Patent Grant
11854041
This disclosure relates generally to audience measurement, and, more particularly, to methods and apparatus to determine impressions corresponding to market segments.
Techniques for monitoring user access to Internet resources such as web pages, advertisements and/or other media have evolved significantly over the years. Some prior systems perform such monitoring primarily through server logs. In particular, entities serving media on the Internet can use such prior systems to log the number of requests received for their media at their server. However, such systems lack a means to determine any characteristics about the persons responsible for the logged requests.
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.
Techniques for monitoring user access to Internet resources such as web pages, advertisements and/or other media have evolved significantly over the years. At one point in the past, such monitoring was done primarily through server logs. In particular, entities serving media on the Internet would log the number of requests received for their media at their server. Basing Internet usage research on server logs is problematic for several reasons. For example, server logs can be tampered with either directly or via zombie programs which repeatedly request media from servers to increase the server log counts corresponding to the requested media. Secondly, media is sometimes retrieved once, cached locally and then repeatedly viewed from the local cache without involving the server in the repeat viewings. Server logs cannot track these views of cached media because reproducing locally cached media does not require re-requesting the media from a server. Thus, server logs are susceptible to both over-counting and under-counting errors.
The inventions disclosed in Blumenau, U.S. Pat. No. 6,108,637, fundamentally changed the way Internet monitoring is performed and overcame the limitations of the server side log monitoring techniques described above. For example, Blumenau disclosed a technique wherein Internet media to be tracked is tagged with beacon instructions. In particular, monitoring instructions are associated with the Hypertext Markup Language (HTML) of the media to be tracked. When a client requests the media, both the media and the beacon instructions are downloaded to the client. The beacon instructions are, thus, executed whenever the media is accessed, be it from a server or from a cache.
The beacon instructions cause monitoring data reflecting information about the access to the media to be sent from the client that downloaded the media to a monitoring entity. Typically, the monitoring entity is an audience measurement entity (AME) (e.g., any entity interested in measuring or tracking audience exposures to advertisements, media, and/or any other media) that did not provide the media to the client and who is a trusted third party for providing accurate usage statistics (e.g., The Nielsen Company, LLC). Advantageously, because the beaconing instructions are associated with the media and executed by the client browser whenever the media is accessed, the monitoring information is provided to the AME irrespective of whether the client is a panelist of the AME.
It is useful, however, to link demographics and/or other user information to the monitoring information. To address this issue, the AME establishes 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 panel, they provide detailed information concerning their identity and demographics (e.g., gender, race, income, home location, occupation, etc.) to the AME. The AME sets a cookie (or other persistent identifier) on the panelist computer that enables the AME to identify the panelist whenever the panelist accesses tagged media and, thus, sends monitoring information to the AME.
Most of the clients providing monitoring information from the tagged pages are not panelists and, thus, are unknown to the AME. Thus, it is necessary to use statistical methods to impute demographic information based on the data collected for panelists to the larger population of users providing data for the tagged media. However, panel sizes of AMEs remain small compared to the general population of users. Thus, a problem is presented as to how to increase panel sizes while ensuring the demographics data of the panel are accurate.
There are many database proprietors operating on the Internet. These database proprietors provide services (e.g., social networking services, email services, media access services, etc.) to large numbers of subscribers. In exchange for the provision of such services, the subscribers register with the database proprietors. As part of this registration, the subscribers provide detailed demographic information. Examples of such database proprietors include social network providers such as Facebook, Myspace, Twitter, etc. These database proprietors set cookies on the computers of their subscribers to enable the database proprietors to recognize registered users when such registered users visit their websites.
Examples disclosed herein can be used to determine media impressions, advertisement impressions, media exposure, and/or advertisement exposure based on user information, which is distributed across different databases (e.g., different website owners, service providers, etc.) on the Internet. Not only do example methods, apparatus, and articles of manufacture disclosed herein enable more accurate correlation of Internet media exposure to user information, but they also effectively extend panel sizes and compositions beyond persons participating in the panel of an audience measurement entity and/or a ratings entity to persons registered in other Internet databases such as the databases of wireless service carriers, mobile software/service providers, social medium sites (e.g., Facebook, Twitter, Google, etc.), and/or any other Internet sites such as Yahoo!, MSN, Apple iTunes, Experian, etc. This extension effectively leverages the media impression tracking capabilities of the AME and the use of databases of non-AME entities such as social media and other websites to create an enormous, demographically accurate panel that results in accurate, reliable measurements of exposures to Internet media such as advertising and/or programming. Examples of such media include web sites, images presented on web sites, and/or streaming media accessible via a computing device (e.g., Amazon Video, Netflix, Hulu, etc.).
Traditionally, AMEs (also referred to herein as “ratings entities”) determine demographic reach for advertising and media programming based on registered panel members. That is, an AME enrolls people who consent to being monitored into a panel. During enrollment, the AME receives demographic information from the enrolling people so that subsequent correlations may be made between advertisement/media exposure to those panelists and different demographic markets. Unlike traditional techniques in which AMEs rely solely on their own panel member data to collect demographics-based audience measurements, example methods, apparatus, and/or articles of manufacture disclosed herein enable an AME to obtain demographic information shared by other entities (e.g., database proprietors) that operate based on user registration models to collect such demographic information. In some examples, the AME also shares its panel member demographic information with such other entities (e.g., database proprietors). Such entities may be referred to as “database proprietors” and include entities such as wireless service carriers, mobile software/service providers, social medium sites (e.g., Facebook, Twitter, Google, etc.), and/or any other Internet sites such as Yahoo!, MSN, Apple iTunes, Experian, etc. that collect demographic data of users which may be in exchange for a service.
As used herein, a user registration model is a model in which users subscribe to services of database proprietors by creating an account and providing demographic-related information about themselves. Receiving demographic information associated with registered users of database proprietors at an AME enables the AME to extend or supplement their panel data with substantially reliable demographics information from external sources (e.g., database proprietors), thus extending the coverage, accuracy, and/or completeness of their demographics-based audience measurements. Such access also enables the AME to measure persons who would not otherwise have joined an AME panel. Any entity having a database identifying demographics of a set of individuals may cooperate with the AME.
Examples disclosed herein may be implemented by an AME (e.g., any entity interested in measuring or tracking audience exposures to advertisements, content, and/or any other media) in cooperation with any number of database proprietors, such as online web services providers, to develop online media exposure metrics. Such database proprietors/online web services providers may be wireless service carriers, mobile software/service providers, social network sites (e.g., Facebook, Twitter, MySpace, etc.), multi-service sites (e.g., Yahoo!, Google, Axiom, Catalina, etc.), online retailer sites (e.g., Amazon.com, Buy.com, etc.), credit reporting sites (e.g., Experian) and/or any other web service(s) site that maintains user registration records.
The use of demographic information from disparate data sources, such as database proprietors, (e.g., high-quality demographic information from the panels of an audience measurement entity and/or registered user data of web service providers) results in improved reporting effectiveness of metrics for both online and offline advertising campaigns. Example techniques disclosed herein use online registration data to identify demographics of users, and/or other user information, and use server impression counts, and/or other techniques to track quantities of impressions attributable to those users. Online web service providers such as wireless service carriers, mobile software/service providers, social network sites (e.g., Facebook, Twitter, MySpace, etc.), multi-service sites (e.g., Yahoo!, Google, Axiom, Catalina, etc.), online retailer sites (e.g., Amazon.com, Buy.com, etc.), credit reporting services (e.g., Experian), etc. (collectively and individually referred to herein as online database proprietors) maintain detailed demographic information (e.g., age, gender, geographic location, race, income level, education level, religion, etc.) collected via user registration processes.
An impression corresponds to a home or individual having been exposed to the corresponding media and/or advertisement. Thus, an impression represents a home or an individual having been exposed to an advertisement or media or group of advertisements or media. In Internet advertising, a quantity of impressions, also referred to as impression counts, is the total number of times an advertisement or advertisement campaign has been accessed by a web population (e.g., including the number of times accessed as decreased by, for example, pop-up blockers and/or increased by, for example, retrieval from local cache memory). An amount of time for which there was exposure may also be measured. As used herein, the term “duration unit” is defined to be an impression that corresponds to a period of time, such as 10 seconds, 30 seconds, a minute, or any other unit. For example, if two minutes of a video are viewed and a duration unit corresponds to one minute, two duration units are credited to the video due to the viewing.
Impression data is collected from mobile devices that extend beyond mobile device panels (e.g., impression data is collected from every user of a particular application on the mobile device, whether or not the user of the mobile device is a member of a panel of mobile device users). While the database proprietors that provide demographic information corresponding to the impressions enable the impressions to be attributed to certain defined demographic groups (e.g., age/gender groups), the database proprietors may not be able to provide information about other demographic factor and/or market segments of interest (e.g., household income, ethnicity, etc.). In such circumstances, impression information cannot be determined or reported for these demographic factors and/or market segments using the database proprietor information.
Furthermore, mobile device audience measurement panels may not have demographic or market segment characteristics that are comparable to the actual audience of an item of media that corresponds to the impressions. As a result, market segment information derived from such mobile device audience measurement panels may be biased, even significantly biased, thereby introducing bias error into the resulting market segment share estimate. Panels of mobile device users may be incomplete because, among other reasons, panelist metering software (used to monitor panelist activity on the mobile devices) may not be installed and/or may not be installable on some types of mobile devices. The absence of metering software may occur because, for example, such metering software is not granted sufficient permissions to provide useful data and/or such metering software requires excess computing resources (e.g., computing resources that negatively affect the exposure of the user using the computing device (e.g., prospective panelists) to a degree in excess of a level that is considered tolerable). For this reason, current mobile device panels may likewise suffer from significant bias error when applied to media impressions for the purposes of analyzing market segment share.
