Methods and apparatus to generate corrected online audience measurement data

Methods and apparatus to generate corrected online audience measurement data are disclosed. An example method includes determining a corrected audience count of streaming media for a demographic group by: calculating a deduplication factor for a demographic group using a first audience count, a second audience count, and a third audience count; estimating a fourth audience count for the demographic group, the fourth audience count indicative of a number of people who accessed the streaming media; estimating a fifth audience count for the demographic group, the fifth audience count indicative of a number of people who accessed text content; and applying the deduplication factor to a sum of the fourth audience count and the fifth audience count to determine the corrected audience count. The example method also includes generating ratings information for the streaming media based on subtracting the corrected audience count from the first audience count.

FIELD OF THE DISCLOSURE

This disclosure relates generally to monitoring media, and, more particularly, to methods and apparatus to generate corrected online audience measurement data.

BACKGROUND

Traditionally, audience measurement entities determine compositions of audiences exposed to media by monitoring registered panel members and extrapolating their behavior onto a larger population of interest. That is, an audience measurement entity enrolls people that consent to being monitored into a panel and collects relatively highly accurate demographic information from those panel members via, for example, in-person, telephonic, and/or online interviews. The audience measurement entity then monitors those panel members to determine media exposure information identifying media (e.g., television programs, radio programs, movies, streaming media, etc.) exposed to those panel members. By combining the media exposure information with the demographic information for the panel members, and by extrapolating the result to the larger population of interest, the audience measurement entity can determine detailed audience measurement information such as media ratings, audience composition, reach, etc. This audience measurement information can be used by advertisers to, for example, place advertisements with specific media to target audiences of specific demographic compositions.

More recent techniques employed by audience measurement entities monitor exposure to Internet accessible media or, more generally, online media. These techniques expand the available set of monitored individuals to a sample population that may or may not include registered panel members. In some such techniques, demographic information for these monitored individuals can be obtained from one or more database proprietors (e.g., social network sites, multi-service sites, online retailer sites, credit services, etc.) with which the individuals subscribe to receive one or more online services. However, the demographic information available from these database proprietor(s) may be self-reported and, thus, unreliable or less reliable than the demographic information typically obtained for panel members registered by an audience measurement entity.

The figures are not to scale. Wherever appropriate, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts.

DETAILED DESCRIPTION

In online audience measurement, collecting information about audiences and impressions from unknown viewers presents a risk of reporting biased demographic compositions in ratings information (e.g., average minute audience, views, duration, and unique audience). Examples disclosed herein reduce the risk of bias in the demographic compositions by correcting for sample bias and/or attribution error present in database proprietor data.

In disclosed examples, an online user visits a website to watch a video that has been provided with a set of instructions or information (e.g., via a software development kit (SDK) provided by an audience measurement entity such as The Nielsen Company). When the online user, who may or may not have a prior relationship with the audience measurement entity, visits the website, different types of messages are generated and sent by the online user's device via a communications network. One or more of the messages are sent to a database proprietor's server and one or more of the messages are sent to the audience measurement entity's servers.

The message(s) sent to the database proprietor's server include a cookie and/or other identifier that enable(s) the database proprietor to match the online user to demographic information. The database proprietor attributes the impression to a user account corresponding to the cookie value, and subsequently aggregates the impressions, sessions, and/or audience count based on the demographics associated with the user accounts. The message(s) sent to the audience measurement entity and/or the database proprietor enable the audience measurement entity and/or the database proprietor to measure the portions of the media (e.g., a video) presented at the online user's device.

among the potential sources of bias in the demographic information provided by the database proprietor is coverage of the online user. For example, not everyone in a population has the database proprietor cookie that enables the database proprietor to match the impression to an online profile. For example, a user may not have an account with the database proprietor and/or the user may have an account with the database proprietor but has deleted the database proprietor cookie or otherwise does not have the database proprietor cookie set at a client device at the time of media exposure via the client device. As a result, the database proprietor is not be able to match the impressions to demographic information. A failure to match impressions results in a failure to report audience, views, or duration for those impressions and, thus, an underestimation of the audience count, view count and/or duration for the demographic group to which the online user belongs.

Another potential source of bias in the demographic information arises from misattribution, in which the online user's device is used by multiple users in a household. The other users may or may not have an account with database proprietor. For example, if a first user that logged into the database proprietor on the device at a first time is not the same user using the device at a second time during a media presentation, any impressions, sessions, audience, and/or duration logged based on the database proprietor cookie corresponding to the first user may be misattributed to an incorrect demographic group. As a result, misattribution may result in overestimation of the audience count and/or duration for the demographic group of the user corresponding to the cookie and underestimation of the audience count and/or duration for the demographic group of the actual user that was exposed to the media.

The example sources of bias described above arise in techniques for measuring online audiences for media in which viewing data is collected from unknown (e.g., anonymous) users and third-party demographic information is used to ascertain the demographic composition of the unknown users. While such techniques provide the benefit of more accurate measurements of larger audiences by including unknown or anonymous users, the use of message transmission from the client devices to the audience measurement entity and/or to the database proprietor as well as the use of cookie (or other identifier) matching at the database proprietor results in the inclusion of the above-described sources of bias in the demographic information obtained from the database proprietor.

Disclosed example methods to generate corrected online audience measurement data include collecting messages indicating impressions of streaming media delivered to computing devices via an Internet. Some disclosed example methods include receiving, from a database proprietor, a first audience count indicative of first numbers of the impressions corresponding to the messages attributed to the streaming media for a demographic group, a second audience count indicative of second numbers of the impressions corresponding to the messages attributed to text accessed by the demographic group, and a third audience count describing third numbers of the impressions corresponding to the messages attributed to the streaming media and the text content for accessed by the demographic group. Some disclosed example methods further include determining a corrected audience count of the streaming media for the demographic group by: estimating a fourth audience count for the demographic group and estimating a fifth audience count for the demographic group. In some examples, the fourth audience count indicative of a first number of people who accessed the streaming media and the fifth audience count indicative of a second number of people who accessed the text content. In some examples, determining a corrected audience count of the streaming media for the demographic group further includes calculating a deduplication factor for the demographic group based on the first audience count, the second audience count, and the third audience count, and applying the deduplication factor to a sum of the fourth audience count and the fifth audience count to determine the corrected audience count. Some example methods include generating audience metrics for the streaming media based on subtracting the corrected audience count from the first audience count.

Some example methods further include generating a first adjusted audience count by adjusting the first audience count to compensate for demographic information being unavailable to the database proprietor, generating a second adjusted audience count by adjusting the second audience count to compensate for the demographic information being unavailable to the database proprietor, and generating a third adjusted audience count by adjusting the third audience count to compensate for the demographic information being unavailable to the database proprietor. In some examples, calculating the deduplication factor is based on the first adjusted audience count, the second adjusted audience count, and the third adjusted audience count.

In some disclosed examples, the fourth audience count is estimated based on a first impressions count and a first frequency of impressions detected by the database proprietor. In some disclosed examples, the first impressions count indicates a presentation of a time segment of the streaming media.

In some disclosed example methods, the first impressions count is determined by applying a first ratio to a second impressions count, where the first ratio indicates a quantity of time sub-segments of the streaming media presented at the devices during the time segment and the second impressions count indicates a number of impressions of second media. Some example methods further include generating a redistributed impressions count by assigning second demographic information to a first impressions for which the second demographic information is unavailable to the database proprietor based on second impressions for which the second demographic information is received from the database proprietor. Some example methods further include applying an impressions matrix to the redistributed impressions count to generate the second impressions count.

Some example methods further include generating a misattribution matrix including a probability that the impressions are attributable to a second demographic group when the database proprietor determines the impressions to correspond to a person in a third demographic group. Some disclosed examples further include converting the misattribution matrix to the impressions matrix, where the impressions matrix indicating numbers of the impressions determined by the database proprietor to correspond to respective ones of demographic groups.

In some examples, the messages include an identifier relating the impressions to respective time segments of the streaming media, a network affiliate distributing the streaming media, a network affiliate distributor providing the streaming media to ones of the computing devices as permitted by the network affiliate, a program distributed by the network affiliate and provided to ones of the computing devices via the network affiliate distributor and of which the streaming media is a part, and an episode that belongs to the program of which the streaming media is at least a part. In some examples, the messages identify at least one of time segments of the streaming media that were presented at the computing devices or impressions of the text accessed via the computing devices.

Disclosed example apparatus to generate corrected online audience measurement data include a first impressions collector, a second impressions collector, a factor generator, an audience calculator, and a ratings data generator. In some examples, the first impressions collector collects messages indicating impressions of streaming media delivered to computing devices via an Internet. In some disclosed examples, second impressions collector receives, from a database proprietor, a first audience count indicative of first numbers of the impressions corresponding to the messages attributed to the streaming media for a demographic group, a second audience count indicative of second numbers of the impressions corresponding to the messages attributed to text accessed by the demographic group, and a third audience count describing third numbers of the impressions corresponding to the messages attributed to the streaming media and the text content for accessed by the demographic group. In some disclosed examples, the factor generator calculates a deduplication factor for the demographic group using the first audience count, the second audience count, and the third audience count. In some examples, the audience calculator determines a corrected audience count of the streaming media for the demographic group by: estimating a fourth audience count for the demographic group, estimating a fifth audience count for the demographic group, and applying the deduplication factor to a sum of the fourth audience count and the fifth audience count. In some examples, the fourth audience count indicative of a first number of people who accessed the streaming media and the fifth audience count indicative of a second number of people who accessed the text. In some examples, the ratings data generator generates ratings information for the streaming media based on subtracting the corrected audience count from the first audience count.

Some disclosed apparatus further include a demographic distributor to generate a first adjusted audience count by adjusting the first audience count to compensate for demographic information being unavailable to the database proprietor, generate a second adjusted audience count by adjusting the second audience count to compensate for the demographic information being unavailable to the database proprietor, and generate a third adjusted audience count by adjusting the third audience count to compensate for the demographic information being unavailable to the database proprietor. In some examples, the factor generator calculates the deduplication factor based on the first adjusted audience count, the second adjusted audience count, and the third adjusted audience count.

In some examples, the audience calculator estimates the fourth audience count based on a first impressions count and a first frequency of impressions detected by the database proprietor. In some examples, the first impressions count indicating presentation of a time segment of the streaming media. Some disclosed examples further includes a ratio calculator to determine the first impressions count by applying a first ratio to a second impressions count. In some examples, the first ratio indicates a quantity of time sub-segments of the streaming media presented at the devices during the time segment and the second impressions count indicates a number of impressions of second media.

Some example apparatus further include a matrix converter to convert a misattribution matrix to an impressions matrix, where the misattribution matrix includes a probability that the impressions are attributable to a second demographic group when the database proprietor determines the impressions to correspond to third second demographic group. In some examples, the impressions matrix indicates numbers of impressions determined by the database proprietor to correspond to respective ones of demographic groups. Some disclosed examples further include an adjuster to apply the impressions matrix to a redistributed impressions count to generate the second impressions count, in which the redistributed impressions count compensate for second demographic information being unavailable to the database proprietor.

In some examples, the messages include an identifier relating the impressions to respective time segments of the streaming media, a network affiliate distributing the streaming media, a network affiliate distributor providing the streaming media to ones of the computing devices as permitted by the network affiliate, a program distributed by the network affiliate and provided to ones of the computing devices via the network affiliate distributor and of which the streaming media is a part, and an episode that belongs to the program of which the streaming media is at least a part. In some example apparatus, the messages identify one of time segments of the streaming media that were presented at the computing devices or the impressions of the text at the computing devices.

Turning to the figures,FIG. 1illustrates example client devices102(e.g.,102a,102b,102c,102d,102e) that report audience counts and/or impressions for online (e.g., Internet-based) media to impression collection entities104to facilitate determining numbers of impressions and sizes of audiences exposed to different online media. An “impression” generally refers to an instance of an individual's exposure to media (e.g., content, advertising, etc.). As used herein, the term “impression collection entity” refers to any entity that collects impression data, such as, for example, audience measurement entities and database proprietors that collect impression data. Duration refers to an amount of time of presentation of media, which may be credited to an impression. For example, an impression may correspond to a duration of 1 minute, 1 minute 30 seconds, 2 minutes, etc.

The client devices102of the illustrated example may be implemented by any device capable of accessing media over a network. For example, the client devices102a-emay be a computer, a tablet, a mobile device, a smart television, or any other Internet-capable device or appliance. Examples disclosed herein may be used to collect impression information for any type of media. As used herein, “media” refers collectively and/or individually to content and/or advertisement(s). Media may include advertising and/or content delivered via web pages, streaming video, streaming audio, Internet protocol television (IPTV), movies, television, radio and/or any other vehicle for delivering media. In some examples, media includes user-generated media that is, for example, uploaded to media upload sites, such as YouTube, and subsequently downloaded and/or streamed by one or more other client devices for playback. Media may also include advertisements. Advertisements are typically distributed with content (e.g., programming). Traditionally, content is provided at little or no cost to the audience because it is subsidized by advertisers that pay to have their advertisements distributed with the content.

In the illustrated example, the client devices102employ web browsers and/or applications (e.g., apps) to access media. Some of the media includes instructions that cause the client devices102to report media monitoring information to one or more of the impression collection entities104. That is, when a client device102of the illustrated example accesses media that is instantiated with (e.g., linked to, embedded with, etc.) one or more monitoring instructions, a web browser and/or application of the client device102executes the one or more instructions (e.g., monitoring instructions, sometimes referred to herein as beacon instruction(s)) in the media and executes the beacon instruction(s) to cause the executing client device102to send a beacon request or impression request108to one or more impression collection entities104via, for example, the Internet110. The beacon request108of the illustrated example includes information about the access to the instantiated media at the corresponding client device102generating the beacon request. Such beacon requests allow monitoring entities, such as the impression collection entities104, to collect impressions for different media accessed via the client devices102. In this manner, the impression collection entities104can generate large impression quantities for different media (e.g., different content and/or advertisement campaigns). Example techniques for using beacon instructions and beacon requests to cause devices to collect impressions for different media accessed via client devices are further disclosed in U.S. Pat. No. 6,108,637 to Blumenau and U.S. Pat. No. 8,370,489 to Mainak, et al., which are incorporated herein by reference in their respective entireties.

The impression collection entities104of the illustrated example include an example audience measurement entity (AME)114and an example database proprietor (DP)116. In the illustrated example, the AME114does not provide the media to the client devices102and is a trusted (e.g., neutral) third party (e.g., The Nielsen Company, LLC, Adobe Systems, etc.) for providing accurate media access statistics. In the illustrated example, the database proprietor116is one of many database proprietors that operate on the Internet to provide one or more services. Such services may include, but are not limited to, email services, social networking services, news media services, cloud storage services, streaming music services, streaming video services, online shopping services, credit monitoring services, etc. Example database proprietors include social network sites (e.g., Facebook, Twitter, MySpace, etc.), multi-service sites (e.g., Yahoo!, Google, etc.), online shopping sites (e.g., Amazon.com, Buy.com, etc.), credit services (e.g., Experian), and/or any other type(s) of web service site(s) that maintain user registration records. In examples disclosed herein, the database proprietor116maintains user account records corresponding to users registered for Internet-based services provided by the database proprietors. That is, in exchange for the provision of services, subscribers register with the database proprietor116. As part of this registration, the subscriber may provide detailed demographic information to the database proprietor116. The demographic information may include, for example, gender, age, ethnicity, income, home location, education level, occupation, etc. In the illustrated example ofFIG. 1, the database proprietor116sets a device/user identifier (e.g., an identifier described below in connection withFIG. 2) on a subscriber's client device102that enables the database proprietor116to identify the subscriber in subsequent interactions.

In the illustrated example, when the database proprietor116receives a beacon/impression request108from a client device102, the database proprietor116requests the client device102to provide the device/user identifier that the database proprietor116had previously set for the client device102. The database proprietor116uses the device/user identifier corresponding to the client device102to identify demographic information in its user account records corresponding to the subscriber of the client device102. In this manner, the database proprietor116can generate “demographic impressions” by associating demographic information with an impression for the media accessed at the client device102. Thus, as used herein, a “demographic impression” is defined to be an impression that is associated with one or more characteristic(s) (e.g., a demographic characteristic) of the person(s) exposed to the media in the impression. Through the use of demographic impressions, which associate monitored (e.g., logged) media impressions with demographic information, it is possible to measure media exposure and, by extension, infer media consumption behaviors across different demographic classifications (e.g., groups) of a sample population of individuals.

In the illustrated example, the AME114establishes a panel of users who have agreed to provide their demographic information and to have their Internet browsing activities monitored. When an individual joins the AME panel, the person provides detailed information concerning the person's identity and demographics (e.g., gender, age, ethnicity, income, home location, occupation, etc.) to the AME114. The AME114sets a device/user identifier (e.g., an identifier described below in connection withFIG. 2) on the person's client device102that enables the AME114to identify the panelist.

In the illustrated example, when the AME114receives a beacon request108from a client device102, the AME114requests the client device102to provide the AME114with the device/user identifier the AME114previously set for the client device102. The AME114uses the device/user identifier corresponding to the client device102to identify demographic information in its user AME panelist records corresponding to the panelist of the client device102. In this manner, the AME114can generate demographic impressions by associating demographic information with an audience for the media accessed at the client device102as identified in the corresponding beacon request.

In the illustrated example, the database proprietor116reports demographic impression data to the AME114. To preserve the anonymity of its subscribers, the demographic impression data may be anonymous demographic impression data and/or aggregated demographic impression data. In the case of anonymous demographic impression data, the database proprietor116reports user-level demographic impression data (e.g., which is resolvable to individual subscribers), but with any personally identifiable information (PII) removed from or obfuscated (e.g., scrambled, hashed, encrypted, etc.) in the reported demographic impression data. For example, anonymous demographic impression data, if reported by the database proprietor116to the AME114, may include respective demographic impression data for each device102from which a beacon request108was received, but with any personal identification information removed from or obfuscated in the reported demographic impression data. In the case of aggregated demographic impression data, individuals are grouped into different demographic classifications, and aggregate demographic data (e.g., which is not resolvable to individual subscribers) for the respective demographic classifications is reported to the AME114. In some examples, the aggregated data is aggregated demographic impression data. In other examples, the database proprietor116is not provided with impression data that is resolvable to a particular media name (but may instead be given a code or the like that the AME114can map to the code) and the reported aggregated demographic data may thus not be mapped to impressions or may be mapped to the code(s) associated with the impressions.

Aggregate demographic data, if reported by the database proprietor116to the AME114, may include first demographic data aggregated for devices102associated with demographic information belonging to a first demographic classification (e.g., a first age group, such as a group which includes ages less than 18 years old), second demographic data for devices102associated with demographic information belonging to a second demographic classification (e.g., a second age group, such as a group which includes ages from 18 years old to 34 years old), etc.

As mentioned above, demographic information available for subscribers of the database proprietor116may be unreliable, or less reliable than the demographic information obtained for panel members registered by the AME114. There are numerous social, psychological and/or online safety reasons why subscribers of the database proprietor116may inaccurately represent or even misrepresent their demographic information, such as age, gender, etc. Accordingly, one or more of the AME114and/or the database proprietor116determine sets of classification probabilities for respective individuals in the sample population for which demographic data is collected. A given set of classification probabilities represents likelihoods that a given individual in a sample population belongs to respective ones of a set of possible demographic classifications. For example, the set of classification probabilities determined for a given individual in a sample population may include a first probability that the individual belongs to a first one of possible demographic classifications (e.g., a first age classification, such as a first age group), a second probability that the individual belongs to a second one of the possible demographic classifications (e.g., a second age classification, such as a second age group), etc. In some examples, the AME114and/or the database proprietor116determine the sets of classification probabilities for individuals of a sample population by combining, with models, decision trees, etc., the individuals' demographic information with other available behavioral data that can be associated with the individuals to estimate, for each individual, the probabilities that the individual belongs to different possible demographic classifications in a set of possible demographic classifications. Example techniques for reporting demographic data from the database proprietor116to the AME114, and for determining sets of classification probabilities representing likelihoods that individuals of a sample population belong to respective possible demographic classifications in a set of possible demographic classifications, are further disclosed in U.S. Patent Publication No. 2012/0072469 (Perez et al.) and U.S. patent application Ser. No. 14/604,394 (Sullivan et al.), which are incorporated herein by reference in their respective entireties.

In the illustrated example, one or both of the AME114and the database proprietor116include example audience data generators to determine ratings data from population sample data having incomplete demographic classifications in accordance with the teachings of this disclosure. For example, the AME114may include an example audience data generator120aand/or the database proprietor116may include an example audience data generator120b. As disclosed in further detail below, the audience data generator(s)120aand/or120bof the illustrated example process sets of classification probabilities determined by the AME114and/or the database proprietor116for monitored individuals of a sample population (e.g., corresponding to a population of individuals associated with the devices102from which beacon requests108were received) to estimate parameters characterizing population attributes (also referred to herein as population attribute parameters) associated with the set of possible demographic classifications.

In some examples, such as when the audience data generator120bis implemented at the database proprietor116, the sets of classification probabilities processed by the audience data generator120bto estimate the population attribute parameters include personal identification information which permits the sets of classification probabilities to be associated with specific individuals. Associating the classification probabilities enables the audience data generator120bto maintain consistent classifications for individuals over time, and the audience data generator120bmay scrub the PII from the impression information prior to reporting impressions based on the classification probabilities. In some examples, such as when the audience data generator120ais implemented at the AME114, the sets of classification probabilities processed by the audience data generator120ato estimate the population attribute parameters are included in reported, anonymous demographic data and, thus, do not include PII. However, the sets of classification probabilities can still be associated with respective, but unknown, individuals using, for example, anonymous identifiers (e.g., hashed identifiers, scrambled identifiers, encrypted identifiers, etc.) included in the anonymous demographic data.

In some examples, such as when the audience data generator120ais implemented at the AME114, the sets of classification probabilities processed by the audience data generator120ato estimate the population attribute parameters are included in reported, aggregate demographic impression data and, thus, do not include personal identification and are not associated with respective individuals but, instead, are associated with respective aggregated groups of individuals. For example, the sets of classification probabilities included in the aggregate demographic impression data may include a first set of classification probabilities representing likelihoods that a first aggregated group of individuals belongs to respective possible demographic classifications in a set of possible demographic classifications, a second set of classification probabilities representing likelihoods that a second aggregated group of individuals belongs to the respective possible demographic classifications in the set of possible demographic classifications, etc.

Using the estimated population attribute parameters, the audience data generator(s)120aand/or120bof the illustrated example determine ratings data for media, as disclosed in further detail below. For example, the audience data generator(s)120aand/or120bmay process the estimated population attribute parameters to further estimate numbers of individuals across different demographic classifications who were exposed to given media, numbers of media impressions across different demographic classifications for the given media, accuracy metrics for the estimate number of individuals and/or numbers of media impressions, numbers of individuals that accessed media multiple times from same and/or different devices, etc.

FIG. 2is an example communication flow diagram200illustrating an example manner in which the AME114and the database proprietor116can cooperate to collect demographic impressions based on client devices102reporting impressions to the AME114and/or the database proprietor116.FIG. 2also shows the example audience data generators120aand120b, which are able to determine ratings data from population sample data having unreliable demographic classifications in accordance with the teachings of this disclosure. The example chain of events shown inFIG. 2occurs when a client device102accesses media for which the client device102reports an impression to the AME114and/or the database proprietor116. In some examples, the client device102reports impressions for accessed media based on instructions (e.g., beacon instructions) embedded in the media that instruct the client device102(e.g., that instruct a web browser or an app executing on the client device102) to send beacon/impression requests (e.g., the beacon/impression requests108ofFIG. 1) to the AME114and/or the database proprietor116. In such examples, the media associated with the beacon instructions is referred to as tagged media. The beacon instructions are machine executable instructions (e.g., code, a script, etc.) which may be contained in the media (e.g., in the HTML of a web page) and/or referenced by the media (e.g., identified by a link in the media that causes the client to request the instructions).

