SYSTEMS AND METHODS FOR A TELEVISION SCORING SERVICE THAT LEARNS TO REACH A TARGET AUDIENCE

Abstract

Television is the largest advertising category in the United States with over 65 billion spent by advertisers per year. A variety of different targeting algorithms are compared, ranging from the traditional age-gender targeting methods employed based on Nielsen ratings, to new approaches that attempt to target high probability buyers using Set Top Box data. The performance of these different algorithms on a real television campaign is shown, and the advantages and limitations of each method are discussed. In contrast to other theoretical work, all methods presented herein are compatible with targeting the existing 115 million Television households in the United States and are implementable on current television delivery systems.

Description

TECHNICAL FIELD

The present disclosure relates to systems and methods for evaluating television media instances for advertisement spots based on various factors for reaching television viewers who are desired product buyers.

BACKGROUND

Television is the largest advertising medium in the United States, with over 65 billion dollars in advertising revenue in 2011. According to Nielsen, approximately 20 times more hours are spent viewing TV as compared to viewings on either the Internet or mobile video. In 2013, there were about twice as many original programs on TV as compared to 2005, and over 60% of viewers were using High Definition (“HD”) TVs.

If there is an area for improvement in TV, it is around how advertising can be effective and targeted to viewers. TV advertising is unlike online advertising because it has traditionally been a broadcast medium, i.e., a one way transmission of TV programs to the viewer with no direct feedback. In online advertising, it is possible to deliver ads to individual persons, via cookies and IP addresses, and to then track the behavior of those persons, including whether they convert after seeing the advertisement by observing their clicks on advertisements and conversions on web sites.

In TV, advertisements may be embedded in a single high definition video stream, and broadcast using over-the-air terrestrial transmission towers, satellite, and/or cable. The single signal transmission enables high bandwidth and very high quality TV signal. However, this introduces significant limitations. Apart from small experimental TV systems, there are currently no available technologies for delivering advertisements one-to-one to households at a scale equivalent to TV broadcasting.

A second major limitation is determining whether a purchase was influenced by the TV advertisement. Standard TV systems do not allow advertisers to know if individuals saw the advertisements. Further, standard TV systems cannot determine if an individual who is purchasing a product or service, saw the advertisement.

Because of these and other limitations, since the 1950s, this medium has been tracked using a 25,000 person, Nielsen “panel” with “diaries.” The individuals on Nielsen's panel could report on what they saw on TV, and then this data could be extrapolated across the United States (115,000,000 households). This panel is both small and yet expensive to maintain. However, in the United States, set top boxes (“STBs”) are now present in over 91.5% of US homes. Further, since 2009, STBs with return path capabilities have proliferated in the United States, comprising over 30% of STBs in households. The number of households with STBs is greater in size than the Nielsen panel, and the scale and richness of detail of STB data allows for new capabilities in TV advertisement targeting.

In order to utilize new capabilities, the present disclosure relates to systems and methods that use current U.S. data collection and U.S. TV broadcasting capabilities. As will be discussed in further detail below, the systems and methods discussed herein provide a framework for understanding certain TV targeting problems and approaches for solving them. Benefits of the present disclosure may include providing detailed descriptions of data formats available for television targeting; formalizing TV advertisement targeting problems into one or more objective functions; identifying variables available for advertisement targeting that can be used for targeting practical TV advertisement campaigns; providing a plurality of algorithms for TV data; and combining the plurality of algorithms to provide desired results.

SUMMARY OF THE DISCLOSURE

According to certain embodiments, methods are disclosed for teaching a television targeting system to reach product buyers. One method includes receiving, at a server, one or more heterogeneous sources of media data, the media data including television viewing events; generating, by the server, a plurality of media asset patterns from the one or more heterogeneous sources of media data, the plurality of media asset patterns being possible media placements which are represented as conjunctive expressions; calculating, by the server, one or more heterogeneous advertisement effectiveness measures for each media asset pattern; calculating, by the server for a plurality of pairs of an advertisement and a media instance, a number of previously placed airings of the advertisement in the media instance; and generating, by the server, a model to predict advertisement effectiveness for each pair of an advertisement and a media instance based on a combination of the ad effectiveness measures and the number of previously placed airings of the advertisement in the media instance.

According to certain embodiments, systems are disclosed for teaching a television targeting system to reach product buyers. One system includes a data storage device storing instructions; and a processor configured to execute the instructions to perform a method including: receiving, at a server, one or more heterogeneous sources of media data, the media data including television viewing events; generating, by the server, a plurality of media asset patterns from the one or more heterogeneous sources of media data, the plurality of media asset patterns being possible media placements which are represented as conjunctive expressions; calculating, by the server, one or more heterogeneous advertisement effectiveness measures for each media asset pattern; calculating, by the server for a plurality of pairs of an advertisement and a media instance, a number of previously placed airings of the advertisement in the media instance; and generating, by the server, a model to predict advertisement effectiveness for each pairing of an advertisement and a media instance based on a combination of the ad effectiveness measures and the number of previously placed airings of the advertisement in the media instance.

Additional objects and advantages of the disclosed embodiments will be set forth in part in the description that follows, and in part will be apparent from the description, or may be learned by practice of the disclosed embodiments. The objects and advantages of the disclosed embodiments will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. As will be apparent from the embodiments below, an advantage to the disclosed systems and methods is that multiple parties may fully utilize their data without allowing others to have direct access to raw data. The disclosed systems and methods discussed below may allow advertisers to understand users' online behaviors through the indirect use of raw data and may maintain privacy of the users and the data.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various exemplary embodiments and together with the description, serve to explain the principles of the disclosed embodiments.

FIG. 1A depicts an exemplary analytics environment and an exemplary system infrastructure for modeling and detailed targeting of television media, according to exemplary embodiments of the present disclosure;

FIG. 1B depicts exemplary data feeds of one or more media agencies of media plan data, according to exemplary embodiments of the present disclosure;

FIG. 1C depicts exemplary data feeds of one or more media agencies of media verification data, according to exemplary embodiments of the present disclosure;

FIG. 1D depicts exemplary data feeds of one or more media agencies of trafficking/distribution data, according to exemplary embodiments of the present disclosure;

FIG. 1E depicts exemplary data feeds of call center data of one or more call centers, according to exemplary embodiments of the present disclosure;

FIG. 1F depicts exemplary data feeds of e-commerce data of one or more e-commerce data vendors, according to exemplary embodiments of the present disclosure;

FIG. 1G depicts exemplary data feeds of order data of one or more data order processing/fulfillment providers, according to exemplary embodiments of the present disclosure;

FIG. 1H depicts exemplary data feeds of consumer data enrichment of one or more audience data enrichment providers from one or more data bureaus, according to exemplary embodiments of the present disclosure;

FIG. 11 depicts exemplary data feeds of guide data of one or more guide services, according to exemplary embodiments of the present disclosure;

FIG. 1J depicts exemplary data feeds of panel data of one or more panel data enrichment providers, according to exemplary embodiments of the present disclosure;

FIG. 2A depicts a graph of person-level conversions per advertisement view for certain products, according to exemplary embodiments of the present disclosure;

FIG. 2B depicts another graph of person-level conversions per advertisement view for certain products, according to exemplary embodiments of the present disclosure;

FIG. 2C depicts a graph of phone calls per million impressions in response to an embedded phone number in a TV advertisement observed after placing the advertisement in the same station-day-hour, according to exemplary embodiments of the present disclosure;

FIG. 3A depicts a graph of three major classes of an ad effectiveness metric including demographic match, phone response per impression (“RPI”), and buyers per impression (“BPI”) versus the size of media being scored, according to exemplary embodiments of the present disclosure;

FIG. 3B depicts a bar graph of usability of the three major classes of an ad effectiveness metric including demographic match, RPI, and BPI, according to exemplary embodiments of the present disclosure;

FIG. 4A depicts a bar graph in which all variables for a given ad effectiveness metric may be selected, according to exemplary embodiments of the present disclosure;

FIG. 4B depicts a bar graph in which missing value variables may be allowed and/or selected, according to exemplary embodiments of the present disclosure;

FIG. 4C depicts a bar graph of a comparison of variables (and weights) selected versus the variable correlations, according to exemplary embodiments of the present disclosure;

FIG. 5 depicts a graph of predicted ad response versus future responses per million impressions, according to exemplary embodiments of the present disclosure;

FIG. 6A depicts a graph of generated media asset pattern being tested over time, according to exemplary embodiments of the present disclosure;

FIG. 6B depicts graph of another generated media asset pattern being tested over time, according to exemplary embodiments of the present disclosure;

FIG. 6C depicts a graph of yet another generated media asset pattern being tested over time, according to exemplary embodiments of the present disclosure;

FIG. 7A depicts exemplary pseudo code in which queries count historical airings by station-day-hour, and count a number of airings in a program, according to exemplary embodiments of the present disclosure;

FIG. 7B depicts another exemplary pseudo code in which queries count historical airings by station-day-hour, and count a number of airings in a program, according to exemplary embodiments of the present disclosure;

FIG. 8 is a simplified functional block diagram of a computer that may be configured as a device or server for executing the methods, according to exemplary embodiments of the present disclosure;

FIGS. 9A-9N depict exemplary charts and graphs of how programs change in popularity, according to exemplary embodiments of the present disclosure;

FIG. 10 depicts an example of a branched model, according to exemplary embodiments of the present disclosure;

FIG. 11 depicts an error analysis of impressions forecasting, according to exemplary embodiments of the present disclosure;

FIG. 12 depicts an exemplary accuracy analysis on various conditions, according to exemplary embodiments of the present disclosure;

FIG. 13 depicts an exemplary process for automated media scoring, according to exemplary embodiments of the present disclosure;

FIG. 14 depicts an example of a sample scored output text file, according to exemplary embodiments of the present disclosure;

FIG. 15 depicts another example of a sample scored output text file, including sample scored output, according to exemplary embodiments of the present disclosure;

FIG. 16 depicts another example JSON output from the scoring service showing a media instance being scored, according to exemplary embodiments of the present disclosure;

FIG. 17 depicts an exemplary graph of standardized score (x-axis) versus buyers per million impressions (y-axis), according to exemplary embodiments of the present disclosure;

FIG. 18 depicts an exemplary graph of a comparison of Media Asset Patterns, according to exemplary embodiments of the present disclosure;

FIG. 19 depicts an exemplary graph depicting that the program is often poorly populated; and

FIGS. 20 and 21 depict an exemplary graph showing that program authority is not as predictive as the program.

DETAIL DESCRIPTION OF EMBODIMENTS

Aspects of the present disclosure, as described herein, relate to determining what television programs to place advertisements on for certain products, by evaluating aspects of the viewers of those television programs. Aspects of the present disclosure involve recognizing that media may be represented and evaluated by the demographics of the people who watch that media. The system may perform a match against media by looking for the television program whose viewers are the closest match to the customers that buy the product to be advertised. After the system finds a close match, it may recommend buying that media (i.e., placing the product ad within that television program). Aspects of the present disclosure may use targeting capabilities, tracking, and delivery, and may add in individualized information to its demographic segment information in order to improve the matching quality.

In one embodiment, the method used by a media buyer may include using Nielsen aggregated data to determine which program to purchase. Furthermore, while a Nielsen panel may be a useful data source and use of this data is described in this disclosure, the Nielsen viewer panel may be somewhat limited by its relatively small size, and limitations in covering certain geographic areas. Accordingly, a variety of enhancements are discussed for making the techniques described below compatible with multiple other data sources (including census data, set top box data, and linked buyer data) so as to create a highly complete and rich profile based on millions of viewers, over 400 variables, and buyers rather than viewers.

Various examples of the present disclosure will now be described. The following description provides specific details for a thorough understanding and enabling description of these examples. One skilled in the relevant art will understand, however, that the present disclosure may be practiced without many of these details. Likewise, one skilled in the relevant art will also understand that the present disclosure may include many other related features not described in detail herein. Additionally, some understood structures or functions may not be shown or described in detail below, so as to avoid unnecessarily obscuring the relevant description.

The terminology used below may be interpreted in its broadest reasonable manner, even though it is being used in conjunction with a detailed description of certain specific examples of the present disclosure. Indeed, certain terms may even be emphasized below; however, any terminology intended to be interpreted in any restricted manner will be overtly and specifically defined as such in this Detailed Description section.

The systems and method of the present disclosure allow for the receiving and processing of TV (media) related data and consumer related data from a plurality of different data sources and of a variety of different data types and formats. Based on the received data, the systems and methods may build a model that may be used to estimate a probability of reaching a particular set of persons. The estimated probability may then be used to determine a value associated with buying an advertisement spot within a television program for the advertisement.

System Architecture

Any suitable system infrastructure may be put into place to receive media related data to develop a model for targeted advertising for television media. FIG. 1A and the following discussion provide a brief, general description of a suitable computing environment in which the present disclosure may be implemented. Although not required, aspects of the present disclosure are described in the context of computer-executable instructions, such as routines executed by a data processing device, e.g., a server computer, wireless device, and/or personal computer. Those skilled in the relevant art will appreciate that aspects of the present disclosure can be practiced with other communications, data processing, or computer system configurations, including: Internet appliances, hand-held devices (including personal digital assistants (“PDAs”)), wearable computers, all manner of cellular or mobile phones (including Voice over IP (“VoIP”) phones), dumb terminals, media players, gaming devices, multi-processor systems, microprocessor-based or programmable consumer electronics, set-top boxes, network PCs, mini-computers, mainframe computers, and the like. Indeed, the terms “computer,” “server,” and the like, are generally used interchangeably herein, and refer to any of the above devices and systems, as well as any data processor.

Aspects of the present disclosure may be embodied in a special purpose computer and/or data processor that is specifically programmed, configured, and/or constructed to perform one or more of the computer-executable instructions explained in detail herein. While aspects of the present disclosure, such as certain functions, are described as being performed exclusively on a single device, the present disclosure may also be practiced in distributed environments where functions or modules are shared among disparate processing devices, which are linked through a communications network, such as a Local Area Network (“LAN”), Wide Area Network (“WAN”), and/or the Internet. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Aspects of the present disclosure may be stored and/or distributed on non-transitory computer-readable media, including magnetically or optically readable computer discs, hard-wired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memory, biological memory, or other data storage media. Alternatively, computer implemented instructions, data structures, screen displays, and other data under aspects of the present disclosure may be distributed over the Internet and/or over other networks (including wireless networks), on a propagated signal on a propagation medium (e.g., an electromagnetic wave(s), a sound wave, etc.) over a period of time, and/or they may be provided on any analog or digital network (packet switched, circuit switched, or other scheme).

Use of the system of FIG. 1A may involve multiple initial steps of setting up data feeds that can be used to receive data for building one or more models as described herein for evaluating television programs, estimating ad effectiveness, and estimating ad response.

One step may be to setup data feeds with one or more media agencies, which may ensure the collection of all the data about what media is being purchased, running, and trafficked to stations. This may also ensure that there is an accurate representation of the available television media. This step may include setting up data feeds for one or more of: media plan data (e.g., as shown in FIG. 1B), which may include data that is produced by media buyers purchasing media to run in the future; media verification data (e.g., as shown in FIG. 1C), which may include data that is generated by third-party verification services; and/or trafficking/distribution data (e.g., as shown in FIG. 1D), which may include sample trafficking instructions and/or order confirmations sent to TV stations; media response data which is the response of viewers to the TV ad, captured either through web activity, phone activity or other responses; TV schedule guide data which comprises data on upcoming program airings, TV set top box data which comprises a record of viewing activity from set top box subscribers; TV panel data which comprises a record of viewing activity from television viewers.