Examples disclosed herein use a hierarchical approach to estimate a panel audience for the panel-based market segment share. As explained in more detail below, the panel-based market segment share estimate may be dependent on a panel sample size being at least a threshold size (e.g., the panel including at least a threshold number of people). A panel is a set of audience members recruited by an audience measurement entity for audience measurement purposes. Panelists provide detailed demographic information to the audience measurement entity and agree to permit the audience measurement entity to monitor their media exposure habits. The panel is created and maintained by the audience measurement entity for measuring audience behavior as a representative sample of an audience of interest (e.g., a portion of the panel audience meeting certain demographic criteria). In some examples, the audience measurement panel (e.g., a television audience panel, a cross-platform audience panel, a radio audience panel, etc.) used to determine the panel-based market segment share mentioned above is selected based on a number of persons of an audience panel in a demographic group and availability of data representative of the media corresponding to the media impressions.
In some disclosed examples, media impression information is collected by an audience measurement entity in cooperation with one or more database proprietors. The example audience measurement entity (or another party) attributes (or allocates) collected impressions to the appropriate demographic groups. This attribution process includes correcting for data collection errors and/or biases. Example methods and apparatus to collect impression data and to attribute demographic information to the impression data are disclosed in U.S. patent application Ser. No. 14/560,947, filed Dec. 4, 2014. The entirety of U.S. patent application Ser. No. 14/560,947 is incorporated herein by reference.
In addition to the demographic makeup of a set of impressions that can be provided by the database proprietor, advertisers may also be interested in one or more specific market segments not available from (and/or not fully represented by the data of) the database proprietor. Such market segments may represent specific portions of the audience that have one or more common characteristics (e.g., race, gender, native language, income, political affiliation, etc.) and who, as a group, are more likely (or less likely) to respond to a stimulus with a particular behavior than the audience as a whole.
As used herein, the term “market segment” refers to a group of people who have or are estimated to have one or more common characteristics. As used herein, the terms “demographics,” “demographic data,” “demographic characteristics,” and/or “demographic information” refer to characteristics that describe a particular person or group of people. Example demographic information includes age, gender, race, income, native language, home location, and/or occupation. In some examples, a market segment of interest is specified using, among other things, one or more demographic characteristics. As used herein, a share of media impressions for a market segment of interest (also referred to as a market segment share) refers to a subset or portion of a larger collection of impressions corresponding to a particular market segment of interest and logged in connection with numerous other market segments.
As an example, in the television ratings context, market segments may be determined by consulting a television audience measurement panel, such as the Nielsen National TV Ratings Panel, that is statistically selected to be representative of the total audience including many market segments. To determine the market share for a market segment in the television ratings context, an audience measurement entity, such as The Nielsen Company, may determine the portion of the total television audience who are in the market segment of interest. The audience measurement entity then determines the market share for the market segment of interest (e.g., a percentage of the audience that is represented by the market segment) by dividing the determined portion by the total estimated audience size.
Examples disclosed herein determine a share (or portion) of media impressions occurring on mobile devices that are attributable to a market segment of interest. In examples disclosed herein, a share refers to a portion of impressions that is a subset of a larger group of impressions. In some examples, the market segment of interest is not identifiable by a database proprietor from which demographic information corresponding to the media impressions is obtained because, for example, the database proprietor does not or cannot collect market segment information for the market segment of interest. The market segment of interest may be defined by a demographic characteristic that is unknown to the database proprietor. In some disclosed examples, an audience panel for a different media platform (e.g., a television audience panel, a radio audience panel, a PC audience panel, etc.) is used to determine the share of media impressions for the market segment of interest, where the audience panel has information about the market segment of interest (e.g., the audience panel has access to the characteristic defining the market segment, which characteristic is not known by the database proprietor). In some examples, the panel is selected to have an audience whose media access behaviors are as similar as possible to the media access behaviors of the mobile device audience corresponding to the media impressions.
Examples disclosed herein use a hierarchical process to calculate a market segment share at a most detailed (e.g., granular) level possible for which accurate data is available. This determination of the level of detail possible is made by identifying one or more relevant data sets that achieve a specified precision for the estimated market segment share. For example, the AME may divide panelists into demographic groups at multiple levels (e.g., hierarchical levels) that range from more granular to less granular by demographic composition. Example hierarchical levels are shown in Table 1 below, in order from least granular level (e.g., Male) to most granular level (e.g., Male, Age 24-35, Income >100K).
As used herein, the term “precision” refers to the amount of sampling error associated with a given metric. Typically, precision increases as sample size increases, and decreases as sample size decreases. As used herein, the term “sample size” refers to the number of panelists exhibiting the behavior of interest (for example, in the audience). However, references to sample size may be replaced with direct measures of precision such as standard error or variance, in which case requirements of sample size thresholds (e.g., minimum sample size thresholds) would be replaced with standard error or variance thresholds (e.g., a maximum standard error threshold, a maximum variance threshold).
If the sample size associated with the market segment share is smaller than a threshold for a particular demographic group (e.g., females, 21-24 years old), disclosed examples combine multiple demographic groups (e.g., the group females, 21-24, the group females, 18-21, and the group females, 24-30) to calculate the market segment share for a larger, combined demographic group (e.g., females, 18-30), which is then used to calculate the impressions attributable to the market segment for one or more of the more detailed demographic groups in the combined groups. Disclosed examples may combine demographic groups multiple times (e.g., combine females 21-24 and females 18-20 to females 18-24, combine females 18-24 and females 25-30 to females 18-30, etc.). Combination may be performed at any level(s) in a hierarchy of demographic groups and/or may be performed on portions of any level(s) in the hierarchy of demographic groups, as described in more detail below. Combining multiple demographic groups increases the sample size and improves the precision for the resulting estimated market segment share. However, each collapse of two or more demographic groups sacrifices granularity and/or potentially increases bias error of the demographic information attributable to the media impressions. By selecting the appropriate granularity and/or sample size, disclosed examples enable determining market segment share for media impressions that occur on mobile devices with an precision that was unattainable using previous methods.
Examples disclosed herein may be used with the Online Campaign Ratings (OCR) systems developed by The Nielsen Company (US), LLC.
Disclosed example methods involve receiving, at a first Internet domain, a first request from a computing device. In the disclosed example methods, the first request is indicative of access to media at the computing device. The disclosed example methods further involve requesting demographic information from a database proprietor, where the demographic information corresponds to the first request. The disclosed example methods further involve determining a number of media impressions that occurred on mobile devices and that are attributable to a first demographic group, where the number of media impressions is based on attributions of the media impressions to the first demographic group by the database proprietor in the demographic information. The disclosed example methods further involve determining whether a first size of a first audience that corresponds to the first demographic group satisfies a threshold, the first audience including panelists in an audience measurement panel maintained by an audience measurement entity. The disclosed example methods further involve determining whether a second size of a second audience satisfies the threshold when the first size of the first audience does not satisfy the threshold. In the disclosed example methods, the second audience includes panelists in the audience measurement panel corresponding to a second demographic group, the first audience is a subset of the second audience, and the first demographic group is a subset of the second demographic group. The disclosed example methods further involve calculating a portion of the media impressions attributable to a market segment and to the second demographic group, based on a portion of the second audience that belongs to the market segment, when the second size of the second audience satisfies the threshold.
In some disclosed example methods, the threshold includes a minimum number of panel audience members. In some disclosed examples, the first demographic group includes one of multiple age and gender groups, and the second demographic group includes a combination of the multiple age and gender groups. In some such example methods, the audience measurement panel comprises a television audience measurement panel.
Some disclosed example methods further involve determining whether a size of a third audience that corresponds to a third demographic group satisfies the threshold, the third audience comprising panelists in the audience measurement panel, where the third audience is a subset of the second audience and the third demographic group is a subset of the second demographic group. When the size of the third audience satisfies the threshold and the size of the first audience does not satisfy the threshold, such example methods further involve calculating a second portion of the media impressions attributable to both the market segment and the third demographic group based on a portion of the third audience that belongs to the market segment, and calculating a portion of the media impressions attributable to both the market segment and the first demographic group based on the portion of the second audience that belongs to the market segment.
In some disclosed example methods calculating the portion of the media impressions involves multiplying a) the portion of the second audience belonging to the market segment and b) an audience size for the second demographic group, where the second demographic group is determined based on i) demographic information obtained from the database proprietor and ii) a number of media impressions counted at an impression collector, to calculate a portion of the second audience that belongs to both the market segment and the second demographic group. In some such example methods, calculating the portion of the media impressions involves multiplying an impression frequency determined from the database proprietor and the portion of the second audience that belongs to both the market segment and the second demographic group.
Some disclosed example methods further involve conserving at least one of computing resources or network resources by calculating the portion of the media impressions attributable to the market segment and to the second demographic group without communicating with non-panel online users to request survey responses about their personal details related to the market segment.