Although the above examples operate based on monitoring instructions associated with media (e.g., a web page, a media file, etc.), in other examples, the client device102reports impressions for accessed media based on instructions associated with (e.g., embedded in) apps or web browsers that execute on the client device102to send beacon/impression requests (e.g., the beacon/impression requests108ofFIG. 1) to the AME114and/or the database proprietor116for media accessed via those apps or web browsers. In such examples, the media itself need not be tagged media. In some examples, the beacon/impression requests (e.g., the beacon/impression requests108ofFIG. 1) include device/user identifiers (e.g., AME IDs and/or DP IDs) as described further below to allow the corresponding AME114and/or the corresponding database proprietor116to associate demographic information with resulting logged impressions.

In the illustrated example, the client device102accesses tagged media206that is tagged with beacon instructions208. The beacon instructions208cause the client device102to send a beacon/impression request212to an AME impressions collector218when the client device102accesses the media206. For example, a web browser and/or app of the client device102executes the beacon instructions208in the media206which instruct the browser and/or app to generate and send the beacon/impression request212. In the illustrated example, the client device102sends the beacon/impression request212using an HTTP (hypertext transfer protocol) request addressed to the URL (uniform resource locator) of the AME impressions collector218at, for example, a first Internet domain of the AME114. The beacon/impression request212of the illustrated example includes a media identifier213identifying the media206(e.g., an identifier that can be used to identify content, an advertisement, content type (e.g., text, video, a combination of both text and video), and/or any other media). In some examples, the beacon/impression request212also includes a site identifier (e.g., a URL) of the website that served the media206to the client device102and/or a host website ID (e.g., www.acme.com) of the website that displays or presents the media206. In the illustrated example, the beacon/impression request212includes a device/user identifier214. In the illustrated example, the device/user identifier214that the client device102provides to the AME impressions collector218in the beacon impression request212is an AME ID because it corresponds to an identifier that the AME114uses to identify a panelist corresponding to the client device102. In other examples, the client device102may not send the device/user identifier214until the client device102receives a request for the same from a server of the AME114in response to, for example, the AME impressions collector218receiving the beacon/impression request212.

In some examples, the device/user identifier214may be a device identifier (e.g., an international mobile equipment identity (IMEI), a mobile equipment identifier (MEID), a media access control (MAC) address, etc.), a web browser unique identifier (e.g., a cookie), a user identifier (e.g., a user name, a login ID, etc.), an Adobe Flash® client identifier, identification information stored in an HTML5 data store (where HTML is an abbreviation for hypertext markup language), and/or any other identifier that the AME114stores in association with demographic information about users of the client devices102. In this manner, when the AME114receives the device/user identifier214, the AME114can obtain demographic information corresponding to a user of the client device102based on the device/user identifier214that the AME114receives from the client device102. In some examples, the device/user identifier214may be encrypted (e.g., hashed) at the client device102so that only an intended final recipient of the device/user identifier214can decrypt the hashed identifier214. For example, if the device/user identifier214is a cookie that is set in the client device102by the AME114, the device/user identifier214can be hashed so that only the AME114can decrypt the device/user identifier214. If the device/user identifier214is an IMEI number, the client device102can hash the device/user identifier214so that only a wireless carrier (e.g., the database proprietor116) can decrypt the hashed identifier214to recover the IMEI for use in accessing demographic information corresponding to the user of the client device102. By hashing the device/user identifier214, an intermediate party (e.g., an intermediate server or entity on the Internet) receiving the beacon request cannot directly identify a user of the client device102.

In response to receiving the beacon/impression request212, the AME impressions collector218logs an impression for the media206by storing the media identifier213contained in the beacon/impression request212. In the illustrated example ofFIG. 2, the AME impressions collector218also uses the device/user identifier214in the beacon/impression request212to identify AME panelist demographic information corresponding to a panelist of the client device102. That is, the device/user identifier214matches a user ID of a panelist member (e.g., a panelist corresponding to a panelist profile maintained and/or stored by the AME114). In this manner, the AME impressions collector218can associate the logged impression with demographic information of a panelist corresponding to the client device102. In some examples, the AME impressions collector218determines (e.g., in accordance with the examples disclosed in U.S. Patent Publication No. 2012/0072469 to Perez et al. and/or U.S. patent application Ser. No. 14/604,394 to Sullivan et al.) a set of classification probabilities for the panelist to include in the demographic information associated with the logged impression. As described above and in further detail below, the set of classification probabilities represent likelihoods that the panelist belongs to respective ones of a set of possible demographic classifications (e.g., such as likelihoods that the panelist belongs to respective ones of a set of possible age groupings, etc.).

In some examples, the beacon request212is an impression message, or I beacon, which is a non-durational message that is transmitted by the client device102when the media206is loaded. In some examples, the beacon/impression request212may not include the device/user identifier214(e.g., if the user of the client device102is not an AME panelist). In such examples, the AME impressions collector218logs impressions regardless of whether the client device102provides the device/user identifier214in the beacon/impression request212(or in response to a request for the identifier214). When the client device102does not provide the device/user identifier214, the AME impressions collector218can still benefit from logging an impression for the media206even though it does not have corresponding demographics. For example, the AME114may still use the logged impression to generate a total impressions count and/or a frequency of impressions (e.g., an average number of impressions per unique audience member) for the media206. Additionally or alternatively, the AME114may obtain demographics information from the database proprietor116for the logged impression if the client device102corresponds to a subscriber of the database proprietor116.

In the illustrated example ofFIG. 2, to compare or supplement panelist demographics (e.g., for accuracy or completeness) of the AME114with demographics from one or more database proprietors (e.g., the database proprietor116), the AME impressions collector218returns a beacon response message222(e.g., a first beacon response) to the client device102including an HTTP “302 Found” re-direct message and a URL of a participating database proprietor116at, for example, a second Internet domain different than the Internet domain of the AME114. In the illustrated example, the HTTP “302 Found” re-direct message in the beacon response222instructs the client device102to send a second beacon request226to the database proprietor116. In other examples, instead of using an HTTP “302 Found” re-direct message, redirects may be implemented using, for example, an iframe source instruction (e.g., <iframe src=“ ”>) or any other instruction that can instruct a client device to send a subsequent beacon request (e.g., the second beacon request226) to a participating database proprietor116. In the illustrated example, the AME impressions collector218determines the database proprietor116specified in the beacon response222using a rule and/or any other suitable type of selection criteria or process. In some examples, the AME impressions collector218determines a particular database proprietor to which to redirect a beacon request based on, for example, empirical data indicative of which database proprietor is most likely to have demographic data for a user corresponding to the device/user identifier214. In some examples, the beacon instructions208include a predefined URL of one or more database proprietors to which the client device102should send follow up beacon requests226. In other examples, the same database proprietor is always identified in the first redirect message (e.g., the beacon response222).

In the illustrated example ofFIG. 2, the beacon/impression request226may include a device/user identifier227that is a DP ID because it is used by the database proprietor116to identify a subscriber of the client device102when logging an impression. In some instances (e.g., in which the database proprietor116has not yet set a DP ID in the client device102), the beacon/impression request226does not include the device/user identifier227. In some examples, the DP ID is not sent until the database proprietor116requests the same (e.g., in response to the beacon/impression request226). In some examples, the device/user identifier227is a device identifier (e.g., an IMEI), an MEID, a MAC address, etc.), a web browser unique identifier (e.g., a cookie), a user identifier (e.g., a user name, a login ID, etc.), an Adobe Flash® client identifier, identification information stored in an HTML5 data store, and/or any other identifier that the database proprietor116stores in association with demographic information about subscribers corresponding to the client devices102. In some examples, the device/user identifier227may be encrypted (e.g., hashed) at the client device102so that only an intended final recipient of the device/user identifier227can decrypt the hashed identifier227. For example, if the device/user identifier227is a cookie that is set in the client device102by the database proprietor116, the device/user identifier227can be hashed so that only the database proprietor116can decrypt the device/user identifier227. If the device/user identifier227is an IMEI number, the client device102can hash the device/user identifier227so that only a wireless carrier (e.g., the database proprietor116) can decrypt the hashed identifier227to recover the IMEI for use in accessing demographic information corresponding to the user of the client device102. By hashing the device/user identifier227, an intermediate party (e.g., an intermediate server or entity on the Internet) receiving the beacon request cannot directly identify a user of the client device102. For example, if the intended final recipient of the device/user identifier227is the database proprietor116, the AME114cannot recover identifier information when the device/user identifier227is hashed by the client device102for decrypting only by the intended database proprietor116.

In the example ofFIG. 2, the beacon instructions208cause the client device102to transmit multiple second beacon requests226a,226bto the database proprietor116and to the AME114. The example second beacon requests226a,226bmay take the form of duration (e.g., indicative of video content) messages (e.g., referred to herein as D beacons or D pings).

In the example ofFIG. 2, D beacons are beacon requests226a,226bthat contain duration information that identify portions of the media206(e.g., video content) that has been presented at the client device102. For example, the D beacons may use the format “D_s_abcde,” where s denotes a time segment of the media206(e.g., an episode of a program distributed by a distributor (e.g., a network affiliate distributor)) being presented at the client device102. The s term can vary between 1 and n, with n being the last segment of the media206. In the D beacon format D_s_abcde, the terms a, b, c, d, and e refer to respective ones of 1st, 2nd, 3rd, 4th, and 5th sub-segments (e.g., minutes) of a segment. A value 0 in any of the terms a, b, c, d, e indicates that the corresponding sub-segment (e.g., the corresponding minute) was not presented and the value 1 indicates that the corresponding sub-segment (e.g., the corresponding minute) was presented. Thus, a D beacon of D_2_01010 indicates that, of a second time segment of the media206(of n time segments making up the media206), the 2nd and 4th sub-segments were presented while the 1st, 3rd, and 5th sub-segments were not presented. In the example ofFIG. 2, the client device102sends the D beacons at the conclusion of designated time segments (e.g., after the 5th minute of a time segment is viewed) and/or at designated time intervals during presentation of the media (e.g., every z minutes, where z sub-segments are represented per D beacon). As used herein, a “time segment” may refer to the larger segments represented by one D beacon (e.g., super-segments) and/or to a sub-segment (e.g., a minute) that is a component of the larger segment.

When the database proprietor116receives the device/user identifier227, the database proprietor116can obtain demographic information corresponding to a user of the client device102based on the device/user identifier227that the database proprietor116receives from the client device102. In some examples, the database proprietor116determines (e.g., in accordance with the examples disclosed in U.S. Patent Publication No. 2012/0072469 to Perez et al. and/or U.S. patent application Ser. No. 14/604,394 to Sullivan et al.) a set of classification probabilities associated with the user of the client device102to include in the demographic information associated with this user. As described above and in further detail below, the set of classification probabilities represent likelihoods that the user belongs to respective ones of a set of possible demographic classifications (e.g., likelihoods that the panelist belongs to respective ones of a set of possible age groupings, etc.).

Although only a single database proprietor116is shown inFIGS. 1 and 2, the impression reporting/collection process ofFIGS. 1 and 2may be implemented using multiple database proprietors. In some such examples, the beacon instructions208cause the client device102to send beacon/impression requests226to numerous database proprietors. For example, the beacon instructions208may cause the client device102to send the beacon/impression requests226to the numerous database proprietors in parallel or in daisy chain fashion. In some such examples, the beacon instructions208cause the client device102to stop sending beacon/impression requests226to database proprietors once a database proprietor has recognized the client device102. In other examples, the beacon instructions208cause the client device102to send beacon/impression requests226to database proprietors so that multiple database proprietors can recognize the client device102and log a corresponding impression. Thus, in some examples, multiple database proprietors are provided the opportunity to log impressions and provide corresponding demographics information if the user of the client device102is a subscriber of services of those database proprietors.

In some examples, prior to sending the beacon response222to the client device102, the AME impressions collector218replaces site IDs (e.g., URLs) of media provider(s) that served the media206with modified site IDs (e.g., substitute site IDs) which are discernable only by the AME114to identify the media provider(s). In some examples, the AME impressions collector218may also replace a host website ID (e.g., www.acme.com) with a modified host site ID (e.g., a substitute host site ID) which is discernable only by the AME114as corresponding to the host website via which the media206is presented. In some examples, the AME impressions collector218also replaces the media identifier213with a modified media identifier213corresponding to the media206. In this way, the media provider of the media206, the host website that presents the media206, and/or the media identifier213are obscured from the database proprietor116, but the database proprietor116can still log impressions based on the modified values (e.g., if such modified values are included in the beacon request226), which can later be deciphered by the AME114after the AME114receives logged impressions from the database proprietor116. In some examples, the AME impressions collector218does not send site IDs, host site IDS, the media identifier213or modified versions thereof in the beacon response222. In such examples, the client device102provides the original, non-modified versions of the media identifier213, site IDs, host IDs, etc. to the database proprietor116.

In the illustrated example, the AME impression collector218maintains a modified ID mapping table228that maps original site IDs with modified (or substitute) site IDs, original host site IDs with modified host site IDs, and/or maps modified media identifiers to the media identifiers such as the media identifier213to obfuscate or hide such information from database proprietors such as the database proprietor116. Also in the illustrated example, the AME impressions collector218encrypts all of the information received in the beacon/impression request212and the modified information to prevent any intercepting parties from decoding the information. The AME impressions collector218of the illustrated example sends the encrypted information in the beacon response222to the client device102so that the client device102can send the encrypted information to the database proprietor116in the beacon/impression request226. In the illustrated example, the AME impressions collector218uses an encryption that can be decrypted by the database proprietor116site specified in the HTTP “302 Found” re-direct message.

Periodically or aperiodically, the impression data collected by the database proprietor116is provided to a DP impressions collector232of the AME114as, for example, batch data. In some examples, the impression data collected from the database proprietor116by the DP impressions collector232is demographic impression data, which includes sets of classification probabilities for individuals of a sample population associated with client devices102from which beacon requests226were received. In some examples, the sets of classification probabilities included in the demographic impression data collected by the DP impressions collector232correspond to respective ones of the individuals in the sample population, and may include personal identification capable of identifying the individuals, or may include obfuscated identification information to preserve the anonymity of individuals who are subscribers of the database proprietor but not panelists of the AME114. In some examples, the sets of classification probabilities included in the demographic impression data collected by the DP impressions collector232correspond to aggregated groups of individuals, which also preserves the anonymity of individuals who are subscribers of the database proprietor.

Additional examples that may be used to implement the beacon instruction processes ofFIG. 2are disclosed in U.S. Pat. No. 8,370,489 to Mainak et al. In addition, other examples that may be used to implement such beacon instructions are disclosed in U.S. Pat. No. 6,108,637 to Blumenau.

In the example ofFIG. 2, the AME114includes the example audience data generator120aofFIG. 1to determine ratings data (e.g., ratings information) using the sets of classification probabilities determined by the AME impressions collector218and/or obtained by the DP impressions collector232. Additionally or alternatively, in the example ofFIG. 2, the database proprietor116includes the example audience data generator120bofFIG. 1to determine media ratings data using the sets of classification probabilities determined by the database proprietor116. A block diagram of an example audience data generator120, which may be used to implement one or both of the example audience data generators120aand/or120b, is illustrated inFIG. 3.

The example audience data generator120ofFIG. 3receives impression information from the AME impressions collector218. Example impression information includes the impression requests212and/or the beacon requests226b. The AME impressions collector218collects and tracks the requests212,226b, as well as the devices102from which the requests212,226bare received.

The example audience data generator120further receives demographic information corresponding to impressions received by the DP116. In some examples, the impressions received at the DP116correspond at least in part to the requests212,226breceived at the AME impressions collector218. The example DP116attempts to determine demographic information for the impressions (e.g., the beacon requests226a), and provides numbers of impressions, numbers of duration units (e.g., minutes, seconds, etc.), and/or numbers of audience members to the AME114.

In examples disclosed herein, the audience data generator120receives aggregated data files containing aggregated numbers of impressions, aggregated numbers of duration units, and aggregated counts of unique audience members organized by groups (e.g., levels of detail) from the DP116. In examples disclosed herein, the aggregated data files are organized by platform (e.g., computing platform). For example, the DP116attributes the aggregated data files to a mobile computing platform or a non-mobile computing platform. As such, the aggregated data files may indicate whether an audience accessed the media206ofFIG. 2via a mobile computing platform (e.g., through a mobile device such as a smartphone, a tablet, a portable media player (PMP), etc.) or via a non-mobile computing platform (e.g., through a desktop computer). In some examples, the DP116may identify ones of the aggregated data files attributed to both the mobile computing platform and the non-mobile computing platform as duplicate data files and attribute the duplicate data files to a total digital computing platform. As such, the aggregated data files are organized by mobile computing platform, non-mobile computing platform, and/or total digital computing platform.

In examples disclosed herein, the aggregated data files are further organized by brand. As used herein, a brand refers to a network affiliate (e.g., a broadcaster, streaming entity, or media provider owned by a company other than an owner of a network by which media is delivered). For example, the DP116may attribute first ones of the aggregated data files to a first brand, or a first network affiliate (e.g., the American Broadcast Company (ABC)), second ones of the aggregated data files to a second brand, or a second network affiliate (e.g., the FOX Broadcasting Company), etc. In examples disclosed herein, the aggregated data files are further organized by sub-brand. As used herein, sub-brand refers to network affiliate distributors (e.g., an asset, a subsidiary, or a division of a network affiliate distributing specified services and media). For example, the DP116may attribute certain ones of the aggregated data files to a first sub-brand, or a first network affiliate distributor (e.g., CBS Television), a second sub-brand, or a second network affiliate distributor (e.g., CBS Sports), a third sub-brand, or a third network affiliate distributor (e.g., Hulu), etc. In examples disclosed herein, a network affiliate may grant permissions to certain ones of network affiliate distributors to allow the certain ones of the network affiliate distributors to provide certain programs and/or episode s to an audience.

In the example ofFIG. 3, the audience data generator120receives the aggregated data files from the DP116organized by: Platform x Device x Brand x Corrected Age×Gender (e.g., platform and device specific brand-level (e.g., network affiliate level) aggregation by age and gender, such as “Platform1, Device1, Brand1, Male, ages 18-24” and “Platform1, Device1, Brand2, Male, ages 25-30”), Platform x Device x Brand x Sub-Brand x Corrected Age x Gender (e.g., platform and device specific sub-brand-level (e.g., network affiliate distributor level) aggregation by age and gender, such as “Platform1, Device1, Brand1, Sub-Brand1, Male, ages 18-24” and “Platform1, Device1, Brand1, Sub-Brand2, Male, ages 25-30”); Platform x Device x Brand x Sub-Brand x Asset Corrected Age x Gender (e.g., platform and device specific asset-level (e.g., program level) aggregation by age and gender, such as “Platform1, Device1, Brand1, Sub-brand1, Asset1, Male, ages 18-24” and “Platform1, Device1, Brand1, Sub-brand1, Asset2, Male, ages 25-30”), Platform x Brand x Sub-Brand x Asset x Episode x Corrected Age x Gender (e.g., episode-level aggregation by platform, age, and gender, such as “Platform1, Brand1, Sub-Brand1, Asset1, Episode1, Male, ages 18-24” and “Platform1, Brand1, Sub-brand1, Asset1, Episode2, Male, ages 18-24”); Platform x Brand x Sub-Brand x Asset x Segment x Corrected Age x Gender (e.g., time segment-level aggregation by age and gender, such as “Platform1, Brand1, Sub-Brand1, Asset1, Minute1(or Minutes1-5), Male, ages 18-24” and “Platform1, Brand1, Sub-Brand1, Asset1, Minute5(or Minutes6-10), Male, ages 18-24”); Platform x Brand x Segment x Corrected Age x Gender (e.g., time segment-level aggregation by age and gender, such as “Platform1, Brand1, Minute1(Or Minutes1-5), male, ages 18-24” and “Platform1, Brand1, Minute5(or Minutes6-10), Male, ages 18-24”).

The audience data generator120also receives aggregated data files containing aggregated numbers of impressions, aggregated numbers of duration units, and aggregated counts of unique audience members organized by groups (e.g., levels of detail) observed at a monitored website (e.g., web site census counts): Platform x Device x Brand (e.g., platform and device specific brand-level (e.g., network affiliate level) aggregation, such as “Platform1, Device1, Brand1” and “Platform1, Device1, Brand2”); Platform x Device x Brand x Sub-Brand (e.g., platform and device specific sub-brand-level (e.g., network affiliate distributor level) aggregation, such as “Platform1, Device1, Brand1, Sub-Brand1” and “Platform1, Device1, Brand1, Sub-Brand2”); Platform x Device x Brand x Sub-Brand x Asset (e.g., platform and device specific asset-level (e.g., program level)) aggregation, such as “Platform1, Device1, Brand1, Sub-Brand1, Asset1” and “Platform1, Device1, Brand1, Sub-Brand1, Asset2”); Platform x Brand x Sub-Brand x Asset×Episode (e.g., episode-level aggregation, such as “Platform1, Brand1, Sub-Brand1, Asset1, Episode1” and “Platform1, Brand1, Sub-brand1, Asset1, Episode2)”; Platform x Brand x Sub-Brand x Asset x Segment (e.g., time segment-level aggregation by age and gender, such as “Platform1, Brand1, Sub-Brand1, Asset1, Minute1(or Minutes1-5)” and “Platform1, brand1, Sub-Brand1, Asset1, Minute5(Or Minutes6-10))”

The example audience data generator120includes an example calibration data collector300. The example calibration data collector300collects or obtains survey calibration data describing platform and device usage statistics of an audience. For example, the survey calibration data may include and/or be based on responses to a survey (e.g., a probability survey) of households selected at random. In some examples, a calibration survey obtains information including the demographic characteristics of a panel member (e.g., age and gender, race, ethnicity, language characteristics, household income, geographic location, etc.), the numbers and/or types of devices (e.g., smartphones, tablet computers, portable media players, desktop computer, etc.) used by a panel member, and/or registrations of panel members with specified database proprietors (e.g., DP116ofFIG. 1). In some examples, the calibration survey obtains, for each panel member of a survey panel, usage characteristics of each of the devices and/or types of devices; programs and episodes typically viewed by the panel member; programs and episodes typically viewed by the panel member on each device and/or type of device; apps used by the panel member on each device and/or type of device; and/or characteristics of interactions with specified database proprietors on each device and/or type of device. The example calibration data collector300obtains the survey calibration data from at least a threshold number of households and, if appropriate, weights the results to be reflective of a general population or audience.

In some other examples, the survey calibration data source includes a survey of established panel(s) of respondents, such as the Nielsen National People Meter (NPM) panel for television ratings. Surveys of established panels often provide higher quality survey calibration data.

The example audience data generator120includes an example platform data manager302, an example device data manager304, an example segment data manager306, an example episode data manager308, an asset data manager310, a sub-brand data manager312, and a brand data manager314. The example platform data manager302, the example device data manager304, the example segment data manager306, the example episode data manager308, the example asset data manager310, the example sub-brand data manager312, and the example brand data manager314each manage the aggregated demographic information received from the DP116and/or from the AME impressions collector218for the respective data levels.