Media plan data may include a station a commercial will run on, an advertiser, topic information, a media cost associated with the purchase, a phone number, and/or a web address that is associated with the commercial for tracking purposes.

Third-party verification services may watermark commercials and monitor when the media was run across all TV stations. The data generated by third-party verification services may be used to verify that a media instance that was purchased for an advertisement spot was actually displayed on TV.

The sample trafficking instructions and/or order confirmation may include a product that was purchased, and instructions that a station is to use when displaying a commercial.

Another step may be to setup data feeds with one or more call centers, which may ensure there is accurate data about callers that called into specific phone numbers. This step may include receiving a call center data feed (e.g., as shown in FIG. 1E). Call center data may include any data associated with phone responses to phone numbers displayed in a commercial.

Yet another step may be to setup one or more data e-commerce vendor data feeds. E-commerce data feeds may be setup to receive recurring data feeds with a vendor and/or internal system of an advertiser that records orders that come in from an advertiser's website (e.g., as shown in FIG. 1F). E-commerce data may include orders that came in on an advertiser's website, customer information, and/or a time, volume, and/or substance of the orders. Another step may be to set up one or more web activity feeds with a vendor and/or internal system of an advertiser that records web activity corresponding to TV broadcasts.

Another step may be to setup one or more data order processing/fulfillment data feeds. Data order processing/fulfillment data feeds may be setup to receive recurring data feeds with order vendor and/or internal system that physically handles the logistics of billing and/or fulfillment. This step may ensure an accounting of subsequent purchases, such as subscriptions and for returns/bad debt, etc., and may ensure accurate accounting for revenue. This step may also include receiving data from a series of retail Point of Sale (“PoS”) systems (e.g., as shown in FIG. 1G). Order data may include a purchase record, subsequent purchases, debt collection information, and return information.

Another step may be to setup one or more audience data enrichment data feeds with one or more data bureaus. This step may ensure that callers, web-converters, and/or ultimate purchasers have their data attributes appended to their record in terms of demographics, psychographics, behavior, etc. (e.g., as shown in FIG. 1H). Examples of data bureaus may include Experian, Acxiom, Claritas, etc. This data may include attributes about consumers from the various data bureaus, such as demographics, psychographics, behavioral information, household information, etc.

Yet another step may be to setup one or more data feeds with one or more guide services. This step may ensure that forward looking guide service data is ingested into the system. This data may be programming based on what is going to run on television for the weeks ahead (e.g., as shown in FIG. 11). This upcoming media may be scored to determine which of this media should be purchased. Program guide data may include data related to a future run of programming, such as a station, time, program name, program type, stars, and general text description.

Another step may be to setup one or more data feeds for panel data enrichment. Data related to purchasers of products on television, set top box viewer records, and/or existing panels may be received as a data feed and appended to an advertiser's purchaser data mentioned above (FIG. 1J). Panel data enrichment may include viewer/responder data, such as demographic, psychographic, and/or behavioral data.

In another step, all of the underlying data may be put into production. For example, all of the data feeds setup from steps one through seven may be loaded into an intermediate format for cleansing, adding identifiers, etc. Personally Identifiable Information (“PII”) may also be split and routed to a separate pipeline for secure storage. As shown in FIG. 1A, an analytics environment 100 may include a media processing system 102, an agency data system 104, an advertiser data system 106, an audience data system 108, and a processed media consumer system 110.

At the next step, media plan data 104a, verification data 104b, and/or trafficking data 104c of the agency data system 104 may be received at a data feed repository 112 of the media processing system 102. Further, call center data 106a, e-commerce data 106b, and/or order management data 106c of advertiser data system 106 may be received at the data feed repository 112. Additionally, viewer panel data 108a, guide data 108b, and/or consumer enrichment data 108c of the audience data system 108 may be received at the data feed repository 112. After one or more of data feeds are received by the feed repository 112, data may be extracted from the data feeds by extractor 114 of media processing system 102.

At another step, business logic/models may be run for matching responses and orders to media (“attribution”). In this step, the data extracted from the data feeds has been ingested into the system at the most granular form. Here, the phone responses may be matched up to media that generated it. The e-commerce orders may be matched using statistical models to the media that likely generated them. As shown in FIG. 1A, transformer 116, aggregator 118, and analytics engine 120 of the media processing system 102 may process the aggregated data of the data feeds. Analytics engine 120 may include various sub-engines, such as experiment engine 120a, match engine 120b, optimize engine 120c, and/or attribute engine 120d, to perform various analytical functions.

At yet another step, the analyzed data may be loaded into databases. For example, the data may have already been aggregated and/or final validation of the results may have been completed. After this, the data may be loaded by loader 122 into one or more databases 124 for use with any of the upstream media systems, such as data consumers system 110. These include the ability to support media planning through purchase suggestions, revenue predictions, pricing suggestions, performance results, etc. One or more databases 124 may include customers database 124, campaign database 124, station inventory database 124, performance database 124, models database 124, and/or PII database 124.

At another step, the analyzed data may be used by presentation module 126. In this step, all of the data may be accessible to the operators of various roles in the media lifecycle. This may include graphical tools for media planning (where the targeting in this application primarily fits), optimization, billing, trafficking, reporting, etc.

The above-described system may be used to gather, process, and analyze TV related data. This data may then be used to identify certain available media instances, or advertisement spots, that an advertiser may purchase to display an advertisement. As will be described in further detail below, advertisement spots, also referred to as media instances, may be evaluated and scored to assist an advertiser in choosing which media instance to purchase.

Media Instances

As described above, a TV media instance, Mi, may be any segment of time on TV that may be purchased for advertising. The media instance, Mi, as an element of the Cartesian product, may be defined as follows:

M _i ∈S×P×D×H×T×G×POD×POS×L

• • where S is station, P is program, D is day-of-week, H is hour-of-day, T is calendar-time, G is geography, POD is the ad-pod, POS is the pod-position, and L is media-length.

Stations may include broadcast and/or cable stations, and may be identified by their respective call letters, such as KIRO and CNN. Geography may include national (nationwide), one or more direct market association areas, such as Miami, Florida, and/or cable zones, such as Comcast Miami Beach.

An ad-pod may be a set of advertisements that run contiguously in time during a commercial break for a TV program. Pod-position may be the sequential order of the advertisement within its pod. Media length may be the duration of the time segment in seconds. Media length, for example, may include 15, 30, 45, and/or 60 second spots.

The present disclosure allows the advertiser to select a set of media instances, M_i, to purchase for advertisement targeting for an ideal audience. The present disclosure also allows the advertiser to provide a bid, CPI(M_i) cost per impression, such that the expected advertisement response per dollar is maximized, as follows:

M _i:max Σ_irpi_Ω(M_i)·I(M_i)

subject to Σ_iCPI(M_i)·I(M_i)≤B and V({M_i})=true

where rpi_Ω(M_i) is the response (also referred to as a conversion, a sale, and/or revenue) per impression or target-audience-concentration per impression or probability-of-target-audience per impression for the given media instance, M_i; I(M_i) are the impressions for media instance, M_i; B is the TV campaign budget; and V determines if the set of media instances, M_i, violates advertiser-defined rotation rules. Rotation rules may be, for example, running an advertisement no more than once per 60 minutes, having no greater than 5% of budget on any one network or day-part, etc. Rotation rules may be defined by TV advertisement buyers and/or broadcast networks.

One embodiment of the present disclosure is to iteratively select media instances in order of value per dollar, as follows:

$M_i:\max \frac{rp{i}_{\Omega }\left(M_i\right)}{\mathrm{CPI}\left(M_i\right)}$

• • subject to rotation rule constraints V until the budget is filled. CPI(M_i) and rpi_Ω(M_i) are both estimates using historical clearing prices and media observations.

Methods will next be described for estimating the response per impression or target-audience-concentration per impression “rpi_Ω(M_i)” part of the formula above.

Media Asset Patterns

A media asset pattern may be any set of variable value instantiations of a media instance. Formally, media asset pattern, may be a subset of instantiated features from the media instance M_im_i,t⊆M_i, for example, a future media instance that is under consideration to buy may be M_i1=(CNN, 8 pm, “Piers Morgan”, Tuesday, Dec. 12, 2012, Pod1, Pos2, 60 s). The following media asset patterns may be used to predict its performance: Station m_i1=(CNN); Station-Hour-Pod m_i2=(CNN, 8 pm, Pod1); Geography-Station m_i3=(National-CNN); and others.

Table 1, below, shows a list of Media Asset Patterns used in one embodiment of the present disclosure.

TABLE 1
Media Asset Pattern types, and RPI functions used in one embodiment
		Response
		per
MapType		impression
NameSanitized	MAPType	calculation	Description
1-MBDemo-Station	Station	TRatio	Match between Panel viewer
			demographics for a station
			and product buyers
2-MBDemo-Program	Program	TRatio	Match between Panel viewer
			demographics for a program
			and product buyers
3-Genre	NULL	TRatio	Match between Panel viewer
			demographics for a program's
			genre classification and
			product buyers
4-MBDemo-Station-	Station-	TRatio	Match between Panel viewer
Rotation	Rotation		demographics for a Station-
			Daypart and product buyers
5-MBDemo-Day of	Day of	TRatio
Week-Hour of Day	Week-
	Hour of Day
6-MBDemo-Day of	Day of	TRatio
Week	Week
7-MBDemo-Hour of Day	Hour of Day	TRatio
8-MediaMarket	MediaMarket	TRatio
9-State	State	TRatio
10-State per capita	State	TRatio	Number of buyers per capita
			in a state
11-DMA per capita	DMA	TRatio	Number of buyers per capita
			in a DMA area
12-Zone	Zone	TRatio	Number of buyers per capita
			in a cable zone area
13-Zone per capita	Zone	TRatio	Number of buyers per capita
			in a cable zone area
14-MBDemo-Station-	Station-	TRatio
Day-Hour	Day-Hour
15-Advertising Patch	Patch	TRatio	Number of buyers per capita
Area per capita			in an advertising patch area
16-STB Device-Station	Station	TRatio	Match between STB Device
			level demographics for Station
			and product buyers
17-STBDevice-Station-	Station-	TRatio
Rotation	Rotation
18-STBDevice-Station-	Station-	TRatio
Day-Hour	Day-Hour
19-STBDevice-Station-	Station-	TRatio
Day	Day
20-STBDevice-Station	Station	TRatio
21-STBDevice-Station-	Station-	TRatio
Rotation	Rotation
22-STBDevice-Station-	Station-	TRatio
Day-Hour	Day-Hour
23-STBDevice-Day of Week	Day of Week	TRatio
24-STBDevice-Station-	Station-	TRatio
Program Authority	Program
	Authority
25-STBDevice-Program	Program	TRatio
26-Zip Code per capita	Zipcode	TRatio	Number of buyers per capita
			in Zipcode
27-STBHead-Station	Station	TRatio	Match between STB Head-
			End level Station viewing
			demographics and buyers
28-STBHead-Program	Program	TRatio
29-STBHead-Day of	Day of	TRatio
Week	Week
30-STBHead-Hour of	Hour of Day	TRatio
Day
31-STBHead-Station-	Station-	TRatio
Rotation	Rotation
32-STBHead-Station-	Station-	TRatio
Day-Hour	Day-Hour
33-USCensus-DMA	DMA	TRatio	Match between US Census
			demographics for DMA and
			product buyers
34-USCensus-Zip Code	Zip Code	TRatio	Match between US Census
			demographics for zip and
			product buyers
35-STBDevice-Station-	Station-	TRatio
Day-Hour-Program	Day-Hour-
	Program
36-STBHead-DMA-	DMA-	TRatio
Station-Day-Hour	Station-
	Day-Hour
37-Telesale-Station	Station	RPI	Phone responses per
			impression historically
			recorded when running on this
			national station (e.g., ABC)
38-Telesale-Station-	Station-	RPI	Phone responses per
Day-Hour	Day-Hour		impression historically
			recorded when running on this
			station-day-hour
39-Telesale-Station-	Station	RPI	Phone responses per
Local			impression historically
			recorded when running on this
			local area station (e.g., KIRO)
40-Telesale-Station-	Station-	RPI
Day-Hour-Local	Day-Hour
41-STBHead-Actual Airings	Airing	Impressions
42-STBHead-DMA-	DMA-	Impressions
Station-Day-Hour-	Station-
Local	Day-Hour
43-Telesale-Phone	Phone	RPI
Response Actual Airings	Response
	Actual
	Airings
44-STBSale-Source	Airing	SourceViewPct
Viewers Actual
45-STBSale-Station-	Station-	SourceViewPct	Buyers per impression
Day-Hour	Day-Hour		measured in the audience of
			this station-day-hour
46-STBSale-Station	Station	SourceViewPct
47-STBSale-Station-	Station-	SourceViewPct
Program	Program
48-AgeGender-	Airing	Impressions
CompetitiveData Source
Actual Airings
49-AgeGender-DMA-	DMA-	Impressions
Station	Station
50-AgeGender-DMA-	DMA-	Impressions
Station-Day-Hour	Station-
	Day-Hour
51-AgeGender-Station-	Station-	TRatio
Day-Hour	Day-Hour
52-AgeGender-Station	Station	TRatio	Match between age-gender
			demographics of panel
			viewers on this station versus
			buyers
53-AgeGender-Station-	Station-	TRatio
Program	Program
54-AgeGender-	Station-	TRatio	Match between age-gender
Syndication Station-	Program		demographics of panel
Program Authority	Authority		viewers on this Syndication
			station versus buyers
55-AgeGender-Program	Program	TRatio
Authority	Authority
56-STBDevice-	Airing	TRatio
ActualAiring
57-Telesale-Station	Station	RPI
58-Telesale-Station-	Station-	RPI
Day-Hour	Day-Hour
59-AgeGender-Station-	Station-	TRatio
Program Authority	Program
	Authority
60-STBHead-Station-	Station-	TRatio
Program Authority	Program
	Authority
61-AgeGender-DMA-	DMA-	Cost
Station-Day-Hour-	Station-
Local	Day-Hour
62-AgeGender-DMA-	DMA-	Cost
Station-Local	Station
63-AgeGender-DMA	DMA	Cost
Station-Program	Station-
Authority-Local	Program
	Authority
64-AgeGender-	Airing	Cost
CompetitiveData Actual
Airings-Local
65-AgeGender-	Station-	Impressions
SpecialEvent-Station-	Program
Program Authority	Authority
66-STBHead-	Station-	Impressions
SpecialEvent-Station-	Program
Program Authority	Authority
67-STBHead-Station-	Station-	Impressions
Day-Hour-Local/Airing	Day-Hour
68-5 minute Attributed	Station-	WPI
Web Spike Station-	Program
Program Authority	Authority
69-5 minute Attributed	Station-	WPI
Web Spike Station-	Day-Hour
Day-Hour
70-5 minute Attributed	Station	WPI
Web Spike Station
71-Day Hour Subtracted	Station-	WPI
Web Response Verified	Program
Airing-Station-	Authority
Program Authority
72-Day Hour Subtracted	Station-	WPI
Web Response Verified	Day-Hour
Airing Station-Day-
Hour
73-Day Hour Baseline	Station-	WPI
Subtracted Web Response	Day-Hour
Verified Airing
Station-Day-Hour
74-STBHead-Station-	Station-	TRatio
Day-Hour-Quarter	Day-Hour-
	Quarter
75-STBHead-Program-	Program	TRatio
Quarter	Authority-
	Quarter
76-AgeGender-Program-	Program	TRatio
Quarter	Authority-
	Quarter
77-STB Head-Weekpart-	Weekpart-	TRatio
Daypart-SpecialEvent-	Daypart-
Station-Program	Station-
Authority	Program
	Authority
78-AgeGender-	Weekpart-	TRatio
Weekpart-Daypart-	Daypart-
SpecialEvent-Station-	Station-
Program Authority	Program
	Authority
79-N-Magazine	Magazine	TRatio
80-STBHead-LocalDMA-	DMA	TRatio
Station-Program	Station-
	Program
	Authority
81-STBHead-STBHead-	Station-	TRatio
Currrent Quarter-	Program
Station-Program	Authority
Authority
82-AgeGender-Station-	Station-	TRatio
Day-Hour	Day-Hour
83-AgeGender-Station-	Station-	TRatio
Program	Program
	Authority
84-STBSale-Station-	Station-	SourceView
Program	Program	MinutesPct
	Authority
84-STBSale-Station-	Station-	SourceView
Day-Hour	Day-Hour	MinutesPct
86-Station-Program	Station-	Impressions
Authority	Program
	Authority-
	Quarter
87-Station-Day-Hour	Station-	Impressions
	Day-Hour-
	Quarter
89-AgeGender-Station-	Station-	Impressions
Day-Hour-Week	Day-Hour-
	Week
90-STBHead-FirstAiring-	Station-	Impressions
Station-Program	Program
	Authority-
	First Airing
91-AgeGender-DMA-	DMA-	TRatio
Station-Day-Hour	Station-
	Day-Hour
92-AttribuedWebSpike-	Station-	WPI
Station-Day-Hour	Day-Hour
93-AgeGender-Station-	Station-	Impressions
Program-PodA	Program
	Authority-
	Pod
94-STBHead-Actual	Same time	Impressions
Airings Minus 7 Days	minus 7
	days
95-STBHead-Actual	Same time	Impressions
Airings Minus 14 Days	minus 14
	days
96-STBHead-Actual	Same time	TRatio
Airings Minus 21 Days	minus 21
	days
97-STBHead-Actual	Same time	Impressions
Airings Minus 28 Days	minus 28
	days
98-STBHead-Station-	Station-	Impressions
Program-Hour	Program-
	Hour
99-STBHead-Actual	Most recent	Impressions
Airings-Station-Program-	known
Hour	airing of
	same
	program
100-STBHead-Actual	Same time	Impressions
Airings-Minus 29 to 42	29-42 days
Days	prior to
	present
101-STBHead-Actual	Same time	Impressions
Airings-Minus 43 to 56	43-56 days
Days	prior to
	present
102-STBHead-Actual	Same time	Impressions
Airings-Minus 57 to 70	57-70 days
Days	prior to
	present
103-AdapTV Video	Digital video	NULL
Publisher Sites	publisher site
104-AdapTV Segments	Digital	NULL
	Segment
105-AgeGender-	Station-	TRatio
Syndication-Station-	Day-Hour
Day-Hour
105-AgeGender-	Station-	TRatio
Syndication-Station-	Program
Program Authority	Authority