Disclosed example apparatus include an impression collector, a demographics determiner, a precision determiner, and a market segment calculator. In disclosed example apparatus, the impression collector receives, at a first Internet domain, a first request from a computing device, where the first request is indicative of access to media at the computing device. In the disclosed example apparatus, the impression collector also requests demographic information from a database proprietor, where the demographic information corresponding to the first request. In the disclosed example apparatus, the demographics determiner determines a number of media impressions that occurred on mobile devices and that are attributable to a first demographic group. In the disclosed example apparatus, the number of media impressions is based on attributions of the media impressions to the first demographic group by the database proprietor in the demographic information. In the disclosed example apparatus, the precision determiner determines whether a first size of a first audience that corresponds to the first demographic group satisfies a threshold, where the first audience includes panelists in an audience measurement panel maintained by an audience measurement entity. In the disclosed example apparatus, the precision determiner also determines whether a second size of a second audience satisfies the threshold when the first size of the first audience does not satisfy the threshold, where the second audience includes panelists in the audience measurement panel and corresponds to a second demographic group. In the disclosed example apparatus, the first audience is a subset of the second audience and the first demographic group is a subset of the second demographic group. In the disclosed example apparatus, the market segment calculator calculates a portion of the media impressions attributable to a market segment and to the second demographic group, based on a portion of the second audience that belongs to the market segment, when the second size of the second audience satisfies the threshold.
In some disclosed examples, the threshold is a minimum audience size. In some example apparatus, the first demographic group includes one of multiple age and gender groups, and the second demographic group includes a combination of the multiple age and gender groups. In some examples, the audience measurement panel comprises a television audience measurement panel.
In some disclosed example apparatus, the precision determiner determines whether a third size of a third audience that corresponds to a third demographic group satisfies the threshold, where the third audience includes panelists in the audience measurement panel. In such disclosed example apparatus, the third audience is a subset of the second audience, and the third demographic group is a subset of the second demographic group. In such example apparatus, when the third size of the third audience satisfies the threshold and the first size of the first audience does not satisfy the threshold: the market segment calculator calculates a second portion of the media impressions attributable to both the market segment and the second demographic group based on a portion of the third audience that belongs to the market segment and calculates a portion of the media impressions attributable to both the market segment and the first demographic group based on the portion of the second audience that belongs to the market segment.
In some disclosed example apparatus, the market segment calculator calculates the portion of the media impressions by multiplying a) the portion of the second audience belonging to the market segment and b) an audience size for the second demographic group determined based on i) demographic information obtained from the database proprietor and ii) a number of media impressions counted at the impression collector, to calculate a portion of the second audience that belongs to both the market segment and the second demographic group. In some such example apparatus, the market segment calculator calculates the portion of the media impressions by multiplying an impression frequency determined from the database proprietor and the portion of the second audience that belongs to both the market segment and the second demographic group.
In some disclosed example apparatus, the market segment calculator conserves at least one of computing resources or network resources by calculating the portion of the media impressions attributable to the market segment and to the second demographic group without communicating with non-panel online users to request survey responses about their personal details related to the market segment.
While examples disclosed herein are described with reference to compensating or adjusting impression information obtained from mobile devices, the examples are also applicable to non-mobile devices such as desktop computers, televisions, video game consoles, set top boxes, and/or other devices.
Examples disclosed herein can be applied to incoming data in real-time or substantially real-time (e.g., within seconds or minutes of receiving the data), and may be used to attribute impression information to market segments (e.g., impressions, duration units) for any desirable time period (e.g., hourly, daily, weekly, monthly, etc.) and/or cumulatively (e.g., applied to impressions and/or duration units collected over numerous time periods). Therefore, examples disclosed herein may provide accurate market segment information to advertisers and/or media distributors to enable more rapid adjustment of media campaign strategies to fit measured market segments than known methods.
Impression and Demographic Information Collection
In the illustrated example of
Any of the example software 114-117 may present media 118 received from a media publisher 120. The media 118 may be an advertisement, video, audio, text, a graphic, a web page, news, educational media, entertainment media, and/or any other type of media. In the illustrated example, a media ID 122 is provided in the media 118 to enable identifying the media 118 so that the AME 108 can credit the media 118 with media impressions when the media 118 is presented on the client device 106 or any other device that is monitored by the AME 108.
The data collector 112 of the illustrated example includes instructions (e.g., Java, java script, or any other computer language or script) that, when executed by the client device 106, cause the client device 106 to collect the media ID 122 of the media 118 presented by the app program 116 and/or the client device 106, and to collect one or more device/user identifier(s) 124 stored in the client device 106. The device/user identifier(s) 124 of the illustrated example include identifiers that can be used by corresponding ones of the partner database proprietors 104a-b to identify the user or users of the client device 106, and to locate user information 102a-b corresponding to the user(s). For example, the device/user identifier(s) 124 may include hardware identifiers (e.g., an international mobile equipment identity (IMEI), a mobile equipment identifier (MEID), a media access control (MAC) address, etc.), an app store identifier (e.g., a Google Android ID, an Apple ID, an Amazon ID, etc.), an open source unique device identifier (OpenUDID), an open device identification number (ODIN), a login identifier (e.g., a username), an email address, user agent data (e.g., application type, operating system, software vendor, software revision, etc.), third-party service identifiers (e.g., advertising service identifiers, device usage analytics service identifiers, demographics collection service identifiers), web storage data, document object model (DOM) storage data, local shared objects (also referred to as “Flash cookies”), etc. In some examples, fewer or more device/user identifier(s) 124 may be used. In addition, although only two partner database proprietors 104a-b are shown in
In some examples, the client device 106 may not allow access to identification information stored in the client device 106. For such instances, the disclosed examples enable the AME 108 to store an AME-provided identifier (e.g., an identifier managed and tracked by the AME 108) in the client device 106 to track media impressions on the client device 106. For example, the AME 108 may provide instructions in the data collector 112 to set an AME-provided identifier in memory space accessible by and/or allocated to the app program 116, and the data collector 112 uses the identifier as a device/user identifier 124. In such examples, the AME-provided identifier set by the data collector 112 persists in the memory space even when the app program 116 and the data collector 112 are not running. In this manner, the same AME-provided identifier can remain associated with the client device 106 for extended durations. In some examples in which the data collector 112 sets an identifier in the client device 106, the AME 108 may recruit a user of the client device 106 as a panelist, and may store user information collected from the user during a panelist registration process and/or collected by monitoring user activities/behavior via the client device 106 and/or any other device used by the user and monitored by the AME 108. In this manner, the AME 108 can associate user information of the user (from panelist data stored by the AME 108) with media impressions attributed to the user on the client device 106.
In the illustrated example, the data collector 112 sends the media ID 122 and the one or more device/user identifier(s) 124 as collected data 126 to the app publisher 110. Alternatively, the data collector 112 may be configured to send the collected data 126 to another collection entity (other than the app publisher 110) that has been contracted by the AME 108 or is partnered with the AME 108 to collect media ID's (e.g., the media ID 122) and device/user identifiers (e.g., the device/user identifier(s) 124) from mobile devices (e.g., the client device 106). In the illustrated example, the app publisher 110 (or a collection entity) sends the media ID 122 and the device/user identifier(s) 124 as impression data 130 to an impression monitoring server 132 at the AME 108. The impression data 130 of the illustrated example may include one media ID 122 and one or more device/user identifier(s) 124 to report a single impression of the media 118, or it may include numerous media ID's 122 and device/user identifier(s) 124 based on numerous instances of collected data (e.g., the collected data 126) received from the client device 106 and/or other mobile devices to report multiple impressions of media.
In the illustrated example, the impression monitoring server 132 stores the impression data 130 in an AME media impressions store 134 (e.g., a database or other data structure). Subsequently, the AME 108 sends the device/user identifier(s) 124 to corresponding partner database proprietors (e.g., the partner database proprietors 104a-b) to receive user information (e.g., the user information 102a-b) corresponding to the device/user identifier(s) 124 from the partner database proprietors 104a-b so that the AME 108 can associate the user information with corresponding media impressions of media (e.g., the media 118) presented at mobile devices (e.g., the client device 106).
In some examples, to protect the privacy of the user of the client device 106, the media identifier 122 and/or the device/user identifier(s) 124 are encrypted before they are sent to the AME 108 and/or to the partner database proprietors 104a-b. In other examples, the media identifier 122 and/or the device/user identifier(s) 124 are not encrypted.
After the AME 108 receives the device/user identifier(s) 124, the AME 108 sends device/user identifier logs 136a-b to corresponding partner database proprietors (e.g., the partner database proprietors 104a-b). In some examples, each of the device/user identifier logs 136a-b may include a single device/user identifier, or it may include numerous aggregate device/user identifiers received over time from one or more mobile devices. After receiving the device/user identifier logs 136a-b, each of the partner database proprietors 104a-b looks up its users corresponding to the device/user identifiers 124 in the respective logs 136a-b. In this manner, each of the partner database proprietors 104a-b collects user information 102a-b corresponding to users identified in the device/user identifier logs 136a-b for sending to the AME 108. For example, if the partner database proprietor 104a is a wireless service provider and the device/user identifier log 136a includes IMEI numbers recognizable by the wireless service provider, the wireless service provider accesses its subscriber records to find users having IMEI numbers matching the IMEI numbers received in the device/user identifier log 136a. When the users are identified, the wireless service provider copies the users' user information to the user information 102a for delivery to the AME 108.
