Patent Publication Number: US-9848239-B2

Title: Projecting person-level viewership from household-level tuning events

Description:
BACKGROUND 
     Television advertising relies on program and network viewership data in order to determine the expected reach of advertising slots. Advertisers are interested in numbers of viewers as well as the demographics of viewers in order to effectively manage television advertising timing and content. Understanding television audience viewing and habits may be useful in supporting advertising planning, buying, and selling. 
     SUMMARY 
     The present disclosure describes techniques for projecting person-level viewership from household-level tuning events. One example method includes accessing tuning data representing television tuning events associated with particular households, each tuning event in the tuning data including an identification of a particular television program associated with the tuning event and a household minutes value representing a number of minutes the particular television program was played at a particular household in association with the tuning event; accessing panelist viewing data representing television viewing events associated with panelists, each viewing event in the viewing data including an identification of a particular television program associated with the viewing event and a panelist minutes value representing a number of minutes a panelist was exposed to the particular television program during the viewing event, each panelist associated with panelist information; and for at least one tuning event represented by the tuning data: accessing household member data representing member information on individual members of a particular household associated with the tuning event; determining a member minutes value associated with each individual member of the particular household for the tuning event, the member minutes value based at least in part on panelist minutes values for television viewing events from at least a portion of the panelist viewing data associated with members of panelist households whose panelist information matches at least a portion of the member information of the individual member; determining a fractional viewership value for each individual member of the particular household associated with the tuning event, the fractional viewership value representing a probability that the individual member watched the television program associated with the tuning event, the fractional viewership value based at least in part on the portion of the panelist viewing data associated with members of panelist households whose panelist information matches at least a portion of the member information of the individual member. 
     Another example method includes accessing tuning data representing television tuning events associated with particular households, each tuning event in the tuning data including an identification of a television program played in association with the tuning event, and a household minutes value representing a number of minutes the television program was played at a particular household in association with the tuning event; accessing panelist viewing data representing television viewing events associated with panelists, each viewing event in the viewing data including an identification of a television program associated with the viewing event, and a panelist minutes value representing a number of minutes a particular panelist was exposed to the television program during the viewing event, each panelist associated with panelist information; identifying group subsets within the tuning data, each group subset including tuning data having matching values for a group criteria including attributes included in the tuning data and the panelist viewing data; identifying a particular television program for which to calculate unique viewers and person-level minutes; and for at least one particular group subset from the identified group subsets within the tuning data: identifying matching panelist viewing data for the particular group subset, the matching panelist viewing data including panelist viewing data having matching values for the group criteria associated with the particular group subset; calculating a unique viewers value for the particular group subset based on the matching panelist viewing data, wherein the unique viewers value represents an estimated number of individual members of households associated with the tuning data included in the group subset that watched the particular television program; calculating a person-level minutes value for the group subset based on the matching panelist viewing data, wherein the person-level minutes value represents an estimated number of minutes individual members of households associated with the tuning data included in the group subset watched the particular television program. 
     Another example method includes accessing tuning data representing television viewing events associated with particular households, each tuning event in the tuning data including an identification of a particular television program watched during the tuning event, and a household minutes value representing a number of minutes the particular television program was played at a particular household in association with the tuning event, the tuning data divided into rostered tuning data for particular households associated with household member data and non-rostered tuning data for particular households not associated with household member data, wherein the household member data includes information about individual members of the particular household; accessing panelist viewing data representing television viewing events associated with panelists, each viewing event in the viewing data including an identification of a particular television program watched during the viewing event, and a panelist minutes value representing a number of minutes a particular panelist member was exposed to the particular television program during the viewing event, each panelist associated with panelist information; generating person-level viewing data for tuning events represented by the rostered tuning data, the person-level viewing data based at least in part on the panelist viewing data and including second viewing events, each second viewing event including a member minutes value and a fractional viewership value associated with an individual member of a household associated with household member data; calculating unique viewers values and person-level minutes values for the non-rostered tuning data based on the panelist viewing data and the person-level viewing data. 
     Implementations of any of the described techniques may include a method or process, an apparatus, a device, a machine, a system, or instructions stored on a computer-readable storage device. The details of particular implementations are set forth in the accompanying drawings and description below. Other features will be apparent from the following description, including the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an example of a system in which television viewership information may be collected and processed to determine audience measurement data. 
         FIG. 2  illustrates an example of a system in which person-level viewing data can be used to project person-level viewership data for a household from household-level tuning data through demographic attribution. 
         FIG. 3  is a flow diagram illustrating an example of a process for using person-level viewing data to project person-level viewership from household-level tuning data through demographic attribution. 
         FIG. 4  is a block diagram applying a demographic attribution model to person-level viewing data and household-level tuning data to project person-level viewership for the household. 
         FIG. 5  illustrates an example of a system in which television viewership information may be collected and processed to determine audience measurement data for non-rostered households. 
         FIG. 6  is a flow diagram illustrating an example of a process for using person-level viewing data to project person-level viewership from non-rostered household-level tuning data. 
         FIG. 7  is a flow diagram illustrating an example of a process for using first person-level viewing data to project second person-level viewing data for rostered households, and using the first and second person-level viewing data to project person-level viewership from non-rostered household-level tuning data. 
     