Examples of various media asset pattern types will now be described in more detail.

Media Asset Pattern Example 1A: Station-Program

TV programs are intuitively what people tune into when watching television. Different programs appeal to different people. For example, viewers of TLC's “I Didn't Know I Was Pregnant” may be different from viewers of SYFY's “Continuum.”

There are over 450,000 weekpart-daypart-programs available to be purchased on TV. The programs may be good predictors of advertisement performance. An example of media asset patterns and their calculated ad effectiveness scores is shown in table 2, below.

TABLE 2
Media Asset Pattern 60 (STBHead-Station-Program) and ad effectiveness scores
MediaAssetPatternKey	sourcesegmentkey	MediaAssetPatternTypeID	Correlation
ABC-Insanity Workout!	110356	60	0.05977
ABC-Inside Edition	110356	60	0.043434
ABC-Inside Story	110356	60	0.194032
ABC-Inside the Big East	110356	60	0.122061
ABC-Inside Washington	110356	60	−0.06444

Media Asset Pattern Example 1B: High-Value Station-Program

In addition to using programs in general, it is also possible to demarcate a special class of programs which may be referred to as “high impact programs.” These programs have high observed impressions per expected impressions for their station-timeslot,

$\frac{I\left(m_P\right)}{I\left(m_{SDH}\right)}.$

Impactful programs may include event programs, such as “The Academy Awards,” football games, and very popular reality programs, e.g., “Dancing with the Stars.” Impactful programs may also include “cultural phenomena,” such as “Honey Badgers!” Table 3 below depicts programs and their respective impressions performance relative to their expected performance in their timeslot. A media asset pattern of High-Impact-Program can then be established and used by the system.

TABLE 3
Station-Program                  RE
NFLN-NFL Football                20.49714
NBC-Super Bowl XLVI              18.06963
NFLN-Postgame                    15.35507
CBS-Super Bowl XLIV              15.2775
ESPN-NFL Football                12.66412
NBCSN-2012 NHL All-Star Game     10.47042
SPD-NASCAR Sprint Cup            10.39651
FOX-Super Bowl XLV               9.862597
E!-Live from the Red Carpet: The 2012 Grammy Awards 4.467404
NBC-Macy's Thanksgiving Day Parade 4.434626
ABC-Oscars Red Carpet Live       4.288276
BBCA-William & Kate: The First Year 4.135
ABC-Dancing With the Stars       4.126531
VH1-2010 MTV Video Music Awards  3.863292
ABC-CMA Awards 2011              3.831977
FUSE-Whitney Houston: A Tribute  3.770582
VH1-2011 Video Music Awards      3.423895
E!-Live from the Red Carpet: The Academy Awards 3.30741
NBC-Voice                        3.305157
CNN-Arizona Republican Presidential Debate 3.086414
CNN-New Hampshire GOP Debate     3.009244
E!-Live from the Red Carpet: Grammys 2.987157
WILD-Honey Badgers               2.939016

Media Asset Pattern Example 2: Station-Day-Hour

Stations often run similar programming in the same station-day-hour timeslots. This information may add value as a predictor, as some demographics may have a propensity to watch TV on certain times of day. For example, high income people tend to watch in prime-time, but not daytime. Weekday, daytime programming may be highly skewed toward older and/or lower income households.

TABLE 4
Media Asset Patterns for MAPType 32-STBHead-Station-Day-Hour
MediaAssetPatternKey	sourcesegmentkey	MediaAssetPatternTypeID	Correlation
ABC-Su-4 pm	110356	32	−0.49971
ABC-Su-5 am	110356	32	0.114984
ABC-Su-5 pm	110356	32	−0.26138
ABC-Su-6 am	110356	32	0.279073
ABC-Su-6 pm	110356	32	0.005131
ABC-Su-7 am	110356	32	0.115856
ABC-Su-7 pm	110356	32	−0.04855
ABC-Su-8 am	110356	32	0.07703
ABC-Su-8 pm	110356	32	−0.32483
ABC-Su-9 am	110356	32	−0.19655
ABC-Su-9 pm	110356	32	−0.43123

Media Asset Pattern Example 3: Program Master and Other Mastered Taxonomies

Program names are often recorded in television panel data and schedules in a variety of inconsistent ways, often because television program data is hand-entered. Thus when a buyer is attempting to buy “Cold Case,” the present disclosure may fail to find a match for “Cold Case” because the panel data might have recorded this as “Cold Case Sat.” In order to address this, the presently disclosed methods may use a series of mapping tables to map native panel strings to “mastered” versions of those strings, which may facilitate matching. The present disclosure also allows editors to inspect the native strings, and uses edit distance to identify similar mastered strings that each native string may be mapped to. These “mastered” program names are then used in media asset patterns. Examples of program master mappings are shown in Tables 5A-5C, below.

Table 5A: Program Master table showing entries for “Cold Case”. “Cold Case” appears in various panel sources described using a variety of strings. These are mapped to a consistent string (ProgramMaster).

TABLE 5A
ExternalProgramMappingID	NielsenShowTitle	Title
137564	COLD CASE	Cold Case
137567	COLD CASE FRI	Cold Case
137568	COLD CASE FRI 2	Cold Case
137569	COLD CASE FRI 3	Cold Case
137570	COLD CASE FRI 4	Cold Case
137571	COLD CASE FRI 5	Cold Case
137572	COLD CASE FRI 6	Cold Case
137573	COLD CASE FRI 7	Cold Case
137574	COLD CASE FRI 8	Cold Case
239948	COLD CASE FRI 9	Cold Case
137575	COLD CASE MON	Cold Case
137576	COLD CASE MON 2	Cold Case
137577	COLD CASE MON 3	Cold Case
225605	COLD CASE MON 4	Cold Case
225606	COLD CASE MON 5	Cold Case
225607	COLD CASE MON 6	Cold Case
137578	COLD CASE SPEC	Cold Case
137579	COLD CASE SUS 2	Cold Case
137580	COLD CASE SYN AT	Cold Case
137581	COLD CASE SYN	Cold Case
	MYNET AT
137582	COLD CASE THURS	Cold Case
137583	COLD CASE THURS 2	Cold Case
137584	COLD CASE THURS 3	Cold Case
137585	COLD CASE WED	Cold Case
137586	COLD CASE WED 2	Cold Case
137587	COLD CASE WED 3	Cold Case
197923	COLD CASE WED 4	Cold Case
197924	COLD CASE WED 5	Cold Case
197925	COLD CASE WED 6	Cold Case
197926	COLD CASE WED 7	Cold Case
137565	COLD CASE FILES	Cold Case Files
137566	COLD CASE FILES	Cold Case Files
	M-F

Table 5B: Program Master table showing entries for “Countdown to the Grammys.”

TABLE 5B
ExternalProgramMappingID	NielsenShowTitle	Title
138687	COUNTDOWN 2010	Countdown to the
	GRAMMYS	Grammys
138691	COUNTDOWN 2011	Countdown to the
	GRAMMYS	Grammys
138713	COUNTDOWN TO THE	Countdown to the
	GRAMMYS	Grammys
143909	GRAMMY FASHION	Grammy Awards Fashion
	WRAP	Wrap
138733	COUNTDOWN: 2012	Grammy Awards Red
	GRAMMYS	Carpet Countdown
143908	GRAMMY AWARDS RED	Grammy Awards Red
	CARPET	Carpet Countdown
143912	GRAMMY RED CARPET	Grammy Awards Red
	CNTDWN	Carpet Countdown
132317	2012 GRAMMY	Grammy Takeover
	TAKEOVER
147822	LRC: 2011 GRAMMYS	Live from the Red Carpet:
		The Grammy Awards
147826	LRC: 2012 GRAMMYS	Live from the Red Carpet:
		The Grammy Awards
143907	GRAMMY AWARDS	The Grammy Awards
147405	LIVE AT THE	The Grammy Awards
	GRAMMYS
143911	GRAMMY NOMINATIN	The Grammy Nominations
	CNCRT SP	Concert Live!!: Countdown
		to Music's Biggest Night

Table 5C: Program Master table showing entries for “Academy Awards Red Carpet.”

TABLE 5C
ExternalProgramMappingID	NielsenShowTitle	Title
132829	ACADEMY AWARDS	Academy Awards Preview
	PREVIEW
132586	84TH OSCAR RED	Academy Awards Red
	CARPET	Carpet
151411	OSCARS RED CARPET	Academy Awards Red
	2010	Carpet
151412	OSCARS RED CARPET	Academy Awards Red
	LIVE 1	Carpet
151413	OSCARS RED CARPET	Academy Awards Red
	LIVE 2	Carpet
151414	OSCARS RED CARPET	Academy Awards Red
	LIVE 3	Carpet
138685	COUNTDOWN ACADEMY	Academy Awards Red
	AWARDS	Carpet Countdown
132634	AA FSHIN WRAP-CARRIE	Academy Awards Red
	ANN	Carpet Fashion Wrap
132827	ACADEMY AWARD FSHIN	Academy Awards Red
	WRAP	Carpet Fashion Wrap
132635	AA ICON STARS LEGEND	Academy Awards: Iconic
	FASH	Stars, Legendary Fashions
225452	ACAD AWRDS ICONIC	Academy Awards: Iconic
	STRS LF	Stars, Legendary Fashions

Table 6, below, depicts an exemplary MediaAssetPatternType 53-AgeGender-Station-Program showing entries like “Academy Award”. Note that these programs are all actually the same program. “LRC” stands for “Live from the Red Carpet.”

TABLE 6
	Source	MediaAsset
MediaAssetPatternKey	segmentkey	PatternTypeID	Correlation
E! - LRC:	110356	53	0.330511
12 ACADEMY
AWARD PT1
E! - LRC:	110356	53	0.249202
12 ACADEMY
AWARD PT2
E! - LRC:	110356	53	0.288986
2010 ACADEMY
AWARDS
E! - LRC:	110356	53	0.252108
2011 ACADEMY
AWARDS

Table 7, below, depicts an exemplary MAPType 59-AgeGender-Station-ProgramMaster showing programs like “Academy Awards”. The various program strings have been remapped to a single canonical program called “Live from the Red Carpet: The Academy Award.”

TABLE 7
		Media
		Asset
	Sourcesegment	Pattern
MediaAssetPatternKey	Key	TypeID	Correlation
E! - Live from	110356	59	0.285531
the Red Carpet: The
Academy Awards

Media Asset Pattern Example 4: Auto-Regressive Airing: Same Program, Same Time, Prior Week

Human viewing behavior is periodic and so viewers of a program this week are likely to have also viewed the same program in the previous week. TV Program episodes are often sequential in that the story builds from one week to the next, or sports games follow events from the previous week, and in the same way, human viewing tends to track the episodes from week to week. During some seasons, viewership increases from episode to episode (e.g., see FIG. 9D, Walking Dead increased in viewership each week). Programs such as American Idol may languish and then their ratings may increase dramatically because of an event. Predicting the next broadcast of Walking Dead or American Idol can use the previous week's (or episode 2 weeks prior or 3 weeks prior) as an estimate. This turns out to be a very effective strategy for predicting the demographics of the next airing for the program.

FIG. 9A depicts how programs often increase in popularity as a season progresses. This is one reason why same-time-last-week is highly predictive of the next airing. If the average for the program over a season is taken, this may not be as good a predictor as same-time-last-week, since the latter has the latest changes in viewership.

FIG. 9B shows the performance of using previous weeks' episodes for predicting future impressions. Error predicting the next episode is lowest when the episode exactly 7 days prior and at the same hour is used. Error is slightly worse using 14 days prior, and slightly worse again using 21 days prior. The figure shows mean absolute error percentage versus number of days since today. Every 7 days the error between the present station-hour and previous is minimized. This shows that using same time last week is a good strategy for predicting demographic viewership of an upcoming broadcast. These may be called “same-program-same-time-last-x-week” features auto-regressive features since we're using lag terms to predict future impressions. Based on the error analysis below, lag terms that are as close as possible to the time of prediction may be used. For a live, running campaign, it may be desirable to verify that actual data is pulled through with as little latency as possible. When looking at same time last week, 25 weeks prior to the present, it may be that the opposite season is viewed. Error may be highest during this period.