In some other examples, the data collector 112 is configured to collect the device/user identifier(s) 124 from the client device 106. The example data collector 112 sends the device/user identifier(s) 124 to the app publisher 110 in the collected data 126, and it also sends the device/user identifier(s) 124 to the media publisher 120. In such other examples, the data collector 112 does not collect the media ID 122 from the media 118 at the client device 106 as the data collector 112 does in the example system 100 of
In some other examples in which the data collector 112 is configured to send the device/user identifier(s) 124 to the media publisher 120, the data collector 112 does not collect the media ID 122 from the media 118 at the client device 106. Instead, the media publisher 120 that publishes the media 118 to the client device 106 also retrieves the media ID 122 from the media 118 that it publishes. The media publisher 120 then associates the media ID 122 with the device/user identifier(s) 124 of the client device 106. The media publisher 120 then sends the media impression data 130, including the media ID 122 and the device/user identifier(s) 124, to the AME 108. For example, when the media publisher 120 sends the media 118 to the client device 106, it does so by identifying the client device 106 as a destination device for the media 118 using one or more of the device/user identifier(s) 124. In this manner, the media publisher 120 can associate the media ID 122 of the media 118 with the device/user identifier(s) 124 of the client device 106 indicating that the media 118 was sent to the particular client device 106 for presentation (e.g., to generate an impression of the media 118). In the illustrated example, after the AME 108 receives the impression data 130 from the media publisher 120, the AME 108 can then send the device/user identifier logs 136a-b to the partner database proprietors 104a-b to request the user information 102a-b as described above in connection with
Although the media publisher 120 is shown separate from the app publisher 110 in
Additionally or alternatively, in contrast with the examples described above in which the client device 106 sends identifiers to the audience measurement entity 108 (e.g., via the application publisher 110, the media publisher 120, and/or another entity), in other examples the client device 106 (e.g., the data collector 112 installed on the client device 106) sends the identifiers (e.g., the user/device identifier(s) 124) directly to the respective database proprietors 104a, 104b (e.g., not via the AME 108). In such examples, the example client device 106 sends the media identifier 122 to the audience measurement entity 108 (e.g., directly or through an intermediary such as via the application publisher 110), but does not send the media identifier 122 to the database proprietors 104a-b.
As mentioned above, the example partner database proprietors 104a-b provide the user information 102a-b to the example AME 108 for matching with the media identifier 122 to form media impression information. As also mentioned above, the database proprietors 104a-b are not provided copies of the media identifier 122. Instead, the client provides the database proprietors 104a-b with impression identifiers 140. An impression identifier uniquely identifies an impression event relative to other impression events of the client device 106 so that an occurrence of an impression at the client device 106 can be distinguished from other occurrences of impressions. However, the impression identifier 140 does not itself identify the media associated with that impression event. In such examples, the impression data 130 from the client device 106 to the AME 108 also includes the impression identifier 140 and the corresponding media identifier 122. To match the user information 102a-b with the media identifier 122, the example partner database proprietors 104a-b provide the user information 102a-b to the AME 108 in association with the impression identifier 140 for the impression event that triggered the collection of the user information 102a-b. In this manner, the AME 108 can match the impression identifier 140 received from the client device 106 to a corresponding impression identifier 140 received from the partner database proprietors 104a-b to associate the media identifier 122 received from the client device 106 with demographic information in the user information 102a-b received from the database proprietors 104a-b. The impression identifier 140 can additionally be used for reducing or avoiding duplication of demographic information. For example, the example partner database proprietors 104a-b may provide the user information 102a-b and the impression identifier 140 to the AME 108 on a per-impression basis (e.g., each time a client device 106 sends a request including an encrypted identifier 124-b and an impression identifier 140 to the partner database proprietor 104a-b) and/or on an aggregated basis (e.g., send a set of user information 102a-b, which may include indications of multiple impressions at a mobile device 102a-b (e.g., multiple impression identifiers 140), to the AME 108 presented at the client device 106).
The impression identifier 140 provided to the AME 108 enables the AME 108 to distinguish unique impressions and avoid overcounting a number of unique users and/or devices viewing the media. For example, the relationship between the user information 102a from the partner A database proprietor 104a and the user information 102b from the partner B database proprietor 104b for the client device 106 is not readily apparent to the AME 108. By including an impression identifier 140 (or any similar identifier), the example AME 108 can associate user information corresponding to the same user between the user information 102a-b based on matching impression identifiers 140 stored in both of the user information 102a-b. The example AME 108 can use such matching impression identifiers 140 across the user information 102a-b to avoid overcounting mobile devices and/or users (e.g., by only counting unique users instead of counting the same user multiple times).
A same user may be counted multiple times if, for example, an impression causes the client device 106 to send multiple user/device identifiers to multiple different database proprietors 104a-b without an impression identifier (e.g., the impression identifier 140). For example, a first one of the database proprietors 104a sends first user information 102a to the AME 108, which signals that an impression occurred. In addition, a second one of the database proprietors 104b sends second user information 102b to the AME 108, which signals (separately) that an impression occurred. In addition, separately, the client device 106 sends an indication of an impression to the AME 108. Without knowing that the user information 102a-b is from the same impression, the AME 108 has an indication from the client device 106 of a single impression and indications from the database proprietors 104a-b of multiple impressions.
To avoid overcounting impressions, the AME 108 can use the impression identifier 140. For example, after looking up user information 102a-b, the example partner database proprietors 104a-b transmit the impression identifier 140 to the AME 108 with corresponding user information 102a-b. The AME 108 matches the impression identifier 140 obtained directly from the client device 106 to the impression identifier 140 received from the database proprietors 104a-b with the user information 102a-b to thereby associate the user information 102a-b with the media identifier 122 and to generate impression information. This is possible because the AME 108 received the media identifier 122 in association with the impression identifier 140 directly from the client device 106. Therefore, the AME 108 can map user data from two or more database proprietors 104a-b to the same media exposure event, thus avoiding double counting.
Each unique impression identifier 140 in the illustrated example is associated with a specific impression of media on the client device 106. The partner database proprietors 104a-b receive the respective user/device identifiers 124 and generate the user information 102a-b independently (e.g., without regard to others of the partner database proprietors 104a-b) and without knowledge of the media identifier 122 involved in the impression. Without an indication that a particular user demographic profile in the user information 102a (received from the partner database proprietor 104a) is associated with (e.g., the result of) the same impression at the client device 106 as a particular user demographic profile in the user information 102b (received from the partner database proprietor 104b independently of the user information 102a received from the partner database proprietor 104a), and without reference to the impression identifier 140, the AME 108 may not be able to associate the user information 102a with the user information 102b and/or cannot determine that the different pieces of user information 102a-b are associated with a same impression and could, therefore, count the user information 102a and the user information 102b as corresponding to two different users/devices and/or two different impressions.
The above examples illustrate methods and apparatus for collecting impression data at an audience measurement entity (or other entity). The examples discussed above may be used to collect impression information for any type of media, including static media (e.g., advertising images), streaming media (e.g., streaming video and/or audio, including content, advertising, and/or other types of media), and/or other types of media. For static media (e.g., media that does not have a time component such as images, text, a webpage, etc.), the example AME 108 records an impression once for each occurrence of the media being presented, delivered, or otherwise provided to the client device 106. For streaming media (e.g., video, audio, etc.), the example AME 108 measures demographics for media occurring over a period of time. For example, the AME 108 (e.g., via the app publisher 110 and/or the media publisher 120) provides beacon instructions to a client application or client software (e.g., the OS 114, the web browser 117, the app 116, etc.) executing on the client device 106 when media is loaded at client application/software 114-117. In some examples, the beacon instructions cause the client application/software 114-117 to transmit a request (e.g., a pingback message) to an impression monitoring server at regular and/or irregular intervals (e.g., every minute, every 30 seconds, every 2 minutes, etc.). By monitoring and/or counting the requests occurring at intervals, the example AME 108 monitors the duration of individual impressions of duration-based media (e.g., video, audio, etc.). The example AME 108 may determine the numbers of impressions (e.g., initial loads) of the duration-based media, the unique audience of duration-based media, and/or the total duration (in units, such as seconds or minutes) of the duration-based media viewed in the numbers of impressions. As used herein, the term “impression information” may include impressions and/or duration units. The example impression monitoring server 132 identifies the requests from the web browser 117 and, in combination with one or more database proprietors, matches the impression information for the media with demographics of the user of the web browser 117.
In some examples, a user loads (e.g., via the browser 117) a web page from a web site publisher, in which the web page corresponds to a particular 60 minute video. As a part of or in addition to the example web page, the web site publisher causes the data collector 112 to send a pingback message (e.g., a beacon request) to a beacon server 142 by, for example, providing the browser 117 with beacon instructions. For example, when the beacon instructions are executed by the example browser 117, the beacon instructions cause the data collector 112 to send pingback messages (e.g., beacon requests, hypertext transfer protocol (HTTP) requests, pings) to the impression monitoring server 132 at designated intervals (e.g., once every minute or any other suitable interval). The example beacon instructions (or a redirect message from, for example, the impression monitoring server 132 or a database proprietor 104a-b) further cause the data collector 112 to send pingback messages or beacon requests to one or more database proprietors 104a-b that collect and/or maintain demographic information about users. The database proprietor 104a-b transmits demographic information about the user associated with the data collector 112 for combining or associating with the impression determined by the impression monitoring server 132. If the user closes the web page containing the video before the end of the video, the beacon instructions are stopped, and the data collector 112 stops sending the pingback messages to the impression monitoring server 132. In some examples, the pingback messages include timestamps and/or other information indicative of the locations in the video to which the numerous pingback messages correspond. By determining a number and/or content of the pingback messages received at the impression monitoring server 132 from the client device 106, the example impression monitoring server 132 can determine that the user watched a particular length of the video (e.g., a portion of the video for which pingback messages were received at the impression monitoring server 132).