    
    
     DETAILED DESCRIPTION 
     Estimates of television (TV) viewership may be used by content providers, advertisers, and others to estimate the number of people that viewed a particular television program, advertisement, sporting event, or other content item. TV viewership data can be collected at multiple levels. For example, person-level television viewership may be measured for a set of viewers, referred to as “panelists,” who, in some cases, have agreed to have their viewing behavior monitored. For example, the measured viewership may be used for the purposes of producing viewing estimates. Each panelist may be associated with demographic information, such as, for example, the gender of the panelist, the age of the panelist, the income of the panelist, a geographic area in which the panelist lives, a size of the panelist&#39;s household, and other information. In some cases, each panelist may be given a monitoring device to keep on their person, which monitors audio signals to determine if the panelist is watching a television program, and identifies the television program being watched. For example, the monitoring device may detect encoded signals within the television program audio identifying the television program. The monitoring device may also identify the television program from the detected audio by other mechanisms, such as, for example, by generating an acoustic fingerprint for the detected audio and consulting a database mapping acoustic fingerprints to television programs. Viewership data for the panelists, or “person-level data,” may be collected from the monitoring devices (e.g., by transferring the data to an external server over a network such as the Internet), and may be further analyzed to determine statistics, trends, and other information regarding viewership of television programs. In some cases, the person-level data may be used to project viewership statistics for larger populations, such as the total number of viewers or viewing minutes for a television program for a particular demographic or a particular geographic area. However, collecting person-level data from panelists is logistically complex, and thus the sample size of the person-level data may be small, leading to a large margin of error in projections of viewership data for larger populations. 
     Television viewership can also be measured at the household level, such as, for example, by a set-top-box logging viewing activity. This “household-level data” may be easier to obtain for a large sample of viewers, but may not provide any information regarding which members of the household (if any) watched a particular television program. It may be difficult to accurately project individual viewership numbers (e.g., the number of unique individual viewers for the particular program) from this household-level data, which, as stated, may not include any information regarding which members of the household (if any) watched the particular television program. 
     Various techniques may be employed for projecting person-level viewership from household-level data. For example, given a set of person-level viewership data and a household-level television viewing event for a particular program, fractional viewership values representing probabilities that individual members of the household participated in the television viewing event may be determined based on a portion of the person-level viewership data matching the demographic profile of each individual member. In particular, viewing events in the person-level data representing individual panelists matching the demographic profile of the individual member may be examined to determine a probability that the individual member viewed the particular program, such as by calculating a ratio of the number of panelists with the demographic profile that watched the program over the total number of panelists with the demographic profile. 
     Similarly, a projected number of viewing minutes that the particular program was viewed by individual members of the household may be determined. In particular, viewing events in the person-level data representing individual panelists matching the demographic profile of an individual member may be examined to determine an average amount of time the panelists viewed the particular program. This number can be used to project the number of minutes that the individual member participated in the particular television viewing event. In some cases, this calculation may be lead to total projected viewing minutes for all individual members of the household is larger than the duration of the television viewing event, representing cases where the projections indicate that multiple people were watching the program in the household at once. 
     By repeating these projections for a large set of household-level viewing events, a larger person-level data set can be projected. Such a set can be used to calculate metrics for larger populations, such as total audience size or total number of viewing minutes for the particular program within households in a certain geographic area, with greater accuracy than may be possible using smaller samples of direct-measured, person-level data. 
     Additional techniques to improve accuracy of the projections, such as weighting the person-level data to remove bias, may be performed, and are described in greater detail below. In addition, although the present disclosure generally describes the present techniques in the context of television viewership calculation, the techniques may be used to perform audience calculation for other types of content, including music, webpages, streaming video over the Internet, and other content. 
     Some or all of the techniques described may provide one or more advantages. For instance, by performing audience projections based on the person-level data and the household-level data, the relative shortcomings of both data sets (e.g, small sample size and lack of specific viewer information, respectively) may be mitigated. Further, the techniques described herein may allow for the integration of person-level data and household-level data from multiple sources into the same model, which may lead to further improvements in projection accuracy. Viewing within particular demographic groups may also be projected for households where individual member information is not known based on the observed and projected person-level data, providing more accurate and useful viewership information to broadcasters, advertisers, and others. 
       FIG. 1  illustrates an example of a system  100  in which television viewership information may be collected and processed to determine audience measurement data. The system  100  includes a number of households, such as household  101 , that include one or more set-top boxes  112  for viewing television programs. The set top boxes  112  record data for tuning events (e.g., viewing events), which may represent a particular television network or program being watched at a particular time. The set top boxes  112  may report these tuning events to a set top box usage collection server  114 , which may store tuning data  116  representing these tuning events in a database or other storage  120 . In addition to tuning events, the tuning data  116  may include data to identify the household  101  and set top box  112 , stream control data, data representing content recorded by the set top box  112 , programs ordered on the set top box  112 , and data about when the set top box  112  was on or off. Other data about the status of the set top box  112  and user interaction with the set top box  112  may also be recorded and included in tuning data  116 . 
     The household  101  include one or more members  102  that use the set-top boxes to watch television. These members  102  may be associated with demographics, such as age and gender, and these demographics may be collected and stored in storage  120  or another storage as household member data  110 . In the example shown, the household  101  includes four members  102 : an 18-year-old male, a 24-year-old female, a 35-year-old female, and a 46-year-old male. Their specific age and gender may be stored in household member data  110 , or the members may instead be associated with demographic groups. For example, each member  102  may be associated with an age group (for example: 18-24, 25-34, 35-44, 45-54, 55-64, or 65+), rather than specific age. This information may also be stored in the household member data  110 . Other demographics may be collected, such as occupation, income, or ethnicity. In addition, a geographic area or location for the household  101  may be stored in the household member data  110 . In some cases, the geographic area or location for the household  101  may be stored as a demographic attribute of the individual members of the household. 
     The demographic information for the household members  102  may be collected in a number of ways. For example, the household  101  may be recruited to be part of a television viewing panel that is used to determine television viewership data. Once the household  101  is recruited, the demographic information may be collected as part of a registration process. 
     In another example, the household  101  may be a part of, or recruited into, an Internet usage panel that is used to determine Internet usage. Demographic information of the household members  102  may be collected when the household  101  is registered to be part of the Internet usage panel. As part of the Internet usage panel, the household  101  may have a panel application installed on one or more client systems in the household. The panel application may collect internet usage data to send to an internet usage collection server. In some implementations, the internet usage data could be used to infer information about household member  102 , such as by comparing internet content accessed by each member  102  with demographic or other information about users accessing the same content. Other methods may be used to capture or confirm information about members  102  of the household  101 , such as survey data or data captured from other household behaviors, or data provided by third party services that attempt to determine demographic data of household members  102 . 
     The system  100  includes a plurality of panelists  130 . The panelists  130  may be persons who have provided demographic and other information about themselves, and who opted to have their television viewing behavior monitored. The panelist data  122  stored in the data store  120  includes the demographic and other information provided by or otherwise obtained from the panelists  130 . In some cases, the panelists  130  may be associated with households having associated information, such as household size, household roster, and other information, which may be included in panelist data  122 . In some implementations, the viewing data  118  may represent viewing data from panelists  130  associated with households including only other panelists, such that a complete representation of all television viewing within the household is included in the viewing data  118 . 
     In some implementations, the viewing data  118 , unlike the tuning data  116 , may represent person-level viewing activity for the individual panelists  130 . For example, a television viewing event in the viewing data  118  may represent a particular panelist  130  watching a television program for a period of time, as opposed to the tuning data  116 , which may represent the television program being watched in the household for a period of time, but may not represent which of the individual members of the household watched the program. 