FIG. 9B depicts how the error forecasting the demographics of the up-coming airing are lowest at the same time, same program last week (the sharp troughs in the above graph). The error is also low same-time-same-program-14-days-ago, and 21 days ago. Going further into the past, the error may increase, however. The further away from the time that is being predicted, the more likely it is that some event has occurred in the show which has changed viewership, or that the schedule may have changed and so a different audience is tuning in. The figure above actually shows performance in predicting household impressions, however demographic prediction has similar behavior.

FIG. 9C depicts how the error forecasting the demographics increases with the number of weeks in the past that is being forecasted. The error actually becomes very high about 180 days prior to the present. This reflects the fact that winter and summer programming tends to be quite different (e.g., summer sports are different from winter). A corollary of the above, is the same-time exactly 1 year ago, is also a good feature for predicting the current demographics. The figure above shows accuracy in forecasting impressions, but demographics follow a similar pattern.

Table 8, below, depicts an exemplary Media Asset Pattern Type 98-Station-Program-Hour-Prior 1 week: This shows the impressions generated by the same program, at the same hour 1 week prior to the airing.

TABLE 8
	MediaAsset
MediaAssetPatternKey	PatternTypeID	HourofDay	Impressions
BRAV - INSIDE THE	98	9	100586
ACTORS STUDIO - 9 am
SCI - FIREFLY - 1 pm	98	13	200248
SYFY - SCARE	98	13	237726
TACTICS - 1 pm
VH1C - MUSIC	98	2	10145
VIDEOS - 2 am
ADSM - COWBOY	98	3	544376
BEBOP - 3 am
FX - BUFFY THE	98	9	269022
VAMPIRE
SLAYER - 9 am
HLN - SHOWBIZ	98	4	52920
TONIGHT - 4 am
LOGO - 30 ROCK - 3 pm	98	15	9254

Media Asset Pattern Example 5: Time-Since-First-Airing

Premiere or First-time-Airings of Episodes for programs such as The Walking Dead tend to attract large viewing audiences. These premieres are often followed by a “same-day encore,” and then some repeats during the week. The audiences are much smaller for repeats that were first shown 40 or 100 days ago.

This phenomenon may be used to create a time-since-first-airing media asset pattern. This is a number 0 or higher (or coded as Station-Program-first-day, Station-Program-first-day encore, Station-Program-first-week, Station-Program-more than 1 week) which can be used to predict the audience and viewing audience impressions given a certain time since the first airing. In order to calculate this, the first detected episode number may be used to take the date of the first airing, and then take the fractional number of days since the first detection.

Table 9, below, depicts an exemplary Media Asset Pattern Type for Time-Since-First-Airing: Times are discretized into 0 (premiere), 0.5 (same-day encore), 7 (same week) and 8 (greater than 1 week since the first detected airing).

TABLE 9
	Days since	mediaasset
Mediaassetpatternkey	first airing	pattern typeid	impressions
CBS - Super Bowl	0.5	90	84939111
XLVII - 0.5
FOX - NFL Super	8	90	76759981
Bowl - 8
CBS - Super Bowl	0	90	69207813
XLVII - 0
FOX - NFL Super	0	90	66525098
Bowl - 0
FOX - Super Bowl	0	90	52947596
Postgame - 0
CBS - Super Bowl	0	90	48895479
on CBS Kick-Off
Show - 0
FOX - Glee - 0.5	0.5	90	31689157
FOX - NFL Football	0.5	90	30361066
Playoffs - 0.5
CBS - NFL Football	0.5	90	28335601
Playoffs - 0.5
CBS - NFL Football	8	90	27484440
Playoffs - 8

FIG. 9D depicts time-since-first-airing (line that is high and then drops) versus viewing impressions for program (line that has the three peaks). Specifically, FIG. 9D shows the viewing behavior of the Walking Dead in the lead-up to a series premiere (first peak). A Walking Dead marathon from the previous season starts at the far left of the graph, followed by season premiere. Then there is a same-day-encore of the premiere in which the program is shown again right after the premiere. Following that, the premiere is shown again during the week. On the second week, the premiere from last week is shown, and then the premiere for week 2 shows.

The above shows how viewership changes fairly dramatically with the premiere, encore, or repeat. A feature called time-since-last-airing may be used to help to predict the viewership of each program. The time-since-first-airing starts at the far left with a high value indicating that these are re-runs from last year. Then when the premiere is shown, the time-since-last-first-airing drops to 0 and there is a spike on viewing. After that it may be possible to see that time-since-first-airing changes between 0 and 7, and that the associated changes in viewing may be seen.

Media Asset Pattern Example 6: Pod Position

Pod position and commercial break are also important features of the ad insertion, and can dramatically affect the viewership and response per impression from the ad. In general the first pod has the highest viewership, and viewership then decreases throughout the commercial break. FIG. 9E shows response per impression as measured by phone response for first airing, middle and last airing in a commercial break. Specifically, FIG. 9E depicts how the first commercial to air in a break has the highest response pre impression. The last has the lowest. On average the performance degradation for the last commercial break is 5 times lower than for the first commercial in the break. FIG. 9F shows response per impression by order in commercial break. Specifically, FIG. 9F depicts how, with each additional commercial, the response per impression from the ad decreases. FIG. 9G suggests that later commercial breaks in a program perform better also. Specifically, FIG. 9G depicts how commercial breaks deeper into the program have higher response per impression. As described above, it may be possible and desirable to incorporate pod position into a representation of the media when estimating the response per impression.

% through pod RPI/RPI(0)
20%           100%
40%           87%
60%           74%
80%           52%
100%          22%

Using the above pod information it is possible to create media asset patterns of the form: Station-Program-PodSequence and to estimate performance of these differentially.

Media Asset Pattern Example 7: Local Market Audiences

TV broadcasts can be performed nationally and locally. Advertisers often execute local TV campaigns when they are trying to get very precise levels of targeting, for example during elections. Often particular geographic markets such as Birmingham AL behave differently to overall national population. For example, Montel may over-perform—have more engaged viewers—in the South and under-perform in the North. It may be possible to represent media as Market-Station-Day-Hour or Market-Station-Program and then measure the ad effectiveness or response per impression from these different markets, and use these in an ad targeting system.

Because there are a large number of local markets (210 DMAs), it is desirable to control the amount of data being retained. One embodiment utilizes a feature whereby it calculates the RPI or ad effectiveness metrics for each market, and then if the RPI metric is not significantly different (as measured in absolute difference) from the national RPI metric or ad effectiveness metric, then the local ad effectiveness metric can be deleted (converted to missing), which as described below, may result in the national RPI or ad effectiveness metric being used. The degree of absolute difference is a parameter that can be used to control how much local data is retained.

Table 10, below, depicts an example Market-Station-Program media asset patterns for a range of geographies and the same program. This shows that the estimated ad effectiveness varies by geography.

TABLE 10
		Media
		Asset
	Sourcesegment	Pattern
MediaAssetPatternKey	key	TypeID	Correlation	MAPID
SACR - KTFK - MLS Cup Soccer	110356	80	−0.09668	40255755
Playoffs
GRSC - WYFF - MLS Cup Soccer	110356	80	0.356705	40127923
Playoffs
NASH - WSMV - MLS Cup Soccer	110356	80	−0.04557	40157609
Playoffs
COLO - WCMH - MLS Cup Soccer	110356	80	−0.49754	40002762
Playoffs
LOUI - WAVE - MLS Cup Soccer	110356	80	−0.48667	40123908
Playoffs
PHL - WCAU - MLS Cup Soccer	110356	80	−0.46398	40224514
Playoffs
CLE - WKYC - MLS Cup Soccer	110356	80	−0.63222	39963073
Playoffs
NOR - WAVY - MLS Cup Soccer	110356	80	−0.6917	40213307
Playoffs
TUL - KJRH - MLS Cup Soccer	110356	80	0.000412	40330064
Playoffs
DAY - WDTN - MLS Cup Soccer	110356	80	−0.37268	40046919
Playoffs
GRNC - WXII - MLS Cup Soccer	110356	80	−0.14151	40366126
Playoffs
MOBI - WPMI - MLS Cup Soccer	110356	80	−0.03149	40074842
Playoffs
OKLA - KFOR - MLS Cup Soccer	110356	80	0.13376	40207308
Playoffs
SACR - KCRA - MLS Cup Soccer	110356	80	−0.64831	40279695
Playoffs
SHRE - KTAL - MLS Cup Soccer	110356	80	0.062029	40322737
Playoffs
CHAT - WRCB - MLS Cup Soccer	110356	80	−0.68651	39969068
Playoffs
SANA - KNIC - MLS Cup Soccer	110356	80	−0.46716	40039296
Playoffs
BIRM - WVTM - MLS Cup Soccer	110356	80	0.007384	39940916
Playoffs

Table 11, below, depicts an example Market-Station-Program media asset patterns and their ad effectiveness scores. The market shown is Birmingham, AL.

TABLE 11
		Media
		Asset
	Sourcesegment	Pattern
MediaAssetPatternKey	key	TypeID	Correlation
BIRM - WVTM - Mister	110356	80	0.393448
Magoo's
Christmas Carol
BIRM - WVTM -	110356	80	0.007384
MLS Cup Soccer
Playoffs
BIRM - WVTM -	110356	80	−0.30599
MLS Major
League Soccer
BIRM - WVTM -	110356	80	−0.11523
Mockingbird
Lane

Media Asset Pattern Example 7: Quarter of Year

Viewership changes throughout the year, in some part in response to programming changes, but in other parts due to different events that occur each year. for example, each December, Hallmark's viewership increases dramatically as they air a variety of family favorite Christmas movies.

As shown in FIG. 9H, and in the table 12 below, in order to incorporate these changes in viewing, it may be possible to create media asset pattern types such as Station-Program-Quarter, and Station-Day-Hour-WeekNumber.

TABLE 12
		Media
		Asset
	Sourcesegment	Pattern
MediaAssetPatternKey	key	TypeID	Correlation
Hollywood Uncensored - Q1	110356	76	0.448947
Hollywood Uncensored - Q2	110356	76	0.59193
Hollywood Uncensored - Q3	110356	76	0.103446
Hollywood Uncensored - Q4	110356	76	0.380187
Hollywood's 10 Best - Q1	110356	76	0.419322
Hollywood's 10 Best - Q2	110356	76	0.416322
Hollywood's 10 Best - Q3	110356	76	0.550515
Hollywood's Greatest Love	110356	76	0.709967
Affairs of All Time - Q1
Hollywood's Greatest Love	110356	76	0.76959
Affairs of All Time - Q2
Hollywood's Greatest Love	110356	76	0.667494
Affairs of All Time - Q3
Hollywood's Hottest Car	110356	76	−0.19692
Chases - Q1
Hollywood's Hottest Car	110356	76	−0.46141
Chases - Q2
Hollywood's Hottest Car	110356	76	−0.2328
Chases - Q4

Media Asset Pattern Example 8: Genre

Media Assets can also be represented by their Genre. Table 13, below, shows genres as classified by Nielsen corporation using their taxonomy, and how programs in those genres were scored for a demographic match to buyers. For example, Devotional is the genre that has the highest correlation with buyers-a result which makes sense as these customers tend to be religious and view a lot of religious programming.

TABLE 13
Media Asset pattern type 3 - Genre
		MediaAssetPattern
MediaAssetPatternKey	sourcesegmentkey	TypeID	Correlation	MAPID	Counts
DEVOTIONAL	110356	3	0.747485	23100	6234
QUIZ-GIVE AWAY	110356	3	0.738476	25200	48425
PRIVATE DETECTIVE	110356	3	0.717184	25143	131313
QUIZ-PANEL	110356	3	0.708613	25201	2841
AUDIENCE	110356	3	0.672347	22253	141666
PARTICIPATION
NEWS	110356	3	0.667453	24746	49792
CONVERSATIONS,	110356	3	0.660163	22893	33610
COLLOQUIES
WESTERN DRAMA	110356	3	0.58834	26245	476043
PAID PROGRAMMING	110356	3	0.574373	24951	3397
SUSPENSE, MYSTERY	110356	3	0.49955	25812	50258

Media Asset Pattern Example 9: Local Market Sales

TV broadcasts occur locally and nationally. It may even be possible to use data about the sales per capita in particular geographic areas to inform the presently disclosed system as to the expected response from these areas when an ad is broadcast in these areas. The media asset pattern type in this case is simply a local market which may or may not include the program information.

Media Asset Pattern Example 10: Programs Containing Keyword

Media Asset Patterns can also be represented by the keywords of program names. An example is shown in table 14, below. When the keywords below are in the program title, impressions are on average higher than expected. It is possible to create Media Asset Patterns for Genre-keyword.

TABLE 14
Keyword        Impressions/Expected(impressions)
playoff        2.57259536
championship   1.340646812
cup            1.339433679
red carpet     1.733993006
academy award  2.098937084
grammy         2.339914967
football draft 5.4533442
final          1.331537746
all-star       1.515066396
live           1.071003292
super bowl     3.665313321
countdown      1.0587158
extreme        0.918094091
draft          1.676420518

Advertisement Response:

Advertisement response is a generalized measure of the concentration of a desired audience within a particular media asset pattern Mi. This may be calculated using several measures including the number of buyers reached by targeting each media asset, phone response per impression, the concentration of targeted audience, and others. In one embodiment, information about response may come from any subsystems of data feeds of advertiser data system 106.

Advertisement response may be represented as R_Ω(P, M), where P is an advertiser's product and M is media. Advertisement response may be a measurement that is positive and monotonic with lift from advertising.

$R_{\Omega }\left(\overline{P},M\right)=B\left(M\right)/I\left(M\right)$

TV Advertisement Response/Ad Effectiveness Measures:

Multiple ad effectiveness measures may be used for helping to estimate response per impression or concentration of target audience per impression. One method may be Target Rating Points (“TRPs”) on Age-Gender.

Target Rating Points (TRPs) on Age-Gender:

Age-gender Target Rating Points may be used as a form of targeting. This form of targeting may be based on the number of persons who match the advertiser's target demographics divided by total viewing persons. A formula representing age-gender TRPs may be represented as:

$r_A\left(P,m_i\right)=100·\frac{\tau \left(m_i,P\right)}{\#Q\left(m_i\right)}$

• • where Q(m_i) is a set of viewers who are watching TV media instance m_i; where this viewing activity was recorded by Nielsen panel; where qk∈Q(m_i); where # is the cardinality of a set; and where #r_T includes persons that match on all demographics.

For example, a calculation of r_A(P, m_i) as 50% may mean that 50% of the people are a match to the desired demographics. Age-gender TRPs may also be calculated using Nielsen “Market Breaks,” such as gender=male|female and/or age=18-24, 25-34, 35-44, 45-54, 55-64, 65+.

Table 15, below, depicts an example of MAPType 59 with Ad Effectiveness of Target Rating Points (TRPs).

TABLE 15
		Media
		Asset
		Pattern
MediaAssetPatternKey	sourcesegmentkey	TypeID	Correlation	MAPID	TRP
ABC - The Silence of	110356	59	0.090847	24240093	0.231977
the Lambs
ABC - The Simpsons	110356	59	0.01147	24242989	0.333813
ABC - The Singing Bee	110356	59	0.182083	24240287	0.29886
ABC - The Soloist	110356	59	−0.26161	24240305	0.253884
ABC - The Stellar	110356	59	−0.04318	24240151	0.340155
Awards
ABC - The Stepford	110356	59	0.273387	24245805	0.305246
Wives
ABC - The Steve	110356	59	0.66973	24243515	0.511859
Harvey Show
ABC - The Steve	110356	59	0.44268	24242061	0.434906
Wilkos Show
ABC - The Suburbans	110356	59	0.714196	24239773	0.553664

Phone Response Per Impression:

When a TV advertisement is run with a 1800 number, it may be possible to match the phone responses on specific 1800 numbers back to the advertisement that was placed. This data may be used to track sales due to the TV advertisement. A specific method may use a series of hour lag terms to predict the number of phone-calls that would be generated on a given hour.