The example audience data generator120ofFIG. 3further includes an example DP data manager316. The example DP data manager316collects, stores, and provides the demographic information (e.g., raw impressions, duration units, and/or audience data per demographic group and/or data level) obtained from the DP116. While the platform data manager302, the device data manager304, the segment data manager306, the episode data manager308, the asset data manager310, the sub-brand data manager312, and the brand data manager314manage the correction and/or calibration of demographic information obtained from the DP116at the respective levels, the example DP data manager316manages the raw demographic information obtained from the DP116for use in the correction and/or calibration of the demographic information.

The example audience data generator120also includes an example demographic distributor318, an example distributed demographic data calculator320, an example matrix generator322, an example episode-segment (E-S) adjuster324, an example census imputer326, an example metrics generator328, an example segment modifier330, an example brand/cross-platform adjuster332, an example content calculator336, and an example ratings data generator338. The example platform data manager302, the example device data manager304, the example segment data manager306, the example episode data manager308, the example asset data manager310, the example sub-brand data manager312, and/or the example brand data manager314may communicate with or notify the example demographic distributor318, the example distributed demographic data calculator320, the example matrix generator322, the example episode-segment (E-S) adjuster324, the example census imputer326, the example metrics generator328, the example segment modifier330, the example brand/cross-platform adjuster332, the example content calculator336, and the example ratings data generator338to correct and/or calibrate the DP demographic information and/or to generate ratings information for online media.

The example demographic distributor318ofFIG. 3may receive communications, notifications, requests, signals, etc. from the platform data manager302, the device data manager304, the segment data manager306, the episode data manager308, the asset data manager310, the sub-brand data manager312, and/or the brand data manager314to distribute impressions, duration units and/or audience members into demographic groups (e.g., demographic groups defined by the AME114and/or another entity, demographic groups corresponding to an audience measurement system such as television ratings, etc.). For example, all or a portion of impression information received from the DP116may have incomplete demographic information (e.g., undetermined age and/or gender information). As described in more detail below, the example demographic distributor318determines distributions of impressions, duration units, and/or audience members by demographic groups, and then distributes any impressions, duration units, and/or audience members for which a demographic group is unknown into the demographic groups based on the determined distribution. In examples disclosed herein, the demographic distributor318may send distributed (e.g., redistributed) impressions, duration units, and/or audience counts to the distributed demographic data calculator320and/or the content calculator336. Example implementations of the demographic distributor318are described below in connection withFIG. 5.

The example distributed demographic data calculator320ofFIG. 3receives redistributed impressions, duration units, and/or audience counts from the demographic distributor318and calculates raw measures for the demographic groups. The distributed demographic data calculator320includes an example duration-per-impression (DPI) calculator340, an example segment frequency calculator (FC)341, an example segment deduplication factor generator (DFG)342, an example episode frequency calculator (FC)343, an example episode deduplication factor generator (DFG)344, an example asset frequency calculator (FC)345, an example asset deduplication factor generator (DFG)346, an example sub-brand frequency calculator (FC)347, an example sub-brand deduplication factor generator (DFG)348, an example brand frequency calculator (FC)349, an example brand deduplication factor generator (DFG)350, an example cross-publisher frequency calculator (CP-FC)351, and an example cross-publisher deduplication factor generator (CP-DFG)352. Example implementations of the distributed demographic data calculator320are described below in connection withFIGS. 6 and 7.

The DPI calculator340calculates a duration-per-impression (DPI) measure for each demographic group using redistributed impressions and redistributed audience counts at the time segment-level.

The segment FC341calculates an impression frequency per audience member, also referred to herein as a frequency, for each demographic group using the redistributed impressions and the redistributed audience counts at the time segment-level.

The segment DFG342calculates a ratio of duplicated audience counts to total audience counts across all platforms y, referred to herein as a deduplication factor, for each demographic group using the redistributed audience counts at the time segment-level.

The episode FC343calculates a frequency for each demographic group using the redistributed impressions and the redistributed audience counts at the episode-level.

The episode DFG344calculates a deduplication factor for each demographic group using the redistributed audience counts at the episode-level.

The asset FC345calculates a frequency for each demographic group using the redistributed impressions and the redistributed audience counts at the asset-level.

The asset DFG346calculates a deduplication factor for each demographic group using the redistributed audience counts at the asset-level.

The sub-brand FC347calculates a frequency for each demographic group using the redistributed impressions and the redistributed audience counts at the sub-brand level.

The sub-brand DFG348calculates a deduplication factor for each demographic group using the redistributed audience counts at the sub-brand level.

The brand FC349calculates a frequency for each demographic group using the redistributed impressions and the redistributed audience counts at the brand-level.

The brand DFG350calculates a deduplication factor for each demographic group using the redistributed audience counts at the brand-level.

The CP-FC351calculates a frequency for each demographic group using the redistributed audience counts at a cross-publisher level. As described in more detail below, the demographic distributor318determines a distribution of impression information (e.g., impressions, duration units, and/or audience counts) at the time segment-level and sums the distributed impression information at the time segment-level, respectively, across all brands b to generate impression information distributed at the cross-publisher level.

The CP-DFG352calculates a deduplication factor for each demographic group using the redistributed audience counts at the cross-publisher level.

The example matrix generator322generates and/or calculates misattribution correction matrices based on the survey calibration data. In examples disclosed herein, the matrix generator322creates a misattribution correction matrix for each combination of device type x and brand b. The matrix generator322receives survey calibration data from the calibration data collector300and calculates probabilities that: (1) a person of a recognized demographic group i is identified by the database proprietor116as a viewer of media, and (2) a person in an actual viewer demographic group j is an actual viewer, which is configured using the survey calibration data. Additional examples that may be used to implement the misattribution correction matrix generation process are disclosed in U.S. Patent Publication No. 2015/0262207 to Rao et al. The example matrix generator322includes an example matrix converter353.

The example matrix converter353converts the misattribution correction matrices to impression counts matrices. In examples disclosed herein, the matrix converter353utilizes a conversion formula to convert a probability of each cell in the misattribution correction matrices to an impressions count probability. In examples disclosed herein, the impression counts matrices are used to correct and/or calibrate impression information and to generate ratings information. Example implementations of the matrix converter353are described below in connection withFIG. 9.

The example E-S adjuster324adjusts impression counts and durations at the episode-level and the time-segment level. In examples disclosed herein, the example E-S adjuster324receives, from the demographic distributor318, and applies redistributed impression counts and redistributed durations at the episode-level and the time-segment level, respectively, to a corresponding impression counts matrix to generate misattribution adjusted impression counts and misattribution adjusted durations to compensate for device sharing. The E-S adjuster324compensates for database proprietor non-coverage error by applying coverage vectors to the misattribution impression counts and durations to generate coverage adjusted impression and durations. As used herein, coverage vectors reflect portions of impressions and/or durations attributable to persons by the database proprietor116. In examples disclosed herein, the E-S adjuster324scales the coverage adjusted impression counts and durations by applying scaling factors. The E-S adjuster324includes an impressions sharing adjuster354, a duration sharing adjuster355, a coverage vector calculator356, an impressions coverage adjuster357, a duration coverage adjuster358, an impression scaling factor generator359, a duration scaling factor generator360, an impressions scaler361, and a duration scaler362. Example implementations of the E-S adjuster324are described below in connection withFIG. 17.

The impressions sharing adjuster354receives impression counts matrices from the matrix converter353and redistributed impressions from the demographic distributor318. In examples disclosed herein, the impressions sharing adjuster354multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the redistributed impressions by a corresponding impression counts matrix to determine misattribution adjusted impressions.

The duration sharing adjuster355receives impression counts matrices from the matrix converter353and redistributed durations from the demographic distributor318. In examples disclosed herein, the duration sharing adjuster355multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the redistributed durations by the corresponding impression counts matrix to determine misattribution adjusted durations.

The coverage vector calculator356calculates coverage adjustment vectors for each brand b and device type x. In examples disclosed herein, the coverage vector calculator356receives survey calibration data from the calibration data collector300and determines a number or portion of persons (e.g., respondents to a survey) in a demographic group that indicate they would be recognized by the database proprietor116when using a particular device type x, as a proportion of the persons in the demographic group that own and have access to that particular device type x. The coverage vector calculator356calculates coverage adjustment vectors by determining a total number of respondents in a demographic group with access to a device of the device type x and dividing by a covered portion of respondents in the demographic group for the device type x based on the survey calibration data.

The impressions coverage adjuster357receives the misattribution adjusted impressions from the impressions sharing adjuster354and the coverage vectors from the coverage vector calculator356. In examples disclosed herein, the impressions coverage adjuster357multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the misattribution adjusted impressions by a corresponding coverage vector to generate coverage adjusted impression counts.

The duration coverage adjuster358receives the misattribution adjusted durations from the duration sharing adjuster355and the coverage vectors from the coverage vector calculator356. In examples disclosed herein, the duration coverage adjuster358multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the misattribution adjusted durations by the corresponding coverage vector to generate coverage adjusted durations.

The impression scaling factor generator359obtains and/or receives the aggregated numbers of impressions from the monitored website (e.g., the web site census counts) at the episode-level and the time-segment level, respectively. In examples disclosed herein, the impression scaling factor generator359determines impression scaling factors for each demographic group, at the episode-level and the time-segment level, respectively, by dividing the aggregated numbers of impressions by a sum of the coverage adjusted impressions across all demographic groups d.

The duration scaling factor generator360obtains and/or receives the aggregated numbers of duration units from the monitored website (e.g., the web site census counts) at the episode-level and the time-segment level, respectively. In examples disclosed herein, the duration scaling factor generator360determines duration scaling factors for each demographic group, at the episode-level and the time-segment level, respectively, by dividing the aggregated numbers of duration units by a sum of the coverage adjusted durations across all demographic groups d.

The impressions scaler361receives the impression scaling factors from the impression scaling factor generator359and multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the coverage adjusted impressions by a corresponding impression scaling factor to generate scaled impressions.

The duration scaler362receives the duration scaling factors from the duration scaling factor generator360and multiplies, for each demographic group at the episode-level and the time-segment level, respectively, the coverage adjusted durations by a corresponding duration scaling factor to generate scaled durations.

The census imputer326further adjusts impression counts and durations at the asset-level, the episode-level, and the time-segment level. In examples disclosed herein, the census imputer326receives scaled impressions and durations from the E-S adjuster324and distributes the scaled impressions and durations at the asset-level. The census imputer326receives, from the monitored website, and imputes the aggregated numbers of impressions and the aggregated numbers of duration units. In examples disclosed herein the census imputer326calculates scaled and adjusted impressions and durations based on the imputation. The census imputer326includes an example impressions adder363, an example duration adder364, an example impressions imputation adder365, an example duration imputation adder366, an example impressions distribution deriver367, an example duration distribution deriver368, an example impressions imputation director369, an example duration imputation director370, an example impressions imputation calculator371, and an example duration imputation calculator372. Example implementations of the census imputer326are described below in connection withFIG. 11.

The impressions adder363receives the scaled impressions at the episode-level and the time-segment level, respectively, from the E-S adjuster324and sums, for each demographic group, the scaled impressions across all episodes e and time-segments i, respectively, to generate asset-level adjusted impressions.

The duration adder363receives the scaled durations at the episode-level and the time-segment level, respectively, from the E-S adjuster324and sums, for each demographic group, the scaled durations across all episode e and time-segments i, respectively, to generate asset-level adjusted durations.

The impressions imputation adder365receives the asset-level adjusted impressions from the impressions adder363and determines sums of the asset-level adjusted impressions across all demographic groups d.

The duration imputation adder366receives the asset-level adjusted durations from the duration adder363and determines sums of the asset-level adjusted durations across all of the demographic groups d.

The impressions distribution deriver367receives the asset-level adjusted impressions from the impressions adder363and the sums of the asset-level adjusted impressions across all demographic groups d from the impressions imputation adder365. In examples disclosed herein, the impressions distribution deriver367determines a ratio, for each demographic group d at the episode-level e and the time-segment level i, respectively, of the asset-level adjusted impressions to the sum of the asset-level adjusted impressions across all demographic groups d to generate adjusted impression demographic distributions.

The duration distribution deriver368receives the asset-level adjusted durations from the duration adder364and the sums of the asset-level adjusted durations across all demographic groups d from the duration imputation adder366. In examples disclosed herein, the duration distribution deriver368determines a ratio, for each demographic group d at the episode-level e and the time-segment level i, respectively, of the asset-level adjusted durations to the sum of the asset-level adjusted durations across all demographic groups d to generate adjusted duration demographic distributions.

The impressions imputation director369obtains site census impressions (e.g., the aggregated numbers of impressions received from the monitored website) and determines whether, at the episode-level e and the time-segment level i, respectively, the site census impressions are logged and the impressions from the database proprietor116are not logged. In examples disclosed herein, the impressions imputation director369imputes the site census impressions by applying, for each demographic group d at the episode-level e and the time-segment level i, respectively, the adjusted impression demographic distributions to the site census impressions to generate imputed impressions.

The duration imputation director370obtains site census durations (e.g., the aggregated numbers of duration units from the monitored website) and determines whether, at the episode level e and the time-segment level i, respectively, the site census durations are logged and the durations from the database proprietor are not logged. In examples disclosed herein, the duration imputation director370imputes the site census durations by applying, for each demographic group d at the episode-level e and the time-segment level i, respectively, the adjusted duration demographic distributions to the site census durations to generate imputed durations.

The impressions imputation calculator371receives the imputed impressions from the impressions imputation director369and the scaled impressions from the E-S adjuster324and determines adjusted and scaled impressions by adding, for each demographic group d at the episode-level e and the segment level i, respectively, the imputed impressions to the scaled impressions.

The duration imputation calculator372receives the imputed durations from the duration imputation director370and the scaled durations from the E-S adjuster324and determines adjusted and scaled durations by adding, for each demographic group d at the episode-level e and the segment level i, respectively, the imputed durations to the scaled durations.

The metrics generator328estimates impression information counts (e.g., estimated impression counts, estimated durations, and estimated audience counts), for each combination of demographic group d and platform y, at the time-segment level i, the episode-level e, the asset-level p, the sub-brand level s, and the brand-level b. In examples disclosed herein, the metrics generator328receives adjusted and scaled impressions and durations from the census imputer326. The metrics generator328also receives frequencies and deduplication factors from the distributed demographic data calculator320. In examples disclosed herein, estimated impression counts, referred to herein as time-segment level estimates (e.g., time-segment level impression estimates, time-segment level duration estimates, and time-segment level audience estimates), are generated at the time-segment level i by the metrics generator328and estimated impression counts, referred to herein as episode-level estimates (e.g., episode-level impression estimates, episode-level duration estimates, and episode-level audience estimates), are generated at the episode level e by the metrics generator328. Additionally, the estimated impression counts rolled up from the time-segment level to the asset-level are referred to herein as first-asset level estimates (e.g., first asset-level impression estimates, first asset-level duration estimates, and first-asset level audience estimates) whereas the estimated impression counts rolled up from the episode-level to the asset-level are referred to herein as second-asset level estimates (e.g., second asset-level impression estimates, second asset-level duration estimates, and second asset-level duration estimates). Example implementations of the metrics generator328are described below in connection withFIGS. 12 and 13.

The segment modifier330adjusts ones of the estimated impression counts for each demographic group d at the time-segment level i. In examples disclosed herein, the segment modifier330receives ones of the time-segment level estimates from the metrics generator328. The segment modifier330calculates impression ratios and duration ratios at a sub-segment level isto adjust ones of the time-segment level estimates. The example segment modifier330includes an example impression sub-segment adder373, an example duration sub-segment adder374, an example impression ratio calculator375, an example duration ratio calculator376, an example impression metrics adjuster377, and an example duration metrics adjuster378. Example implementation of the segment modifier330are described below in connection withFIG. 21.

The impression sub-segment adder373receives time-segment level impression estimates from the metrics generator328. In examples disclosed herein, the impression sub-segment adder373identifies or tracks, for each time-segment level impression estimate, durational segments (e.g., D beacons) representative of impressions identified by the AME114and the DP116. The impression sub-segment adder373may identify, for each time-segment level impression estimate determined for platform y, brand b, sub-brand s, asset p, time-segment z, and demographic d, sub-segments of the corresponding D beacons. For example, if the time-segment level impression estimate is partially derived from a D beacon of D_2_01010, the sub-segment adder373identifies the sub-segments (e.g., 01010) and determines a quantity of total identified sub-segments in the D beacon (e.g., a total of 5 sub-segments). As such, the impression sub-segment adder373determines a sum of sub-segments isof the media206for each of the time-segment level impression estimates. In this manner, the impression sub-segment adder373may calculate a sum of sub-segments for all D beacons corresponding to the time-segment level impression estimates.

The duration sub-segment adder374receives time-segment level duration estimates from the metrics generator328. In examples disclosed herein, the duration sub-segment adder374identifies or tracks, for each time-segment level duration estimate, the durational segments (e.g., the D beacons). The duration sub-segment adder374may identify, for each time-segment level duration estimate determined for platform y, brand b, sub-brand s, asset p, time segment z, and demographic d, durations of the corresponding D beacons. For example, if the time-segment level duration estimate is partially derived from the D beacon of the aforementioned example (e.g., D_2_01010), the duration sub-segment adder374identifies the durations of the sub-segments (e.g., each sub-segment is equal to a duration of one minute) and determines a quantity of total identified sub-segment durations in the D beacon (e.g., a total duration of 5 minutes). As such, the duration sub-segment adder374determines a sum of sub-segment durations of the media206for each of the time-segment level duration estimates. In this manner, the duration sub-segment adder374may calculate a sum of sub-segment durations for all D beacons corresponding to the time-segment level duration estimates.

The impression ratio calculator375receives time-segment level impression estimates from the metrics generator328and the sums of the sub-segments isfor each time-segment z from the impression sub-segment adder373. In examples disclosed herein, the impression ratio calculator375calculates an impression ratio for each combination of platform y, brand b, sub-brand s, asset p, time segment z, and demographic d, by dividing a quantity of time sub-segments presented (e.g., credited as impressions) in the time-segment level impression estimates by the sum of the sub-segments is, for the demographic group d and the time-segment z. For example, if time-segment level impression estimate is partially derived from the D beacon, D_2_01010, the quantity of time sub-segments presented in the time-segment level impression estimate is 2 (e.g., of the 5 sub-segments, the 2nd and 4th sub-segments of the media206were credited as being presented). Thus, the ratio of the time-segment level impression estimate (e.g., 2) and the sum of the sub-segments is for the time-segment z (e.g., 5) is the impression ratio (e.g., 2/5). In examples disclosed herein, the impression ratio calculator375may determine an impression ratio for each corresponding D beacon and sum the impression ratios across all D beacons.

The duration ratio calculator376receives time-segment level duration estimates from the metrics generator328and the sums of the sub-segment durations for each time-segment z from the duration sub-segment adder374. In examples disclosed herein, the duration ratio calculator376calculates a duration ratio for each combination of platform y, brand b, sub-brand s, asset p, time segment z, and demographic d, by dividing a quantity of presented time sub-segment durations (e.g., credited as durations) of the time-segment level duration estimate by the sum of the sub-segment durations for the demographic group d and the time-segment z. For example, if a time-segment level duration estimate is partially derived from the D beacon, D_2_01010, the quantity of presented time sub-segment durations in the time-segment level duration estimate is 2 minutes (e.g., of the 5 sub-segments, each one minute in duration, the 2ndand 4thsub-segments of the media206were credited as being presented). Thus, the ratio of the time-segment level duration estimate (e.g., 2 minutes) and the sum of the sub-segment durations for the time-segment z (e.g., 5 minutes) is the duration ratio (e.g., 2 minute/5 minutes=2/5). In examples disclosed herein, the duration ratio calculator376may determine a duration ratio for each corresponding D beacon and sum the impression ratios across all D beacons.

The impression metrics adjuster377receives first asset-level impressions from the metrics generator328and the impression ratios from the impression ratio calculator375. In examples disclosed herein, the impression metrics adjuster377adjusts the first asset-level impressions by applying (e.g., multiplying), for each platform y, brand b, sub-brand s, asset p, segment z, and demographic d, the impression ratio of the segment z and the demographic d to the first-asset level impressions of the demographic d. The resulting product produces adjusted time-segment level impression estimates.

The duration metrics adjuster378receives first asset-level durations from the metrics generator328and the duration ratios from the duration ratio calculator376. In examples disclosed herein, the durations metrics adjuster378adjusts the first asset-level durations by applying (e.g., multiplying), for each platform y, brand b, sub-brand s, asset p, segment z, and demographic d, the duration ratio of the segment z and the demographic d to the first-asset level durations of the demographic d. The resulting product produces adjusted time-segment level duration estimates.

The example brand/cross-platform adjuster332adjusts the adjusted time-segment level impression and duration estimates at the brand-level b. In examples disclosed herein, the brand/cross-platform adjuster332receives time segment-level estimates from the segment modifier330and frequencies and deduplication factors from the distributed demographic data calculator320. The brand/cross-platform adjuster332includes an example impressions adjuster379, an example duration adjuster380, an example audience adjuster381, an example impression total digital (TD) calculator382, an example duration total digital (TD) calculator383, and an example audience total digital (TD) calculator384. Example implementation of the brand/cross-platform adjuster332is described below in connection withFIG. 22.

The impressions adjuster379receives the adjusted time-segment-level impression estimates from the segment modifier330and calculates, for each demographic group d, adjusted brand-level impression estimates (e.g., brand-level impression counts) by summing the adjusted time segment-level impression estimates across all assets p within sub-brands s. Additionally or alternatively, the example impressions adjuster379calculates, for each demographic group d, cross publisher adjusted brand-level impression estimates (e.g., cross-publisher adjusted brand-level impression counts) by summing the adjusted brand-level impression estimates across all brands b (e.g., across all network affiliates providing a segment z of the media206).

The duration adjuster380receives the adjusted time segment-level duration estimates (e.g., the adjusted time segment-level durations) from the segment modifier330and calculates, for each demographic group d, adjusted-brand level duration estimates by summing the time-segment level duration estimates across all assets p within sub-brands s. Additionally or alternatively, the duration adjuster380calculates, for each demographic group d, cross publisher adjusted-brand level durations by summing the adjusted brand-level durations across all brands b.

The audience adjuster381receives the adjusted brand-level impression estimates from the impression adjuster379and the frequency at the brand-level b, referred to herein as brand-level frequency, from the brand FC349. In examples disclosed herein, the audience adjuster381calculates adjusted brand-level audience estimates by dividing, for each demographic group d, the adjusted brand-level impression estimate by the brand-level frequency. Additionally or alternatively, the example audience adjuster receives the frequency at the cross publisher level, referred to herein as cross publisher-level frequency, from the CP-FC351. In examples disclosed herein, the audience adjuster381calculates cross publisher adjusted brand-level audience estimates (e.g., cross publisher adjusted brand-level audience counts) by dividing, for each demographic group d, the cross publisher adjusted brand-level impression count by the cross publisher-level frequency.

The impressions TD calculator382receives the adjusted brand-level impression estimates from the impressions adjuster379and determines total digital brand-level impression estimates, by adding, for each demographic group d, adjusted brand-level impression counts presented at a time segment z via a non-mobile (e.g., desktop) computing platform to adjusted brand-level impression counts presented at the time segment z via a mobile computing platform. Additionally or alternatively, the impressions TD calculator382determines cross publisher total digital brand-level impression estimates (e.g., cross publisher total digital brand-level impression counts), by adding, for each demographic group d, cross publisher adjusted brand-level impression estimates presented at a time segment z via a non-mobile (e.g., desktop) computing platform to cross publisher adjusted brand-level impression estimates presented at the time segment z via a mobile computing platform.