     Each of the panelists  130  is associated with a viewing monitor  132 . In some cases, the viewing monitors  132  may be portable computing devices carried by each of the panelists  130  that monitor television viewing by the panelist carrying the device. For example, the viewing monitors  132  may be devices operable to capture and analyze sound information to determine if the panelist  130  is watching a particular television program. In some cases, the viewing monitors  132  may extract encoded signals from the sound information identifying the particular television program being watched by the panelist  130 . The viewing monitors  132  may also identify the particular television program from the sound information using other mechanisms, such as, for example, by generating acoustic fingerprint from the sound information in querying a database mapping known acoustic fingerprints to television programs. In some implementations, the viewing monitors  132  may monitor other types of information to determine a television program being watched by the panelist  130 , such as, for example, video information, radio frequency (RF) signals, infrared (IR) signals, or other information. 
     The viewing monitors  132  produce viewing data  118  representing viewing activity by the panelists  130 . In some implementations, the viewing monitors  132  may provide viewing data  118  directly to the data store  120 . The viewing monitors  132  may also provide the viewing data  118  to a separate collection server or set of servers, and the viewing data  118  may be acquired by or otherwise stored in the data store  120 . In some implementations, the viewing data  118  includes information regarding television viewing events, such as, for example, a television program being watched, a television network, an entity operating the television network, a start time and stop time for the television viewing event, an identifier of the panelist  130  associated with the television viewing event, or other information. 
     When reporting tuning events, the set top boxes  112  may not be able to directly report the particular household member or members  102  associated with each tuning event. For example, in some implementations, the tuning data  116  may include episode viewership for the household  101 , but may not include a breakdown of the viewership of individual members of the household  102 . As described further below, the household member data  110 , tuning data  116 , viewing data  118 , and panelist data  122  may be used to determine, for a given program, values for members  102  of the household  101  that represent the probability that the corresponding member  102  watched the program, as well as to project a number of watched minutes for the viewing event for each individual member  102  of the household  101 . These values can be aggregated for various demographic groups in order to generate demographic viewership data for the episode, program, or network. 
       FIG. 2  illustrates an example of a system in which person-level viewing data can be used to generate projected person-level viewing data from household-level tuning data through demographic attribution. The system  200  includes a reporting server  202 . The reporting server  202  may be implemented using, for example, a general-purpose computer capable of responding to and executing instructions in a defined manner, a personal computer, a special-purpose computer, a workstation, a server, or a mobile device. The reporting server  202  may receive instructions from, for example, a software application, a program, a piece of code, a device, a computer, a computer system, or a combination thereof, which independently or collectively direct operations. The instructions may be embodied permanently or temporarily in any type of machine, component, equipment, or other physical storage medium that is capable of being used by the reporting server  202 . 
     The reporting server  202  executes instructions that implement a measurement data processor  204 , a data aggregation processor  206 , and a report generation module  208 . The measurement data processor  204  includes a pre-processing module  204   a , a minute assignment module  204   b , and a fractional viewership module  204   c . The measurement data processor  204  and report generation module  208  may be operable to generate viewership data based on the household member data  110 , tuning data,  116 , viewing data  118 , and panelist data  122  and use that data to generate one or more reports  210  that include information regarding episode-level, program-level, network-level, or entity-level viewership. 
       FIG. 3  is a flow diagram illustrating an example of a process for using person-level viewing data can to project person-level viewership from household-level tuning data through demographic attribution. The following describes the process  300  as being performed by components of the reporting server  202  with respect to data associated with the household  101 . However, the process  300  may be performed by other systems or system configurations and implemented with respect to other members of the viewing audience. 
     The pre-processing module  204   a  accesses the collected data  201 , including household member data  110 , tuning data  116 , viewing data  118 , and panelist data  122  ( 302 ). The pre-processing module  204   a  may perform one or more pre-processing functions on the household member data  110 , tuning data  116 , viewing data  118 , and panelist data  122  as appropriate. 
     In some cases, the pre-processing module  204   a  may identify particular elements of the household member data  110  for use in comparison with the panelist data  122  associated with the viewing data  118 , such as age category, gender, race, occupation, geographic area, or other elements. Information about the household as a whole, such as household size or income, may also be identified for use. Each household member  102  may be identified by one or more demographic dimensions relevant to the particular application of the demographic attribution model. 
     In some cases, the pre-processing module  204   a  may sort the household member data  110  into particular demographic categories for demographic attribution. The pre-processing module  204   a  may also sort the viewing data  118  into particular demographic categories based on the particular panelist  132  associated with each viewing event in the viewing data  118 . The pre-processing module  204   a  may identify the particular program associated with a household event within the tuning data  116 . In some cases, the pre-processing module  204   a  may examine the demographic distribution of the panelists  132  associated with the viewing data  118 , and may apply weighting factors to the viewing data  118  to correct any bias in the demographics of the panelists. For example, if the panelists  132  include 80% females and 20% males, the pre-processing module  204   a  may apply a negative weighting factor to data from female panelists and a positive weighting factor to male panelists to correct for the gender bias in the sample. This gender bias is indicated by the demographics of the sample being dissimilar to the demographics of the population as a whole (i.e., an 80% female sample may not be representative of the overall population, which is roughly 50% female). 
     In some implementations, the pre-processing module may extract the tuning event data for the television viewing event from a larger collection of tuning data  116  involving multiple tuning events. In some implementations, other relevant tuning events may also be extracted (such as simultaneous events as further described below). 
     In some implementations, the television viewing event may be associated with a particular episode of a television program. In some implementations, the television viewing event may not be associated with a particular program but may be associated with tuning data  116  such as a date, time, and television network. 
     The pre-processing module  204   a  may extract the viewing data  118  for demographic groups matching the individual members of the household  101  and demographic groups from households that match the household  101  as a whole. For the example of the household  101  as shown in  FIG. 1 , the pre-processing module  204   a  may extract the viewing data for 18-year-old males in households with four people, 24-year-old females in households with four people, 35-year-old females in households with four people, and 46-year-old males in households with four people. 
     The viewing data  118  may be for the viewership of television viewing events sharing one or more characteristics with the television viewing event represented by the tuning data  116 . For example, where the tuning data  116  represents a television viewing event represented by a particular episode of a television program, the viewing data  118  may be for the viewership of the television program by the panelists  132 . If the television viewing event is represented by a date, time, and network, the viewing data  118  may be for the viewership of the network at the date and time by the panelists  132 . 
     Actions  304  and  306  are performed for each television viewing event represented by the tuning data  116 . For each television viewing event, the fractional viewership module  204   c  determines fractional viewership values for the individual members of the particular household based on the household member data  110 , the viewing data  118 , and the panelist data  122  ( 304 ). The fractional viewership values for a given member of the household represents the probability that member viewed the particular event. 
     In some cases, the fractional viewership module  204   c  calculates the fractional viewership values for a particular television viewing event in the tuning data  116  for each member  102  of a household  101  by determining a set of the viewing data  118  associated with panelists  130  having matching demographic information to the individual members  102  of the household  101 . For example, to determine a fractional viewership value for the 18-year-old male member of household  101 , the fractional viewership module  204   c  may examine viewing data  118  associated with panelists in the same demographic group (e.g., ages 18 to 24). The fractional viewership module  204   c  may calculate the percentage of panelists associated with this set of the viewing data  118  that watched the television program associated with the particular television viewing event, and may assign this percentage as the fractional viewership value for the 18-year-old male member of household  101  for the particular television viewing event. In some implementations, a combination of demographic groups may be matched between the individual members  102  and the panelists  130 , including, but not limited to, gender, race/ethnicity, income, occupation, household size, household demographic makeup, geographic area, or other groups. 
     More formally, the probability of a member i with demographic characteristics d i  viewing content s in a household h can be computed to represent the viewership of that member. In addition to the demographic characteristics of the member i (d i ), the demographic characteristics of all household members in h, denoted by D h , are taken into account in determining the probability of viewing. The person demographic characteristics, d i , can include age and gender, while the characteristics of the household, D h , can include the household size, the race/ethnicity, and a collection of age/gender entries of all persons in the household (we refer to this as ‘household composition”). The probability of a member i in household h to view content s can be calculated in two steps: 
     The first step estimates the total number of persons viewing the content in household h. This is obtained using a persons-to-viewers conversion factor learned from the training data (person-level data):
 