The client device 106 of the illustrated example executes a client application/software 114-117 that is directed to a host website (e.g., www.acme.com) from which the media 118 (e.g., audio, video, interactive media, streaming media, etc.) is obtained for presenting via the client device 106. In the illustrated example, the media 118 (e.g., advertisements and/or content) is tagged with identifier information (e.g., a media ID 122, a creative type ID, a placement ID, a publisher source uniform resource locator (URL), etc.) and a beacon instruction. The example beacon instruction causes the client application/software 114-117 to request further beacon instructions from a beacon server 142 that will instruct the client application/software 114-117 on how and where to send beacon requests to report impressions of the media 118. For example, the example client application/software 114-117 transmits a request including an identification of the media 118 (e.g., the media identifier 122) to the beacon server 142. The beacon server 142 then generates and returns beacon instructions 144 to the example client device 106. Although the beacon server 142 and the impression monitoring server 132 are shown separately, in some examples the beacon server 142 and the impression monitoring server 132 are combined. In the illustrated example, beacon instructions 144 include URLs of one or more database proprietors (e.g., one or more of the partner database proprietors 104a-b) or any other server to which the client device 106 should send beacon requests (e.g., impression requests). In some examples, a pingback message or beacon request may be implemented as an HTTP request. However, whereas a transmitted HTTP request identifies a webpage or other resource to be downloaded, the pingback message or beacon request includes the audience measurement information (e.g., ad campaign identification, content identifier, and/or device/user identification information) as its payload. The server to which the pingback message or beacon request is directed is programmed to log the audience measurement data of the pingback message or beacon request as an impression (e.g., an ad and/or content impression depending on the nature of the media tagged with the beaconing instructions). In some examples, the beacon instructions received with the tagged media 118 include the beacon instructions 144. In such examples, the client application/software 114-117 does not need to request beacon instructions 144 from a beacon server 142 because the beacon instructions 144 are already provided in the tagged media 118.
When the beacon instructions 144 are executed by the client device 106, the beacon instructions 144 cause the client device 106 to send beacon requests (e.g., repeatedly at designated intervals) to a remote server (e.g., the impression monitoring server 132, the media publisher 120, the database proprietors 104a-b, or another server) specified in the beacon instructions 144. In the illustrated example, the specified server is a server of the audience measurement entity 108, namely, at the impression monitoring server 132. The beacon instructions 144 may be implemented using Javascript or any other types of instructions or script executable via a client application (e.g., a web browser) including, for example, Java, HTML, etc.
The example AME 108 of
Examples that may be used to implement the system of
Attributing Impression Information to Market Segments
The examples of
The example impression monitoring server 132 of
The example impression collector 202 of
The impression requests may be implemented, for example, as HTTP requests. However, whereas a transmitted HTML request identifies a webpage or other resource to be downloaded, the impression request includes the audience measurement information (e.g., ad campaign identification, a content identifier, and/or user identification information) as its payload. The example impression requests are dummy HTTP requests requesting a resource (e.g., a web page) but to which a web page is not served by the impression collector 202. The dummy HTTP request serves as an impression request that effectively requests the impression collector 202 to log an impression for corresponding accessed media that is identified in the dummy HTTP impression request.
The example impression collector 202 of
The example demographics determiner 204 of
The example precision determiner 206 of
When the precision determiner 206 determines that the size of the panel audience (that corresponds to the demographic group of interest) does not satisfy the threshold at a higher level of granularity (e.g., the first level), the example precision determiner 206 determines whether the size of a second, larger audience at a lower level of granularity (e.g., the second level, the third level, etc.) satisfies the threshold. In particular, the precision determiner 206 uses as the second audience a larger set of panelists from the audience measurement panel that includes the first set of panelists plus additional panelists that correspond to a larger (e.g., less granular) demographic group. For example, if the demographic group of the first set was an M35-39 (male, ages 35-39) demographic group, the precision determiner 206 may create the second set by widening the age demographic to use panel audience panelists in the group M35-54 (male, ages 35-54). Alternatively, the precision determiner 206 may widen the gender category to include persons (i.e., male and female) ages 35-39 in the second group. Alternatively, the precision determiner 206 may widen both the gender and the age categories such that the second set of panelists are those panelists who are persons of either gender and are ages 35-54. In other words, the precision determiner 206 selects the second set of panelists having less granular demographics panel audience than the first group of panelists. The first set of panelists is a subset of the second set of panelists. The example precision determiner 206 may generate any number of demographic granularity levels (e.g., 2, 3, 4, or more), testing increasingly larger sets of panelists until the precision determiner 206 identifies a set of panelists that satisfies the threshold.
In some examples, precision determiner 206 includes non-demographic based requirements in addition to the panel audience size, such as a requirement to have a certain number of mobile devices and/or type(s) of mobile devices present in the household of the audience members. Such requirements may be instituted to improve the representation of the overall population by the panel audience. In such examples, an audience member may be excluded from the panel audience (referred to as a set of panelists above) if the audience member and/or the household of the audience member do not meet the non-demographic based requirements. The precision determiner 206 determines whether the panel audience for a demographic group, excluding those panel audience members that are in the demographic group but do not meet the non-demographic based criteria, satisfies the threshold (e.g., is at least a minimum number of audience members). If not, the precision determiner 206 may cause the demographics determiner 204 to select a panel audience for a demographic group that includes a larger number of panel audience members, as discussed in more detail below.
The example market segment calculator 208 of
In some examples, such as when a higher-granularity demographic level panel audience does not have enough panelists to result in an acceptable sample size in the calculation of attributable impressions, the market segment calculator 208 calculates the portion of the media impressions that are attributable to the market segment and the demographic group by multiplying a) the proportion of the panel audience at a lower-granularity demographic level that includes the panel audience at the higher-granularity demographic level and that belongs to the market segment and b) a number of impressions attributable to the demographic group at the highest-granularity demographic level. For example, if the market segment calculator 208 determines that the F21-24 panel audience (e.g., at highest granularity level) would not result in an acceptable sample size for determining the impressions attributable to the market segment of interest and the F21-24 demographic group, the market segment calculator 208 selects a demographic group at the next-highest granularity level that includes the F21-24 demographic group, such as the F21-30 demographic group. The market segment calculator 208 then calculates the portion of the media impressions for the F21-24 attributable to the market segment of interest by multiplying a) the proportion of persons in the F21-30 panel audience who belong to the market segment of interest and b) the media impressions attributable to the F21-24 panel audience. If the F21-30 panel audience also does not provide an acceptable sample size, the example market segment calculator 208 may repeat the calculation using the next-highest granularity demographic level to determine the proportion of the panel audience that belongs to the market segment of interest, while also using the media impressions attributable to the F21-24 panel audience.
The example market segment calculator 208 determines the total audience size (e.g., not limited to the panel audience) for each demographic group based on, for example, information obtained from a database proprietor (e.g., the database proprietor 104a of
In the example of
The example precision determiner 206 selects the highest granularity level for each demographic group at which the panel audience satisfies the threshold, regardless of whether the precision determiner 206 selects a lower-granularity level for other demographic groups (e.g., where the selected lower-granularity level may include other demographic groups for which the highest granularity level may be selected). For example, the precision determiner 206 may determine that, for a M25-29 demographic group, a lower-granularity level “persons 21-34” panel audience is to be used to achieve satisfactory precision. However, rather than using the “persons 21-34” demographic group for each of the demographic groups falling within the “persons 21-34” lower-granularity level demographic group, the example precision determiner 206 selects the highest-granularity level demographic groups for the demographic groups for which the corresponding panel audience satisfies the threshold (e.g., the M21-24, M30-34, F21-24, F25-29, and F30-34 demographic groups).
Table 1 below illustrates an example approach for estimating market section audiences by device type, and in total (across device types, and across TV households and Cross-Platform Homes (CPH)). The example market segment calculator 208 of
In Table 2 above, A (or Pdb) is a panel audience for the dth demographic group (e.g., females of ages 18-21 (F18-21), females of ages 18-30 (F18-30), persons of age 2 and over, etc.) and the bth market segment (e.g., household income $50,000-59,999). In Table 2 above, B (or Pd) is a panel audience for the dth demographic group. Therefore, the ratio A/B is the portion (e.g., percentage or fraction) of the dth demographic group made up by the bth market segment. A and B above are obtained from an audience panel (e.g., panel-based), such as a statistically-selected television audience panel, a statistically-selected PC audience panel, and/or a statistically selected radio audience panel maintained by an audience measurement entity such as The Nielsen Company. The audience may be, for example, the number of persons in the panel audience that fit the criteria (e.g., the dth demographic group, the bth market segment, etc.).
The panel audience A (or Pdb) and the panel audience B (or Pd) are obtained from the demographics determiner 204 based on a demographic group selected by the precision determiner 206 (e.g., at a level that results in a satisfactory precision). For example, when the precision determiner 206 selects a demographic group level (e.g., a granularity level), the example demographics determiner 204 determines a number of persons in the panel audience that are known to be in the dth demographic group (e.g., the demographic group of interest, a lower-granularity-level demographic group that includes the demographic group of interest, etc.) and the bth market segment. The example market segment calculator 208 obtains A and B from the panel database 146 based on a demographic group selected by the precision determiner 206.