The duration TD calculator383receives the adjusted brand-level durations from the duration adjuster380and determines total digital brand-level durations, by adding, for each demographic group d, adjusted brand-level durations presented at a time segment z via a non-mobile (e.g., desktop) computing platform to adjusted brand-level impression durations presented at the time segment z via a mobile computing platform. Additionally or alternatively, the duration TD calculator383determines cross publisher total digital brand-level durations, by adding, for each demographic group d, cross publisher adjusted brand-level duration estimates presented at a time segment z via a non-mobile (e.g., desktop) computing platform to cross publisher adjusted brand-level duration estimates presented at the time segment z via a mobile computing platform.

The audience TD calculator384receives the deduplication factors at the brand level b, referred to herein as brand-level deduplication factors, from the brand DFG350. The audience TD calculator384also receives the adjusted brand-level audience counts from the audience adjuster381. In examples disclosed herein, the TD calculator384determines total digital brand-level audience counts by adding, for each demographic group d, adjusted brand-level audience counts presented at a time segment z via a non-mobile (e.g., desktop) computing platform to adjusted brand-level audience counts presented at the time segment z via a mobile computing platform and multiplying the sum by a corresponding brand-level deduplication factor.

In some examples, the audience TD calculator384receives the deduplication factors at the cross publisher level, referred to herein as cross publisher-level deduplication factors, from the CP-DFG352. The example audience TD calculator384determines cross publisher total digital brand-level audience estimates by adding, for each demographic group d, cross publisher adjusted brand-level audience estimates presented at a time segment z via a non-mobile (e.g., desktop) computing platform to cross publisher adjusted brand-level audience estimates presented at the time segment z via a mobile computing platform and multiplying the sum by a corresponding cross publisher-level deduplication factor.

The brand/cross-platform adjuster332also adjusts the adjusted time-segment level impression and duration estimates at the cross-publisher level. In examples disclosed herein, the brand/cross-platform adjuster332determines the adjusted brand-level impression counts, and receives the frequencies and deduplication factors from the distributed demographic data calculator320.

The content calculator336receives redistributed impression counts, duration units, and/or audience counts organized by content type c (e.g., text content t, video content v, or both text and video content w) from the demographic distributor318. The example content calculator336also receives adjusted brand-level impression estimates from the impressions adjuster379. In examples disclosed herein, the content calculator336generates text and video (T/V) deduplication factors based on the audience counts organized by content type c. In examples disclosed herein, the content calculator336determines corrected audience counts (e.g., total content audience counts) based on the (T/V) deduplication factors and the adjusted brand-level audience estimates. The corrected audience counts indicate a quantity of audience members, of a demographic group d, that were presented text content t and/or video content v for a particular segment z of the media206ofFIG. 2. In examples disclosed herein, the corrected audience counts are based on a quantity of audience members that accessed text content t, referred to herein as text audience counts, and a quantity of audience members that accessed video content v, referred to herein as video audience counts. The content calculator336includes an example text and video (T/V) deduplication factor generator (DFG)391, an example text and video (T/V) audience calculator392, an example total digital (TD) deduplication factor generator (DFG)393, and an example total digital (TD) audience calculator394. Example implementation of the content calculator adjuster336is described below in connection withFIG. 24.

The T/V DFG391receives audience counts redistributed by text content t, video content v, and both text and video content w from the demographic distributor318. In examples disclosed herein, the TN DFG391calculates, for each demographic d and time segment z, a T/V deduplication factor by dividing a redistributed audience count indicative of both text and video w (e.g., a duplicated content audience count) by a sum of a redistributed audience count indicative of text t (e.g., a text redistributed audience count) and a redistributed audience count indicative of video v (e.g., a video redistributed audience count).

The T/V audience calculator392receives adjusted brand-level impression estimates from the impressions adjuster379. The T/V audience calculator392also generates TN deduplication factors using the T/V DFG391. In examples disclosed herein, the T/V audience calculator392calculates a text frequency by dividing, for each demographic group d, an impression count redistributed by text t by an audience count redistributed by text t. The T/V audience calculator392also calculates a video frequency by dividing, for each demographic group d, an impression count redistributed by video v by an audience count redistributed by video v. In examples disclosed herein the T/V audience calculator392determines, for each demographic d, a text audience count by dividing an adjusted brand-level impression estimate by the text frequency. The T/V audience calculator392further determines, for each demographic d, a video audience count by dividing the adjusted brand-level impression estimate by the video frequency. In examples disclosed herein, the TN audience calculator392calculates a corrected audience count (e.g., a total content audience count), for each demographic d, by applying a T/V deduplication factor to a sum of the text audience count and the video audience count.

The TD DFG393receives audience counts redistributed by text content t, video content v, and both text and video content w from the demographic distributor318. In examples disclosed herein, the TD DFG393determines, for each demographic d, a total digital (TD) text and video (T/V) deduplication factor by dividing a duplicated platform audience count (e.g., an audience count, based on all audience counts redistributed by content type c, credited to both a non-mobile computing platform and a mobile computing platform) by a sum of a non-mobile computing platform audience count (e.g., an audience count, based on all audience counts redistributed by content type c, credited to the non-mobile computing platform) and a mobile computing platform audience count (e.g., an audience count, based on all audience counts redistributed by content type c, credited to the mobile computing platform).

The TD audience calculator394receives the TD T/V deduplication factors from the TD DFG393. The TD audience calculator394also accesses and/or receives the adjusted brand-level impression estimates from the impressions adjuster379. In examples disclosed herein, the TD audience calculator394calculates a total digital text frequency by dividing, for each demographic group d, an impression count redistributed by text t across all platforms y by an audience count redistributed by text t across all platforms y. The TD audience calculator394also calculates a total digital video frequency by dividing, for each demographic group d, an impression count redistributed by video v across all platforms y by an audience count redistributed by video v across all platforms y. In examples disclosed herein the TD audience calculator394determines, for each demographic d, a total digital text audience count by dividing an adjusted brand-level impression estimate by the total digital text frequency. The TD audience calculator394further determines, for each demographic d, a total digital video audience count by dividing the adjusted brand-level impression estimate by the total digital video frequency. In examples disclosed herein, the TD audience calculator394calculates a total digital corrected audience count (e.g., a total content audience count), for each demographic d, by applying a TD T/V deduplication factor to a sum of the total digital text audience count and the total digital video audience count.

The ratings data generator338generates ratings information for media of interest for the demographic groups d using corrected and/or adjusted impression and/or audience information (e.g., the corrected audience counts, the total content audience counts, etc.).

While an example manner of implementing the audience data generator120a-bofFIGS. 1 and/or 2is illustrated inFIG. 3, one or more of the elements, processes and/or devices illustrated inFIG. 3may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example calibration data collector300, the example platform data manager302, the example device data manager304, the example segment data manager306, the example episode data manager308, the example asset data manager310, the example sub-brand data manager312, the example brand data manager314, the example database proprietor (DP) data manager316, the example demographic distributor318, the example distributed demographic data calculator320, the example matrix generator322, the example episode-segment (E-S) adjuster324, the example census imputer326, the example metrics generator328, the example segment modifier330, the example brand/cross-platform adjuster332, the example content calculator336, the example ratings data generator338, the example duration-per-impression (DPI) calculator340, the example segment frequency calculator (FC)341, the example segment deduplication factor generator (DFG)342, the example episode frequency calculator (FC)343, the example episode deduplication factor generator (DFG)344, the example asset frequency calculator (FC)345, the example asset deduplication factor generator (DFG)346, the example sub-brand frequency calculator (FC)347, the example sub-brand deduplication factor generator (DFG)348, the example brand frequency calculator (FC)349, the example brand deduplication factor generator (DFG)350, the example cross publisher frequency calculator (CP-FC)351, the example cross publisher deduplication factor generator (CP-DFG)352, the example matrix converter353, the example impressions sharing adjuster354, the example duration sharing adjuster355, the example coverage vector calculator356, the example impressions coverage adjuster357, the example duration coverage adjuster358, the example impression scaling factor generator359, the example duration scaling factor generator360, the example impressions scaler361, the example duration scaler362, the example impressions adder363, the example duration adder364, the example impressions imputation adder365, the example duration imputation adder366, the example impressions distribution deriver367, the example duration distribution deriver368, the example impressions imputation director369, the example duration imputation director370, the example impressions imputation calculator371, the example duration imputation calculator372, the example impression sub-segment adder373, the example duration sub-segment adder374, the example impression ratio calculator375, the example duration ratio calculator376, the example impression metrics adjuster377, the example duration metrics adjuster378, the example impressions adjuster379, the example duration adjuster380, the example audience adjuster381, the example impressions total digital (TD) calculator382, the example duration total digital (TD) calculator383, the example audience total digital (TD) calculator384, the example text and video (TN) deduplication factor generator (DFG)391, the example text and video (TN) audience calculator392, the example total digital (TD) deduplication factor generator393, the example total digital (TD) audience calculator394and/or, more generally, the example audience data generator120ofFIGS. 1, 2, and/or3may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example calibration data collector300, the example platform data manager302, the example device data manager304, the example segment data manager306, the example episode data manager308, the example asset data manager310, the example sub-brand data manager312, the example brand data manager314, the example database proprietor (DP) data manager316, the example demographic distributor318, the example distributed demographic data calculator320, the example matrix generator322, the example episode-segment (E-S) adjuster324, the example census imputer326, the example metrics generator328, the example segment modifier330, the example brand/cross-platform adjuster332, the example content calculator336, the example ratings data generator338, the example duration-per-impression (DPI) calculator340, the example segment frequency calculator (FC)341, the example segment deduplication factor generator (DFG)342, the example episode frequency calculator (FC)343, the example episode deduplication factor generator (DFG)344, the example asset frequency calculator (FC)345, the example asset deduplication factor generator (DFG)346, the example sub-brand frequency calculator (FC)347, the example sub-brand deduplication factor generator (DFG)348, the example brand frequency calculator (FC)349, the example brand deduplication factor generator (DFG)350, the example cross publisher frequency calculator (CP-FC)351, the example cross publisher deduplication factor generator (CP-DFG)352, the example matrix converter353, the example impressions sharing adjuster354, the example duration sharing adjuster355, the example coverage vector calculator356, the example impressions coverage adjuster357, the example duration coverage adjuster358, the example impression scaling factor generator359, the example duration scaling factor generator360, the example impressions scaler361, the example duration scaler362, the example impressions adder363, the example duration adder364, the example impressions imputation adder365, the example duration imputation adder366, the example impressions distribution deriver367, the example duration distribution deriver368, the example impressions imputation director369, the example duration imputation director370, the example impressions imputation calculator371, the example duration imputation calculator372, the example impression sub-segment adder373, the example duration sub-segment adder374, the example impression ratio calculator375, the example duration ratio calculator376, the example impression metrics adjuster377, the example duration metrics adjuster378, the example impressions adjuster379, the example duration adjuster380, the example audience adjuster381, the example impressions total digital (TD) calculator382, the example duration total digital (TD) calculator383, the example audience total digital (TD) calculator384, the example text and video (T/V) deduplication factor generator (DFG)391, the example text and video (T/V) audience calculator392, the example total digital (TD) deduplication factor generator393, the example total digital (TD) audience calculator394, and/or, more generally, the example audience data generator120could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example calibration data collector300, the example platform data manager302, the example device data manager304, the example segment data manager306, the example episode data manager308, the example asset data manager310, the example sub-brand data manager312, the example brand data manager314, the example database proprietor (DP) data manager316, the example demographic distributor318, the example distributed demographic data calculator320, the example matrix generator322, the example episode-segment (E-S) adjuster324, the example census imputer326, the example metrics generator328, the example segment modifier330, the example brand/cross-platform adjuster332, the example content calculator336, the example ratings data generator338, the example duration-per-impression (DPI) calculator340, the example segment frequency calculator (FC)341, the example segment deduplication factor generator (DFG)342, the example episode frequency calculator (FC)343, the example episode deduplication factor generator (DFG)344, the example asset frequency calculator (FC)345, the example asset deduplication factor generator (DFG)346, the example sub-brand frequency calculator (FC)347, the example sub-brand deduplication factor generator (DFG)348, the example brand frequency calculator (FC)349, the example brand deduplication factor generator (DFG)350, the example cross publisher frequency calculator (CP-FC)351, the example cross publisher deduplication factor generator (CP-DFG)352, the example matrix converter353, the example impressions sharing adjuster354, the example duration sharing adjuster355, the example coverage vector calculator356, the example impressions coverage adjuster357, the example duration coverage adjuster358, the example impression scaling factor generator359, the example duration scaling factor generator360, the example impressions scaler361, the example duration scaler362, the example impressions adder363, the example duration adder364, the example impressions imputation adder365, the example duration imputation adder366, the example impressions distribution deriver367, the example duration distribution deriver368, the example impressions imputation director369, the example duration imputation director370, the example impressions imputation calculator371, the example duration imputation calculator372, the example impression sub-segment adder373, the example duration sub-segment adder374, the example impression ratio calculator375, the example duration ratio calculator376, the example impression metrics adjuster377, the example duration metrics adjuster378, the example impressions adjuster379, the example duration adjuster380, the example audience adjuster381, the example impressions total digital (TD) calculator382, the example duration total digital (TD) calculator383, the example audience total digital (TD) calculator384, the example text and video (T/V) deduplication factor generator (DFG)391, the example text and video (TN) audience calculator392, the example total digital (TD) deduplication factor generator393, and/or the example total digital (TD) audience calculator394and/or, more generally, the example audience data generator120is/are hereby expressly defined to include a tangible computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. storing the software and/or firmware. Further still, the example audience data generator120ofFIGS. 1, 2 and/or 3may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated inFIG. 3, and/or may include more than one of any or all of the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions for implementing the audience data generator120ofFIGS. 1, 2, and/or3are shown inFIGS. 4-7 and 9-16. In this example, the machine readable instructions comprise a program for execution by a processor such as the processor1712shown in the example processor platform1700discussed below in connection withFIG. 17. The program(s) may be embodied in software stored on a tangible computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory associated with the processor1712, but the entire program and/or parts thereof could alternatively be executed by a device other than the processor1712and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowchart illustrated inFIGS. 4-7 and 9-16, many other methods of implementing the example audience data generator120may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined.

FIG. 4is a flowchart representative of example machine readable instructions400which may be executed to implement the audience data generator120ofFIGS. 1-3to determine rating data from population sample data having incomplete demographic classifications.

The example AME impressions collector218collects messages (e.g., the beacon requests212,226b) indicating impressions of a video delivered to devices (e.g., the client device102) via the Internet (block402).

The example DP impressions collector232receives, from the DP116, a first audience count describing first numbers of impressions corresponding to the messages attributed to the video for a demographic group (Block404). The example DP impressions collector232receives, from the DP116, a second audience count describing second numbers of impressions corresponding to the messages attributed to text content for the demographic group (Block406). The example DP impressions collector232receives, from the DP116, a third audience count describing third numbers of impressions corresponding to the messages attributed to the video and the text content for the demographic group (Block408).

The example audience data generator120ofFIGS. 1, 2, and/or3(e.g., via the content calculator336ofFIG. 3) determines a corrected audience count of the video for the demographic group. The example T/V calculator392estimates a fourth audience count for the demographic group (Block410). The fourth audience count indicates a number of people who accessed the video. The example TN calculator392also estimates a fifth audience count for the demographic group (Block412). The fifth audience count indicates a number of people who accessed the text content.

The example audience data generator120(e.g., via the T/V DFG391) calculates a deduplication factor for the demographic group using the first audience count, the second audience count, and the third audience count (Block414).

The example audience data generator120(e.g., via the T/V audience calculator392) applies the deduplication factor to a sum of the fourth audience count and the fifth audience count to determine the corrected audience count (Block416).

The example audience data generator (e.g., via the ratings data generator338) generates ratings information for the video based on subtracting the corrected audience count from the first audience count (Block418).

The example instructions400ofFIG. 4may then end. More detailed examples of implementing the instructions ofFIG. 4are described below with reference toFIGS. 4-7 and 9-16.

Gender Redistribution at the Episode Level

FIG. 5is a flowchart representative of example machine readable instructions500which may be executed to implement the audience data generator ofFIGS. 1-3to perform demographic redistribution of database proprietor demographic data. The example instructions500ofFIG. 5may be executed to perform demographic redistribution at any of multiple levels. Example levels include the hierarchical levels for which the audience data generator receives aggregate demographic data for impressions, audience counts, and/or duration from a database proprietor. For example, demographic redistribution may occur for impressions, audience counts, and/or duration attributed by the database proprietor to a combination of a platform, a device type, an age group, a time segment, an episode, an asset, a sub-brand, and/or a brand. The example demographic distributor318ofFIG. 3may perform the instructions500ofFIG. 5to redistribute impressions, durations, and/or unique audience members for which the DP116is unable to determine a gender at the example time segment level, the example episode level, the example asset level, the example sub-brand level, and/or the example brand level. The example instructions500are described below with respect to a redistribution of a gender group, but may be modified to redistribute DP demographic data according to any demographic quality.

In some examples, aggregated demographic information may not be collected for impressions, audience counts, and/or durations at some hierarchical levels. In such examples, the corresponding gender redistribution blocks may be omitted from the instructions500.

Table 1 illustrates an example demographic redistribution of database proprietor demographic data at the episode level. Table 2 illustrates an example demographic redistribution of database proprietor demographic data at the asset level. Table 3 illustrates an example demographic redistribution of demographic data at the sub-brand level. Table 4 illustrates an example demographic redistribution of database proprietor demographic data at the brand level.

The example demographic distributor318receives as input post-Decision-Tree (DT) episode-level data from the DP116ofFIGS. 1 and/or 2(e.g., via the DP data manager316ofFIG. 3). As shown in Table 1, the episode-level demographic data includes episode-level impression counts, episode-level durations, and episode-level unique audience counts, which correspond to a brand b, a sub-brand s, an asset p, and an episode e. The DP116aggregates and de-duplicates the impression counts, the durations, and the unique audience counts of Table 1 for all time segments included in an identified episode. The impression counts, the durations, and the unique audience counts of Table 1 are attributed to demographic groups (e.g., male, age 12+ and female, age 12+) by the DP116. The example DP116also provides the impression counts, the durations, and the unique audience counts of Table 1 for an unknown demographic group, which corresponds to impression counts, durations, and unique audience members that the DP116counted based on the pings or requests, but for which the DP116was unable to identify demographic groups.

Similarly, the example demographic distributor318receives as input post-Decision-Tree (DT) asset-level demographic data, sub-brand-level demographic data, and brand-level demographic data from the DP116ofFIGS. 1 and/or 2(e.g., via the DP data manager316ofFIG. 3). As shown in Table 2, the asset-level demographic data includes asset-level impression counts, asset-level durations, and asset-level unique audience counts, which correspond to a brand b, a sub-brand s, and an asset p. The example impression counts, durations, and unique audience counts of Table 2 are attributed to demographic groups (e.g., male, age 12+ and female, age 12+) by the DP116. As shown in Table 3, the sub-brand level demographic data includes sub-brand level impression counts, sub-brand level durations, and sub-brand level unique audience counts, which correspond to a brand b, and a sub-brand s. The example impression counts, durations, and unique audience counts of Table 3 are attributed to demographic groups (e.g., male, age 12+ and female, age 12+) by the DP116. As shown in Table 4, the brand-level demographic data includes brand-level impression counts, brand-level durations, and brand-level unique audience counts, which correspond to a brand b. The example impression counts, durations, and unique audience counts of Table 4 are attributed to demographic groups (e.g., male, age 12+ and female, age 12+) by the DP116. The example demographic data of Tables 1-4 above may be further specified according to a platform and/or a device type, but the specified device type and the specified platform are omitted from Tables 1-4 for brevity.

Because the demographic distributor318may execute similar instructions to perform distribution at any of the episode level, the asset level, the sub-brand level, and the brand-level, the example instructions500ofFIG. 5may be applied at the example episode level, the example asset level, the example sub-brand level, and/or the example brand-level.

In the example ofFIG. 5, the example demographic distributor318ofFIG. 3selects a combination of a brand b/sub-brand s/asset p/episode e, a platform y, a device type x, an age group a, and a gender group g (Block502). In this example, the gender groups “male” and “female” are referred to by indices g=1 and 2, and an unknown gender group is referred to using the index g=0.

The demographic distributor318creates a gender impression distribution (e.g., gImpsRatiob/s/p/e,y,x,a,g) (block504). For example, the gender impression distribution gImpsRatiob/s/p/e,y,x,a,gmay be a ratio of the impression counts of Tables 1, 2, 3, and/or 4 for the selected brand b/sub-brand s/asset p/episode e, platform y, device type x, age group a, and gender group g (e.g., Impsb/s/p/e,y,x,a,g) and total impression counts across all of the gender groups (e.g., for g=1 and g=2) for the selected age group a and the selected brand b/sub-brand s/asset p/episode e. In the example of Table 1, the demographic distributor318determines an impression distribution of 59.6% for the male demographic group D1for the selected episode (e.g., 12,557/(12,557+8,496)) and 40.4% for the female demographic group D2for the selected episode (e.g., 8,496/(12,557+8,496). In the example of Table 2, the demographic distributor318determines an impression distribution of 56.8% for the male demographic group D1for the selected asset (e.g., 15,751/(15,751+11,987)) and 43.2% for the female demographic group D2for the selected asset (e.g., 11,987/(15,751+11,987)). In the example of Table 3, the demographic distributor318determines an impression distribution of 60% for the male demographic group D1for the selected sub-brand (e.g., 21,345/(21,345+16,789)) and 40.0% for the female demographic group D2for the selected sub-brand (e.g., 16,789/(21,345+16,789)). In the example of Table 4, the demographic distributor318determines an impression distribution of 66% for the male demographic group D1for the selected brand (e.g., 42,323/(42,323+21,789)) and 34% for the female demographic group D2for the selected brand (e.g., 21,789/(42,323+21,789)). Equation 1 below may be used to implement block504.

The demographic distributor318creates a gender duration distribution (e.g., gDurationRatiob/s/p/e,y,x,a,g) (Block506). For example, the gender duration distribution gDurationRatiob/s/p/e,y,x,a,gmay be a ratio of the durations of Tables 1, 2, 3, and/or 4 for the selected brand b/sub-brand s/asset p/episode e, platform y, device type x, age group a, and gender group g (e.g., Durationb/s/p/e,y,x,a,g) and total durations across all of the gender groups (e.g., for g=1 and g=2) for the selected age group a and the selected brand b/sub-brand s/asset p/episode e. In the example of Table 1, the demographic distributor318determines a duration distribution of 60.4% for the male demographic group D1for the selected episode (e.g., 138,868 minutes/(138,868 minutes+91,004 minutes)) and 39.6% for the female demographic group D2for the selected episode (e.g., 91,004 minutes/(138,868 minutes+91,004 minutes). In the example of Table 2, the demographic distributor318determines a duration distribution of 54.7% for the male demographic group D1for the selected asset (e.g., 175,312 minutes/(175,312 minutes+145,119 minutes)) and 45.3% for the female demographic group D2for the selected asset (e.g., 145,119 minutes/(175,312 minutes+145,119 minutes)). In the example of Table 3, the demographic distributor318determines a duration distribution of 51.7% for the male demographic group D1for the selected sub-brand (e.g., 211,467 minutes/(211,467 minutes+197,651 minutes)) and 48.3% for the female demographic group D2for the selected sub-brand (e.g., 197,651 minutes/(211,467 minutes+197,651 minutes)). In the example of Table 4, the demographic distributor318determines a duration distribution of 57.4% for the male demographic group D1for the selected brand (e.g., 292,178 minutes/(292,178 minutes+216,782 minutes)) and 42.6% for the female demographic group D2for the selected brand (e.g., 216,782 minutes/(292,178 minutes+216,782 minutes)). Equation 2 below may be used to implement block506.