 C   Dh,s   =n   Dh,s   /N   Dh  
 
     where C Dh,s  is the conversion factor, n Dh,s  is the projected number of viewers of content s in households characterized by D h , and N Dh  is the total projected number of persons in households characterized by D h . The expected number of viewers in household h can be calculated by multiplying the conversion factor by the number of persons in h. 
     The second step in assigning the probability of viewing to a member i of the household consists of attributing the correct proportion of viewers computed in the first step to i. The proportion is obtained from the training data as follows:
 
 P   Dh,di,s   =n   di,Dh,s   /N   Dh,s  
 
     where n di,Dh,s  denotes the projected number of viewers of s with demographics di and belonging to households characterized by D h , and N Dh,s  consists of the projected number of persons in households characterized by D h  and that had a tuning event of s. 
     To calculate the probability that member i viewed content s in household h, the results of the first and second steps are used:
 
 P ( V   i   s   |d   i   ,D   h )= P   Dh,di,s   ×C   Dh,s   ×N   h  
 
     where N h  is the number of persons in household h. Note that the proportions computed in step 2 are in some cases normalized to add up to 1.0. 
     The minute assignment module  204   b  of the measurement data processor  204  determines member minute values for the particular television viewing event for each individual member of the household based on the viewing data  118  ( 306 ). The member minute values represent the projected number of minutes each individual member of the household participated in the television viewing event. In some cases, the minute assignment module  204   b  may determine a set of the viewing data  118  associated with panelists  130  having matching demographic information to the individual members  102  of the household  101 . The minute assignment module  204   b  may also use the same set of viewing data determined by the fractional viewership module  204   c  at  304 . From this set of viewing data, the minute assignment module  204   b  may determine an average number of minutes that panelists  130  represented by the set watched the television program represented by the particular television viewing event. From this, the minute assignment module  204   b  may project a number of minutes that the individual member participated in the particular television viewing event. For example, if the set of viewing data indicates that panelists  130  with matching demographics to the individual member watched an average of seven minutes of the television program, but the television viewing event indicates that the program was viewed for 30 minutes by the household, the individual member can be projected to watched seven minutes of the total 30 minute viewing event. In some cases, the member minute values for all individual members in the household for a particular viewing event may sum to a value greater than the number of minutes for the household for the particular viewing event. This represents cases of “co-viewing” of the television program by multiple individual members at once, and provides a more granular view of television viewing activity within the household. 
     In some cases, the minute assignment module for assigning minutes of content s to member i in household h also follows two steps. 
     The first step estimates the total number of person-minutes of content in household h based on the observed household minutes. By using the person-level data and filtering to those households where all members are known, the household minutes can be computed by counting the number of unduplicated minutes across the members. The household-minute-to-person-minute conversion factor is then obtained from the training data (person-level data):
 