In Table 2 above, C (or Cdt) is a calibrated census audience size (or impression counts) for the dth demographic group and the tth device type out of n device types (e.g., device types t1, t2, . . . tn). The example demographics determiner 204 of
In some examples, the demographics determiner 204 converts the media impression count and/or the duration unit count to a unique audience size using a frequency measure determined for the dth demographic group (e.g., by the database proprietor, by the audience measurement entity, etc.). As used herein, frequency is defined to refer to the ratio of an impression count to a unique audience size. Therefore, frequency may be considered an average number of impressions per person in the audience. A value for C may be a number of media impressions obtained using the example methods and apparatus disclosed in U.S. patent application Ser. No. 14/560,947.
In Table 2 above, D (or Ddbt) is a calculated value representing a mobile audience size (or number of impressions) for the dth demographic group, the bth market segment, and the tth device type. The example market segment calculator 208 of
In Table 2 above, E (or Ddb) is a mobile audience size (or number of impressions) for the dth demographic group and the bth market segment. The example market segment calculator 208 calculates E as the sum of D (or Ddbt) for all of the device types t1-tn. For example, the market segment calculator 208 may calculate E using the formula
In Table 2 above, F (or Tdb) is the existing audience size for the dth demographic group and the bth market segment. The existing audience size refers to an audience size for media presented using a different platform than the platform represented by D in Table 2 above. For example, the market segment calculator 208 obtains the existing audience F as an audience for the media of interest for devices not included in the set of device types t1-tn. For example, if the set of device types t1-tn include mobile device types such as smartphones, tablet computers, and/or portable media players, the existing audience F may reflect audiences of device types such as desktop computers, televisions, and/or radios. Existing audience sizes may be different based on the particular service (e.g., national vs local service). For example, in national reporting, the existing audience size may include TV-only audience (or radio only), or may include combined TV and PC (or radio and PC) audiences depending on the networks' respective participation in extended access programs that monitor media access on devices such as personal computers. As an example for local reporting, existing audience sizes may only include TV audiences (or radio audiences).
Example methods and apparatus for determining an audience for computing devices are disclosed in U.S. Pat. No. 6,108,637 to Blumenau. Example methods and apparatus for determining an audience for television and/or radio are described in U.S. Pat. No. 5,481,294 to Thomas et al. and/or U.S. Pat. No. 5,504,518 to Ellis et al. The entireties of U.S. Pat. Nos. 6,108,637, 5,481,294, and 5,504,518 are incorporated herein by reference.
In Table 2 above, G is a total of the mobile audience size (e.g., E or Ddb) and existing audience size (e.g., F or Tdb) for the market segment for the dth demographic group and the bth market segment. In the example above, the market segment calculator 208 calculates G using the equation G=F+E=Tdb+Ddb. Therefore, the above example can provide an audience for a market segment of interest for media impressions occurring on mobile devices and/or for total accesses of the media across devices (e.g., mobile devices, non-mobile computing devices, television, radio, etc.). The example market segment calculator 208 may calculate the audience for the market segment of interest in one or more selected demographic groups using the panel audiences selected by the precision determiner 206 for each of the selected demographic groups. Additionally or alternatively, the market segment calculator 208 may sum the audiences across multiple demographic groups to determine the audience for the entire market segment of interest.
As discussed above, the example precision determiner 206 determines the sample size associated with the panel-based market segment calculations to determine whether the sample size traverses a threshold. The data source of the panel-based share (e.g., the data source, such as a television audience measurement panel, used to obtain A and B in Table 2 above) and the demographic granularity level at which the share is computed, are based on the numbers of viewers and availability of the panel data. Availability of panel data refers to whether audience measurement panel data can be obtained from a desired source for a demographic group. The demographic granularity level refers to the granularity of the demographic groups used to determine the market segment share. For example, the females of ages 18-21, females of ages 21-24, females of ages 24-27, and females of ages 28-30 demographic groups at a first demographic granularity level all have a higher granularity than a females of ages 18-30 demographic group at a second demographic granularity level and/or a persons of ages 2+ (e.g., male and female, ages 2 and up) demographic group at a third demographic granularity level. Table 3 below illustrates example sets of demographic groups at three different demographic granularity levels.
In the example of Table 3 above, each granularity level is a surjection of the immediately preceding level (e.g., Demographic Granularity Level 1 precedes Demographic Granularity Level 2 in the example Table 3 above). In other words, each demographic group at one demographic granularity level in Table 3 above (e.g., the female, ages 12-14 demographic group in Demographic Granularity Level 1) fits into (or is mapped to) a demographic group at each of the higher demographic granularity levels in Table 3 above (e.g., Teens, ages 12-17 in Demographic Granularity Level 2, Persons 2+ in Demographic Granularity Level 3).
At each demographic granularity level in Table 3 above, the market segment calculator 208 determines the market segment share of impressions and/or audience members based on the ratio of market segment projected audience (or impressions) (e.g., A in Table 1 above) to total projected audience (or impressions) (e.g., B in Table 1 above).
In some examples, the precision determiner 206 uses a hierarchical approach so that the demographic granularity level expected to be most highly correlated with mobile viewing by the market segment is considered first. Thus, the example market segment calculator 208 calculates the mobile audience size (or impressions) E for the dth demographic group and the bth market segment according to Table 1 above for the demographic groups d at Demographic Granularity Level 1 of Table 3 above. For example, the example demographics determiner 204 provides the market segment projected audience A and the total projected audience B of Table 1 above are obtained for each of the 30 demographic groups at Demographic Granularity Level 1 of Table 3 above. The calibrated census audience size C of Table 1 above is also obtained for each of the 30 demographic groups d at Demographic Granularity Level 1 of Table 3 above. The example market segment calculator 208 calculates the mobile audience size D of Table 1 for each of the 30 demographic groups d at Demographic Granularity Level 1 of Table 3 above. The market segment calculator 208 calculates the mobile audience size (or number of impressions) E of Table 1 for each of the 30 demographic groups d at Demographic Granularity Level 1 of Table 3 above.
In some examples, if the precision determiner 206 determines that the threshold for the panel audience B of Table 1 above (e.g., a minimum audience) is not satisfied for one or more of the demographic groups at a particular demographic granularity level, the example market segment calculator 208 uses another demographic granularity level (e.g., a less granular level having more panelists) to calculate the impressions and/or audience for the market segment. For example, the precision determiner 206 may compare the panel audience B to the threshold prior to the market segment calculator 208 calculating the mobile audience size (or number of impressions) E at that demographic granularity level.
For example, if the precision determiner 206 determines that a first demographic group (e.g., female, ages 18-20) at Demographic Granularity Level 1 of Table 3 above has a panel audience (e.g., B of Table 1 above) of fewer than an example threshold of 30 people, then the precision determiner 206 determines whether the next demographic granularity level (e.g., Demographic Granularity Level 2 of Table 3 above) has a panel audience of fewer than the example threshold. In this example, the precision determiner 206 would determine whether the Persons, ages 18-24 demographic group audience has at least 30 people. If the Persons, ages 18-24 demographic group audience has at least 30 people, the example precision determiner 206 instructs the market segment calculator to use the Persons, ages 18-24 demographic group to determine the market segment projected audience A and the total projected audience B to calculate the mobile audience (or number of impressions) E for the female, ages 18-20 demographic group. The market segment calculator 208 would then obtain the panel audience A for the Persons, ages 18-24 demographic group and the panel audience B for the Persons, ages 18-24 demographic group from the panel database 146.
In some examples, the Demographic Granularity Levels and demographic groups in each of the levels may be defined using an established audience panel such as the Nielsen National TV Ratings panel. However, other panels may be used that have a correlation between the panel member behavior and mobile device user behavior that is higher than, for example, the correlation between the Nielsen National TV Ratings panel and mobile device user behavior.
In some examples, the market segment calculator 208 calculates separate shares for the same market segment for different time-shifted viewing streams. A time-shifted viewing stream refers to media that is accessed from a source of time-shifted media access, such as a particular web site, a particular service, or a particular mobile device application. Time-shifted refers to accessing archived or recorded media at a later time and/or date relative to the time/date at which the media was broadcast or distributed for real-time access during a scheduled time/date. In some other examples, the market segment calculator 208 calculates a single share for the market segment based on a single stream, and the single share is then utilized for all measured time-shifted viewing streams.
Table 4 below illustrates another example set of granularity levels that may be used by the example market segment calculator 208 to calculate a share of impressions and/or audience for a market segment. The example granularity levels of Table 4 below may be used to measure market segment share for, for example, local media telecasts (as opposed to national media telecasts) on local stations (e.g., national network affiliates).
In the example of Table 4 above, BB refers to a set of “building block” demographic categories, such as the set of national building block demographic categories (e.g., age/gender groups) used in the Nielsen National TV Ratings panel. The BB demographic groups are an example of a “most granular” set of demographic groups in the granularity levels, and may be replaced with other sets of demographic groups based on panel data availability. In the example of Table 4 above, Demo (6) refers to the example demographic groups in Demographic Granularity Level 2 of Table 3 above, including: 1) Children, ages 2-11; 2) Teens, ages 12-17; 3) Persons, ages 18-24; 4) Persons, ages 25-34; 5) Persons, ages 35-54; and 6) Persons, ages 55+. In the example of Table 4 above, the Day/Station/Time period (e.g., ¼ hour, ½ hour, 1 hour) refers to the local station, day, and day-part (e.g., a designated ¼ hour, a designated ½ hour, a designated hour, etc.) from which the panel audience is estimated or determined. For example, an audience measured from a panel on a particular ¼ hour (e.g., 5:00 P.M.-5:15 P.M., etc.) of a particular day (e.g., Friday, Friday, Nov. 28, 2014, etc.) from a particular station (e.g., a local CBS affiliate station) may be used.