The demographic distributor318creates a gender audience distribution (e.g., gUARatiob/s/p/e,y/x,a,g) (Block508). For example, the gender audience distribution gUARatiob/s/p/e,y/x,a,gmay be a ratio of the unique audience count of Tables 1, 2, 3, and/or 4 for the selected brand b/sub-brand s/asset p/episode e, platform y/device type x, age group a, and gender group g (e.g., UAb/s/p/e,y/x,a,g) and total unique audience count across all of the gender groups (e.g., for g=1 and g=2) for the selected age group a and the selected brand b/sub-brand s/asset p/episode e. In some examples, the demographic distributor318creates the gender audience distribution gUARatiob,s,p,e,y,a,gfor a platform y but does not distinguish between device types x. In the example of Table 1, the demographic distributor318determines an audience distribution of 54.7% for the male demographic group D1for the selected episode (e.g., 105/(105+87)) and 45.3% for the female demographic group D2for the selected episode (e.g., 87/(105+87))). In the example of Table 2, the demographic distributor318determines an audience distribution of 38.5% for the male demographic group D1for the selected asset (e.g., 125/(125+200)) and 61.5% for the female demographic group D2for the selected asset (e.g., 200/(125+200))). In the example of Table 3, the demographic distributor318determines an audience distribution of 45.5% for the male demographic group D1for the selected sub-brand (e.g., 250/(250+300)) and 54.5% for the female demographic group D2for the selected sub-brand (e.g., 250/(250+300)). In the example of Table 4, the demographic distributor318determines an audience distribution of 42.5% for the male demographic group D1for the selected brand (e.g., 352/(352+476)) and 57.5% for the female demographic group D2for the selected brand (e.g., 476/(352+476)). Equation 3 below may be used to implement block506.

The example demographic distributor318determines whether there are additional combinations of brand b/sub-brand s/asset p/episode e, platform y, device type x, age group a, and known gender g (block510). If there are additional combinations (e.g., combinations for which the distributions of the impression counts, the durations, and the unique audience counts are to be created) (block510), control returns to block502.

When there are no more combinations of brand b/sub-brand s/asset p/episode e, platform y, device type x, age group a, and known gender g (block510), the demographic distributor318selects a combination of a brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a (block512).

The demographic distributor318distributes the unknown gender impressions (block514). For example, the demographic distributor318may distribute the unknown gender impressions to generate redistributed impressions rDPImpsb/s/p/e,y,x,dby applying the gender impression distribution gImpsRatiob/s/p/e,y,x,a,gof Tables 1, 2, 3, and/or 4, to the impression counts, of Tables 1, 2, 3, and/or 4, respectively, for which the gender is unknown (e.g., unknown gender impressions) (Impsb/s/p/e,y,x,a,0) for the selected brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a. In the example of Table 1, the demographic distributor318determines 13,980 redistributed impression counts for the male demographic group D1(e.g., 12,557+0.596*2,387=13,980) and 9,460 redistributed impression counts for the female demographic group D2(e.g., 8,496+0.404*2,387=9,460) by applying (e.g., multiplying) the respective gender impression distribution of Table 1 to the unknown gender impressions. In the example of Table 2, the demographic distributor318determines 17,107 redistributed impression counts for the male demographic group D1(e.g., 15,751+0.568*2,387=17,107) and 13,018 redistributed impression counts for the female demographic group D2(e.g., 15,751+0.432*2,387=13,108) by applying (e.g., multiplying) the respective gender impression distribution of Table 2 to the unknown gender impressions. In the example of Table 3, the demographic distributor318determines 22,777 redistributed impression counts for the male demographic group D1(e.g., 21,345+0.60*2,387=22,777) and 17,744 redistributed impression counts for the female demographic group D2(e.g., 16,789+0.40*2,387=17,744) by applying (e.g., multiplying) the respective gender impression distribution of Table 3 to the unknown gender impressions. In the example of Table 4, the demographic distributor318determines 43,898 redistributed impression counts for the male demographic group D1(e.g., 42,434+0.66*2,387=43,898) and 22,601 redistributed impression counts for the female demographic group D2(e.g., 21,789+0.34*2,387=22,601) by applying (e.g., multiplying) the respective gender impression distribution of Table 4 to the unknown gender impressions. As shown in Tables 1-4, the total DP impression counts are equal to the respective total redistributed impressions. Equation 4 below may be used to implement block514.
rDPImpsb/s/p/e,y,x,d=Impsb/s/p/e,y,x,a,g+(Impsb/s/p/e,y,x,a,0*gImpsRatiob/s/p/e,y,x,a,g)   (Equation 4)

The demographic distributor318distributes the unknown gender durations (block516). For example, the demographic distributor318may distribute the unknown duration impressions to generate redistributed durations rDPDurationb/s/p/e,y,x,dby applying the gender duration distribution gDurationRatiob/s/p/e,y,x,a,gof Tables 1, 2, 3, and/or 4, to the durations, of Tables 1, 2, 3, and/or 4, respectively, for which the gender is unknown (e.g., unknown gender durations) (Durationb/s/p/e,y,x,a,0) for the selected brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a. In the example of Table 1, the demographic distributor318determines a redistributed duration of 153,307 minutes for the male demographic group D1(e.g., 138,868 minutes+0.604*23,906 minutes=13,980 minutes) and a redistributed duration of 100,471 minutes for the female demographic group D2(e.g., 91,004 minutes+0.396*23,906 minutes=100,471 minutes) by applying (e.g., multiplying) the respective gender duration distribution of Table 1 to the unknown gender duration. In the example of Table 2, the demographic distributor318determines a redistributed duration of 188,389 minutes for the male demographic group D1(e.g., 175,312 minutes+0.547*23,906 minutes=188,389 minutes) and a redistributed duration of 155,948 minutes for the female demographic group D2(e.g., 145,119 minutes+0.432*23,906 minutes=155,948 minutes) by applying (e.g., multiplying) the respective gender duration distribution of Table 2 to the unknown gender durations. In the example of Table 3, the demographic distributor318determines a redistributed duration of 223,826 minutes for the male demographic group D1(e.g., 211,467 minutes+0.517*23,906 minutes=223,826 minutes) and a redistributed duration of 209,198 minutes for the female demographic group D2(e.g., 197,651 minutes+0.483*23,906 minutes=209,198 minutes) by applying (e.g., multiplying) the respective gender duration distribution of Table 3 to the unknown gender duration. In the example of Table 4, the demographic distributor318determines a redistributed duration of 305,900 minutes for the male demographic group D1(e.g., 292,178 minutes+0.574*23,906 minutes=305,900 minutes) and a redistributed duration of 226,966 minutes for the female demographic group D2(e.g., 216,782 minutes+0.426*23,906 minutes=226,966 minutes) by applying (e.g., multiplying) the respective gender duration distribution of Table 4 to the unknown gender duration. As shown in Tables 1-4, the total DP durations are equal to the respective total redistributed durations. Equation 5 below may be used to implement block516.
rDPDurationb/s/p/e,y,x,d=Durationb/s/p/e,y,x,a,g+(Durationb/s/p/e,y,x,a,0*gDurationRatiob/s/p/e,y,x,a,g)   (Equation 5)

The demographic distributor318distributes the unknown gender audience (block518). For example, the demographic distributor318may distribute the unknown gender audience to generate redistributed unique audiences rDPUAb/s/p/e,y/x,dby applying the gender audience distribution gUARatiob/s/p/e,y,x,a,gof Tables 1, 2, 3, and/or 4, to the unique audience counts, of Tables 1, 2, 3, and/or 4, respectively, for which the gender is unknown (e.g., unknown gender audience) (UAb/s/p/e,y,x,a,0) for the selected brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a. In the example of Table 1, the demographic distributor318determines 116 redistributed unique audience members (e.g., a redistributed unique audience count) for the male demographic group D1(e.g., 105+0.547*20=116) and 96 redistributed unique audience members for the female demographic group D2(e.g., 87+0.453*20=96) by applying (e.g., multiplying) the respective gender audience distribution of Table 1 to the unknown gender unique audience. In the example of Table 2, the demographic distributor318determines 133 redistributed unique audience members for the male demographic group D1(e.g., 125+0.386*20=133) and 212 redistributed unique audience members for the female demographic group D2(e.g., 200+0.615*20=212) by applying (e.g., multiplying) the respective gender audience distribution of Table 2 to the unknown gender unique audience. In the example of Table 3, the demographic distributor318determines 259 redistributed unique audience members for the male demographic group D1(e.g., 250+0.455*20=259) and 311 redistributed unique audience members for the female demographic group D2(e.g., 300+0.545*20=311) by applying (e.g., multiplying) the respective gender audience distribution of Table 3 to the unknown gender unique audience. In the example of Table 4, the demographic distributor318determines 360 redistributed unique audience members for the male demographic group D1(e.g., 352+0.425*20=360) and 488 redistributed unique audience members for the female demographic group D2(e.g., 476+0.575*20=488) by applying (e.g., multiplying) the respective gender audience distribution of Table 4 to the unknown gender unique audience. As shown in Tables 1-4, the total DP unique audiences are equal to the respective total redistributed unique audiences. Equation 6 below may be used to implement block518.
rDPUAb/s/p/e,y/x,d=UAb/s/p/e,y/x,a,g+(UAb/s/p/e,y/x,a,0*gUARatiob/s/p/e,y/x,a,g)   (Equation 6)

In examples disclosed herein, the redistributed audience (e.g., rDPUAb/s/p/e,y,x,a,g), when determined at the episode level, is determine for each platform y but not device type x.

The demographic distributor318determines whether there are additional combinations of brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a (block520). If there are additional combinations (e.g., combinations for which the redistributed impression counts, the redistributed durations, and the redistributed unique audience counts are to be created) (block520), control returns to block512. When there are no more combinations of brand b/sub-brand s/asset p/episode e, platform y, device type x, and age group a (block520), the example instructions500ofFIG. 5end.

In some other examples, the instructions500ofFIG. 5are performed for combinations of a first time segment level including demographic data corresponding to a brand b, sub-brand s, asset p, segment z; a second time segment level including demographic data corresponding to a brand b and a segment z; a cross-publisher level including demographic data corresponding to a platform y, a brand b, a segment z, and an age group a; and/or a text/video (T/V) segment level including demographic data corresponding to a content type c (e.g., either text content t, video content v, or both text content and video content w), a brand b, a segment z, and a platform y.

FIG. 6is a flowchart representative of example machine readable instructions600which may be executed to implement the audience data generator120ofFIGS. 1-3to calculate raw measures for the demographic groups using redistributed demographic information. The example instructions600are described below with reference to the example distributed demographic data calculator320ofFIG. 3.

The example instructions600may be executed to implement the distributed demographic data calculator320to calculate frequencies and deduplication factors at the episode-level e, the asset-level p, the sub-brand level s, the brand-level b, the second time segment level, and/or the cross-publisher level. In some examples, the example instructions600may include calculating duration-per-impression (DPI) measures at the first time segment level. In examples disclosed herein, the distributed demographic data calculator320receives redistributed demographic data from the demographic distributor318. The redistributed demographic data received from the demographic distributor includes redistributed impressions (e.g., rDPImpsb/s/p/e,y,x,d(e.g., at the example episode-level e, the example asset-level p, the example sub-brand level s, the example brand-level b, the example first time segment level, the example second time segment level, and/or the example cross-publisher level), redistributed audiences rDPUAb/s/p/e,y/x,d(e.g., at the example episode-level e, the example asset-level p, the example sub-brand level s, and the example brand-level b, and/or the example cross-publisher level), and/or redistributed durations rDPDurationb,s,p,i,y,x,d(e.g., at the example first time segment level).

The example episode FC343receives the redistributed impressions rDPImpsb,s,p,e,y,x,dand the redistributed audiences rDPUAb,s,p,e,y,dat the at the example episode-level e, the example asset-level p, the example sub-brand level s, the example brand-level b, the example first time segment level, the example second time segment level, and/or the example cross-publisher level (e.g., fromFIG. 5). Additionally, the episode DFG344receives the redistributed audiences rDPUAb,s,p,e,y,dat the at the example episode-level e, the example asset-level p, the example sub-brand level s, the example brand-level b, the example first time segment level, the example second time segment level, and/or the example cross-publisher level. In the example instructions600ofFIG. 6, the episode FC343calculates raw database proprietor frequency for each combination of brand b/sub-brand s/asset p/episode e/time segment z, platform y, device type x, and/or demographic group d (e.g., rDPFreqb,s,p,e,y,d) (block602). The episode FC343calculates the episode-level frequency rDPFreqb,s,p,e,y,dby dividing, for each combination of brand b/sub-brand s/asset p/episode e/time segment z, platform y, device type x, and/or demographic group d, the redistributed impressions (e.g., rDPImpsb,s,p,e,y,x,d) across all device types x (e.g., rDPImpsb,s,p,e,y,d) by the redistributed audiences (e.g., rDPUAb,s,p,e,y,d). In some examples, the episode-level frequency rDPFreqb,s,p,e,y,dis not calculated for each device type x at the episode-level e because the unique audience counts at the episode-level e (e.g., UAb,s,p,e,y,a,g) provided by the DP116ofFIGS. 1 and/or 2are not organized by device type x. Equation 7 below may be used to implement block602.
rDBFreqb,s,p,e,y,d=rDPImpsb,s,p,e,y,d/rDPUAb,s,p,e,y,d(Equation 7)

The episode DFG344calculates a deduplication factor (e.g., Dedup_FTD,b,s,p,e,d) for each combination of brand b/sub-brand s/asset p/episode e/time segment z, platform y, device type x, and/or demographic group d (block604). For example, the episode DFG344may calculate, for each combination of brand b/sub-brand s/asset p/episode e/time segment z, platform y, device type x, and/or demographic group d, the episode-level deduplication factor Dedup_FTD,b,s,p,e,dby dividing an episode-level gender audience attributed to both the mobile computing platform and the non-mobile computing platform (e.g., the total digital platform), (e.g., rDPUATD,b,s,p,e,d), a duplicated episode-level audience, by a sum of an episode-level gender audience attributed to the non-mobile computing platform (e.g., rDPUADesktop,b,s,p,e,d) and an episode-level gender audience attributed to the mobile computing platform (e.g., rDPUAMobile,b,s,p,e,d). Equation 8 below may be used to implement block604.
Dedup_FTD,b,s,p,e,d=rDPUATD,b,s,p,e,d/rDPUADesktop,b,s,p,e,d+rDPUAMobile,b,s,p,e,d(Equation 8)

After calculating the deduplication factor, the example instructions600end. In some examples, the raw DP frequency (block602) and the deduplication factor (block604) may be calculated for combinations of brand b, sub-brand s, asset p, platform y, device type x, and demographic group d; combinations of brand b, sub-brand s, platform y, device type x, and demographic d; and/or Calculate raw DP frequency for each brand b, platform y, device type x, and demographic group d.

FIG. 7is a flowchart representative of example machine readable instructions700which may be executed to implement the audience data generator ofFIGS. 1-3to calculate a frequency and a deduplication factor associated with database proprietor demographic data at the time segment level and the cross-publisher level.

The distributed demographic data calculator320calculates raw measures associated with demographic data redistributed at the first time segment level, the second time segment level, and the cross-publisher level based on the example instructions700ofFIG. 7. In examples disclosed herein, the DPI calculator340receives the redistributed impressions rDPImpsb,s,p,i,y,x,dand the redistributed durations rDPDurationb,s,p,i,y,x,dfrom the demographic distributor318(e.g., at the first time segment level). The segment FC341receives the redistributed impressions rDPImpsb,i,y,x,dand the redistributed audiences rDPUAb,i,y,x,dfrom the demographic distributor318(e.g., at the second time segment level). The segment DFG342receives the redistributed audiences rDPUAb,i,y,x,dfrom the demographic distributor318(e.g., at the second time segment level). Additionally, the CP-FC351receives the redistributed impressions rDBCPImpsb,i,y,dand the redistributed audience counts rDBCPUAb,i,y,dfrom the demographic distributor318(e.g., cross-publisher level).

The DPI calculator340ofFIG. 3calculates a duration-per-impression (DPI) measure (e.g., rDPDPIb,s,p,i,y,x,d) (block702). For example, the DPI calculator340may calculate the DPI measure, for each combination of brand b, sub-brand s, asset p, segment z, platform y, device type x, and demographic d, by dividing a respective redistributed duration rDPDurationb,s,p,i,y,x,dby a respective redistributed impressions rDPImpsb,s,p,i,y,x,d. Equation 9 below may be used to implement block702.
rDPDPIb,s,p,i,y,x,d=rDPDurationb,s,p,i,y,x,d/rDPImpsb,s,p,i,y,x,d(Equation 9)

The segment FC341calculates a segment level frequency (e.g., rDPFreqb,i,y,d) (block704). For example, the segment FC341may calculate the segment level frequency rDPFreqb,i,y,d(e.g., at the second time segment level) by dividing, for each combination of brand b, segment z, platform y, and demographic d, respective redistributed impressions rDPImpsb,i,y,x,dacross all device types x (e.g., rDPImpsb,i,y,d) by a respective redistributed audience rDPUAb,i,y,x,dacross all device types x (e.g., rDPUAb,i,y,d). Equation 10 below may be used to implement block704.
rDPFreqb,i,y,d=rDPImpsb,i,y,d/rDPUAb,i,y,d(Equation 10)

The segment DFG342calculates a segment level deduplication factor (e.g., Dedup_FTD,b,i,d) (block706). For example, the segment DFG342may calculate, for each combination of brand b, segment z, and demographic group d, the segment-level deduplication factor Dedup_FTD,b,i,dby dividing a segment level gender audience attributed to the total digital platform (e.g., rDPUATD,b,i,d), a duplicated segment-level audience, by a sum of a segment-level gender audience attributed to the non-mobile computing platform (e.g., rDPUADesktop,b,i,d) and a segment-level gender audience attributed to the mobile computing platform (e.g., rDPUAMobile,b,i,d). In some examples, the duplicated segment-level audience (e.g., rDPUATD,b,i,d), the segment-level gender audience attributed to the non-mobile computing platform (e.g., rDPUADesktop,b,i,d), and the segment-level gender audience attributed to the mobile computing platform (e.g., rDPUAMobile,b,i,d) are derived from the redistributed audiences rDPUAb,i,y,x,dacross all device types x. Equation 11 below may be used to implement block706.
Dedup_FTD,b,i,d=rDPUATD,b,i,d/(rDPUADesktop,b,i,d+rDPUAMobile,b,i,d)   (Equation 11)

The CP-FC351calculates a cross-publisher level frequency (e.g., rDPCPFreqb,i,y,d) (block708). For example, the CP-FC351may calculate the cross publisher level frequency rDPCPFreqb,i,y,dby dividing, for each combination of brand b, segment z, platform y, and demographic d, respective redistributed impressions rDPCPImpsb,i,y,x,dacross all device types x (e.g., rDPCPImpsb,i,y,d) by a respective redistributed audience rDPCPUAb,i,y,x,dacross all device types x (e.g., rDPCPUAb,i,y,d). Equation 12 below may be used to implement block708.
rDPCPFreqb,i,y,d=rDPCPImpsb,i,y,d/rDPCPUAb,i,y,d(Equation 12)

The CP-DFG352calculates a cross publisher level deduplication factor (e.g., Dedup_F_CPTD,b,i,d) (block710). For example, the CP-DFG352may calculate, for each combination of brand b, segment z, and demographic group d, the cross-publisher level deduplication factor Dedup_F_CPTD,b,i,dby dividing a cross-publisher level gender audience attributed to the total digital platform (e.g., rDPCPUATD,b,i,d), a duplicated cross publisher level audience, by a sum of a cross-publisher level gender audience attributed to the non-mobile computing platform (e.g., rDPCPUADesktop,b,i,d) and a cross-publisher level gender audience attributed to the mobile computing platform (e.g., rDPCPUAMobile,b,i,d). Equation 13 below may be used to implement block710.
Dedup_F_CPTD,b,i,d=rDPCPUATD,b,i,d/(rDPCPUADesktop,b,i,d+rDPUAMobile,b,i,d)   (Equation 13)

The example instructions700end.

Misattribution Correction Matrix Generation and Transformation

FIG. 8illustrates an example misattribution correction matrix800(e.g., for a particular brand b and device type x) generated by the matrix generator322. The example matrix generator322may generate the example misattribution correction matrix800as described in U.S. Patent Publication No. 2015/0262207. The entirety of U.S. Patent Publication No. 2015/0262207 is incorporated herein by reference. In the illustrated example ofFIG. 8, the columns of the misattribution correction matrix800(database proprietor demographic groups i802) represent demographic groups identified by the DP116associated with impression information, and the rows of the misattribution correction matrix800(actual demographic groups j804) represent the demographic groups actually viewing the media206ofFIG. 2and corresponding to the survey calibration data.

Each cell of the misattribution correction matrix800corresponding to a demographic group i802and a demographic group j804includes a probability (e.g., represented as a percentage). In examples disclosed herein, the probability is a probability that (1) a person of the demographic group j is identified by the DP116as a viewer of the media206and (2) a person in the actual demographic group j is an actual viewer of the media206. Thus, when a person is identified by the DP116as being a person in the demographic group i, the actual or true viewer is a person in the actual viewer demographic group j, determined from the survey calibration data. As shown inFIG. 8, the values of each of the columns sum to 100%. Therefore, all of the impression information attributed to recognized demographic group i is accounted for.

FIG. 9is a flow chart representative of example machine readable instructions900which may be executed to implement the audience data generator120ofFIGS. 1, 2, and/or3to convert or transform the misattribution correction matrices into impression count matrices IMb,s,p,y,x,d. The example matrix converter353ofFIG. 3may perform the instructions900ofFIG. 9to convert the misattribution correction matrices generated by the matrix generator322to impression count matrices for each combination of brand b and device type x. In examples disclosed herein, the impression count matrices are generated using the asset-level frequencies rDPFreqb,s,p,y,x,dgenerated by the asset FC345ofFIG. 3. The matrix converter353may apply a conversion formula, or a conversion equation, to each cell of each of the misattribution correction matrices to convert the misattribution correction matrices to impression count matrices.

The matrix converter353may obtain asset-level frequencies (e.g., rDPFreqb,s,p,y,x,d) from the asset FC345ofFIG. 3. The matrix converter353also obtains and/or accesses the misattribution correction matrices (e.g., the example misattribution correction matrix800ofFIG. 8) generated by the matrix generator322for each combination of brand b and device type x.

The matrix converter353selects a misattribution correction matrix800for a combination of a device type x, a platform y, a brand b, a sub-brand s and an asset p (block902).