 F   Dh,s   =m   Dh,s   /M   Dh  
 
     where F Dh,s  is the conversion factor, m Dh,s  is the number of person minutes of content s in households characterized by D h , and M Dh  is the number of household minutes in households characterized by D h . The expected number of person minutes in household h can be calculated by multiplying the conversion factor by the number of household minutes in h. 
     The second step in assigning the probability of viewing to a member i of the household consists of attributing the correct proportion of person minutes computed in the first step to i. The proportion is obtained from the training data as follows:
 
 Q   Dh,di,s   =m   di,Dh,s   /M   Dh,s  
 
     where m di,Dh,s  denotes the number of person minutes of s with demographics di and belonging to households characterized by D h , and M Dh,s  consists of the total number of person minutes in households characterized by D h  and that had a tuning event of s. 
     To calculate the probability that member i viewed content s in household h, the results of the first and second steps are used:
 
 M   i   =Q   Dh,di,s   ×F   Dh,s   ×M   h  
 
     where M i  is the number of minutes assigned to member i, and M h  is the number of household minutes in household h. Note that the proportions computed in step 2 are in some cases normalized to add up to 1.0. In some cases, the data aggregation processor  206  aggregates the household viewership data from the household  101  with further household viewership data accumulated from other households in order to generate various levels of viewership data. For example, the various sets of household viewership data may be aggregated to determine episode viewership data for the episode corresponding to the tuning event. The episode viewership data may itself be broken down into a number of demographics or other group measurements based on what is relevant to the television entity or to advertisers. 
     The data aggregation processor  206  may further aggregate the episode viewership data for multiple episodes into program viewership data reflecting the viewership demographics for the program as a whole. Program viewership data may be further aggregated from multiple programs appearing on the same network in order to determine network viewership data. If two or more networks are owned by the same entity, the network viewership data may be further aggregated into entity viewership data. At each level, the data may continue to isolate and report on various demographics or may aggregate one or more of the demographic dimensions. 
     The data aggregation processor  206  may aggregate the data for the tuning events in different households representing the same episode. This produces episode viewership data. In some implementations, aggregating the viewership data involves summing the fractional viewership data in each demographic category and dividing by the number of individuals in that category to arrive at a probability profile for the episode. 
     In some implementations, an equation for the number of people N s  who viewed the episode s can be calculated as: 
     
       
         
           
             
               N 
               s 
             
             = 
             
               
                 ∑ 
                 k 
               
               ⁢ 
               
                 
                   p 
                   
                     k 
                     , 
                     
                       D 
                       h 
                     
                   
                   s 
                 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 
                   ∀ 
                   
                     k 
                     ∈ 
                     Sample 
                   
                 
               
             
           
         
       
     
     That is, the sum of the adjusted fractional viewership values over the sample is equal to the estimated number of people from the sample who watched the episode s. As described above, these fractional viewership values p s   k,Dh  may include adjustments for the duration of the tuning event relative to the episode duration and other factors. 
     The data aggregation processor  206  may aggregate the episode viewership data representing episodes of the same program in order to generate program viewership data. In some implementations, the desired statistic is how many viewers are estimated to have seen at least one episode of the program. In this case, the data aggregation processor  206  may use the fractional viewership data associated with each episode and individual in order to produce fractional values representing each individual having watched at least one episode of the program. For example, the fractional viewership values v 1  to v 4  for a single individual over four episodes may be used to produce a program viewership value v p  representing the probability that the individual watched at least one episode of the program:
 
 v   p =1−(1− v   1 )*(1− v   2 )*(1− v   3 )*(1− v   4 )
 
     In this way, each household member will contribute at most 1 viewer to the program viewership data. 
     Formally, for all episodes s of a program {dot over (s)}, the probability of each person i having viewed at least one episode s is: 
               p     i   ,     D   h         s   .       =     1   -       ∏   m     ⁢           ⁢       [     1   -     p     i   ,     D   h         s   m         ]     ⁢           ⁢     ∀       s   m     ∈     s   .                     
The de-duplicated number of persons who viewed the program {dot over (s)} can be calculated by summing up these probabilities. The summation can also be conditioned on a demographic segment.
 
     The data aggregation processor  206  may use similar methods to aggregate program viewership data over multiple programs in order to generate product network viewership data. For example, similar to the calculation above for program viewership data, for all programs {dot over (s)} on a network {umlaut over (s)}, the probability of each person i having viewed one of the programs {dot over (s)} is: 
               p     i   ,     D   h         s   ¨       =     1   -       ∏   n     ⁢       [     1   -     p     i   ,     D   h           s   .     n         ]     ⁢           ⁢     ∀         s   .     n     ∈     s   ¨                     
The de-duplicated number of persons who viewed a program on network {umlaut over (s)} can be calculated by summing up these probabilities. The summation can also be conditioned on a demographic segment.
 
