. In some embodiments, the system iteratively scales an estimated viewership count in a given market based on existing subscription data across all the markets and demographic characteristics of the various markets. The system may also redistribute estimates based on market capacity.

Abstract: Various of the disclosed embodiments contemplate television viewing behavior data collected from a plurality of television set top boxes by using aggregation to detect an excess or a deficit in viewership for a group of television set top boxes. In some embodiments, a group of set top boxes can be associated with a particular television service provider, cable television head-end, or data warehouse. Additionally, some embodiments can clean television viewing behavior data by detecting and correcting aberrant viewership in a time series, e.g. based on a weekly or an approximately monthly frequency. In some embodiments, the aberrant viewership can be detected by calculating a minimum expected number of viewers for a day and comparing it to the actual number of households that reported viewers for that day.

Abstract: A system and method are disclosed that measures audience engagement with a program based on the time a user spends viewing the program. A program's ability to retain viewers is referred to as the “stickiness” of the program. The viewership for a program is determined by the system and then compared to viewership of various other programs in order to assess the audience retention for the analyzed program as compared to the retention of the other programs. The system calculates a “stickiness index” for the program, which is a numerical representation of the analyzed program's stickiness as compared to the average of the other programs. The system may generate a report including the analyzed data and calculated stickiness index for advertisers, agencies, media sellers, or other parties that are interested in assessing viewership of the analyzed program.

Abstract: A system and method for cleaning television viewing behavior data collected from set top boxes by detecting and correcting various problems that can occur in the viewing data. Three problems that may be detected and corrected by the system include: overflows of memory buffers; inclusion of non-human generated tuning events; and presence of clock offsets. After cleaning the television viewing behavior data, the cleaned data may be used to analyze audience viewing behavior in a manner that achieves a higher degree of accuracy than can be achieved by using uncleaned television viewing behavior data.

Abstract: A system and method for cleaning television viewing behavior data collected from set top boxes by detecting and correcting various problems that can occur in the viewing data. Three problems that may be detected and corrected by the system include: overflows of memory buffers; inclusion of non-human generated tuning events; and presence of clock offsets. After cleaning the television viewing behavior data, the cleaned data may be used to analyze audience viewing behavior in a manner that achieves a higher degree of accuracy than can be achieved by using uncleaned television viewing behavior data.

Abstract: Various systems and methods for generating and augmenting viewership datasets are disclosed. In particular, some embodiments prepare the datasets for further analysis by supplementing missing information based upon available data. The system may organize viewership data from disparate formats into a unified form to facilitate analysis and projection of non-reporting device data. In some embodiments, the projections may scale existing cumulative determinations based on information regarding the presence and character of non-reporting devices in different geographic markets.

Abstract: Various of the disclosed embodiments contemplate television viewing behavior data collected from a plurality of television set top boxes by using aggregation to detect an excess or a deficit in viewership for a group of television set top boxes. In some embodiments, a group of set top boxes can be associated with a particular television service provider, cable television head-end, or data warehouse. Additionally, some embodiments can clean television viewing behavior data by detecting and correcting aberrant viewership in a time series, e.g. based on a weekly or an approximately monthly frequency. In some embodiments, the aberrant viewership can be detected by calculating a minimum expected number of viewers for a day and comparing it to the actual number of households that reported viewers for that day.

Abstract: A system and method for analyzing the viewing behavior of end users that are viewing video content to determine the likely presence of one or more advertisements being viewed and to assess the viewers who stop viewing during the course of such advertisements. The system periodically or continuously receives tune data reflecting the viewing behavior of end users, analyzes the cumulative viewing behavior reflected by the received tune data, and delivers a report of the analysis to advertisers, agencies, media sellers, or other parties that are interested in measuring the effectiveness of advertisements on end users. The analysis by the system can be done over multiple different types of content-distribution platforms.

Abstract: A system and method are disclosed that measures audience engagement with a program based on the time a user spends viewing the program. A program's ability to retain viewers is referred to as the “stickiness” of the program. The viewership for a program is determined by the system and then compared to viewership of various other programs in order to assess the audience retention for the analyzed program as compared to the retention of the other programs. The system calculates a “stickiness index” for the program, which is a numerical representation of the analyzed program's stickiness as compared to the average of the other programs. The system may generate a report including the analyzed data and calculated stickiness index for advertisers, agencies, media sellers, or other parties that are interested in assessing viewership of the analyzed program.

Abstract: A method cleans television viewing behavior data collected from a plurality of television set top boxes by using aggregation to detect an excess or a deficit in viewership for a group of television set top boxes. In various aspects, the group of set top boxes may be associated with a particular television service provider, cable television head-end, or data warehouse. Additionally, the method can clean television viewing behavior data by detecting and correcting aberrant viewership in a time series, that is based on a weekly or an approximately monthly frequency. The aberrant viewership can be detected by calculating a minimum expected number of viewers for a day, and comparing it to the actual number of households that reported viewers for that day.