The method of the present disclosure exposes hour and day-lag terms for historical phone response, and then trains a system to predict a probability of phone response from an upcoming media spot. The method of the present disclosure may be represented by the formulas:

$r_F\left(P,m_{i,T}\right)=\sum _j\frac{\mathrm{CALL}\left(m_{j,T}\right)}{I\left(m_{j,T}\right)}$ $r_B\left(P,m_{i,T}\right)=\sum _{j}w*r_T\left(m_{T,j}\right)$

• • where CALL (m_j,T) are the number of calls from airing m_j,T.

Table 16, below, depicts an example of: Media Asset Pattern Type 38-Station-Day-Hour with Ad Effectiveness equal to Phone Responses Per Impression for a Life Insurance product, including a selection of scores for CNN.

TABLE 16
MediaAssetPatternKey	sourcesegmentkey	MediaAssetPatternTypeID	MAPID	Responses	ImpressionsScored	AllocatedResponses	RPM
CNN-M-4 pm	10105	38	2244019	49	704008	52.50	74.58
CNN-Su-2 pm	10105	38	2244044	18	450867	26.44	58.64
CNN-Th-12 pm	10105	38	2244052	55	628320	62.47	99.42
CNN-Th-4 pm	10105	38	2244058	41	651779	49.84	76.46
CNN-Th-6 pm	10105	38	2248057	44	635173	52.83	83.18
CNN-Tu-6 pm	10105	38	2244075	47	561477	60.93	108.51
CNN-W-3 pm	10105	38	2252728	65	790970	75.08	94.92
CNN-W-5 pm	10105	38	15323064	78	880141	91.55	104.01
DEST-M-2 pm	10105	38	16838702	22	67779	47.93	707.09
FNEW-M-6 am	10105	38	2244807	22	576185	27.89	48.41

Table 17, below, depicts an example of a Media Asset Pattern Type 38-Station-Day-Hour with Ad Effectiveness equal to Phone Responses Per Impression for a Life Insurance product. Scores ordered by RPI descending.

TABLE 17
		MediaAsset
MediaAsset	source	Pattern	Model	Version				Impressions	Allocated
PatternKey	segmentkey	TypeID	ID	ID	Correlation	MAPID	Responses	Scored	Responses	RPM
INSP-	10105	38	1	1	NULL	16838870	44	114204	47.05	411.94
Tu-3 pm
INSP-M-	10105	38	1	1	NULL	16838852	11	30665	12.16	396.53
10 am
INSP-	10105	38	1	1	NULL	16838876	27	75963	28.16	370.66
W-12 pm
INSP-M-	10105	38	1	1	NULL	16838854	35	101068	36.95	365.58
3 pm
INSP-	10105	38	1	1	NULL	16838874	29	84622	30.56	361.17
W-1 pm
INSP-F-	10105	38	1	1	NULL	16838847	34	102853	36.32	353.08
3 pm
INSP-	10105	38	1	1	NULL	16838861	34	104691	36.03	344.18
Th-2 pm
DEST-	10105	38	1	1	NULL	16838702	22	67779	47.93	707.09
M-2 pm
INSP-	10105	38	1	1	NULL	16838878	33	102452	36.12	352.54
W-3 pm
INSP-	10105	38	1	1	NULL	16838868	23	72138	25.93	359.42
Tu-12 pm
INSP-F-	10105	38	1	1	NULL	16838846	31	102531	34.45	336.02
2 pm
INSP-F-	10105	38	1	1	NULL	16838844	11	36509	14.54	398.34
10 am
INSP-	10105	38	1	1	NULL	16838877	29	97157	30.21	310.90
W-2 pm
OWN-F-	10105	38	1	1	NULL	16839819	37	124507	38.75	311.23
3 pm
SYFY-	10105	38	1	1	NULL	2267509	86	291972	95.34	326.54
Th-12 pm
INSP-	10105	38	1	1	NULL	16838869	28	96224	29.74	309.09
Tu-2 pm

Buyer Ratings:

Buyer targeting may look for media that has a high rate of observed buyers per impression, and targets those programs. An algorithm that may not be trained by itself, such as a self-learning algorithm and/or recursive algorithm, may score a percent of buyers observed in each media, which may be referred to as “buyer ratings.” The following expression defines buyer ratings.

$r_C\left(P,m_{i,T}\right)=\sum _j\frac{B\left(m_j\right)}{I\left(m_j\right)}$

Table 18, below, depicts an example of a Media Asset Pattern Type 47-Station-Program Buyers per impression in the audience (SourceViewPct).

TABLE 18
	Source	MediaAsset			Source
MediaAssetPatternKey	segmentkey	PatternTypeID	MAPID	Counts	ViewPct
ABC - Masters Report	110356	47	24391966	8	0.011834
2012
ABC - Maury	110356	47	24390571	60	0.011121
ABC - MDA Show of	110356	47	24392333	2	0.004651
Strength
ABC - MEGASTUNTS:	110356	47	24392638	28	0.007943
Highwire Over Niagara
Falls - Live!
ABC - Michael	110356	47	24390572	21	0.012567
Jackson: BAD25
ABC - Mirror Mirror	110356	47	17083484	3	0.014085
ABC - Miss Augusta	110356	47	24392664	2	0.019802
Christmas Fantasy
Parade
ABC - Missing	110356	47	17124974	76	0.00693
ABC - Modern Family	110356	47	17082109	125	0.005773

Table 19, below, depicts an example of a Media Asset Pattern Type 47-Station-Program Buyers per impression, sorted in order of highest buyers per impression programs to lowest for Life Insurance Product. A variety of religious programs show up as having high buyers per impression.

TABLE 19
		Media Asset
	Source	Pattern Type			Source
MediaAssetPatternKey	segmentkey	ID	MAPID	Count	ViewPct
WBIH-Times Square	110356	47	24408592	13	0.039275
Church
WBIH-North	110356	47	24403807	26	0.031325
Jacksonville Baptist
Church
WBIH-Day of	110356	47	24406111	18	0.029412
Discovery
BET-Redemption of a	110356	47	24391388	18	0.027231
Dog
WBIH-Truth That	110356	47	24406620	12	0.026906
Transforms with Dr. D.
James Kennedy
WBIH-Wretched with	110356	47	24406853	12	0.025641
Todd Friel
WBIH-Inside the	110356	47	24403800	16	0.025276
Wildside
WBIH-First	110356	47	24408336	14	0.024138
Presbyterian Church
WBIH-Gospel	110356	47	24402598	13	0.023508

High Dimensional Demographic Matching:

In one embodiment, demographic match across 3,000 variables between an ad product buyer and each media asset pattern may also be used. Similar to age-gender matching, demographic mapping may use a thousand times more variables and a different match calculation due to the high dimensionality. The demographic match between an ad product and media may be defined as follows:

$r_E\left(\stackrel{\_}{P},{\stackrel{\_}{M}}_i\right)=\frac{{\stackrel{\_}{P}}^{+}·{\stackrel{\_}{M}}_i^{+}}{❘{\stackrel{\_}{P}}^{+}❘·❘{\stackrel{\_}{M}}_i^{+}❘}$

• • where P is a vector of demographics representing the average buyer demographic readings, and M is a vector of demographics for the media placement.

Table 20, below, depicts an example of a Media Asset Pattern Type 24-Station-Program with Ad Effectiveness=High Dimensional Demographic Match between Buyers and Set Top Box Viewers of Program. Selection for DIY channel.

TABLE 20
	Source	MediaAsset
MediaAssetPatternKey	segmentkey	PatternTypeID	Correlation
DIY-Knitty Gritty	110356	24	−0.0029
DIY-Make a Move	110356	24	0.544038
DIY-Man Caves	110356	24	−0.12583
DIY-Marriage Under	110356	24	−0.28237
Construction
DIY-Massive Moves	110356	24	0.628383
DIY-Mega Dens	110356	24	−0.07638
DIY-Million Dollar	110356	24	−0.039
Contractor

Table 21, below, depicts an example of a Media Asset Pattern Type 24-Station-Program with Ad Effectiveness=High Dimensional Demographic Match between Buyers and Set Top Box Viewers of Program. Top several programs by correlation for a Life Insurance product.

TABLE 21
	Source	MediaAsset
MediaAssetPatternKey	segmentkey	PatternTypeID	Correlation
WE-A Stand Up Mother	110356	24	0.896803
TVGN-Angel Eyes	110356	24	0.869202
BBCA-Amazon Super	110356	24	0.849512
River
NGC-Tsunami: Killer	110356	24	0.834906
Waves
INSP-Wisdom Keys: The	110356	24	0.834243
Transforming Power of
Change with Mike
Murdock
TVGN-Safe Harbour	110356	24	0.828137

Web Spike Per Impression:

If TV broadcasts are aligned in time and geography with web traffic, the difference in web visits due to each broadcast may be calculated by comparing web activity a few minutes before and after the broadcast. These web spike effects may be highest within about 1 minute to about 5 minutes of an airing. Details on calculation of web spike per impression may be as follows:

$r_F\left(P,m_{i,T}\right)=\sum _j\frac{\Delta W\left(m_{j,T}\right)}{I\left(m_{j,T}\right)}$

• • where Δ_W(m_j,T)=W(m_j,T,t,g)−W(m_j,T,t,g) is the difference in web activity at time t2 vs t1, from the same geographic area.

Table 22, below, depicts an example of a Media Asset Pattern 69 Station-Day-Hour with Ad Effectiveness measure equal to Web Spike Response per impression. Table below is sorted in order of highest web spike response per impression to lowest for a different advertising product (identified by sourcekey=110401). This is a product that appeals to women 25-34. The top networks showing up for webspike response are Soap (SOAP), Comedy (Com), Discovery Health and Fitness (DFH).

TABLE 22
MediaAsset	Source	MediaAsset
PatternKey	segmentkey	PatternTypeID	MAPID	WPI
SOAP-Su-	110401	69	17110544	0.00322
3 pm
COM-Tu-	110401	69	17110163	0.003025
1 pm
DFH-Tu-	110401	69	17110598	0.002895
11 am
DFH-W-2 pm	110401	69	17110286	0.00273
DFH-M-7 am	110401	69	17110172	0.002596
COM-W-1 pm	110401	69	17110586	0.002539
DFH-M-1 pm	110401	69	17110279	0.002291
COM-Th-	110401	69	17110381	0.002148
1 pm
COM-Tu-	110401	69	17110377	0.00211
12 pm
DFH-Th-	110401	69	17110816	0.00206
3 pm
COM-M-	110401	69	17110374	0.002018
10 am
DFH-Tu-	110401	69	17110065	0.001955
1 pm

The ad targeting algorithms, as shown below, may be a combination of one or more of: (i) ad effectiveness metric; and (ii) media asset pattern type. For example, stbheadmatch-station-day-hour may mean high dimensional match with set top box data using statistics on station-day-hours (e.g., CNN-Tues-8 pm's demographic match between target and viewing audience).

Table 23, bellows, shows the correlation between each ad effectiveness measure and a particular response per impression measure. For example, Media Asset Pattern Type 32-STBHead-Station-Day-Hour has a high correlation with buyers per million (0.8471) and is present 93.9% of the time.

TABLE 23
Feature	R	% present
32-STBHead-Station-Day-Hour	0.8471	0.9391
40-Telesale-Station-Day-Hour-Local	0.8245	0.4775
60-STBHead-Station-Program Authority	0.7585	0.2385
5-MBDemo-Day of Week-Hour of Day	0.7552	1
39-Telesale-Station-Local	0.7498	0.7451
65-AgeGender-SpecialEvent-Station-Program	0.6964	0.0081
Authority
118-Reach-Station-Day-Hour	0.6597	0.2688
45-Sale-Station-Day-Hour	0.6471	0.8938
31-STBHead-Station-Rotation	0.6102	0.9391
59-AgeGender-Station-Program Authority	0.4901	0.2037
28-STBHead-Program	0.4801	0.5162
124-Reach-Program Authority	0.4544	0.465
30-STBHead-Hour of Day	0.4424	1
7-MBDemo-Hour of Day	0.4121	1
27-STBHead-Station	0.3886	0.9391
55-AgeGender-Program Authority	0.3771	0.5985
53-AgeGender-Station-Program	0.3262	0.153
58-Telesale-Station-Day-Hour	0.2793	0.802
46-Sale-Station	0.26	0.9087
51-AgeGender-Station-Day-Hour	0.2478	0.8313
6-MBDemo-Day of Week	0.2283	1
24-STBDevice-Program Authority	0.199	0.463
29-STBHead-Day of Week	0.1601	1
25-STBDevice-Program	0.1446	0.415
52-AgeGender-Station	0.1099	0.9009
57-Telesale-Station	0.1079	0.8702
33-USCensus-DMA	0.0162	0.8073

TABLE 24
Feature performance for predicting future household impressions
		Mean
Maptype	Present	abs	mean
I94-STBHead Actual Airings Minus 7 Days	35%	14%	0%
I95-STBHead Minus 14 Days	33%	15%	0%
I96-STBHead Minus 21 Days	32%	18%	1%
I97-STBHead Minus 28 Days	31%	19%	0%
I74-STBHead Station-Day-Hour-Quarter	98%	24%	−5%
I77-STBHead Weekpart-Daypart-Station-	0%	26%	2%
Program Authority-High Value
I87-STBHead Current Quarter	98%	26%	−4%
Station-Day-Hour
I32-STBHead Station-Day-Hour	96%	29%	−12%
I90-STBHead First Airings Station-	78%	29%	−3%
Program Authority
I99-STBHead Actual Airings Prior	56%	30%	−8%
Station-Program-Hour
I86-STBHead Current Quarter Station-	85%	31%	2%
Program Authority
I60-STBHead Station-Program Authority	76%	33%	−2%
I61-AgeGender Local Station-Day-Hour	92%	34%	−1%
I31-STBHead Station-Rotation	96%	34%	−12%
I82-AgeGender2 Station-Day-Hour	92%	34%	2%
I51-AgeGender Station-Day-Hour	92%	36%	1%
I98-AgeGender2 Current Station-	36%	37%	−5%
Program-Hour
I63-AgeGender Local Station-	72%	41%	7%
Program Authority
I27-STBHead Station	96%	43%	−7%
I59-AgeGender Station-Program Authority	69%	46%	15%
I75-STBHead Program Authority-Quarter	90%	46%	−2%
I53-AgeGender Station-Program	45%	49%	21%
I78-AgeGender Weekpart-Daypart-	3%	50%	24%
Station-Program Authority-High Value
I52-AgeGender Station	94%	52%	15%
I28-STBHead Program	78%	52%	−2%
I66-STBHead Station-Program Authority-	0%	59%	37%
High Value
I76-AgeGender Program Authority-Quarter	73%	60%	−9%
I65-AgeGender Station-Program	6%	62%	39%
Authority-High Value
I55-AgeGender Program Authority	77%	63%	−7%
I30-STBHead Hour of Day	100%	71%	−28%

Properties of TV Ad Targeting Algorithms:

One element affecting an ad effective metric's ability to be used may be their sparsity. The most sparse data may be STB buyer data, which may be known persons who have bought the advertiser's product, and are also detected watching a particular program. The probability of detection of these customers may be small.