Using the example granularity levels of Table 4, the example precision determiner 206 first tests the panel audience size (e.g., B of Table 1) at granularity level 1 (e.g., BB demographic groups, with the panel audience determined from the day/station/¼ hour corresponding to the media impression) to determine whether each of the example demographic groups in BB have at least a threshold panel audience (e.g., whether the panel audience B of Table 1 above is greater than a threshold audience size). If not all of the demographic groups (BB) have at least the threshold panel audience, the example precision determiner 206 tests the panel audience size at granularity level 2 (e.g., Demo (6) demographic groups, with the panel audience determined from the day/station/¼ hour corresponding to the media impression).
If the third example granularity level of Table 4 above (e.g., persons of ages 2+, with the panel audience determined from the day/station/¼ hour corresponding to the media impression) does not have at least a threshold panel audience size, the example granularity level 4 is used, which increases the day-part time frame to ½ hour and returns to test the demographic groups at the highest level of granularity (e.g., the BB demographic groups).
The example precision determiner 206 progresses through the panel audience size at each subsequent granularity level (e.g., levels 1-9 of Table 4 above) until the precision determiner 206 determines that the granularity level has a panel audience size that meets the threshold for all of the demographic groups at the granularity level. When the precision determiner 206 identifies a granularity level that has a panel audience size that meets the threshold for all of the demographic groups, the example market segment calculator 208 obtains the market segment projected audience A, the total projected audience B, and the calibrated census audience size C of Table 1 above for the demographic groups at the identified granularity level and the market segment of interest.
For example, if all of the demographic groups in Demo (6) of Table 4 satisfy the threshold for the panel audience size at the ¼ hour day-part, the example market segment calculator 208 obtains 6 sets of data for each of the market segment projected audience A, the total projected audience B, and the calibrated census audience size C. The example market segment calculator 208 calculates values for the mobile audience (or number of impressions) E for each of the six demographic groups as described above. In some examples, the market segment calculator 208 obtains multiple values of the calibrated census audience size C and/or multiple values of the mobile audience size (or number of impressions) D are calculated for each demographic group d for the multiple device types t of Table 1 above. For example, a first value of the calibrated census audience size C may be obtained for a first device type and a second value of the calibrated census audience size C may be obtained for a second device type.
A second input table 304 includes impressions for the example age-based demographic categories and the example device type of “smartphone.” Similarly, a third input table 306 includes impressions for the example age-based demographic categories and the example device type of “tablet/personal media player (PMP).” The input tables 304, 306 of
Using the first input table 302 and the second input table 304, the example market segment calculator 208 calculates the impressions (D1) for the smartphone type and for the example market segment to generate the output table 308 for the smartphone device type. The values in the output table 308 correspond to the number of impressions (or audience size) D in Table 1 above. For example, the market segment calculator 208 multiplies the 100 impressions (C1) in the ages 12-17 demographic group in the table 304 by 0.4 (e.g., the ratio A/B) in the ages 12-17 demographic group in table 302 to equal 40 impressions (D1) in the table 308.
Similarly, using the first input table 302 and the third input table 306, the example market segment calculator 208 calculates the impressions (D2) for the tablet/personal media player device type for the market segment to generate table 310 for the tablet/personal media player device type. For example, the market segment calculator 208 multiplies the 200 impressions (C2) in the ages 12-17 demographic group in the table 306 by 0.4 (e.g., the ratio A/B) in the ages 12-17 demographic group in table 302 to equal 80 impressions (D2) in the table 310.
The example output tables 308, 310 of
The example market segment calculator 208 further computes the impression counts for each of the demographic groups for all of the device types (e.g., E of Table 1) by summing the impressions (D1+D2) for each of the device types per demographic group. The output of the calculation of E is shown in table 312 of
In some cases, some demographic groups at a particular granularity level may have at least the threshold panel audience while others of the demographic groups at that granularity level do not have the threshold panel audience. In some examples, each demographic group uses the panel audience size (e.g., A and/or B) for the applicable demographic group having the highest granularity. Using the example of Table 3 above, all of the example demographic groups female of ages 25-29, female of ages 30-34, male of ages 25-29, and male of ages 0-34 are in the Demographic Granularity Level 1 and also map to the demographic group Persons of ages 25-34 in Demographic Granularity Level 2. Assume, for example, each of the demographic groups female of ages 25-29, female of ages 30-34, and male of ages 25-29 have a panel audience size (e.g., B of Table 1 above) that is less than the threshold. Also assume that the example demographic group male of ages 30-34 has a panel audience size that is greater than the threshold. For the example demographic groups female of ages 25-29, female of ages 30-34, male of ages 25-29, the sample size is insufficient at Demographic Granularity Level 1 to provide an acceptable precision of a resulting mobile device audience if the panel audience size is used for those demographic groups. Therefore, the panel audience for the Persons of ages 25-34 demographic group is used to calculate the mobile device audience (e.g., E of Table 1 above) for the demographic groups female of ages 25-29, female of ages 30-34, male of ages 25-29. However, because the panel audience size for the male of ages 30-34 demographic group is greater than the threshold, the panel audience size for the male of ages 30-34 group is used (instead of the Persons of ages 25-34 demographic group used for the other demographic groups) to calculate the mobile device audience (e.g., E of Table 1 above).
Using the less granular Demographic Granularity Level 2 to calculate the mobile device audience for the demographic groups female of ages 25-29, female of ages 30-34, male of ages 25-29, as in the example above, could result in a higher bias or error for the calculated mobile device audience for these groups (e.g., due to less correlation between the panel audience behavior and the mobile device user behavior), but increases the precision. In contrast, because the sample size of the audience in the male of ages 30-34 demographic group is sufficiently large, the mobile device audience for the male of ages 30-34 demographic group has a lower bias while also having a precision within defined limits.
While an example manner of implementing the impression monitoring server 132 of
Flowcharts representative of example machine readable instructions for implementing the impression monitoring server 132 of
As mentioned above, the example processes of
The example precision determiner 206 of
The example precision determiner 206 determines whether the panel audience size (e.g., B of Table 1 above) at the selected demographic granularity level for the selected demographic group is less than a threshold (block 408). For example, the threshold may be a minimum sample size (e.g., a minimum number of people in an established audience measurement panel who belong to the demographic group audience) for the demographic group in the panel.
When the panel audience size is not less than (e.g., satisfies) the threshold (block 408), the example precision determiner 206 sets the selected demographic group d to use the panel audience at the selected demographic granularity level for attributing audience and/or impressions to a market segment s (block 410). In the illustrated example, the market segment s is the market segment of interest selected at block 402. For example, the precision determiner 206 may provide instructions to the market segment calculator 208 to use the panel audience at the selected demographic granularity level for determining A, B, and/or the ratio A/B, of Table 1 above to calculate D, E, and/or G of Table 1 above.
If the panel audience size is less than the threshold (block 408), the panel audience for the demographic granularity level selected at block 406 will not be used to attributed collected impressions to the market segment s selected at block 402. In such an instance, the example precision determiner 206 determines whether there are additional demographic granularity levels (block 412). If there are additional demographic granularity levels (block 412), the example returns to block 406 to select another demographic granularity level. For example, the precision determiner 206 may select a next-lowest demographic granularity level relative to the previously-selected demographic granularity level, such as Demographic Granularity Level 4 of Table 3 above or the Demographic Granularity Level 4 of Table 4 above.
If there are no additional demographic granularity levels (block 412), the example precision determiner 206 determines that the share for the market segment of interest cannot be estimated with sufficient reliability for the selected demographic group d (block 414). For example, because a sufficient sample size cannot be obtained for one or more demographic groups for the market segment of interest, the precision of any calculated audience size or impression count may be considered to be unacceptably low.
After setting the selected demographic group d to use the panel audience at the selected demographic granularity level (block 410), or after determining that the share for the market segment s cannot be estimated with sufficient reliability (block 414), the precision determiner 206 determines whether there are additional demographic groups to be tested for the selected market segment of interest (block 416). If there are additional demographic groups to be tested for the selected market segment of interest (block 416), control returns to block 404 to select another demographic group (e.g., the demographic group female, ages 6-8 of Table 3 above, a second one of the BB demographic groups of Table 4 above, etc.).
When there are no more demographic groups to be tested (e.g., all of the demographic groups have been assigned a demographic granularity level and/or a corresponding panel audience size) (block 416), the example market segment calculator 208 of
The example precision determiner 206 determines whether additional market segment shares are to be estimated (block 420). If there are additional market segment shares to be estimated (block 420), the example returns to block 402 to select another market segment of interest. When there are no more market segment shares to be estimated (block 420), the example instructions 400 end.
The example precision determiner 206 selects a market segment of interest (block 502). For example, the selected market segment of interest may be defined by one or more particular demographic attributes such as household income range, ethnicity, etc. The example precision determiner 206 selects a day-part granularity level (block 504). For example, the day-part granularity level may be a particular day/station/¾ hour combination as discussed above with reference to Table 4.
The example precision determiner 206 selects a demographic granularity level (block 506). For example, the demographic granularity level may be the Demographic Granularity Level 1 of Table 3 above or the Demographic Granularity Level 1 of Table 4 above. The example precision determiner 206 selects a demographic group in the selected granularity level (block 508). For example, the selected demographic group may be the female, ages 2-5 demographic group of Table 3 above or a first demographic group of the BB demographic groups of Table 4 above.