     The data aggregation processor  206  may similarly aggregate network viewership data over multiple commonly-owned networks in order to generate entity viewership data. For example, for all networks {umlaut over (s)} owned by a common entity e, the probability of each person i having viewed one of the networks {umlaut over (s)} is: 
     
       
         
           
             
               p 
               
                 i 
                 , 
                 
                   D 
                   h 
                 
               
               e 
             
             = 
             
               1 
               - 
               
                 
                   ∏ 
                   n 
                 
                 ⁢ 
                 
                   
                     [ 
                     
                       1 
                       - 
                       
                         p 
                         
                           i 
                           , 
                           
                             D 
                             h 
                           
                         
                         
                           
                             s 
                             ¨ 
                           
                           n 
                         
                       
                     
                     ] 
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     ∀ 
                     
                       
                         
                           s 
                           ¨ 
                         
                         n 
                       
                       ∈ 
                       e 
                     
                   
                 
               
             
           
         
       
     
     The de-duplicated number of persons who viewed a network owned by entity e can be calculated by summing up these probabilities. The summation can also be conditioned on a demographic segment. 
     In some implementations, each step of the process may use one or more of the aggregation techniques described above, or others appropriate to allowing for overlap and other factors relevant to aggregation at that level. 
     The report generation module  208  may generate viewership reports  210  based on the aggregated viewership data. These reports may include data at any level of aggregation, and may be specified by a user. Reports may include the viewership data of various demographic groups as estimated through the use of demographic attribution. Entities may request particular demographic data and data at a particular level of aggregation. 
     For example, a program-level report may show that a particular program has been watched by 12% of males ages 18-24. A network-level report may show that 45% of viewers of a particular network are females above age 40. An entity-level report may show that 57% of males and 25% of females watched at least one of the entity&#39;s sports networks during the time period representing this year&#39;s regular baseball season. 
     Because a household may have more than one set top box and more than one display device, at times there may be more than one program episode being viewed at a time by members of a household. In some implementations, the existence of more than one program episode being viewed at the same time in a household may affect the fractional values determined for members of that household for one or both of the viewing events. 
     In some implementations, the pre-processing module  204   a  identifies simultaneous events associated with the same household generated by set top boxes. Simultaneous events are those that include at least some overlap in the times in which the events are shown. In some cases, simultaneous events may have to have at least a threshold amount of overlap to be considered simultaneous; that is, nominal overlap between the first and last minutes of events that are primarily at different times may not be identified as simultaneous. 
     Applying the demographic attribution model as described above generally assumes that viewing data  118 , representing the demographics of panelists and whether or not they watch a particular program, is known for the program associated with the event for which event viewership data is to be determined. More formally, in determining P(V i   s |d i , H), the probability that a particular household member i watched the episode s given the member&#39;s demographic information d i  and household size H, the following equation is used: 
     
       
         
           
             
               P 
               ⁡ 
               
                 ( 
                 
                   
                     
                       V 
                       i 
                       s 
                     
                     ❘ 
                     
                       d 
                       i 
                     
                   
                   , 
                   H 
                 
                 ) 
               
             
             = 
             
               
                 n 
                 
                   
                     d 
                     i 
                   
                   , 
                   H 
                 
               
               
                 N 
                 
                   
                     d 
                     i 
                   
                   , 
                   H 
                 
               
             
           
         
       
     
     Where n di,H  is the number of persons with demographic characteristics d i  and household size H within the viewing data  118  who are recorded as watching a particular episode of the program, and N di,H  is the total number of panelists with demographic characteristics d i  and household size H within the viewing data  118 . 
     In some implementations, the collected data  201  may not include adequate viewing data  118  for a particular program. Formally, the variable n di,H  from the above equation is not available, and so the above equation cannot be used to determine P(V i   s |d i , H). However, other techniques may be available for determining the probabilities of each household member watching the program. For example, the collected data  201  may include viewership data for the network n and viewership data for a particular time of viewing, δ (such as between 8 pm and 8:30 pm). Formally, the data store  120  may include data sufficient to determine P(V i   n |d i , H), the probability that a particular household member i watches the network n, and P(V i   δ |d i , H), the probability that a particular household member i watches television at time δ. 
     In some implementations, the following equation can be applied:
 
 P ( V   i   s   |d   i   ,H )= P ( V   i   n   |d   i   ,H )× P ( V   i   δ   |d   i   ,H )
 