The matrix converter353selects a combination of demographic groups i and j in the selected misattribution matrix800(block904). For example, the matrix converter353may select one of the demographic groups i and one of the demographic groups j of the selected misattribution matrix. To illustrate, the matrix converter353may select the “D1” (e.g., Female, ages 02-12) demographic group from the database proprietor demographic group i802and the “D1” (e.g., Female, ages 02-12) demographic group from the actual demographic group j804in the misattribution correction matrix800.

The matrix converter353converts a misattribution probability of the selected combination of demographic groups i and j to an impression probability (block906). For example, the matrix converter353may use Equation 14 below to implement block906for a selected probability (e.g., cell) of the misattribution matrix800ofFIG. 9to convert each cell of a misattribution matrix to an impression probability.
IMb,s,p,y,x,DPDemoi,TrueDemoj=(Mb,x,DPDemoi,TrueDemoj×rDPFreqb,s,p,y,x,d)/(Σj=1dMb,x,DPDemoi,TrueDemoj×rDPFreqb,s,p,y,x,d)  (Equation 14)

The matrix converter353determines whether there are additional combinations of demographic groups i and j (block908). If there are additional combinations of demographic groups i and j, control returns to block904to select another combination of demographic groups i and j. When there are no more combinations of the demographic groups i and j (block908), the example matrix converter353determines whether there are additional combinations of platform y, device type x, brand b, sub-brand s, and asset p (block910). If there are additional combinations, the example instructions900return to block902to select another misattribution correction matrix for conversion. In this manner, the matrix converter353converts each misattribution correction matrix corresponding to a brand b and a device type x to an impression counts matrix including impression probabilities for each brand b, sub-brand s, asset p, platform y, and device type x. When there are no additional combinations of platform y, device type x, brand b, sub-brand s, and asset p (block910), the example instructions900ofFIG. 9end.

Sharing Adjustments, Coverage Adjustments, and Scaling at the Episode-Level and the First Time Segment Level

FIG. 10is a flow chart representative of example machine readable instructions1000which may be executed to implement the audience data generator120ofFIGS. 1-3to compensate impression data for inaccuracies related to demographic misattribution and database proprietor coverage at the episode level e and the first time segment level. The example E-S adjuster324ofFIG. 3may perform the instructions1000ofFIG. 10to adjust the redistributed impressions rDPImpsb,s,p,e,y,x,dand rDPImpsb,s,p,i,y,x,dat the episode-level and the first time segment level, respectively, and to adjust the redistributed durations rDPDurationb,s,p,e,y,x,dand rDPDurationb,s,p,i,y,x,dat the episode-level and the first time segment level, respectively, to compensate for misattribution and coverage error.

The impression sharing adjuster354and the duration sharing adjuster355receive the redistributed impressions rDPImpsb,s,p,e,y,x,dand rDPImpsb,s,p,i,y,x,dand the redistributed durations rDPDurationb,s,p,e,y,x,dand rDPDurationb,s,p,i,y,x,dfrom the demographic distributor318ofFIG. 3. Additionally, the impression sharing adjuster354receives and/or obtains the impression count matrices from the matrix converter353ofFIG. 3.

Because the E-S adjuster324may execute similar instructions to perform the example instructions1000at any of the episode-level and the first time segment level, the example instructions1000ofFIG. 10are described below using the episode level and the first time segment level in the alternative (e.g., episode/segment).

The E-S adjuster324selects a combination of platform y, device type x, brand b, sub-brand s, episode e/segment z, and demographic d (block1002). The E-S adjuster324selects an impression counts matrix (block1004). For example, the E-S adjuster324selects an impression matrix corresponding to the selected platform y, device type x, brand b, sub-brand s, and asset p. The impressions sharing adjuster354determines misattribution adjusted impressions (block1006). In examples disclosed herein, the impressions sharing adjuster354applies (e.g., performs a dot product calculation) the selected impression counts matrix (e.g., IMb,s,p,y,x,d) to the redistributed impressions (e.g., rDPImpsb,s,p,e,y,x,d/rDPImpsb,s,p,i,y,x,d) corresponding to the selected combination of platform y, device type x, brand b, sub-brand s, episode e/segment z, and demographic d. For example, the impressions sharing adjuster354multiplies each cell of the impression counts matrix to the redistributed impressions to determine misattribution adjusted impressions (e.g., mImpsb,s,p,y,x,e/i,d) for each demographic d.

The duration sharing adjuster355determines misattribution adjusted durations (block1008). In examples disclosed herein, the duration sharing adjuster355applies (e.g., performs a dot product calculation) the selected impression counts matrix (e.g., IMb,s,p,y,x,d) to the redistributed durations (e.g., rDPDurationb,s,p,e,y,x,d/rDPDurationb,s,p,i,y,x,d) corresponding to the selected combination of platform y, device type x, brand b, sub-brand s, episode e/segment z, and demographic d. For example, the duration sharing adjuster355multiplies each cell of the impression counts matrix to the redistributed durations to determine misattribution adjusted durations (e.g., mDurationb,s,p,y,x,e/i,d) for each demographic d.

The coverage vector calculator356calculates coverage adjustment vectors (block1010). The coverage vector calculator356receives survey calibration data from the calibration data collector300ofFIG. 3. The coverage vector calculator356calculates coverage adjustment vectors (e.g., Cb,x) for each combination of brand b and device type x using the survey calibration data. The coverage vector calculator determines a total number of respondents in a demographic group with access to a device of a device type x and media (e.g., the media206ofFIG. 2) provided by a brand b and divides by a covered portion of respondents (e.g., recognized by the DP116) in the demographic group for a device type x and a brand b.

The impressions coverage adjuster357calculates coverage adjusted impressions (block1012). In examples disclosed herein, the impressions coverage adjuster357receives the misattribution adjusted impressions mImpsb,s,p,y,x,e/i,dfrom the impressions sharing adjuster354and the coverage adjustment vectors Cb,xfrom the coverage vector calculator356. The impressions coverage adjuster357multiplies the misattribution adjusted impressions by a coverage adjustment vector Cb,xcorresponding to the selected brand b and device type x. The resultant product is coverage adjusted impressions cImpsb,s,p,e/i,y,x,d. Equation 15 below may be used to implement block1012.
cImpsb,s,p,e/i,y,x,d=mImpsb,s,p,e/i,y,x,d*Cb,x(Equation 15)

The duration coverage adjuster358calculates coverage adjusted durations (block1014). In examples disclosed herein, the duration coverage adjuster358receives the misattribution adjusted durations mDurationb,s,p,y,x,e/i,dfrom the duration sharing adjuster355and the coverage adjustment vectors Cb,xfrom the coverage vector calculator356. The duration coverage adjuster358multiplies misattribution adjusted durations by a coverage adjustment vector Cb,xcorresponding to the selected brand b, and device type x. The resultant product is coverage adjusted durations cDurationb,s,p,e/i,y,x,d. Equation 16 below may be used to implement block1014.
cDurationb,s,p,e/i,y,x,d=mDurationb,s,p,e/i,y,x,d*Cb,x(Equation 16)

The E-S adjuster324determines whether there are additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block1016). If there are additional combinations, the example instructions1000return to block1002. When the E-S adjuster324determines no additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d are available (block1016), the E-S adjuster324obtains census impression counts SCImpsb,s,p,e/i,y,x,dand census durations SCDurationb,s,p,e/i,y,x,dfrom the monitored website (e.g., the web site census) corresponding to the brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block1018).

The E-S adjuster324selects a combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block1020). The impression scaling factor generator359obtains the census impression counts SCImpsb,s,p,e/i,y,x,dand receives the coverage adjusted impressions cImpsb,s,p,e/i,y,x,dfrom the impressions coverage adjuster357. The impression scaling factor generator359determines an impression scaling factor (block1022). The impression scaling factor generator359generates, or calculates, for the selected combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, an impression scaling factor ISFb,s,p,e/i,y,x,dby dividing a corresponding census impression count SCImpsb,s,p,e/i,y,x,dby a sum of corresponding coverage adjusted impressions across all demographic groups d. Equation 17 below may be used to implement block1022.

The duration scaling factor generator360obtains the census durations SCDurationb,s,p,e/i,y,x,dand receives the coverage adjusted durations cDurationb,s,p,e/i,y,x,dfrom the duration coverage adjuster358. The duration scaling factor generator360determines a duration scaling factor (block1024). The duration scaling factor generator360calculates, for the selected combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, a duration scaling factor DSFb,s,p,e/i,y,x,dby dividing a corresponding census duration SCDurationb,s,p,e/i,y,x,dby a sum of corresponding coverage adjusted durations across all demographic groups d. Equation 18 below may be used to implement block1024.

The impression scaler361applies the impression scaling factor to the coverage adjusted impressions to determine scaled impressions (block1026). In examples disclosed herein, the impressions scaler361receives the coverage adjusted impressions cImpsb,s,p,e/i,y,x,dfrom the impressions coverage adjuster357and the impression scaling factor ISFb,s,p,e/i,y,x,dfrom the impression scaling factor generator359. For each combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, the impression scaler361applies (e.g., multiplies), corresponding coverage adjusted impressions by the impression scaling factor to determine scaled impressions sImpb,s,p,e/i,y,x,d. Equation 19 below may be used to implement block1026.
sImpsb,s,p,e/i,y,x,d=cImpsb,s,p,e/i,y,x,d*ISFb,s,p,e/i,y,x,d(Equation 19)

The duration scaler362applies the duration scaling factor to the coverage adjusted durations to determine scaled durations (block1028). In examples disclosed herein, the duration scaler362receives the coverage adjusted durations cDurationb,s,p,e/i,y,x,dfrom the duration coverage adjuster358and the duration scaling factor DSFb,s,p,e/i,y,x,dfrom the duration scaling factor generator360. For each combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, the duration scaler362applies (e.g., multiplies), corresponding coverage adjusted durations by the duration scaling factor to determine scaled durations sDurationb,s,p,e/i,y,x,d. Equation 20 below may be used to implement block1028.
sDurationb,s,p,e/i,y,x,d=cDurationb,s,p,e/i,y,x,d*DSFb,s,pe/i,y,x,d(Equation 20)

The E-S adjuster324determines whether there are additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block1030). If there are additional combinations, the example instructions return to block1020. When the E-S adjuster324determines there are no additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block1030), the example instructions1000ofFIG. 10end.

Adjusting, Scaling, and Imputation at the Episode Level and the First Time Segment Level

FIG. 11is a flow chart representative of example machine readable instructions1100which may be executed to implement the audience data generator120ofFIGS. 1, 2, and/or3to adjust database proprietor demographic data and to impute cite census impression information. The example census imputer326ofFIG. 3may perform the instructions1100ofFIG. 11to adjust the scaled impressions sImpb,s,p,e/i,y,x,dand the scaled durations sDurationb,s,p,e/i,y,x,dat the asset-level and to impute cite census impressions SCImpsb,s,p,e/i,y,x,dand cite census durations SCDurationb,s,p,e/i,y,x,d.

Because the census imputer326may execute similar instructions to perform the example instructions1100at any of the episode-level and the first time segment level, the example instructions1100ofFIG. 11are described below using the episode level and the first time segment level in the alternative (e.g., episode/segment).

The census imputer326obtains the scaled impressions sImpb,s,p,e/i,y,x,dand the scaled durations sDurationb,s,p,e/i,y,x,dfrom the E-S adjuster324(block1102). In examples disclosed herein, the impressions adder363obtains the scaled impressions sImpb,s,p,e/i,y,x,dand the duration adder364obtains the scaled durations sDurationb,s,p,e/i,y,x,d. The census imputer326selects a combination of brand b, sub-brand s, asset p, platform y, device type x, and demographic d (block1104).

The impressions adder363calculates asset-level adjusted impressions (block1106). In examples disclosed herein, the impressions adder363adds, or sums, for each combination of brand b, sub-brand s, asset p, platform y, device type x, and demographic d, corresponding scaled impressions sImpb,s,p,e/i,y,x,dacross all episodes e or segments i included in the selected asset p. For example, the impressions adder363scales, or rolls up, the scaled impressions across all episodes e/segments i of the selected asset p to generate asset-level adjusted impressions ase/iImpsb,s,p,y,x,d. Equation 21 below may be used to implement block1106. In Equation 21, n is the total number of episode/segment components.
ase/iImpsb,s,p,y,x,d=Σe/i=1nsImpsb,s,p,e/i,y,x,d(Equation 21)

The duration adder364calculates asset-level adjusted durations (block1108). In examples disclosed herein, the duration adder364adds, or sums, for each combination of brand b, sub-brand s, asset p, platform y, device type x, and demographic d, corresponding scaled durations sDurationsb,s,p,e/i,y,x,dacross all episodes e/segments i included in the selected asset p. For example, the duration adder364scales, or rolls up, the scaled durations across all episodes e/segments i of the selected asset p to generate asset-level adjusted durations ase/iDurationb,s,p,y,x,d. Equation 22 below may be used to implement block1108. In Equation 22, n is the total number of episode/segment components.
ase/iDurationb,s,p,y,x,d=Σe/i=1nsDurationb,s,p,e/i,y,x,d(Equation 22)

The census imputer326determines whether there are any additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block1110). If there are additional combinations (block1110), the example instructions1100return to block1104.

When the census imputer326determines there are no additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d (block1110), the census imputer326selects a demographic d (block1112). The impressions distribution deriver367receives the asset-level adjusted impressions ase/iImpsb,s,p,y,x,dand the sum of the asset-level adjusted impressions across all demographic groups d (Σd=1nase/iImpsb,s,p,y,x,d) from the impressions adder363and the impressions imputation adder365, respectively.

The impressions distribution deriver367distributes the asset-level adjusted impressions (block1114). In examples disclosed herein, the impressions distribution deriver367distributes the asset-level adjusted impressions ase/iImpsb,s,p,y,x,dby dividing, for each combination of brand b, sub-brand s, asset p, platform y, device type x, and demographic d, a corresponding asset-level adjusted impression by a corresponding sum of asset-level adjusted impressions across all demographics d. The resulting distribution is referred to herein as an adjusted and scaled impression demographic distribution ase/iImpDemoDistrb,s,p,y,x,d. Equation 23 below may be used to implement block1114.
ase/iImpDemoDistrb,s,p,y,x,d=asImpsb,s,p,y,x,d/(Σd=1nase/iImpsb,s,p,y,x,d)  (Equation 23)

The duration distribution deriver368receives the asset-level adjusted durations ase/iDurationb,s,p,y,x,dand the sum of the asset-level adjusted durations across all demographic groups d ase/iDurationb,s,p,y,x,d) from the duration adder364and the duration imputation adder365, respectively. The duration distribution deriver368distributes the asset-level adjusted durations (block1116). In examples disclosed herein, the duration distribution deriver368distributes the asset-level adjusted durations ase/iDurationb,s,p,y,x,dby dividing, for each combination of brand b, sub-brand s, asset p, platform y, device type x, and demographic d, a corresponding asset-level adjusted duration by a corresponding sum of asset-level adjusted durations across all demographics d. The resulting distribution is referred to herein as an adjusted and scaled duration demographic distribution ase/iDurationDemoDistrb,s,p,y,x,d. Equation 24 below may be used to implement block1116.
ase/iDurationDemoDistrb,s,p,y,x,d=ase/iDurationsb,s,p,y,x,d/(Σd=1nase/iDurationb,s,p,y,x,d)   (Equation 24)

The census imputer326selects an episode e/segment z (block1118). The impressions imputation director369subsequently accesses the site census impressions SCImpsb,s,p,e/i,y,x,d. The impressions imputation director369also receives and/or accesses the adjusted and scaled asset-level impression demographic distribution ase/iImpDemoDistrb,s,p,y,x,dfrom the impressions distribution deriver367. The impressions imputation director369determines whether there are site census impressions logged and no DP impressions logged for the selected episode e/segment z (block1120). For example, there may be some site census impressions SCImpsb,s,p,e/i,y,x,didentified by the impressions imputation director369for which DP impressions have not been received (e.g., the DP116ofFIGS. 1 and/or 2did not recognize any impressions for the episode e/segment z selected at block1118), but one or more impressions have been received from the site census. When there are site census impressions SCImpsb,s,p,e/i,y,x,dlogged and no DP impressions for the selected episode e/segment z (block1120), the impressions imputation director369imputes the census impressions with the adjusted and scaled asset-level impression distribution (block1122). For example, the impressions imputation director369may apply (e.g., multiply), for each combination of selected brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, the adjusted and scaled impression demographic distribution ase/iImpDemoDistrb,s,p,y,x,dto the site census impressions SCImpsb,s,p,e/i,y,x,dto generate imputed impressions ie/iImpp,b,s,p,y,x,d. Equation 25 below may be used to implement block1122.
ie/iImpp,b,s,p,y,x,d=ase/iImpDemoDistb,s,p,y,x,d*SCImpsb,s,p,e/i,y,x,d(Equation 25)

After imputing the impressions (block1122), or when the impressions distribution director369determines there are no site census impressions logged and no DP impressions for the selected episode e/segment z (block1120), the example duration imputation director370determines whether there are site census durations logged and no DP durations logged for the selected episode e/segment z (block1124). For example, there may be some site census durations SCDurationb,s,p,e/i,y,x,didentified by the duration imputation director370for which DP durations have not been received (e.g., the DP116ofFIGS. 1 and/or 2did not recognize any durations for the episode e/segment z selected at block1118), but one or more durations have been received from the site census. The duration imputation director370accesses the site census durations SCDurationb,s,p,e/i,y,x,d. The duration imputation director370also receives and/or accesses the adjusted and scaled asset-level duration demographic distribution ase/iDurationDemoDistrb,s,p,y,x,dfrom the duration distribution deriver368. When there are site census durations SCDurationsb,s,p,e/i,y,x,dlogged and no DP durations for the selected episode e/segment z, (block1124), the duration imputation director370imputes the census durations with the adjusted and scaled asset-level duration distribution (block1126). For example, the duration imputation director370may apply (e.g., multiply), for each combination of selected brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, the adjusted and scaled duration demographic distribution ase/iDurationDemoDistrb,s,p,y,x,dto the site census durations SCDurationb,s,p,e/i,y,x,dto generate imputed durations ie/iDurationp,b,s,p,y,x,d. Equation 26 below may be used to implement block1126.
ie/iDurationp,b,s,p,y,x,d=ase/iDurationDemoDistrb,s,p,y,x,d*SCDuration,s,p,e/i,y,x,d(Equation 26)

After imputing the durations (block1126), or when the duration imputation director370determines there are no site census impressions logged and no DP impressions for the selected episode e/segment z (block1124), the impressions imputation calculator371calculates scaled and adjusted impressions (block1128). The impressions imputation calculator371receives and/or access the scaled impressions sImpb,s,p,e/i,y,x,dfrom the E-S adjuster324and the imputed impressions ie/iImpp,b,s,p,y,x,dfrom the impressions imputation director369. The impression imputation calculator371calculates scaled and adjusted impressions asImpb,s,p,e/i,y,x,d, for each combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, by applying (e.g., adding) the imputed impressions ie/iImpp,b,s,p,y,x,dto the scaled impressions sImpb,s,p,e/i,y,x,d. Equation 27 below may be used to implement block1128.
asImpb,s,p,e/i,y,x,d=sImpb,s,p,e/i,y,x,d*ie/iImpp,b,s,p,y,x,d(Equation 27)

The duration imputation calculator372calculates scaled and adjusted durations (bock1130). The duration imputation calculator372receives and/or access the scaled durations sDurationb,s,p,e/i,y,x,dfrom the E-S adjuster324and the imputed durations ie/iDurationp,b,s,p,y,x,dfrom the duration imputation director370. The duration imputation calculator372calculates scaled and adjusted durations asDurationb,s,p,e/i,y,x,d,for each combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, device type x, and demographic d, by applying (e.g., adding) the imputed durations ie/iDurationp,b,s,p,y,x,dto the scaled durations sDurationb,s,p,e/i,y,x,d. Equation 28 below may be used to implement block1130.
asDurationb,s,p,e/i,y,x,d=sDurationb,s,p,e/i,y,x,d*ie/iDurationp,b,s,p,y,x,d(Equation 28)

The census imputer326determines whether there are additional episodes e/segments i (block1132). If the census imputer326determines there are additional episodes e/segments i (block1132), the example instructions1100return to block1118. In this manner, the census imputer326determines scaled and adjusted impressions asImpb,s,p,e/i,y,x,dand durations asDurationb,s,p,e/i,y,x,dfor each episode e/segment z. When the census imputer326determines there are no more episodes e/segments i (block1132), the census imputer326determines whether there are additional demographic groups d (block1134).

When the census imputer326determines there are additional demographics d to select (block1134), the example instructions1100return to block1112. In this manner, the census imputer326determines scaled and adjusted impressions asImpb,s,p,e/i,y,x,dand durations asDurationb,s,p,e/i,y,x,dfor each demographic group d. When the census imputer326determines there are no more demographic groups d to select (block1134), the census imputer326determines whether there are additional combinations of brand b, sub-brand s, asset p, platform y, and device type x (block1136).

When there are additional combinations of brand b, sub-brand s, asset p, platform y, and device type x (block1136), the example instructions1100ofFIG. 11return to block1112. In this manner, the census imputer326determines scaled and adjusted impressions asImpb,s,p,e/i,y,x,dand durations asDurationb,s,p,e/i,y,x,dfor each combination of brand b, sub-brand s, asset p, platform y, and device type x. When the census imputer326determines there are no additional combinations of brand b, sub-brand s, asset p, platform y, and device type x (block1136), the example instructions1100ofFIG. 11end.

FIG. 12is a flow chart representative of example machine readable instructions1200which may be executed to implement the audience data generator120ofFIGS. 1, 2, and/or3to calculate ratings information at the episode level, the first time segment level, and the second time segment level. The metrics generator328FIG. 3may perform the instructions1200ofFIG. 12to estimate impression counts, durations, and unique audiences based on the scaled and adjusted impressions asImpb,s,p,e/i,y,x,dand durations asDurationb,s,p,e/i,y,x,dgenerated by the census imputer326and illustrated inFIG. 11.

Because the metrics generator328may execute similar instructions to perform the example instructions1200at any of the asset level, the sub-brand level, the brand level, the episode-level and/or the second time segment level, the example instructions1200ofFIG. 12are described below using the asset level, the sub-brand level, the brand level, the episode-level and/or the second time segment level in the alternative (e.g., episode/segment). In some examples, the metrics generator328iterates the instructions1200beginning at a lower level (e.g., episode level, time segment level) and moving to higher (e.g., broader) levels (e.g., asset level, sub-brand level, brand level) with successive iterations by using the results from the immediately prior iteration for the next iteration. For example, the metrics generator328may use the episode/segment level results to execute the instructions1200at the asset level, use the asset level results to execute the instructions1200at the sub-brand level, and use the sub-brand level results to execute the instructions1200at the brand level. The example instructions1200are described with reference to the episode/time segment level.