One key reason for sparsity may be because each person must be matched in both STB data and advertiser data.

High dimensional demographic matching may not be as impacted by sparsity because it may aggregate all STB data into a demographic vector, and then may match using this vector. By converting to a demographic vector, it may be possible to eliminate the need for “cross-domain” person-to-person linkage.

FIG. 3A depicts an analysis of the three major classes of ad effectiveness metric: (a) demographic match, (b) phone response per impression (“RPI”), and (c) buyers per impression (“BPI”) versus the size of media being scored. The y-axis may indicate the correlation coefficient between the predicted phone responses and actual phone responses in the future. The x-axis may indicate the number of impressions generated by the media that is being scored. Each data point may indicate a quartiled set of airings, with their correlation coefficient for predicting future phone response. A linear fit may be added to each set of points to provide an idea of the accuracy trend for that ad effectiveness metric versus impressions.

As shown in FIG. 3A, phone RPI performs very well and is sloped upwards, which may indicate that as an airing has more impressions, prediction improves. For large airings, such as around 50,000 impressions in size, the correlation coefficient may average about 0.6. However, for programs with fewer than 1,100 impressions, RPI prediction performance may degrade.

Demographic matching may have a shallower slope, as shown in FIG. 3A. Its prediction may improve with more impressions, but it may be out-performed on high impression airings by RPI. However, one differentiator of the demographic match method may be that the shallow slope means that it continues to show good prediction performance far down the list of airings, into very low impression airings. This may be an advantage for the demographic match method, and may indicate that the entire TV spectrum may be scored and used with this method.

FIG. 3A also depicts BPI. Because of the high sparsity associated with BPI, this method may be useful on airings over 600,000 impressions in size. However, the slope of BPI may be quite steep. It may be possible that BPI might out-pace RPI, and may be a more predictive variable with enough set top boxes and/or the right advertiser that is generating numerous purchases.

In terms of usable predictions (scoring airings with impressions such that prediction performance is above 0), in one exemplary, non-limiting embodiment, demographic match covered, e.g., 99% of all airings, RPI covered, e.g., 57% of all airings, and BPI covered, e.g., only 0.5% of all airings, as shown in FIG. 3B. Thus, the sparsity analysis may indicate that all three methods may be useful from an operational standpoint. In some embodiments, demographic matching may beat all methods on low impression airings (<6,000 impressions), RPI may be effective on medium impression sizes, and BPI may be incorporated on airings with >600,000 impressions.

Exemplary Robust Algorithm:

One benefit of the present disclosure is that the below described targeting algorithm is able to use all of the above-described data and methods which allows for a “hyper-targeted” TV campaign. In order to build a combined algorithm, various problems introduced by the different metrics and range of each algorithm may be overcome. Further, the combined algorithm may be able to select features that are most predictive, and may be trained.

Model:

In one embodiment, a model consistent with the present disclosure may receive all of the available media asset patterns m_i,t and ad effectiveness measures r_a(m_i,t). The model may also use them to predict the ad response per impression R_Ω(M_i). This may include a supervised learning problem, as ad effectiveness information may be available for every airing, and thus, the system may be trained to predict the quantity based on historical examples. The model of the present disclosure may include a stacked estimator where each ad effectiveness model r_a(m_i,t) is an expert, and the assembly is trained to predict ad response R_Ω(M_i).

$R_{\Omega }\left(M_i\right)=Z^{-1}\left(y,{\mu }_{\Omega },{\sigma }_{\Omega }\right)y=\sum _t{w}_t{x}_t{x}_t=Z\left(r_t\left(m_t\right),{\mu }_t,{\sigma }_t\right)$

The predictors x_t and ad response target y=Z(R_Ω, μ, σ) may be standardized, as discussed below. In order to handle so many different variables, the model may be able to standardize the different variable and may select the variables that are most useful for predicting its target to avoid over-fitting.

Variable Standardization:

In one embodiment, different ad effectiveness variables, such as telephone response per impression (RPI), buyers per impression (BPI), and demographic match, may be used. Each of these variables may have a different set of units. In order to handle these different scales, variables may be transformed, as follows:

$x_t=Z\left(r_t\right);y=Z\left(R_{\Omega }\right);Z\left(a\right)=\left(a-\mu \right)/\sigma$

When training the system to predict standardized target y for each ad effectiveness predictor x_t, each predictor may be effectively measuring the relationship between a change of a unit standard deviation in its distribution, to what that translates into in terms of standard deviations of movement in the target variable. This may have several useful properties, such as no constant terms, interpretability, and/or usability.

A constant term may be in effect removed and the co-variance may be measured. The constant term may be “added back” later when the prediction is converted back into target unit. Interpretability may allow standardizing variables on the same scale. When estimating weights, weights in order of magnitude may be read off, and thus, variables that are contributing most to the prediction may be seen. Usability may allow users to enter their own weights if they have some domain knowledge. Because of standardization, w=0.4 intuitively means that 40% of the decision may be based on this variable.

Constraints Due to Ad Theory:

There may be certain constraints that may be imposed on the model due to experimental findings from advertising theory. Ad theory suggests that as the traits of the ad match the product more, response to advertising should increase. Thus, the following propositions for ad effectiveness metrics may be: (1) ad effectiveness ∀i:x_iy>0 (since each ad effectiveness metric may be positively correlated with ad response); and (2) given a model predicting ad response y=Σw_tx_t∀t:w_t≥0, the effect of improved ad effectiveness may be zero or positive on ad response.

Minimum Weight Constraints:

In order to be consistent with the above-mentioned propositions, a positivity constraint in weights may be:

W _t≥0

Sum of Weight Constraints:

For reasons of robustness in production, it may be desired to ensure that predictions do not extrapolate higher or lower than a range of values that has been previously observed. For example, a weight of 2 may allow the system to predict outside of the range of the ad response variable. To ensure the sum of weight constraints, all weights may sum to 1. As a result of this additional constraint, a formula may be:

$\begin{array}{cc}1\ge w_t\ge 0\wedge {\sum }_{t=1}^T{w}_t=1& \left(2\right)\end{array}$

Low Data Behavior/Variable Participation Thresholds:

Each media asset pattern may cover a certain number of historical airings. For each media asset pattern, m, the sum the number of impressions observed may be I(m). Accordingly, the ad effectiveness measures may be unreliable on small amounts of data. Bayesian priors may be used to “fill-in” performance when there is less information available, modifying the ad effectiveness score as follows:

$r=e^{-\alpha ·I\left(m\right)}·r+\left(1-e^{-\alpha ·I\left(m\right)}\right)·r_{\mathrm{PRIOR}}$

• • where α is a parameter that governs how many impressions are collected for the posterior estimate to be favored more heavily than the prior.

However, Bayesian priors may be incorrect and may involve creation themselves. Since there may be hundreds of thousands of variables per product (not to mention hundreds of products), a large number of parameters may be set. Thus, an effect of poorly set priors may be significant as they cause variables that may have been good predictors to be spoiled, and the training process to be unable to weight them properly.

The system of the present disclosure may be able to work reliably with minimal human intervention. Thus, the system may be trained using participation thresholds. IMIN may be defined as the minimum impressions allowed on a particular media asset pattern. If a media asset pattern fails to meet this threshold, it may be converted to a missing value, and thus, does not participate further. The prediction formula for handling missing values may be defined as:

$\mathrm{if}I\left({\stackrel{\_}{m}}_{i,t}\right)<I_{\mathrm{MIN}}\vee {\sigma }_t=0\mathrm{then}{w}_t=0;x_t=\mathrm{MV}$

Missing Value Handling:

In certain embodiments, a particular media asset pattern may be missing and/or otherwise may be unable to report a value. For example, a system may not have enough data on a program to be able to provide a prediction. When this happens, the system may use a more general media asset pattern type, such as the station, to provide a prediction. Missing value handling may allow the system to operate in cases where a variable is not available and/or a variable is zeroed out, and missing value handling may allow other variables that are present to be used to create a prediction.

For production robustness, media asset pattern types may be defined with small weights, so that if there is a failure then the system may default to one of these more general media asset pattern types. For example, if station-day-hour is undefined, then station may be defined but at a very low weight. Thus, a significant weight may not be given to missing values.

Transforming into Target Units:

The standardized predictions may be converted into the original units. This may be performed by inverting the z-score transform

$Z^{-1}\left[y\right]=y{\sigma }_j+{\mu }_j$

• • where j is the ad effectiveness measure that is being reported. The Z-1 transform may be similar to performing a programming language cast operation into the appropriate units.

Training Algorithm:

Weight training may use a subspace trust-region method that is designed to operation for values 0 to 1 and sum of weights=1 constraints, as shown below:

$w_t\min E=\min {{\sum }_i\left[\left(\frac{1}{{\sum }_{t=1}^T{w}_t}{\sum }_{t=1}^T{w}_t{x}_t\right)-y^{*}\right]}^{2}1\ge w_t\ge 0\wedge \sum _{t=1}^T{w}_t=1\mathrm{If}{x}_t=\mathrm{MV}\mathrm{then}{w}_t=0$

A forward-backward selection algorithm may be used to select new features to include in the model.

Different Target Value Types:

The Scoring Service can score response per impression (tratio). It can also predict Impressions, Cost Per Impression (predicted price), (phone) Response Per Impression, Web Response per impression, TRP (target rating points) and others. The list of target value types supported by the system are shown in Table x. In each case, the system uses the common set of media asset patterns defined earlier, with the ad effectiveness metric also defined earlier, to predict the target metric of interest.

TABLE 25
Target value types supported by Scoring Service
TargetValueTypeID	TargetValueType	MinValue	MaxValue
1	TRatio	−1	1
2	RPI	0	NULL
3	SourceViewPct	0	1
4	Impressions	0	NULL
5	CPM	0	NULL
6	Cost	0	NULL
7	TRP	0	NULL
8	TRPImpressions	0	NULL
9	TRPTImpressions	0	NULL
10	ReachPct	0	1
11	WPI	0	1
12	SourceViewMinutesPct	0	1

For example, in order to predict Impressions, the system has expected Impressions defined for each media asset pattern type defined earlier. The system then performs a linear combination of its weighted features to predict upcoming impressions.

Exemplary Weight Training for Forecasting Impressions:

An example forecast is below for the case of impressions. Impressions don't need to undergo standardization and so the example is fairly simple. Let's say that we're trying to estimate the impressions for media instance Mi=(“Little House on The Prairie”, Hallmark, Sun 6 pm, Jun. 9, 2013). The Media Asset Pattern Types that match this airing are shown in Table 26A and 26B below:

Exemplary Media Asset Patterns and Weights:

TABLE 26A
Map	MediaAssetPatternKey
Type	MediaAssetPatternTypeID	Impressions	weight
1	HALL	340,497
2	LITTLE HOUSE ON PRAIRIE	92,730
4	HALL-Sa-Su-9a-8p	481,519
5	Sun-6-9 PM	164,671	.25
7	6-9 PM	117,448
14	HALL-Su-6 pm	569,995
18	HALL-Su-6 pm	194,377
27	HALL	276,393
28	Little House on the Prairie	221,556
30	6-9 PM	281,602
31	HALL-Sa-Su-9a-8p	403,137
32	HALL-Su-6 pm	490,169	.25
37	HALL	264,917
38	HALL-Su-6 pm	395,824
45	HALL-Su-6 pm
46	HALL
47	HALL-Little House on the Prairie
49	HALL

TABLE 26B
Map
Type	MediaAssetPatternKeyMediaAssetPatternTypeID	Impressions	weight
51	HALL-Su-6 pm	747,144
52	HALL	403,255
55	Little House on the Prairie	171,506
57	HALL	232,439
58	HALL-Su-6 pm	290,595
59	HALL-Little House on the Prairie	320,361	.25
60	HALL-Little House on the Prairie	261,492
61	National-HALL-Su-6 pm	725,637
63	National-HALL-Little House on the Prairie	290,635
74	HALL-Su-6 pm-Q2	447,017
75	Little House on the Prairie-Q2	201,296
76	Little House on the Prairie-Q2	161,460
82	HALL-Su-6 pm	809,827
83	HALL-Little House on the Prairie	297,056
84	HALL-Little House on the Prairie
85	HALL-Su-6 pm
86	HALL-Little House on the Prairie-Q22012	232,881	.25
87	HALL-Su-6 pm-Q22012	344,353
89	HALL-Su-6 pm-Week 23	553,533

In one embodiment, given that there may be weights on Maptypes 86, 59, 32, and 5 with 0.25 weight each, this results in the following:

$\mathrm{Forecast}\mathrm{Impressions}=$ $\left(164,671*w1+490,169*w2+320,361*w3+232,881*w4\right)/$ $\mathrm{sum}\left(w1\dots 4\right)=264,971$

Also, assuming that the actual impressions from that airing are ultimately found to be equal to: Actual=292,497, then error can then be calculated as below:

$\mathrm{Error}=\left(\mathrm{Forecast}-\mathrm{Actual}\right)=27,527$

Based on hundreds of thousands of examples of forecasts and actuals, the system may be trained to adjust its weights to minimize forecasting error. It may also be possible to implement variable selection process to iteratively add variables and determine if they improve the fit, and then attempt to remove variables is a similar manner to determine if there is redundance (forward-backward algorithm).

Exemplary Fatigue and Pod Adjustments During Training:

One of the objectives of the present disclosure is to accurately predict a Response Per Impression metric for a future TV broadcast. One challenge is that campaigns are rarely starting for the first time. Often the advertiser has aired their commercial on a range of different networks, and this has caused their commercial to create fatigue on these different networks.

Previous airings cause a variety of challenges for training a model to estimate future Response Per Impression. Historical data on response per impression (eg. phone response) will be distorted because of low fatigue on early airings, and high fatigue on later airings.

For example, the advertiser may have bought “Wheel of Fortune” heavily in the past. When a model is trained to predict Response Per Impression, the historical “Wheel of Fortune” will include data from when “Wheel of Fortune” was first being bought, and so the historical performance may over-estimate the performance that it may be possible to achieve if “Wheel of Fortune” is purchased today.

In order to account for fatigue, it may be desirable to adjust historical airing performance to “reverse out” the impact of fatigue. One example of how to do this is to adjust historical Response Per Impression estimates per below:

$\mathrm{RPI\_historical}=\mathrm{RPI\_historical}*\ln \left(\mathrm{airingcount}+1\right)$

The above fatigue adjustments should be used for ad effectiveness metrics which are related to human response, such as phone response per impression, web response per impression, and the like. Fatigue adjustments aren't needed for ad effectiveness metrics which aren't affected by human response, such as buyers per impression, or age-gender TRP estimates. These latter metrics will be the same whether or not the ad has aired in these spots previously.

Another factor which can make it difficult to predict future RPI performance is variation in historical pod position. Often media buyers negotiate rotations and may be agnostic to particular pod positioning. The pod that the ad airs in has a dramatic impact on response from the ad. The first pod has highest response, and the later the ad appears in the commercial break, the lower is the response. For the 5th ad in a commercial break, performance is just 30% of the 1^st ad. This is a huge performance change, and a major variable which needs to be taken into account. One example for how to take this into consideration is to estimate RPI as a function of pod position, and then to adjust as below:

Table 27, below, shows RPI position adjustments empirically measured in a live TV campaign.