     Given a household member&#39;s demographics and household size, this equation determines the probability of an individual i watching an episode s for which program viewership data as a product of the probability that the individual watches the network n multiplied by the probability that the individual watches television during the particular time of viewing associated with the episode is not available. Similar alternatives may be available in order to accommodate other missing data as necessary. 
       FIG. 4  is a block diagram applying a demographic attribution model to person-level viewing data and household-level tuning data to project person-level viewership for the household. As shown in a tuning events table  402 , a television viewing event occurred on January 2 associated with the show “DWTS” on the network “ABC.” Another tuning event occurred on January 5 associated with the show “SNL” on the network NBC. The table  404  shows a roster of panelists having their television viewing activity monitored (as previously described relative to  FIG. 1 ), their associated demographic groups, and their associated households. Table  406  includes television viewing events representing the panelists watching the shows “DWTS” and “SNL.” 
     Table  408  includes a household roster for the household associated with the tuning events in table  402 . As shown, that also includes an 18-year-old male, 24-year-old female, a 35-year-old female, and a 46-year-old male. 
     Demographic attribution model  401  is used to project person-level viewing events in the table  410  from the tuning events in table  402 , the household roster in table  408 , the panelist roster in table  404 , and the panelist viewing events in table  406 . For explanatory purposes, the following discussion will focus on example event  416  in the person level viewing events in table  410 . 
     As shown, viewing event  416  associates a demographic of “18-year-old male” to the viewing event for “SNL” for set-top box  1 . The viewing event  416  represents the projected person level viewing event for the 18-year-old male in the household (person ID 74369). The viewing event  460  includes a views value of 0.500. This value is a fractional viewership value indicating that the 18-year-old male has a 50% probability of watching this viewing event. In some cases, this probability may be calculated based on panelist viewing events for panelists in the same demographic group (i.e., panelist IDs  1 - 4 ). The table  406  shows panelist viewing events for panelist IDs  1  and  2  only. Accordingly, two of the possible four panelists from this demographic watched “SNL,” yielding a probability of 50% that the 18-year-old male in the household roster in that demographic watched “SNL.” 
     The viewing event  416  also includes a minutes value of 45 representing the projected number of minutes the 18-year-old male watched “SNL.” In some cases, this minutes value may, again, be calculated based on panelist viewing events for panelists in the same demographic group (i.e., panelist IDs  1 - 4 ). The table  406  shows panelist viewing events for panelist IDs  1  and  2  only. Panelist ID  1  watched “SNL” from 8:03 PM until 9:03 PM (60 minutes). Panelist ID  2  watched “SNL” from 8:00 PM until 8:30 PM (30 minutes). Accordingly, panelists matching the demographics of the 18 -year-old male member of the household watched “SNL” for an average of 45 minutes (i.e., 60minutes+30 minutes/2 panelists). 
     In some implementations, panelists in the demographic that did not watch the show may be included with a zero minute value in an average minutes per audience member calculation. For example, if the table  406  included 2 other 18-year old males who did not watch “SNL,” the average minutes per audience member calculation for the program would represent that each 18-year-old male audience member watched the program for 22.5 minutes (i.e., 60 minutes+30 minutes+0 minutes+0 minutes/4 panelists).  FIG. 5  illustrates an example of a system  500  in which television viewership information may be collected and processed to determine audience measurement data for non-rostered households. Similarly to the system  100  of  FIG. 1 , system  500  includes a household  501  including set-top-boxes  512  for viewing television content. The set-top-boxes  512  reporting tuning events to the STB collection server  514 , which stores the tuning events as tuning  518  in data store  520 . The data store  520  also includes household member data  510  having information for individual members of households. In some cases, the household  501  may not be associated with information regarding individual members of the household, making household  501  a “non-rostered” household. 
     The data store  520  also includes viewing data  518 . In some cases, the viewing data  518  may include observed person-level viewing data from one or more panelists associated with panelist data  522 , such as described relative to  FIG. 1 . The viewing data  518  may also include projected person-level viewing data calculated for tuning data  518  for rostered households associated with individual member information in the household member data  510  (e.g., household  101  from  FIG. 1 ) as described in  FIG. 3 . 
       FIG. 6  is a flow diagram illustrating an example of a process  600  for using person-level viewing data to project person-level viewership from non-rostered household-level tuning data. The following describes the process  600  as being performed by components of the reporting server  202  with respect to data associated with the household  501 . However, the process  600  may be performed by other systems or system configurations and implemented with respect to other members of the viewing audience. 
     The measurement data processor  204  accesses tuning data  516  and person-level viewing data  518  ( 602 ). The tuning data  516  may include tuning data for rostered households (e.g., household  101 ) and non-rostered households (e.g., household  501 ). In some cases, the tuning data  516  includes a particular television program watched during each viewing event, and a household minutes value representing a number of minutes the particular household watched the particular television program during each viewing event. The person-level viewing data  518  may represent television viewing events associated with panelist members of other households different than the particular household, and may include a particular television program watched during the viewing event, and a panelist minutes value representing a number of minutes the particular panelist member watched the particular television program, each panelist member associated with panelist information. In some cases, the person-level viewing data includes observed viewing data obtained through monitoring viewing behavior of a plurality of panelists, and modeled viewing data obtained by applying statistical projections to household-level data (e.g., the data produced by the process  300  of  FIG. 3 ). 
     The measurement data processor  204  identifies group subsets including tuning data  516  having matching values for a grouping criteria including attributes included in the tuning data and the person-level viewing data ( 604 ). For example, the measurement data processor  204  may identify group subsets of tuning data  516  for particular geographic areas, such that each group subset includes tuning data for households in that region (e.g., a group subset for Texas, another group subset for New York, etc.). The grouping criteria for the group subsets may also include other criteria or combinations of criteria, including number of set-top-boxes, household size, and other criteria. 
     For each particular group subset identified at  604 , the measurement data processor  204  identifies matching person-level viewing data including person-level viewing data that matches values for group criteria associated with the particular group subset ( 606 ). For example, for a group subset for the geographic area “Texas,” the measurement data processor  204  may identify matching person-level viewing from the viewing data  518  also associated with “Texas.” 
     The measurement data processor  204  calculates a unique viewers value for the group subset based on the matching person-level viewing data ( 608 ). In some cases, the unique viewers value represents an estimated number of individual members of households associated with the tuning data included in the group subset that watched the particular television program. In some implementations, calculating the unique viewers value includes determining a members per household number for the matching person-level viewing data, wherein the unique viewers value for the particular group subset is based at least in part on the members per household number. 
     Calculating the unique viewers value may include calculating a set of demographic unique viewers values, wherein each demographic unique viewers values is associated with a particular one of a plurality of demographic groups and represents an estimated number of individual members in the particular demographic group from households associated with the tuning data included in the group subset that watched the particular television program. 
     For example, to calculate a unique viewers value for a show called “SNL,” the measurement data processor  204  may determine a members per household number of 2.5 for Texas by determining the average number of persons in households represented by the viewing data  518  (e.g., by dividing the total number of persons in all households by the number of households). The measurement data processor  204  may also determine that 80% of viewers represented by the viewing data  518  watched SNL. If the non-rostered tuning data represents 10 households, the measurement data processor  204  may estimate that these households include 25 individual members (e.g., 10 households×2.5 members per household), and that 20 of them watched SNL based on 80% viewership. If the viewing data shows that viewers for SNL were 60% male and 40% female, the measurement data processor  204  may estimate that 12 males (60% of the 20 estimated viewers) and 8 females (40% of the 20 estimated viewers) watched SNL in the 10 households. 