The metrics generator328obtains the scaled and adjusted impressions and the scaled and adjusted durations from the census imputer (block1202). In examples disclosed herein, the metrics generator328obtains the scaled and adjusted impressions asImpb,s,p,e/i,y,x,dfrom the impressions imputation calculator371and the scaled and adjusted durations asDurationb,s,p,e/i,y,x,dfrom the duration imputation calculator372. The metrics generator328selects a combination of brand b, sub-brand s, asset p, episode e/segment z, platform y, and demographic group d (block1204). The metrics generator328subsequently calculates episode-/segment-level impression estimates estImpb,s,p,e/i,y,dacross all device types x (block1206). In examples disclosed herein, the metrics generator328calculates segment-level and episode-level impression estimates by summing, or adding together all adjusted and scaled impressions asImpb,s,p,e/i,y,x,dacross all device types x for the selected combination. In some examples, the segment-level impression estimates are performed at the first time segment level. Equation 29 below may be used to implement block1206. In Equation 29, n represents the total number of device types x.
estImpb,s,p,e/i,y,d=Σx=1nasImpsb,s,p,e/i,y,d(Equation 29)

The metrics generator328calculates episode-/segment-level duration estimates across all device types x (block1208). In examples disclosed herein, the metrics generator328calculates segment-level and episode-level duration estimates estDurationb,s,p,e/i,y,dby summing, or adding together all adjusted and scaled durations asDurationb,s,p,e/i,y,x,dacross all device types x for the selected combination. In some examples, the segment-level duration estimates are performed at the first time segment level. Equation 30 below may be used to implement block1208. In Equation 30, n represents the total number of device types x.
estDurationb,s,p,e/i,y,d=Σx=1nasDurationb,s,p,e/i,y,d(Equation 30)

The metrics generator328accesses and/or receives all frequencies and deduplication factors from the distributed demographic data calculator320ofFIG. 3. The metrics generator328calculates episode-/segment-level unique audience estimates (block1210). In examples disclosed herein, the metrics generator328calculates the episode-level unique audience estimates estUAb,s,p,e,y,dby dividing, for the selected combination, the episode-level impression estimates estImpb,s,p,e,y,dby the episode-level frequency rDPFreqb,s,p,e,y,dof equation 7. Alternatively, the metrics generator328calculates the segment-level unique audience estimates estUAb,i,y,dby dividing, for the selected combination, the segment-level impression estimates estImpb,s,p,i,y,dby the segment-level frequency rDPFreqb,i,y,dof equation 10. In some examples, the segment-level unique audience estimates are performed at the second time segment level. Equation 31 below may be used to implement block1210.
estUAb/(b,s,p),e/i,y,d=estImpb/(b,s,p),e/i,y,d/rDPFreqb/(b,s,p),/i,y,d(Equation 31)

The metrics generator328determines whether there are additional combinations of brand b, sub-brand s, asset p, episode e/segment z, platform y, and demographic d (block1212). When the metrics generator328determines there are additional combinations (block1212), the example instructions return to block1204. When the metrics generator328determines there are no additional combinations (block1212), the metrics generator selects a combination of brand b, sub-brand s, asset p, episode e/segment z, and demographic d (block1214).

The metrics generator328calculates total digital episode-/segment-level impression estimates across all platforms y (block1216). The metrics generator328calculates the total digital episode-/segment-level impression estimates estImpTD,b,s,p,e/i,dby adding episode-/segment-level impressions across the non-mobile (e.g., desktop) computing platform estImpDesktop,b,s,p,e/i,dto episode-/segment-level impressions across the mobile computing platform estImpMobile,b,s,p,e/i,d. In some examples, the total digital segment-level impression estimates are performed at the first time segment level. Equation 32 below may be used to implement block1216.
estImpTD,b,s,p,e/i,d=estIMPDesktop,b,s,p,e/i,d+estImpMobile,b,s,p,e/i,d(Equation 32)

The metrics generator328calculates total digital episode-level/segment-level duration estimates across all platforms y (block1218). The metrics generator328calculates the total digital episode-level/segment-level duration estimates estDurationTD,b,s,p,e/i,dby adding episode-level/segment-level durations across the non-mobile (e.g., desktop) computing platform estDurationDesktop,b,s,p,e/i,dto episode-/segment-level durations across the mobile computing platform estDurationMobile,b,s,p,e/i,d. In some examples, the total digital segment-level duration estimates are performed at the first time segment level. Equation 33 below may be used to implement block1218.
estDurationTD,b,s,p,e/i,d=estDurationDesktop,b,s,p,e/i,d+estDurationMobile,b,s,p,e/i,d(Equation 33)

The metrics generator328calculates total digital episode-/segment-level audience estimates across all platforms y using deduplication factors (block1220). In examples disclosed herein, the metrics generator328calculates total digital episode-level audience estimates estUATDb,s,p,e,dby adding, for the selected combination of brand b, sub-brand s, asset p, episode e, and demographic group d, episode-level audience estimates across the non-mobile (e.g., desktop) computing platform estUADesktopb,s,p,e,dand episode-level audience estimates across the mobile computing platform estUAMobile,b,s,p,e,dand multiplying the sum by the episode-level deduplication factor Dedup_FTD,b,s,p,e,dof equation 8. Alternatively, the metrics generator328calculates total digital segment-level audience estimates estUATD,b,i,dby adding, for the selected combination of brand b, sub-brand s, asset p, segment z, and demographic group d, segment-level audience estimates across the non-mobile (e.g., desktop) computing platform estUADesktop,b,i,dand segment-level audience estimates across the mobile computing platform estUAMobile,b,i,dand multiplying the sum by the segment-level deduplication factor Dedup_FTD,b,i,dof equation 11. In some examples, the segment-level audience estimates are performed at the second time segment level. Equation 34 below may be used to implement block1220.
estUATD,b/(b,s,p),e/i,d=(estUADesktop,b/(b,s,p),e/i,d+estUAMobile,b/(b,s,p),e/i,d)*Dedup_FTD,b/(b,s,p),e/i,d(Equation 34)

The metrics generator328determines whether there are additional combinations of brand b, sub-brand s, asset p, episode e/segment z, and demographic d (block1222). When there are additional combinations (block1222), the example instructions1200return to block1214. When there are no additional combinations (block1222), the example instructions1200end.

FIG. 13is a flow chart representative of example machine readable instructions1300which may be executed to implement the audience data generator120ofFIGS. 1, 2, and/or3to adjust the ratings information at the first time segment level using impression data at a time sub-segment level. The segment modifier330ofFIG. 3may perform the instructions1300ofFIG. 13to determine impression ratios, duration ratios, and to adjust asset-level impression estimates estImpb,s,p,y,d(equation 9) and asset-level duration estimates estDurationb,s,p,y,d(equation 5).

In examples disclosed herein, the segment modifier330receives segment-level impression estimates estImpb,s,p,i,y,d(e.g., at the first time segment level) (Equation 29) and segment-level duration estimates estDurationb,s,p,i,y,d(e.g., at the first time segment level) (Equation 30) from the metrics generator328ofFIG. 3. The segment modifier330selects a combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d (block1302).

The impression sub-segment adder373determines a sum of sub-segments is(block1304). In examples disclosed herein, the impression sub-segment adder373accesses the segment-level impression estimates estImpb,s,p,i,y,dand sums, for the selected combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d, all time sub-segments isfor a corresponding (e.g., matching the selection) segment-level impression estimate estImpb,s,p,i,y,d(e.g., all time sub-segments isincluded in the selected segment z). The impression sub-segment adder373may determine the sum of the segment-level impression estimates across all time sub-segments is, referred to herein as the impression sub-segment level sum (e.g., SumEstImpb,s,p,i,y,d), by analyzing D beacons (e.g., beacon request226bofFIG. 2) associated with (e.g., used to derive) the segment-level impression estimates estImpb,s,p,i,y,d. For example, if a segment level impression estimate estImpb,s,p,i,y,dcollected from a first device type x and corresponding to the selected brand b, sub-brand s, asset p, segment z, platform y, and demographic d, is derived from a D beacon of D_2_01010, the impression sub-segment adder373identifies the sub-segments is(e.g., 01010) and credits the segment as having five sub-segments. Further, if a segment level impression estimate estImpb,s,p,i,y,dcollected from a second device type x and corresponding to the selected brand b, sub-brand s, asset p, segment z, platform y, and demographic d, is derived from a D beacon of D_2_00100, the impression sub-segment adder373identifies the sub-segments is(e.g., 00100) and credits the segment as having five sub-segments. Thus, the impression sub-segment adder373may determine the impression sub-segment level sum SumEstImpb,s,p,i,y,dis ten (e.g., five sub-segments of D_2_01010 plus five sub-segments of D_2_00100). Equation 35 below may be used to implement block1304. In Equation 35 below, z(s) is the first sub-segment zsof the segment z and n is the total number of sub-segments in the segment z.
SumEstImpb,s,p,i,y,d=Σi(s)=1nestImpb,s,p,i,y,d(Equation 35)

The duration sub-segment adder374determines a duration of sub-segments is(block1306). In examples disclosed herein, the duration sub-segment adder374accesses the segment-level duration estimates estDurationb,s,p,i,y,dand sums, for the selected combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d, durations of all time sub-segments isfor a corresponding (e.g., matching the selection) segment-level duration estimate estDurationb,s,p,i,y,d(e.g., summing durations of each time sub-segments isincluded in the selected segment z). The duration sub-segment adder374may determine the sum of the segment-level duration estimates across all time sub-segment zsdurations, referred to herein as the duration sub-segment level sum (e.g., SumEstDurationb,s,p,i,y,d), by analyzing D beacons (e.g., beacon request226bofFIG. 2) associated with (e.g., used to derive) the segment-level duration estimates estDurationb,s,p,i,y,d. For example, if a segment level duration estimate estDurationb,s,p,i,y,dcollected from a first device type x and corresponding to the selected brand b, sub-brand s, asset p, segment z, platform y, and demographic d, is derived from a D beacon of D_2_01010, wherein each time sub-segment zsrepresents a duration of one minute, the duration sub-segment adder374identifies the durations of the sub-segments is(e.g., 01010) and credits the segment as having a duration of five minutes. Further, if a segment level duration estimate estDurationb,s,p,i,y,dcollected from a second device type x and corresponding to the selected brand b, sub-brand s, asset p, segment z, platform y, and demographic d, is derived from a D beacon of D_2_00100, wherein each time sub-segment zsrepresents a duration of one minute, the duration sub-segment adder374identifies the durations of the sub-segments is(e.g., 00100) and credits the segment as having a duration of five minutes. Thus, the duration sub-segment adder374may determine the duration sub-segment level sum SumEstDurationb,s,p,i,y,dto be ten minutes (e.g., a five minute duration for D_2_01010 plus a five minute duration for D_2_00100). Equation 36 below may be used to implement block1306. In Equation 36 below, where i(s) is the first sub-segment zsduration of the segment z and n is the last sub-segment zsduration of the segment z.
SumEstDurationb,s,p,i,y,d=Σi(s)=1nestDurationb,s,p,i,y,d(Equation 36)

The impression ratio calculator375receives the impression sub-segment level sum SumEstImpb,s,p,i,y,dfrom the impression sub-segment adder373. The impression ratio calculator375also receives the segment-level impression estimates estImpb,s,p,i,y,d(e.g., at the first time segment level) (Equation 29) from the metrics generator328. The impression ratio calculator375subsequently calculates an impression ratio (block1308). In examples disclosed herein, the impression ratio calculator375identifies sub-segments isof the segment-level impression estimates estImpb,s,p,i,y,dthat are credited as impressions (e.g., presented via the client device102ofFIGS. 1 and/or 2). For example, the impression ratio calculator375may analyze D beacons (e.g., beacon request226bofFIG. 2) associated with (e.g., used to derive) the segment-level impression estimates estImpb,s,p,i,y,dand identify a quantity of time sub-segments isthat are credited as impressions. For example, if the segment-level impression estimate estImpb,s,p,i,y,dcollected from the first device type x (e.g., provided as an example in the aforementioned description of block1304(derived from the D beacon D_2_01010)), the impression ratio calculator375determines that two time sub-segments were presented (e.g., the 2ndtime sub-segment and the 4thtime sub-segment). Thus, the impression ratio calculator375determines the quantity of time sub-segments credited as impressions to be two. Further, if the segment-level impression estimate estImpb,s,p,i,y,dcollected from the second device type x (e.g., provided as an example in the aforementioned description of block1304(derived from the D beacon D_2_00100)), the impression ratio calculator375determines that one time sub-segment was presented (e.g., the 3rdtime sub-segment). Thus, the impression ratio calculator375determines the quantity of time sub-segments credited as impressions to be one. Moreover, the impression calculator375determines the quantity of time sub-segments credited as impressions for the selected combination (e.g., estiImpb,s,p,i,y,d) to be three (e.g., two time sub-segments presented in D_2_01010 plus one time sub-segment presented in D_2_00100). In examples disclosed herein, the impression ratio calculator375calculates the impression ratio EstImpRatiob,s,p,i,y,dfor the selected combination by dividing the quantity of time sub-segments credited as impressions estiImpb,s,p,i,y,dby the impression sub-segment level sum SumEstImpb,s,p,i,y,d. As such, the impression ratio calculator375calculates an impression ratio of 3/10 or 0.30 by dividing the quantity of time sub-segments credited as impressions estiImpb,s,p,i,y,d(e.g.,3) by the impression sub-segment level sum SumEstImpb,s,p,i,y,d(e.g., 10). Equation 37 below may be used to implement block1308.
EstImpRatiob,s,p,i,y,d=estiImpb,s,p,i,y,d/SumEstImpb,s,p,i,y,d(Equation 37)

The duration ratio calculator376receives the duration sub-segment level sum SumEstDurationb,s,p,i,y,dfrom the duration sub-segment adder374. The duration ratio calculator376also receives the segment-level duration estimates estDurationb,s,p,i,y,d(e.g., at the first time segment level) (Equation 30) from the metrics generator328. The duration ratio calculator376subsequently calculates a duration ratio (block1310). In examples disclosed herein, the duration ratio calculator376identifies sub-segments isof the segment-level duration estimates estDurationb,s,p,i,y,dthat are credited as presented durations (e.g., presented via the client device102ofFIGS. 1 and/or 2). For example, the duration ratio calculator376may analyze D beacons (e.g., the beacon request226bofFIG. 2) associated with (e.g., used to derive) the segment-level duration estimates estDurationb,s,p,i,y,dand identify a duration of the time sub-segments ispresented. For example, if the segment-level duration estimate estDurationb,s,p,i,y,dcollected from the first device type x (e.g., provided as an example in the aforementioned description of block1306(derived from the D beacon D_2_01010)), the duration ratio calculator376determines that two time sub-segments were presented (e.g., the 2ndtime sub-segment and the 4thtime sub-segment). Thus, the duration ratio calculator376determines the duration of the time sub-segments ispresented to be two minutes. Further, if the segment-level duration estimate estDurationb,s,p,i,y,dcollected from the second device type x (e.g., provided as an example in the aforementioned description of block1306(derived from the D beacon D_2_00100)), the duration ratio calculator376determines that one time sub-segment was presented (e.g., the 3rdtime sub-segment). Thus, the duration ratio calculator376determines the duration of the time sub-segments ispresented to be one minute. Moreover, the duration calculator376determines the duration of time sub-segments presented for the selected combination (e.g., estiDurationb,s,p,i,y,d) to be three minutes (e.g., two minutes for the two time sub-segments presented in D_2_01010 plus one minute for the one time sub-segment presented in D_2_00100). In examples disclosed herein, the duration ratio calculator376calculates the duration ratio EstDurationRatiob,s,p,i,y,dfor the selected combination by dividing the duration of time sub-segments presented for the selected combination estiDurationb,s,p,i,y,dby the duration sub-segment level sum SumEstDurationb,s,p,i,y,d. As such, the duration ratio calculator376calculates a duration ratio of 3/10 or 0.30 by dividing the duration of time sub-segments presented estiDurationb,s,p,i,y,d(e.g., 3 minutes) by the duration sub-segment level sum SumEstDurationb,s,p,i,y,d(e.g., 10 minutes). Equation 38 below may be used to implement block1310.
EstDurationRatiob,s,p,i,y,d=estiDurationb,s,p,i,y,d/SumEstDurationb,s,p,i,y,d(Equation 38)

The impression metrics adjuster377receives the impression ratio EstImpRatiob,s,p,i,y,dfrom the impression ratio calculator375and the asset-level impression estimates estImpb,s,p,y,d(equation 29) (e.g., first asset-level impressions) from the metrics generator328. The impression metrics adjuster377subsequently adjusts the asset-level impression estimates with the impression ratio (block1312). In examples disclosed herein, the impression metrics adjuster377adjusts the asset-level impression estimates estImpb,s,p,y,dby applying (e.g., multiplying), for the selected combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d, the impression ratio EstImpRatiob,s,p,i,y,dto the asset-level impression estimates estImpb,s,p,y,d. As a result, the impression metrics adjuster377generates adjusted segment-level impression estimates estImpb,s,p,i,y,dby applying the impression ratio EstImpRatiob,s,p,i,y,dto the asset-level impression estimates estImpb,s,p,y,d. Equation 39 below may be used to implement block1312.
estImpb,s,p,i,y,d=EstImpRatiob,s,p,i,y,d*estImpb,s,p,y,d(Equation 39)

The duration metrics adjuster378receives the duration ratio EstDurationRatiob,s,p,i,y,dfrom the duration ratio calculator376and the asset-level duration estimates estDurationb,s,p,y,d(Equation 30) (e.g., first asset-level durations) from the metrics generator328. The duration metrics adjuster378subsequently adjusts the asset-level duration estimates with the duration ratio (block1314). In examples disclosed herein, the duration metrics adjuster378adjusts the asset-level duration estimates estDurationb,s,p,y,dby applying (e.g., multiplying), for the selected combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d, the duration ratio EstDurationRatiob,s,p,i,y,dto the asset-level duration estimates estDurationb,s,p,y,d. As a result, the duration metrics adjuster378generates adjusted segment-level duration estimates estDurationb,s,p,i,y,dby applying the duration ratio EstDurationRatiob,s,p,i,y,dto the asset-level duration estimates estDurationb,s,p,y,d. Equation 40 below may be used to implement block1314.
estDurationb,s,p,i,y,d=EstDurationRatiob,s,p,i,y,d*estDurationb,s,p,y,d(Equation 40)

The segment modifier330determines whether there are additional combinations of brand b, sub-brand s, asset p, segment z, platform y, and demographic d (block1316). When the segment modifier330determines there are additional combinations (block1316), the example instructions1300return to block1302. In this manner, the segment modifier330generates impression ratios, duration ratios, adjusted segment-level impression estimates, and adjusted segment-level duration estimates across all combination of brand b, sub-brand s, asset p, segment z, platform y, and demographic d. When the segment modifier330determines there are no additional combinations of brand b, sub-brand s, asset p, segment z, platform y, and demographic d (block1316), the example instructions1300ofFIG. 13end.

FIG. 14is a flow chart representative of example machine readable instructions1400which may be executed to implement the audience data generator120ofFIGS. 1, 2, and/or3to adjust the ratings information at the brand level. The brand/cross-platform adjuster332ofFIG. 3may perform the instructions1400ofFIG. 14to estimate impression counts and durations using the adjusted asset-level impression estimates estImpsb,s,p,i,y,dand the adjusted asset-level duration estimates estDurationb,s,p,i,y,dgenerated by the segment modifier330and illustrated inFIG. 13.

The brand/cross-platform adjuster332accesses and/or receives the adjusted asset-level impression estimates estImpsb,s,p,i,y,dand the adjusted asset-level durations estDurationb,s,p,i,y,dfrom the segment modifier330. In examples disclosed herein, the brand/cross-platform adjuster332also accesses and/or receives the segment level frequency rDPFreqb,i,y,d(e.g., at the second time segment level) (Equation 10) from the segment FC341of the distributed demographic data calculator320ofFIG. 3. The brand/cross-platform adjuster332further accesses and/or receives the segment level deduplication factor Dedup_FTD,b,i,d(Equation 11) from the segment DFG342.

The brand/cross-platform adjuster332selects a combination of brand b, segment z, platform y, and demographic d (block1402). The impressions adjuster379subsequently calculates adjusted brand-level impression estimates (Block1404). In examples disclosed herein, the impressions adjuster379calculates adjusted brand-level impression estimates estImpb,i,y,dfor the selected combination of brand b, segment z, platform y, and demographic d by summing the adjusted asset-level impression estimates estImpsb,s,p,i,y,dacross all assets p within sub-brands s. Equation 41 below may be used to implement block1404. In the example Equation 41 below, n represents all assets p within a sub-brand s.
estImpb,i,y,d=Σp=1naestImpsb,s,p,i,y,d(Equation 41)

The duration adjuster380calculates adjusted brand-level duration estimates (Block1406). In examples disclosed herein, the duration adjuster380calculates adjusted brand-level duration estimates estDurationb,i,y,dfor the selected combination of brand b, segment z, platform y, and demographic d by summing the adjusted asset-level duration estimates estDurationb,s,p,i,y,dacross all assets p within sub-brands s. Equation 42 below may be used to implement block1406. In the example Equation 42 below, n represents all assets p within a sub-brand s.
estDurationb,i,y,d=Σp=1nestDurationb,s,p,i,y,d(Equation 42)

The audience adjuster381calculates adjusted brand-level audience estimates (block1408). In examples disclosed herein, the audience adjuster381calculates adjusted brand-level audience estimates estUAb,i,y,dfor the selected combination of brand b, segment z, platform y, and demographic d by dividing a respective, adjusted brand-level impression estimate estImpb,i,y,dby a respective, segment level frequency rDPFreqb,i,y,d(e.g., at the second time segment level) (Equation 10). Equation 43 may be used to implement block1408.
estUAb,i,y,d=estImpb,i,y,d/rDPFreqb,i,y,d(Equation 43)

The brand/cross-platform adjuster332determines whether there are additional combinations of brand b, segment z, platform y, and demographic group d (block1410). When the brand/cross-platform adjuster332determines there are additional combinations (block1410), the example instructions1400return to block1402. When the brand/cross-platform adjuster332determines there are no additional combinations (block1410), the brand/cross-platform adjuster332selects a combination of a brand b, segment z, and demographic d (block1412).

The impressions TD calculator382determines adjusted total-digital brand-level impression estimates across all platforms y (block1414). In examples disclosed herein, the impressions TD calculator382calculates the adjusted total digital brand-level impression estimates estImpTD,b,i,dby adding adjusted brand-level impression estimates across the non-mobile (e.g., desktop) computing platform estImpDesktop,b,i,dto adjusted brand-level impression estimates across the mobile computing platform estImpMobile,b,i,dEquation 44 below may be used to implement block1414.
estImpTD,b,i,d=estImpDesktop,b,i,d+estImpMobile,b,i,d(Equation 44)

The duration TD calculator383determines adjusted total-digital brand-level duration estimates across all platforms y (block1416). In examples disclosed herein, the duration TD calculator383calculates the adjusted total digital brand-level duration estimates estDurationTD,b,i,dby adding adjusted brand-level duration estimates across the non-mobile (e.g., desktop) computing platform estDurationDesktop,b,i,dto adjusted brand-level duration estimates across the mobile computing platform estDurationMobile,b,i,dEquation 45 below may be used to implement block1416.
estDurationTD,b,i,d=estDurationDesktop,b,i,d+estDurationMobile,b,i,d(Equation 45)

The audience TD calculator384calculates adjusted total digital brand-level audience estimates across all platforms y using deduplication factors (block1418). In examples disclosed herein, the audience TD calculator384calculates adjusted total digital brand-level audience estimates estUATD,b,i,dby adding, for the selected combination of brand b, segment z, and demographic group d, adjusted brand-level audience estimates across the non-mobile (e.g., desktop) computing platform estUADesktop,b,i,dand adjusted brand-level audience estimates across the mobile computing platform estUAMobile,b,i,dand multiplying the sum by the segment level deduplication factor Dedup_FTD,b,i,d(Equation 11). Equation 46 below may be used to implement block1418.
estUATD,b,i,d=(estUADesktop,b,i,d+estUAMobile,b,i,d)*Dedup_FTD,b,i,d(Equation 46)

The brand/cross-platform adjuster332determines whether there are additional combinations of brand b, segment z, and demographic d (block1420). When there are additional combinations (block1420), the example instructions1400return to block1412. When there are no additional combinations (block1420), the example instructions1400end.