TABLE 27
% through		pod-	Pod-	Pod-	Pod-
pod	all	size >=3	size >=5	size >=7	size >=9
20%	100%	100%	100%	100%	100%
40%	87%	87%	89%	93%	75%
60%	74%	72%	54%	78%	99%
80%	52%	52%	43%	63%	50%
100%	22%	19%	13%	12%	10%

It is then possible to calculate an RPI-position1-equivalent metric by adjusting the historical RPI metrics as follows:

$\mathrm{RPI\_historical}=\mathrm{RPI\_historical}\left(1\right)/\mathrm{RPI\_historical}\left(\mathrm{pod}\right)$

Exemplary RPI Adjustments During Training:

Response per impression metrics that are divided by impressions can be volatile when there are few impressions. In many cases it is possible to log-transform the RPI metric being estimated to make it robust to these outliers. This often results in far better accuracy than leaving the RPI metric un-normalized.

RPI_historical=In(RPI_historical)

Exemplary Weights on Specific Media Asset Patterns:

A weight may be applied to an entire class of media asset patterns. For example, CNN, NBC, BRAVO, may all be weighted the same amount, and additional data encapsulated by an ad's effectiveness on CNN, NBC, and BRAVO may vary. An example of this is shown in Table 26, which describes the training process in detail. Table 26 shows an example where CNN-Tues-7 pm, CNN-Tues-8 pm, etc, all receive a weight of 0.5. The RPI score for each of these different times can of course be different, and in the example, CNN-Tues-8 pm has the highest RPM (0.5).

In one embodiment of the present disclosure, knowledge of a specific media pattern (e.g., CNN) that is equal to a value may be important for predicting an ad's effectiveness (see Table 1-3). For example, a media asset pattern of a program may be set to a weight of, e.g., 0.4. However, when the program is “The Academy Awards,” the weight may be set to 1.0. In one embodiment, special media asset patterns may be set up to cover a specific media asset pattern, and the other media asset patterns may be set to null. Table 1-3 shows an example of this: CNN-Tues-8 pm receives a weight of 0.5. This indicates that the system should “pay greater attention” to the Station-Day-Hour MAPType when the value is equal to CNN-Tues-8 pm. This is also equivalent to creating a new Media Asset Pattern Type which is equal to the specific MAP string which is being differentially weighted.

Media Asset Pattern Dummy Variable Mining:

Mining to find these special media asset patterns may involve a rule extraction algorithm. For example, the algorithm may search various search spaces, i.e., media asset patterns (station, program, genre, day, and hour). Mining may use the systems in an environment, such as the environment shown in FIG. 1A, to receive and analyze the airings. In mining, the system may identify predicates which have a high support, meaning they have been tested and found to be true on a large number of samples, and have a high confidence, meaning that the probability of a conversion or purchase is high.

The system may generate every possible combination of a media asset pattern. By working from most general media asset patterns first, the system may ensure adequate “support.” Further, the system may form children media asset patterns from the general asset patterns. For example, generated media asset patterns may include: (DIY-Mon-9 pm-11 pm-Documentary); (DIY-Mon-9 pm-11 pm); (DIY-Mon); (DIY); (Documentary); (DIY-9 pm-11 pm); (Mon-9 pm-11 pm); (Mon); and (9 pm-11 pm). The system may also remove generated media asset patterns that are redundant, unlikely to be usable, and/or unlikely to be valuable, such as generated media asset patterns (Mon-9 pm-11 pm); (Mon); and (9 pm-11 pm).

There may also be constraints on a search space. Media asset patterns may be set to not allow collapsibility, which may occur if a child media asset pattern (e.g., ID-Tuesday-8 pm) is predictive, and the parent media asset pattern (e.g., ID-Tuesday) is also predictive. Thus, a child media asset pattern may be deleted (or “collapsed”), and the parent media asset pattern may be used. This may minimize a number of media asset patterns that need to be comprehended by human analysts and/or a machine learning algorithm consistent with this disclosure. This may also allow media asset patterns to work at as general a level as possible.

An example implementation may be set as follows: a media asset pattern is significant at p<0.1 level; orders from media asset pattern >=1; cost per card from media asset pattern <$10,000; and/or above average performance only.

E[Response|Media Asset Pattern]>E[Response].

An example result may be shown as shown in Table 2 below:

Table 28, below, depicts how weights can be applied to Media Asset Pattern Types as a whole, where all MAP strings receive the same weight.

TABLE 28
	Ad Effectiveness estimate (calculated
	demographic match between buyer
Media Asset Pattern	demographics and viewer demographics)	weight
CNN-Tues-7 pm	0.2	0.5
CNN-Tues-8 pm	0.5	0.5
CNN-Tues-9 pm	0.2	0.5
CNN-Tues-10 pm	0.2	0.5
CNN-Tues-11 pm	0.2	0.5

Table 29, below, depicts how weights can be applied to specific Media Asset Patterns. Different MAP strings can receive different weight.

TABLE 29
	Ad Effectivness estimate (calculated
	demographic match between buyer
Media Asset Pattern	demographics and viewer demographics)	Weight
CNN-Tues-7 pm	0.2	0.5
CNN-Tues-8 pm	0.5	0.9
CNN-Tues-9 pm	0.2	0.5
CNN-Tues-10 pm	0.2	0.5
CNN-Tues-11 pm	0.2	0.5

TABLE 30
									cost
									per
Media	M			Resp					card
Asset	Media	R		p-	Pattern		Potential-O	Potential-R	(best
Pattern	Cost	Responses	Placements	value	Type	Arity	Orders	Responses	case)
ENN-	3123.75	6	12	0.024496	SWHD	4	1	6	1561.875
Weekend-
9 pm-
11 pm-Sun
GC-	2184.5	5	12	0.073612	SWHD	4	1	5	1092.25
WeekDay-
11 pm-
3 am-Tue
TWC-	1020	2	2	0.049254	SWHD	4	1	2	340
Weekend-
3 pm-6 pm-
Sun
HGTV	75264.1	54	60	5.51E−29	S	1	672	9072	9408.012
ID	177458.7	285	364	2.9E−114	S	1	3024	47880	8066.307
MLC	40200.75	39	162	0.062159	S	1	504	6552	6700.125
NGC	201144	229	994	0.024503	S	1	2352	38472	9142.909
MLC-	1721.25	6	11	0.015742	SWH	3	24	144	860.625
WeekDay-
11 am-
3 pm
NGC-	24059.25	51	162	0.001633	SWH	3	120	1224	3007.406
WeekDay-
11 am-
3 pm
NGC-	14318.25	40	56	5.31E−15	SWH	3	48	960	2863.65
WeekDay-
9 pm-
11 pm
WBBH-	361.2498	6	1	0.05	SWH	3	24	144	180.6249
Weekend-
5 am-7 am
KCOY-	8653	11	38	0.088364	SW	2	96	528	2163.25
Weekend
Documentary,	81736	338	282	0.05	G	1	0	0	2818.483
General
Documentary,	106.25	11	8	0.05	G	1	0	0	26.5625
News
Sports	19779.5	38	98	8.98E−05	G	1	0	0	6593.167
Commentary

As shown in FIGS. 6A, 6B, and 6C, the generated media asset patterns are shown being tested over time. The dots of the graphs indicate dates when the generated media asset pattern was effectively tested in a live TV campaign by having an airing that matched the pattern. Each of these airings may be an opportunity to collect more data on the media asset pattern. After generating the media asset patterns, as shown in FIGS. 6A, 6B, and 6C, media asset patterns may be employed to determine which of the media asset patterns may be set up as a dummy pattern, and which may be included as another media asset pattern type.

Special Branching Structure and High-Order Features:

The model can be improved by adding structure to detect a variety of conditions. In one embodiment these conditions are implemented using a decision tree in which given a certain condition, a weighted model is executed. However these conditions could also be implemented as features themselves, incorporated as interaction terms or the like. Special conditions may include:

• • 1. First-runs: Premieres like Walking Dead often generate 3-4 million impressions in a premiere, but in a second run only 600,000. This is a huge difference in impressions, and drives most of the error. In one embodiment, special branching logic may be used for first-runs now, so that they are recognized and then estimated based on historical first-runs. A branch may be implemented as
    • if time-since-first-airing<1 then<premiere model>
    • where <premiere model> is a weighted model described above and where the features are selected.
  • 2. Local airing: Local airings can obtain value from a variety of local media asset pattern types.

Table 31, below, shows trained weights for local airings, and table below that shows performance predicting local response per impression for two different advertisers.

TABLE 31
	Input
Variable	id	w	cadaline	cadaline_test	wexpert	wadaline	present
55-AgeGender	33	0.002806	0.203434	0.228209	0.15155	0.162887	0.645773
Program
Authority
21-	21	0.150112	0.210645	0.244288	0.151548	0.104872	0.674908
STBDevice
Station-
Rotation
36-STBHead	28	0.525256	0.270852	0.256366	0.144855	0.216903	0.357569
Local DMA-
Station-Day-
Hour
74-STBHead	37	0.369	0.265005	0.249109	0.124555	0.179191	0.749886
Station-Day-
Hour-Quarter
83-	44	0.690138	0.28115	0.358626	0.123371	0.228219	0.36516
AgeGender2
Station-
Program
Authority
32-STBHead	27	0.688419	0.257169	0.236305	0.109395	0.167552	0.7501
Station-Day-
Hour
82-	43	0.083145	0.084368	0.116748	0.085555	0.050617	0.31611
AgeGender2
Station-Day-
Hour
SourceViewPct85	58	0.678007	0.182726	0.21657	0.080691	0.101695	0.76106
80-STBHead	42	0.641174	0.193971	0.047278	0.019383	0.09252	0.137607
Local DMA-
Station-
Program
Authority
SourceViewPct47	56	0.625855	0.214057	0.200428	0.005974	0.136449	0.370292
78-AgeGender	41	0.855387	0.480057	0.525905	0.002717	0.473033	0.001042
Weekpart-
Daypart-
Station-
Program
Authority-
High Value
SourceViewPct84	57	0.322128	0.157052	0.205061	0.000404	0.098444	0.641496
76-AgeGender	39	0.509407	0.217669	0.239749	0.000001	0.172282	0.643527
Program
Authority-
Quarter
59-AgeGender	34	0.640022	0.180905	0.206033	0	0.151945	0.62225
Station-
Program
Authority
			corr	percent
	Segment	corrw	logw	present
	local	0.273461	0.188556	0.211168
	sourcekey
	110384
	Local	0.251984	0.265557	0.036193
	sourcekey
	110356

• • 3. High demographic volatility: Some networks have a great deal of variation from week to week in terms of viewership and perhaps even their schedule of programs. As shown in Table 32, below, FS-1 is a sports network and the particular sport shown in each weekly timeslot changes every week. Basketball viewers and volleyball viewers are very different, and this shows up in the demographics of the viewers. For these “high demographic volatility networks”, features which use same-time-last-week, or even historical station-day-hour performance can be highly inaccurate, and it tends to be better to use features based around the Station-Program.

A branch may be created, as follows:

• • If tratio_network_volatility >0.21 then <high-tratio-volatility-model>

Where <high-tratio-volatility-model> is trained on airings which are on networks that have high tratio volatility. In practice, it may be expected that the features selected for the model above will tend to have more program-specific features.

TABLE 32
Date	Day	Impressions	Network	Program
Aug. 31, 2013	3	761,342	Fox	COLL
			Sports 1	FOOTBALL:
				PAC 12 L
Sep. 7, 2013	3	1,000,501	Fox	COLL
			Sports 1	FOOTBALL:
				PAC 12 L
Sep. 14, 2013	3	543,084	Fox	COLL
			Sports 1	FOOTBALL:
				PAC 12 L
Oct. 5, 2013	3	444,578	Fox	COLL
			Sports 1	FOOTBALL:
				BIG 12 L
Oct. 19, 2013	3	1,499,663	Fox	COLL
			Sports 1	FOOTBALL:
				PAC 12 L
Oct. 26, 2013	3	710,916	Fox	COLL
			Sports 1	FOOTBALL:
				PAC 12 L
Nov. 2, 2013	3	192,953	Fox	FOX
			Sports 1	SPORTS
				LIVE L
Nov. 9, 2013	3	306,723	Fox	FOX
			Sports 1	SPORTS
				LIVE L
Nov. 16, 2013	3	234,935	Fox	FOX
			Sports 1	SPORTS
				LIVE
Nov. 23, 2013	3	193,520	Fox	COLL
			Sports 1	FOOTBALL:
				BIG 12 L
Nov. 30, 2013	3	1,026,377	Fox	ULTIMATE
			Sports 1	FIGHTER
				FINALE L
Dec. 7, 2013	3	149,952	Fox	FOX
			Sports 1	SPORTS
				LIVE L
Dec. 14, 2013	3	137,760	Fox	FOX
			Sports 1	SPORTS
				LIVE L
Dec. 21, 2013	3	122,551	Fox	FOX
			Sports 1	SPORTS
				LIVE L

As shown in Table 32, above, TV Network FS1 has high variability in viewership for its programs even during the same day of week, hour-of-day, and program name. Variability can also be caused when networks change their schedules (eg. showing volleyball, basketball, football, etc in the same timeslots). When there is high demographic volatility as above, forecasting viewership and response from the upcoming airing will be more accurate when using program-specific features.

Table 33, below, depicts exemplary low demographic volatility networks.