The example precision determiner 206 determines whether the panel audience size (e.g., B of Table 1 above) for the selected demographic group is less than a threshold (block 510). For example, the threshold may be a minimum sample size for the demographic group in the panel. If the panel audience size for the selected demographic group is less than a threshold (block 510), the example precision determiner 206 determines whether there are additional demographic granularity levels (block 512). If there are additional demographic granularity levels (block 512), control returns to block 506 to select another demographic granularity level. For example, a next demographic granularity level, such as Demographic Granularity Level 2 of Table 3 above or the Demographic Granularity Level 2 of Table 4 above, may be selected.
If there are no additional demographic granularity levels (block 512), the example precision determiner 206 determines whether there are additional day-part granularity level(s) available (block 514). For example, a day/station/½ hour granularity level may be used. If there are additional day-part granularity level(s) available (block 514), control returns to block 504 to select another day-part granularity level.
When there are no more day-part granularity level(s) available (block 514), the example precision determiner 206 determines that the share of impressions for the selected market segment of interest cannot be estimated with sufficient reliability (block 516). For example, because a sufficient sample size cannot be obtained for one or more demographic groups for the selected market segment of interest, the precision of any calculated audience or impressions may be considered to be unacceptably low.
If, in block 510, the panel audience size for the selected demographic group is not less than a threshold, the example precision determiner 206 determines whether there are additional demographic groups to be tested at the selected demographic granularity level (block 518). If there are additional demographic groups to be tested at the selected demographic granularity level (block 518), control returns to block 508 to select another demographic group (e.g., the demographic group female, ages 6-8 of Table 3 above, a second one of the BB demographic groups of Table 4 above, etc.).
When there are no more demographic groups to be tested at the selected demographic granularity level (e.g., all of the demographic groups at the selected granularity level have at least a threshold panel audience size) (block 518), the example market segment calculator 208 estimates the share for the market segment of interest using the selected demographic granularity level (block 520). For example, the market segment calculator 208 may estimate the mobile audience E of Table 1 above using the demographic groups in the selected demographic granularity level. Example instructions that may be executed to estimate the mobile audience E of Table 1 above are described below with reference to
After estimating the share of the market segment of interest (block 520), or if the share cannot be estimated with sufficient reliability (block 516), the example market segment calculator 208 determines whether additional market segment shares are to be estimated (block 522). If there are additional market segment shares to be estimated (block 522), control returns to block 502 to select another market segment of interest. When there are no more market segment shares to be estimated (block 522), the example instructions 500 end.
The example market segment calculator 208 selects a demographic group (e.g., a demographic group d as described above with reference to Table 1) (block 602). The market segment calculator 208 also selects a device type t (e.g., a device type t as described above with reference to Table 1) (block 604).
The example market segment calculator 208 accesses a panel audience size for the demographic group d at a selected demographic granularity level and the selected market segment s (e.g., A as described with reference to Table 1 above) (block 606). The market segment s was previously selected by the precision determiner 206 in block 402 of
The market segment calculator 208 of this example also accesses a panel based audience size for the demographic group d at the selected demographic granularity level (e.g., B as described with reference to Table 1 above) (block 608).
The example market segment calculator 208 further accesses a calibrated census audience size for the selected demographic group d at the determined demographic granularity level and the selected device type t (e.g., C as described with reference to Table 1 above) (block 610). For example, the market segment calculator 208 may obtain the calibrated census audience size C from the demographics determiner 204 of
The example market segment calculator 208 calculates a device audience for the selected demographic group d, the selected market segment s, and the selected device type t (e.g., D as described with reference to Table 1 above) (block 612). For example, the market segment calculator 208 may calculate the device audience D using the formula D=C×(A/B), which applies the ratio or percentage of the market segment to the panelists in the selected demographic group d to the number of impressions and/or audience size attributed to the selected demographic group d by the demographics determiner 204.
The example market segment calculator 208 determines whether there are additional mobile device types t for which to calculate a device audience D (block 614). If there are additional device types (block 614), control returns to block 604 to select a next device type. When there are no more device types (block 614), the example market segment calculator 208 calculates a total device audience (e.g., E of Table 1 discussed above) for the selected demographic group d, the selected market segment s, and all device types t1-tn (e.g., devices types for which a device audience D was calculated) (block 616). For example, the market segment calculator 208 may calculate the total device audience E using the formula E=ΣtD, or the summation of D over all device types t.
The example market segment calculator 208 accesses the non-mobile audience size F for the selected demographic group d at the determined demographic granularity level and the selected market segment s (e.g., F as described above with reference to Table 1) (block 618). For example, when the total device audience E calculated in block 616 represents only mobile devices, the market segment calculator 208 may access the audience for one or more non-mobile device types (e.g., television, radio, desktop computer, etc.).
The example market segment calculator 208 calculates a total audience size for the demographic group d at the determined demographic granularity level and for the market segment s (e.g., G as described above with reference to Table 1) (block 620). For example, the market segment calculator 208 may calculate the total audience size G using the formula G=F+E. The resulting total audience size and/or number of impressions G may be used as a cross-platform ratings measure and/or an audience measurement.
The example market segment calculator 208 determines whether there are additional demographic groups for which to calculate total audience size G (block 622). For example, the market segment calculator 208 may calculate the device audience (block 616) and/or the total audience (block 620) for multiple demographic groups. If there are additional demographic groups (block 622), control returns to block 602 to select another demographic group d. When there are no additional demographic groups (block 622), the example instructions 600 end and control returns to a calling function such as block 418 of
The processor platform 700 of the illustrated example includes a processor 712. The processor 712 of the illustrated example is hardware. For example, the processor 712 can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer. The example processor 712 of
The processor 712 of the illustrated example includes a local memory 713 (e.g., a cache). The processor 712 of the illustrated example is in communication with a main memory including a volatile memory 714 and a non-volatile memory 716 via a bus 718. The volatile memory 714 may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory 716 may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory 714, 716 is controlled by a memory controller.
The processor platform 700 of the illustrated example also includes an interface circuit 720. The interface circuit 720 may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a Peripheral Component Interconnect (PCI) express interface.
In the illustrated example, one or more input devices 722 are connected to the interface circuit 720. The input device(s) 722 permit(s) a user to enter data and commands into the processor 712. 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.
One or more output devices 724 are also connected to the interface circuit 720 of the illustrated example. The output devices 724 can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a tactile output device, a light emitting diode (LED), a printer and/or speakers). The interface circuit 720 of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip or a graphics driver processor.
The interface circuit 720 of 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 network 726 (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.).
The processor platform 700 of the illustrated example also includes one or more mass storage devices 728 for storing software and/or data. Examples of such mass storage devices 728 include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives.
The coded instructions 732 of
From the foregoing, it will be appreciated that methods, apparatus and articles of manufacture have been disclosed which enhance the operations of a computer to improve the attribution accuracy of distribution of impression-based data such as unique audience sizes, impression counts, and duration units to market segments so that computers and processing systems therein can be relied upon to produce audience analysis information with higher accuracies. In some examples, computer operations can be made more efficient based on the above equations and techniques for attributing unique audience sizes, impression counts, and/or duration units to market segments. That is, through the use of these processes, computers can operate more efficiently by relatively quickly determining parameters and applying those parameters through the above disclosed techniques to determine the correct attributions. For example, using example processes disclosed herein, a computer can more efficiently and effectively attribute impressions, audience sizes, and/or durational units in development or test data logged by the AME 108 and the database proprietors 104a-b without using large amounts of network communication bandwidth (e.g., conserving network communication bandwidth) and without using large amounts of computer processing resources (e.g., conserving processing resources) to continuously communicate with individual online users (e.g., non-panel online users) to request survey responses about their online media access habits and personal details (e.g., personal details relating to which market segment(s) they belong) and without needing to rely on such continuous survey responses from such online users. Survey responses from online users can be inaccurate due to inabilities or unwillingness of users to recollect online media accesses and/or to divulge personal details for audience measurement purposes. Survey responses can also be incomplete, which could require additional processor resources to identify and supplement incomplete survey responses. As such, examples disclosed herein more efficiently and effectively attribute of impressions, unique audience sizes, and/or durational units to market segments. Such corrected data is useful in subsequent processing for identifying exposure performances of different media so that media providers, advertisers, product manufacturers, and/or service providers can make more informed decisions on how to spend advertising dollars and/or media production and distribution dollars.
Although certain example methods, apparatus and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent.
This patent arises from a continuation of U.S. patent application Ser. No. 16/429,792, filed on Jun. 3, 2019, which is a continuation of U.S. patent application Ser. No. 14/729,870 (now U.S. Pat. No. 10,311,464), filed on Jun. 3, 2015, which claims priority to U.S. Provisional Patent Application No. 62/026,001, filed Jul. 17, 2014, and to U.S. Provisional Patent Application No. 62/117,253, filed Feb. 17, 2015, all of which are hereby incorporated herein by reference in their entireties.
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| Number | Date | Country | |
|---|---|---|---|
| 20210342879 A1 | Nov 2021 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62117253 | Feb 2015 | US | |
| 62026001 | Jul 2014 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16429792 | Jun 2019 | US |
| Child | 17378455 | US | |
| Parent | 14729870 | Jun 2015 | US |
| Child | 16429792 | US |