FIG. 15is a flow chart representative of example machine readable instructions1500which may be executed to implement the audience data generator120ofFIGS. 1-3to calculate ratings information at the cross-publisher level. The brand/cross-platform adjuster332ofFIG. 3may perform the instructions1500ofFIG. 15to estimate impression counts and durations using the adjusted brand-level impression estimates estImpsb,i,y,dand the adjusted brand-level duration estimates estDurationb,i,y,dgenerated by the brand/cross-platform adjuster332.

The brand/cross-platform adjuster332accesses and/or receives the adjusted brand-level impression estimates estImpsb,i,y,dand the adjusted brand-level durations estDurationb,i,y,dfrom the brand/cross-platform adjuster332. In examples disclosed herein, the brand/cross-platform adjuster332also accesses and/or receives the cross-publisher level frequency (e.g., rDPCPFreqb,i,y,d) (Equation 12) from the CP-FC351of the distributed demographic data calculator320ofFIG. 3. The brand/cross-platform adjuster332further accesses and/or receives the cross-publisher level deduplication factor Dedup_F_CPTD,b,i,d(Equation 13) from the CP-DFG352.

The brand/cross-platform adjuster332selects a combination of brand b, segment z, platform y, and demographic d (block1502). The impressions adjuster381subsequently calculates cross-publisher adjusted brand-level impression estimates (Block1504). In examples disclosed herein, the impressions adjuster381calculates cross-publisher adjusted brand-level impression estimates estCPImpb,i,y,dfor the selected combination of brand b, segment z, platform y, and demographic d by summing the adjusted brand-level impression estimates estImpsb,i,y,d(Equation 29) across all brands b (e.g., across all network affiliates providing a segment z of the media206ofFIG. 2). Equation 47 below may be used to implement block1504. In example Equation 47 below, n represents all brands b included within a cross-publisher report.
estCPImpb,i,y,d=Σb=1nestImpsb,i,y,d(Equation 47)

The duration adjuster380calculates cross-publisher adjusted brand-level duration estimates (Block1506). In examples disclosed herein, the duration adjuster380calculates cross-publisher adjusted brand-level duration estimates estCPDurationb,i,y,dfor the selected combination of brand b, segment z, platform y, and demographic d by summing the adjusted brand-level duration estimates estDurationb,i,y,d(Equation 30) across all brands b (e.g., across all network affiliates providing a segment z of the media206ofFIG. 2). Equation 48 below may be used to implement block1506. In example Equation 48 below, n represents all brands b included within a cross-publisher report.
estCPDurationb,i,y,d=Σb=1nestDurationb,i,y,d(Equation 48)

The audience adjuster381calculates cross-publisher adjusted brand-level audience estimates (block1508). In examples disclosed herein, the audience adjuster381calculates cross-publisher adjusted brand-level audience estimates estCPUAb,i,y,dfor the selected combination of brand b, segment z, platform y, and demographic d by dividing a respective, cross-publisher adjusted brand-level impression estimate estCPImpb,i,y,d(Equation 47) by a respective, cross-publisher level frequency rDPCPFreqb,i,y,d(Equation 12). Equation 49 may be used to implement block1508.
estCPUAb,i,y,d=estCPImpb,i,y,d/rDPCPFreqb,i,y,d(Equation 49)

The brand/cross-platform adjuster332determines whether there are additional combinations of brand b, segment z, platform y, and demographic group d (block1510). When the brand/cross-platform adjuster332determines there are additional combinations (block1510), the example instructions1500return to block1502. When the brand/cross-platform adjuster332determines there are no additional combinations (block1510), the brand/cross-platform adjuster332selects a combination of a brand b, segment z, and demographic d (block1512).

The impressions TD calculator382determines cross-publisher total-digital brand-level impression estimates across all platforms y (block1514). In examples disclosed herein, the impressions TD calculator382calculates the cross-publisher total digital brand-level impression estimates estCPImpTD,b,i,dby adding cross-publisher adjusted brand-level impression estimates across the non-mobile (e.g., desktop) computing platform estCPImpDesktop,b,i,dto cross-publisher adjusted brand-level impression estimates across the mobile computing platform estCPImpMobile,b,i,dEquation 50 below may be used to implement block1514.
estCPImpTD,b,i,d=estCPImpDesktop,b,i,d+estCPImpMobile,b,i,d(Equation 50)

The duration TD calculator383determines cross-publisher total-digital brand-level duration estimates across all platforms y (block1516). In examples disclosed herein, the duration TD calculator383calculates the cross-publisher total digital brand-level duration estimates estCPDurationTD,b,i,dby adding cross-publisher adjusted brand-level duration estimates across the non-mobile (e.g., desktop) computing platform estCPDurationDesktop,b,i,dto cross-publisher adjusted brand-level duration estimates across the mobile computing platform estCPDurationMobile,b,i,d. Equation 51 below may be used to implement block1516.
estCPDurationTD,b,i,d=estCPDurationDesktop,b,i,d+estCPDurationMobile,b,i,d(Equation 51)

The audience TD calculator384calculates cross-publisher total digital brand-level audience estimates across all platforms y using deduplication factors (block1518). In examples disclosed herein, the audience TD calculator384calculates cross-publisher total digital brand-level audience estimates estCPUATD,b,i,dby adding, for the selected combination of brand b, segment z, and demographic group d, cross-publisher adjusted brand-level audience estimates across the non-mobile (e.g., desktop) computing platform estCPUADesktop,b,i,dand cross-publisher adjusted brand-level audience estimates across the mobile computing platform estCPUAMobile,b,i,dand multiplying the sum by the cross-publisher level deduplication factor Dedup_F_CPTD,b,i,d(Equation 13). Equation 52 below may be used to implement block1518.
estCPUATD,b,i,d=(estCPUADesktop,b,i,d+estCPUAMobile,b,i,d)*Dedup_F_CPTD,b,i,d(Equation 52)

The brand/cross-platform adjuster332determines whether there are additional combinations of brand b, segment z, and demographic d (block1520). When there are additional combinations (block1520), the example instructions1500return to block1512. When there are no additional combinations (block1520), the example instructions1500end.

FIG. 16is a flow chart representative of example machine readable instructions1600which may be executed to implement the audience data generator120ofFIGS. 1-3to generate ratings information by media content type c at the text and video (TN) segment level. The content calculator336ofFIG. 3may perform the instructions1600ofFIG. 16to estimate a corrected audience count using the redistributed impression information organized by content type c.

The content calculator336accesses and/or receives the redistributed impressions rDPImpsc(t,v,w),b,i,y,d(Equation 4), the redistributed durations rDPDurationc(t,v,w),b,i,y,d(Equation 5), and the redistributed audience rDPUAc(t,v,w),b,i,y,d(Equation 6) from the demographic distributor318. The content calculator336also accesses and/or receives the adjusted brand-level impression estimates estImpb,i,y,d(Equation 29) from the brand/cross-platform adjuster332.

The content calculator336selects a combination of brand b, segment z, platform y, and demographic d (block1602). The TN DFG391subsequently calculates a text and video deduplication factor (block1604). In examples disclosed herein, the T/V DFG391identifies, for the selected combination of brand b, segment z, platform y, and demographic d, the redistributed audience presented both the text content and the video content w, rDPUAv,b,i,y,d, the redistributed audience presented the video content v, rDPUAv,b,i,y,d, and the redistributed audience presented the text content t, rDPUAt,b,i,y,d. In examples disclosed herein, the T/V DFG391calculates a text and video (T/V) deduplication factor (e.g., T&V_Dedup_Fb,i,y,d) by dividing the redistributed audience presented both the text content and the video content w, rDPUAw,b,i,y,d, by a sum of the redistributed audience presented the video content v, rDPUAv,b,i,y,d, and the redistributed audience presented the text content t, rDPUAt,b,i,y,d. Equation 53 below may be used to implement block1604.
T&V_Dedup_Fb,i,y,d=rDPUAw,b,i,y,d/(rDPUAv,b,i,y,d+rDPUAt,b,i,y,d)   (Equation 53)

The T/V audience calculator392subsequently calculates a total content audience (block1606). In examples disclosed herein, the T/V audience calculator392determines the total content audience (e.g., estUATCb,i,y,d), referred to herein as a corrected audience count, using the adjusted brand-level impression estimates estImpb,i,y,d(Equation 29). The T/V audience calculator392receives the text and video (T/V) deduplication factor T&V_Dedup_Fb,i,y,dfrom the T/V DFG391. The T/V audience calculator392calculates the total content audience estUATCb,i,y,d, for the selected brand b, segment z, platform y, and demographic d, by summing a video content audience estUAVideob,i,y,dand a text content audience estUATextb,i,y,dand applying (e.g., multiplying) the sum by the text and video (T/V) deduplication factor T&V_Dedup_Fb,i,y,d. In examples disclosed herein, the video content audience estUAVideob,i,y,dis representative of a quantity of audience members of a demographic group d (e.g., M13-17, M40-44, F25-29, etc.) that accessed and were presented a segment z of video content v via a particular computing platform y (e.g., non-mobile or mobile) from a brand b (e.g., a network affiliate providing the media206ofFIG. 2). The text content audience estUATextb,i,y,dis representative of a quantity of audience members of the same demographic group d that accessed and were concurrently presented text content t when the segment z of the video content v was presented via the same computing platform y from the same brand b. Equation 54 may be used to implement block1606.
estUATCb,i,y,d=(estUAVideob,i,y,d+estUATextb,i,y,d)*T&V_Dedup_Fb,i,y,d(Equation 54)

In examples disclosed herein, the T/V audience calculator392may determine the video content audience estUAVideob,i,y,da and the text content audience estUATextb,i,y,dusing the redistributed impressions indicative of access to text content t and video content v, rDPImpsT/V,b,i,y,d, the redistributed audiences indicative of access to text content t and video content v, rDPUAT/V,b,i,y,d, and the adjusted brand-level impression estimates estImpb,i,y,d. For example, the T/V audience calculator392may first calculate a video impression frequency rDPFreqVideob,i,y,dby dividing, for the selected combination of brand b, segment z, platform y, and demographic d of block1602, the redistributed impressions indicative of access to video content v, rDPImpsv,b,i,y,d, by the redistributed audience indicative of access to the video content v, rDPUAv,b,i,y,d. Equation 55 below may be used to calculate the video impression frequency rDPFreqVideob,i,y,d.
rDPFreqVideob,i,y,dd=rDPImpsv,b,i,y,d/rDPUAv,b,i,y,d(Equation 55)

Similarly, the T/V audience calculator392may also calculate a text impression frequency rDPFreqTextb,i,y,dby dividing, for the selected combination of brand b, segment z, platform y, and demographic d of block1602, the redistributed impressions indicative of access to text content t, rDPImpst,b,i,y,d, by the redistributed audience indicative of access to the text content t, rDPUAt,b,i,y,d. Equation 56 below may be used to calculate the text impression frequency rDPFreqTextb,i,y,d.
rDPFreqTextb,i,y,d<rDPImpst,b,i,y,d/rDPUAt,b,i,y,d(Equation 56)

To determine the video content audience estUAVideob,i,y,dthe T/V audience calculator392divides, for the selected combination of brand b, segment z, platform y, and demographic d of block1602, the adjusted brand-level impression estimates estImpb,i,y,dby the video impression frequency rDPFreqVideob,i,y,d. Equation 57 below may be used to calculate the video content audience used in equation 54 and implemented in block1606.
estUAVideob,i,y,d=estImpb,i,y,d/rDPFreqVideob,i,y,d(Equation 57)

Similarly, to determine the text content audience estUATextb,i,y,d, the T/V audience calculator392divides, for the selected combination of brand b, segment z, platform y, and demographic d of block1602, the adjusted brand-level impression estimates estImpb,i,y,dby the text impression frequency rDPFreqTextb,i,y,d. Equation 58 below may be used to calculate the text content audience used in equation 54 and implemented in block1606.
estUATextb,i,y,d=estImpb,i,y,d/rDPFreqTextb,i,y,d(Equation 58)

The content calculator336determines whether there are additional combinations of brand b, segment z, platform y, and demographic d (block1608). When the content calculator336determines there are additional combinations (block1608), the example instructions1600return to block1602. In this manner, the content calculator336determines a T/V deduplication factor and corrected audience counts for each combination of brand b, segment z, platform y, and demographic group d. When the content calculator336determines there are no additional combinations (block1608), the content calculator336selects a combination of brand b, segment z, and demographic d (block1610).

The TD DFG393then calculates a total digital, text and video deduplication factor (block1612). In examples disclosed herein, the TD DFG393calculates the total digital, text and video deduplication factor T&V_Dedup_FTD,b,i,dfor text and video (T/V) segment level demographic data provided to the audience data generator120aby the DP116. The TD DFG393calculates the total digital, text and video deduplication factor T&V_Dedup_FTD,b,i,dby dividing, for the selected combination of brand b, segment z, and demographic d, a redistributed audience across all content types c (e.g., text t, video v, and both text and video w) that is attributed to the total digital platform rcDPUATD,b,i,dby a sum of a redistributed audience across all content types c (e.g., text t, video v, and both text and video w) that is attributed to the non-mobile (e.g., desktop) computing platform rcDPUADesktop,b,i,dand a redistributed audience across all content types (e.g., text t, video v, and both text and video w) that is attributed to the mobile computing platform rcDPUAMobile,b,i,d. Equation 59 below may be used to implement block1612.
T&V_Dedup_FTD,b,i,d=rcDPUATD,b,i,d/(rcDPUADesktop,b,i,d+rcDPUAMobile,b,i,d)   (Equation 59)

The TD audience calculator394calculates a total digital audience (block1614). In examples disclosed herein, the TD audience calculator394receives the total digital, text and video deduplication factor T&V_Dedup_FTD,b,i,dfrom the TD DFG393. The TD audience calculator394also accesses and/or receives the adjusted brand-level impression estimates estImpb,i,y,d(Equation 29). The TD audience calculator394calculates the total digital, total content audience estUATCTD,b,i,d, for the selected brand b, segment z, and demographic d, by summing a total digital video content audience estUAVideoTD,b,i,dand a total digital text content audience estUATextTD,b,i,dand applying (e.g., multiplying) the sum by the total digital, text and video (T/V) deduplication factor T&V_Dedup_FTD,b,i,d. In examples disclosed herein, the total digital video content audience estUAVideoTD,b,i,dis representative of a quantity of audience members of a demographic group d (e.g., M13-17, M40-44, F25-29, etc.) that accessed and were presented a segment z of video content v via both the non-mobile computing platform and the mobile computing platform from a brand b (e.g., a network affiliate providing the media206ofFIG. 2). The total digital text content audience estUATextTD,b,i,dis representative of a quantity of audience members of the same demographic group d that accessed and were concurrently presented text content t when the segment z of the video content v was presented via the mobile computing platform and the non-mobile computing platform from the same brand b. Equation 60 may be used to implement block1616.
estUATCTD,b,i,d=(estUAVideoTD,b,i,d+estUATextTD,b,i,d)*T&V_Dedup_FTD,b,i,d(Equation 60)

In examples disclosed herein, the TD audience calculator394may determine the total digital video content audience estUAVideoTD,b,i,dand the total digital text content audience estUATextTD,b,i,dusing the redistributed impressions indicative of access to text content t and video content v across all platforms y, rDPImpsT/V,b,i,d, the redistributed audiences indicative of access to text content t and video content v across all platforms y, rDPUAT/V,b,i,d, and the adjusted brand-level impression estimates across all platforms y estImpb,i,d. For example, the TD audience calculator394may first calculate a total digital video impression frequency rDPFreqVideoTD,b,i,dby dividing, for the selected combination of brand b, segment z, and demographic d of block1610, the redistributed impressions indicative of access to video content v across all platforms y, rDPImpsv,b,i,d, by the redistributed audience indicative of access to the video content v across all platforms y, rDPUAv,b,i,d. Equation 61 below may be used to calculate the total digital video impression frequency rDPFreqVideoTD,b,i,d.
rDPFreqVideo=rDPImpsv,b,i,d/rDPUAv,b,i,d(Equation 61)

Similarly, the TD audience calculator394may also calculate a total digital text impression frequency rDPFreqTextTD,b,i,dby dividing, for the selected combination of brand b, segment z, and demographic d of block1610, the redistributed impressions indicative of access to text content t across all platforms y, rDPImpst,b,i,d, by the redistributed audience indicative of access to the text content t across all platforms y, rDPUAt,b,i,d. Equation 62 below may be used to calculate the total digital text impression frequency rDPFreqTextTD,b,i,d.
rDPFreqTextTD,b,i,d=rDPImpst,b,i,d/rDPUAt,b,i,d(Equation 62)

To determine the total digital video content audience estUAVideoTD,b,i,d, the TD audience calculator394divides, for the selected combination of brand b, segment z, and demographic d of block1610, the adjusted brand-level impression estimates across all platforms estImpb,i,dby the total digital video impression frequency rDPFreqVideoTD,b,i,dEquation 63 below may be used to calculate the total digital video content audience used in equation 63 and implemented in block1614.
estUAVideoTD,b,i,d=estImpb,i,d/rDPFreqVideoTD,b,i,d(Equation 63)

Similarly, to determine the total digital text content audience estUATextTD,b,i,d, the TD audience calculator394divides, for the selected combination of brand b, segment z, and demographic d of block1610, the adjusted brand-level impression estimates across all platforms y estImpb,i,dby the total digital text impression frequency rDPFreqTextTD,b,i,d. Equation 64 below may be used to calculate the total digital text content audience used in equation 60 and implemented in block1614.
estUATextTD,b,i,d=estImpb,i,d/rDPFreqTextTD,b,i,d(Equation 64)

The content calculator336determines whether there are additional combinations of brand b, segment z, and demographic d (block1616). When the content calculator336determines there are additional combinations (block1616), the example instructions1600return to block1610. When the content calculator336determines there are no additional combinations (block1616), the example instructions1600ofFIG. 16end.

In some examples, the corrected audience counts calculated by the T/V audience calculator392in the illustrated example ofFIG. 16may be used to generating ratings information for the video. For example, the ratings data generator338may generate ratings information for the video by subtracting the corrected audience counts estUATCb,i,y,dfrom the text and video (T/V) segment level demographic data provided to the audience data generator120aby the DP116(e.g., the audience counts provided by the DP116before redistribution by the demographic distributor318). In this manner, the audience data generator120may determine a DP duplicated audience count (e.g., an overcompensating number of unique audience members identified by the DP116that were credited as being presented text content t and video content v).

FIG. 17is a block diagram of an example processor platform1700capable of executing the instructions ofFIGS. 4-7 and 9-16to implement the audience data generator120ofFIGS. 1, 2, and/or3. The processor platform1700can be, for example, a server, a personal computer, or any other type of computing device.

The processor platform1700of the illustrated example includes a processor1712. The processor1712of the illustrated example is hardware. For example, the processor1712can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer.

The example processor1712ofFIG. 17may implement the example calibration data collector300, the example platform data manager302, the example device data manager304, the example segment data manager306, the example episode data manager308, the example asset data manager310, the example sub-brand data manager312, the example brand data manager314, the example database proprietor (DP) data manager316, the example demographic distributor318, the example distributed demographic data calculator320, the example matrix generator322, the example episode-segment (E-S) adjuster324, the example census imputer326, the example metrics generator328, the example segment modifier330, the example brand/cross-platform adjuster332, the example content calculator336, the example ratings data generator338, the example duration-per-impression (DPI) calculator340, the example segment frequency calculator (FC)341, the example segment deduplication factor generator (DFG)342, the example episode frequency calculator (FC)343, the example episode deduplication factor generator (DFG)344, the example asset frequency calculator (FC)345, the example asset deduplication factor generator (DFG)346, the example sub-brand frequency calculator (FC)347, the example sub-brand deduplication factor generator (DFG)348, the example brand frequency calculator (FC)349, the example brand deduplication factor generator (DFG)350, the example cross publisher frequency calculator (CP-FC)351, the example cross publisher deduplication factor generator (CP-DFG)352, the example matrix converter353, the example impressions sharing adjuster354, the example duration sharing adjuster355, the example coverage vector calculator356, the example impressions coverage adjuster357, the example duration coverage adjuster358, the example impression scaling factor generator359, the example duration scaling factor generator360, the example impressions scaler361, the example duration scaler362, the example impressions adder363, the example duration adder364, the example impressions imputation adder365, the example duration imputation adder366, the example impressions distribution deriver367, the example duration distribution deriver368, the example impressions imputation director369, the example duration imputation director370, the example impressions imputation calculator371, the example duration imputation calculator372, the example impression sub-segment adder373, the example duration sub-segment adder374, the example impression ratio calculator375, the example duration ratio calculator376, the example impression metrics adjuster377, the example duration metrics adjuster378, the example impressions adjuster379, the example duration adjuster380, the example audience adjuster381, the example impressions total digital (TD) calculator382, the example duration total digital (TD) calculator383, the example audience total digital (TD) calculator384, the example text and video (TN) deduplication factor generator (DFG)391, the example text and video (TN) audience calculator392, the example total digital (TD) deduplication factor generator393, the example total digital (TD) audience calculator394and/or, more generally, the example audience data generator120ofFIGS. 1, 2, and/or3.

The processor1712of the illustrated example includes a local memory1713(e.g., a cache). The processor1712of the illustrated example is in communication with a main memory including a volatile memory1714and a non-volatile memory1716via a bus1718. The volatile memory1714may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAIVIBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory1716may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory1714,1716is controlled by a memory controller.

The processor platform1700of the illustrated example also includes an interface circuit1720. The interface circuit1720may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices1722are connected to the interface circuit1720. The input device(s)1722permit(s) a user to enter data and commands into the processor1712. The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system.

The interface circuit1720of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem and/or network interface card to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network1726(e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform1700of the illustrated example also includes one or more mass storage devices1728for storing software and/or data. Examples of such mass storage devices1728include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives.

The coded instructions1732ofFIGS. 4-7 and 9-16may be stored in the mass storage device1728, in the volatile memory1714, in the non-volatile memory1716, and/or on a removable tangible computer readable storage medium such as a CD or DVD.

From the foregoing, it will appreciate that the above disclosed methods, apparatus and articles of manufacture provide a solution to the problem of inaccuracies due to techniques used in online audience measurement. Benefits to online audience measurement, which is an inherently network-based technology, obtained from disclosed example methods, apparatus, and articles of manufacture include a reduction in required network communications that would be necessary to attribute to demographic groups those impressions that are not identifiable by one or more database proprietors. For example, disclosed examples avoid transmitting queries to secondary database proprietors and/or reduce processing and network resources used by the mobile devices as part of the online audience measurement techniques. At the same time, the improved accuracy of ratings information that can be generated using disclosed examples permits a more efficient and more beneficial distribution of advertising resources to viewers of online media by providing rapid, and, more importantly, accurate online audience measurement that enables advertisers to change distributions of advertising resources in response to audience measurement information.

Disclosed examples also improve the accuracy of audience measurement for Internet-delivered media such as streaming videos by correcting for measurement errors arising from problems inherent to computer networks. For example, beacon requests and/or other messages described herein can be dropped or otherwise not delivered to the intended destination (e.g., audience measurement entity, a database proprietor, etc.), which in at least some instances can lead to non-negligible measurement bias.