     The measurement data processor  204  calculates a person-level minutes value for the group subset based on the matching person-level viewing data ( 610 ). In some implementations, the person-level minutes value represents an estimated number of minutes individual members of households associated with the tuning data included in the group subset watched the particular television program. In some cases, calculating the person-level minutes value for the group subset includes determining a co-viewing factor for the matching person-level viewing data representing a ratio of household-level viewing minutes to person-level viewing minutes for the group subset, wherein the person-level minutes value for the group subset is based at least in part on the co-viewing factor. 
     Calculating the person-level minutes value for the group subset may include determining a co-viewing factor for the matching person-level viewing data representing a ratio of household-level viewing minutes to person-level viewing minutes for the group subset, wherein the person-level minutes value for the group subset is based at least in part on the co-viewing factor. In some cases, calculating the person-level minutes value includes calculating a set of demographic person-level minutes values, wherein each demographic person-level minutes value is associated with a particular one of a plurality of demographic groups and represents an estimated number of minutes individual members in the particular demographic group from households associated with the tuning data included in the group subset watched the particular television program. 
     For example, to calculate a person-level minutes value for a show called “SNL,” the measurement data processor  204  may determine a co-viewing factor of 1.5 for Texas by determining a ratio of person-level minutes to household minutes in households in Texas represented by the viewing data  518  (e.g., by dividing the total number of minutes members watched SNL by total number of minutes households watched SNL). If the non-rostered tuning data represents 10 households that watched SNL for 100 minutes, the measurement data processor  204  may estimate that individual members of these households watched SNL for 150 minutes (e.g., 100 household-level×1.5 co-viewing factor). If the viewing data shows that the person-level minutes in the viewing data for Texas were split such that 80% of the minutes were viewed by males and 20% by females, the measurement data processor  204  may estimate that 120 minutes of the estimated 150 minutes were viewed by males (80% of the 150 estimated minutes) and 30 minutes were viewed by females (20% of the 150 estimated minutes). 
     In some cases, the plurality of demographic groups are associated with a demographic criteria such as, for example, age, gender, race, or income. 
       FIG. 7  is a flow diagram illustrating an example of a process  700  for using panelist viewing data to project second person-level viewing data for rostered households, and using the panelist and person-level viewing data to project person-level viewership from non-rostered household-level tuning data. The following describes the process  700  as being performed by components of the reporting server  202  with respect to data associated with the households  101  and  501 . However, the process  700  may be performed by other systems or system configurations and implemented with respect to other members of the viewing audience. 
     The measurement data processor  204  accesses tuning data  516  representing tuning events associated with particular households ( 702 ). The tuning data  516  may include an indication of a particular television program played during each tuning event, and a household minutes value representing a number of minutes the particular television program played during each viewing event. The tuning data  516  may be divided into rostered tuning data for particular households having household member data representing member information on individual members of the particular household and non-rostered tuning data for particular households without household member data. 
     The measurement data processor  204  accesses panelist viewing data representing television viewing events associated with panelist members ( 704 ). The viewing data may include an indication of a particular television program watched during the viewing event, and a panelist minutes value representing a number of minutes the particular panelist member was exposed to the particular television program, each panelist member associated with panelist information. In some cases, a panelist is exposed to a particular television program if the panelist watched the television program, is near enough to the television playing the program for a monitoring device to detect signals representing the television program. 
     The measurement data processor  204  generates person-level viewing data for tuning events represented by the rostered tuning data based at least in part on the first person-level viewing data and rostered tuning data ( 706 ). The person-level viewing data may include member minutes values and fractional viewership values associated with each individual member of the particular households for tuning events associated with the particular households. In some cases, generating the person-level viewing data includes, for each television viewing event represented by the rostered tuning data, determining a member minutes value associated with an individual member of the particular household for the television viewing event represented by the rostered tuning data, the member minutes value based at least in part on panelist minutes values from a portion of the panelist viewing data associated with panelists associated with panelist information matching at least a portion of the member information of the individual member; and determining a fractional viewership value for each individual member of the particular household representing a probability that the individual member watched the tuning event, the fractional viewership value based at least in part on the portion of the panelist viewing data associated with panelist members associated with panelist information matching at least a portion of the member information of the individual member. In some implementations, this may be performed as described relative to  FIG. 3 . 
     The measurement data processor  204  calculates unique viewers values and person-level minutes values for the non-rostered tuning data based on the panelist viewing data and the person-level viewing data ( 708 ). In some cases, calculating unique viewers values and person-level minutes values for the non-rostered tuning data includes, for each particular group subset, identifying matching person-level viewing data including panelist and person-level viewing data that matches values for the group criteria associated with the particular group subset. A unique viewers value for the group subset may then be calculated based on the matching person-level viewing data, wherein the unique viewers value represents an estimated number of individual members of households associated with the non-rostered tuning data included in the group subset that watched the particular television program. A person-level minutes value for the group subset may also be calculated based on the matching person-level viewing data, wherein the person-level minutes value represents an estimated number of minutes individual members of households associated with the non-rostered tuning data included in the group subset watched the particular television program. In some implementations, this may be performed as described relative to  FIG. 6 . 
     Although specific examples using various equations of probability are described herein, the methods described herein can be used with a variety of probability and statistical techniques and are not limited to only the equations and examples shown. 
     Systems for Implementation 
     The techniques described herein can be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them. The techniques can be implemented as a computer program product, i.e., a computer program tangibly embodied in an information carrier, e.g., in a machine-readable storage device, in machine-readable storage medium, in a computer-readable storage device or, in computer-readable storage medium for execution by, or to control the operation of, data processing apparatus, e.g., a programmable processor, a computer, or multiple computers. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network. 
     Method steps of the techniques can be performed by one or more programmable processors executing a computer program to perform functions of the techniques by operating on input data and generating output. Method steps can also be performed by, and apparatus of the techniques can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). 
     Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, such as, magnetic, magneto-optical disks, or optical disks. Information carriers suitable for embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as, EPROM, EEPROM, and flash memory devices; magnetic disks, such as, internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in special purpose logic circuitry. 
     A number of implementations of the techniques have been described. Nevertheless, it will be understood that various modifications may be made. For example, useful results still could be achieved if steps of the disclosed techniques were performed in a different order and/or if components in the disclosed systems were combined in a different manner and/or replaced or supplemented by other components.