TABLE 33
		absdiff_32minustra-	meandiff_32minustra-
callletters	stdevdiff	tioactual	tioactual
DSNY	0.059737758	0.046014225	−0.008227945
DXD	0.062597609	0.047358082	−0.004374518
SONYETA	0.081600238	0.064690547	−0.014652289
ENCWEST	0.081625726	0.064803192	0.014160601
TNNK	0.08834976	0.066093817	−0.00068341
BOOM	0.092145548	0.069325094	−0.010345114
NKTN	0.09298583	0.072888674	−0.015737483
QVC	0.098509047	0.076902623	0.022356379
BET	0.099818146	0.078824512	0.002835602
GSN	0.106359947	0.079360001	0.010407444
NKJR	0.101681394	0.08343749	−0.00293748
HMC	0.107588397	0.084501056	0.010272793
HLN	0.114161717	0.085217182	0.019339856
HGTV	0.114381219	0.086356672	0.007490231
TWC	0.115132704	0.087603514	0.001213892
TCM	0.112659355	0.087656798	0.011851204
MTV	0.117015838	0.089119835	−0.006694463
RFD	0.118062793	0.091784897	−0.007267901

TABLE 34
High demographic volatility networks
FS2	0.381882208	0.311891721	−0.02898613
EPIX	0.388108476	0.318164344	−0.005403923
STARZCIN	0.388334105	0.319495054	0.068866594
HDNETM	0.392414522	0.319642416	−0.099978969
INDIE	0.396644462	0.329708841	0.009744889
BYUTV	0.408502593	0.330631201	−0.030426538
NUVO	0.422968523	0.338527025	0.01763275
AECN	0.420555876	0.346678381	−0.012042394
STARZCOM	0.424315761	0.350517543	0.037716248
AMC	0.446219442	0.352825329	0.02867856
MAVTV	0.430753303	0.359980424	−0.015734196
CNBCW	0.42843112	0.368438834	0.118018351
IFC	0.465177115	0.370091136	0.040695151
ENCO	0.447039619	0.371379583	0.037977942
LOGO	0.455418266	0.378725362	−0.009302438
UHD	0.465517888	0.394160019	−0.007067299

TABLE 35
Low volatility station-programs
ESPN	NBA Face to Face With	0.000131	0.075843	0.075843	−0.45279	−0.52863
	Hannah Storm
SHOW	To Live and Die in L.A.	0.000129	0.016037	−0.01604	−0.14113	−0.12509
FX	Knock Off	0.000103	0.030574	0.030574	0.427313	0.39674
SHOWCSE	Even the Rain	9.95E−05	0.089048	−0.08905	−0.26051	−0.17146
GALA	Santo vs. el rey del crimen	9.83E−05	0.059348	−0.05935	0.154556	0.213904
SYFY	Messengers 2: The	9.02E−05	0.019637	−0.01964	0.571118	0.590755
	Scarecrow
ESQR	Rocco’s Dinner Party	8.55E−05	0.071287	−0.07129	−0.49515	−0.42387
ESQR	ROCCOS DINNER PARTY	8.55E−05	0.071287	−0.07129	−0.49515	−0.42387
TMC	The Advocate	5.80E−05	0.028745	0.028745	0.210809	0.182063
LIFE	To Have and to Hold	4.73E−05	0.006146	0.006146	0.335054	0.328908
5STAR M	Salvation Road	1.81E−05	0.127159	−0.12716	−0.03399	0.093165
FOXD	HOOTERS ANGELS 2011	7.16E−06	0.042774	−0.04277	0.242489	0.285263
FOXD	HOOTERS SNOW ANGELS	7.16E−06	0.042774	−0.04277	0.242489	0.285263
FOXD	Hooters’ Snow Angels	7.16E−06	0.042774	−0.04277	0.242489	0.285263
FOXD	The Hooters 2011 Snow	7.16E−06	0.042774	−0.04277	0.242489	0.285263
	Angels

Below is a sample of SQL code for calculating volatility by network select

• • --a.sourcesegmentkey,
  • --bb.stationmasterid,
  • c.callletters,
  • --bb.DayNumberOfWeek, bb.hourofday,
  • stdev (a.correlation-b.correlation) stdevdiff,
  • avg(abs (a.correlation-b.correlation)) absdiff_32minustratioactual,
  • avg(a.correlation-b.correlation) meandiff_32minustratioactual,
  • avg(a.correlation) tratiom32, avg(b.correlation) tratioactual
  • --a.correlation tratio32, b.correlation tratioactual
  • --*
  • from
  • (select * from [tahoma\sql2008r2].demographics.scoring.modelsourcemapscore
  • where mediaassetpatterntypeid=32
  • and not sourcesegmentkey like ‘% NC--%’
  • and sourcesegmentkey=‘110402’
  • ) a
  • inner join
  • (select * from [tahoma\sql2008r2].demographics.scoring.map
  • where mediaassetpatterntypeid=32
  • ) bb
  • on a.mapid=bb.mapid
  • and a.mediaassetpatterntypeid=bb.mediaassetpatterntypeid
  • inner join
  • (select * from dw1.demographics.scoring.modelsourcemapscoreactuals
  • where not sourcesegmentkey like ‘% NC--%’
  • ) b
  • on bb.StationMasterID=b.stationmasterid
  • --and cast (cast (a.AirDate as date) as datetime)=b.airdate
  • and bb.DayNumberOfWeek=datepart (weekday,b.airdate)--b.dayofweek
  • and bb.hourofday=b.hourofday
  • and bb.marketmasterid=b.marketmasterid
  • and a.sourcesegmentkey=b.sourcesegmentkey
  • inner join dw1.demographics.dim.station c
  • on b.stationmasterid=c.stationmasterid
  • group by
  • --a.sourcesegmentkey,
  • --bb.stationmasterid,
  • c.callletters
  • --,bb.DayNumberOfWeek, bb.hourofday
  • order by
  • stdev (a.correlation-b.correlation) desc

The table below shows trained model for estimating RPI for an airing which has high demographic volatility. The system makes use of Buyers per million features to increase its accuracy. Table 36, below, shows the prediction performance on airings.

TABLE 36
	Input
Variable	id	w	cadaline	cadaline_test	wexpert	wadaline	present
SourceViewPct84	57	−5.25274	0.753682	0.765462	0.149844	0.97362	0.641496
SourceViewPct85	58	0.060485	0.69115	0.717313	0.149839	0.968607	0.76106
28-STBHead Program	25	−2.88135	0.667029	0.688489	0.149833	1.115365	0.46505
75-STBHead Program Authority-	38	1.316007	0.552701	0.651104	0.123854	1.050081	0.543583
Quarter
76-AgeGender Program	39	2.262887	0.487257	0.52321	0.121283	0.597223	0.643527
Authority-Quarter
32-STBHead Station-Day-	27	3.010398	0.539881	0.534069	0.094589	0.939228	0.7501
Hour
51-AgeGender Station-Day-	29	−2.81101	0.485359	0.477517	0.041236	0.512429	0.60378
Hour
55-AgeGender Program	33	−1.31578	0.50033	0.537834	0.038298	0.622985	0.645773
Authority
60-STBHead Station-Program	35	1.09807	0.634798	0.56089	0.032457	1.407408	0.62217
Authority
18-STBDevice-STB Station-	19	1.092676	0.366499	0.427077	0.030492	0.369149	0.168827
Day-Hour
53-AgeGender Station-	31	1.66064	0.49126	0.520857	0.030395	0.475935	0.099142
Program
65-AgeGender Station-	36	−1.04116	0.266688	0.422249	0.02721	0.32742	0.011334
Program Authority-High Value
24-STBDevice STB Station-	22	0.928707	0.464556	0.386469	0.007012	0.357844	0.124856
Program Authority
83-AgeGender2 Station-	44	1.639985	0.627654	0.666194	0.003657	0.601391	0.36516
Program Authority
96-STBHead Actual Airings	45	−0.25876	0.262946	−0.03297	0.000001	0.281401	0.079335
Minus 21 Days
			corr	percent
	Segment	corrw	logw	present
	pred full set	0.400229	0.083105	0.794018
	national sourcekey 110356	0.44727	0.430632	0.117586
	national sourcekey 110384	0.120526	0.2906	0.05616
	national sourcekey 110424	0.303704	0.26144	0.016439
	volatile tratio	0.71906	0.699078	0.025741
	stable tratio	0.274205	0.290363	0.017348
	national	0.43957	0.438388	0.190185
	high national imps	0.424759	0.468252	0.003876
	low national imps	0.333314	0.276243	0.019727
	PMIC Dental Local	0.160301	0.188145	0.036193

• • 4. Syndication: Syndication on television refers to when re-runs of a program are broadcast on another network, and then distributed to a range of local stations. The local stations may or may not carry the syndicated program, creating a distribution footprint that can be fairly unique. Syndication is often priced more favorably than other national broadcasts. In order to estimate syndicated airings, a variety of syndication-specific branches and features are used. Syndicated programs can be thought of as comprising a hierarchy with “program” being the most general representation of the airing (eg. “Judge Karen's Court”). Syndication-network (eg. “SYN-CBSUNI—Judge Karen's Court”) is next in level of granularity. Syndication-network-program-daypart is the most granular. Each of these features can be used when predicting the response per impression from a particular airing.

It may be possible to create a special branch for syndicated airings as follows:

• • If<syndicated airing> then <syndicationmodel>

FIG. 9I depicts an exemplary embodiment of a series of programs, syndication programs, syndication sub-station program, and syndication sub-station program day/week.

Tables 37A-37C below depict examples of different features used for predicting syndicated airings: Maptype 83==syndicated-station-program; maptype 83==syndicated program; maptype 76==Program-quarter of year.

TABLE 37A
MediaAssetPatternKey	sourcesegmentkey	MediaAssetPatternTypeID	Correlation
SYN-20THCTV-American Dad!	110356	83	−0.61966
SYN-20THCTV-Are We There Yet?	110356	83	−0.46726
SYN-20THCTV-Bones	110356	83	0.568095
SYN-20THCTV-Burn Notice	110356	83	0.604625
SYN-20THCTV-Century 20	110356	83	−0.05786
SYN-20THCTV-Century 21	110356	83	0.333567

TABLE 37B
MediaAssetPatternKey	sourcesegmentkey	MediaAssetPatternTypeID	Correlation
SYN-'Til Death	110356	106	−0.61339
SYN-30 Rock	110356	106	−0.62784
SYN-Access Hollywood	110356	106	0.3335
SYN-Access Hollywood Live	110356	106	0.274231
SYN-According to Jim	110356	106	−0.73037
SYN-America Now	110356	106	0.133193

TABLE 37C
			Model	Version
MediaAssetPatternKey	sourcesegmentkey	MediaAssetPatternTypeID	ID	ID	Correlation
Judge Mathis-Q1	110356	76	1	1	0.805666
Judge Mathis-Q2	110356	76	1	1	0.786072
Judge Mathis-Q3	110356	76	1	1	0.776222
Judge Mathis-Q4	110356	76	1	1	0.78142

Table 38, below, depicts syndicated features and degree of predictiveness for estimating response per impression where RPI is phone response per impression.

TABLE 38
Maptype	corr	present %
106-AgeGender2 Syndication Overall Station-	0.53	44%
Program Authority
75-STBHead Program Authority-Quarter	0.49	24%
83-AgeGender2 Station-Program Authority	0.48	43%
76-AgeGender Program Authority-Quarter	0.32	71%
TRP59	0.10	100%
TRP	0.08	100%
54-AgeGender Syndication Program	0.08	100%
51-AgeGender Station-Day-Hour	0.04	100%
TRP51	0.04	100%
105-AgeGender2 Syndication Overall	0.03	29%
Station-Day-Hour
25-STBDevice-STB Program Name	(0.17)	24%

The table in FIG. 9J depicts trained weights for a syndication branch of model, where “WExpert are the weights.”

• • 5. High impression airings:
    • Error tends to show a pattern by impression decile-so that there is high percentage error on the smallest airings, low error on medium sized broadcasts, and then an uptick in error on the biggest impression airings. Those big impression airings tend to be “destination programs” like “Price is Right”, “Revenge”, and so on. Although the uptick in error on these looks small (eg. only 20%) actually these are the airings that are producing most total error in each campaign. Therefore reduction on error here will dramatically increase campaign performance. The model may be broken so as to have branches for large-airing programs above 1 million impressions. For these programs, the variables selected tend to comprise network-program estimates, rather than time of day variables, since the programs carry the audience and not the other way around.
  • e. Below are variables selected for this model-yellow indicates station-program variables, and blue station-day-hour. Most of the variables (8/10) are station-program.

TABLE 39
Var	Pres	weight
I77-STBHead Weekpart-Daypart-Station-	2%	22.124%
Program Authority-High Value
I74-STBHead Station-Day-Hour-Quarter	100%	20.999%
I78-AgeGender Weekpart-Daypart-Station-	15%	16.872%
Program Authority-High Value
I75-STBHead Program Authority-Quarter	100%	11.155%
I99-STBHead Actual Airings Prior Station-	89%	9.382%
Program-Hour
I98-AgeGender2 Current Station-Program-Hour	60%	8.682%
I65-CompetitiveData Station-	37%	6.234%
Program Authority-High Value
I82-AgeGender2 Station-Day-Hour	100%	2.829%
I86-STBHead Current Quarter Station-	100%	1.325%
Program Authority
I60-STBHead Station-Program Authority	89%	0.304%
I66-STBHead Station-Program Authority-	3%	0.044%
High Value
I51-AgeGender Station-Day-Hour	100%	0.037%
I32-STBHead Station-Day-Hour	100%	0.009%
I28-STBHead Program	78%	0.003%
I87-STBHead Current Quarter Station-Day-Hour	100%	0.003%

FIGS. 9K-9L depict variable weights and percentages associated with the above table.

• • 6. Low impression airings
    • As described above, the error pattern tends to be high percentage error on small airings, where this is in part due to the intrinsically small size of the airings. Nevertheless, it may still be desirable to reduce error on these airings, since it is conceivable that an advertiser might be executing a campaign using local cable, local broadcast, or small national networks. It may be possible to create another branch to cover these cases. For these small airings, their performance tends to be dependent very much on the time of day and network, rather than the particular program that is playing. For example, in order to estimate Military channel viewership, it turns out the best variables are the time of day—it seems that people watching this network really tend to tune into generic Military programming, rather than audiences looking for specific programming.
    • A variable selection routine may be run for all airings with <50,000 impressions. This ends up automatically selecting features that are station-day-hour based, and not selecting station-program features.
    • FIGS. 9M-9N depicts the features that are being used. Please note the features in blue indicate that they are Station-day-hour based, and yellow are Station-program based. No color indicates not classified into either category. It is possible to see that 6/7 features are Station-day-hour. Only “high value” programs (present <1% of the time) require the use of Station-program feature, and the weight is relatively low.

TABLE 40
Var	Present	weight
I94-STBHead Actual Airings Minus 7 Days	24%	18.1204%
I97-STBHead Actual Airings Minus 28 Days	23%	17.4835%
I51-AgeGender Station-Day-Hour	77%	16.9061%
I87-STBHead Current Quarter	89%	12.7661%
Station-Day-Hour
I95-STBHead Actual Airings Minus 14 Days	23%	12.3543%
I74-STBHead Station-Day-Hour-Quarter	89%	11.0287%
I32-STBHead Station-Day-Hour	87%	3.8501%
I99-STBHead Actual Airings Prior	40%	2.6417%
Station-Program-Hour
I82-AgeGender2 Station-Day-Hour	79%	1.7760%
I77-STBHead Weekpart-Daypart-Station-	0%	1.3571%
Program Authority-High Value
I96-STBHead Actual Airings Minus 21 Days	23%	1.3080%
I98-AgeGender2 Current Station-Program-	31%	0.4054%
Hour
I61-AgeGender Local Station-Day-Hour	77%	0.0025%

FIG. 10 depicts an example of a branched model.

FIG. 11 depicts an error analysis of impressions forecasting. This shows that the premiere and prime-time programs tend to generate most of the error in the system. Because of this, branches are created to detect prime-time and premiere episodes, and then a model is used which is specialized for operating on those cases. In practice large-impression TV airings tend to result in a model that selects more program-specific attributes.

FIG. 12 depicts an exemplary accuracy analysis on various conditions.

Exemplary Variable Selection:

Variable participation may be limited due to participation thresholds which remove variables, missing value handling, which enables the system to elegantly operate with missing features, and forward-backward selection, which aggressively removes variables that do not make a significant contribution to the model. FIGS. 4A, 4B, and 4C depict different selections of variables. For example, FIG. 4A depicts variables selected in cases in which all variables that are present are used, FIG. 4B depicts variables selected in cases in which missing values are allowed, and FIG. 4C depicts a comparison of the variables selected (and weights) versus the variable correlations. FIG. 5 depicts predicted ad response versus future responses per million impressions.

Exemplary Effects of Fatigue:

Extensive surveys and meta-studies of hundreds of publications have concluded that advertisement response shows diminishing returns when displayed to the same target audience over time. A version of the embodiment will take into account the decrease in performance during repeated exposures of advertising in the same positions, which may be referred to as estimates of “fatigue.”

One embodiment estimates fatigue as a function of individual advertisement exposures of persons participating using a panel. In this embodiment the viewers of a program are known and it may be possible to count the number of times the viewer had the TV on while the ad was on. This approach requires the existence of a panel and their viewing activity.

A second embodiment may estimate fatigue by counting airings delivered to the same program or station-time-of-day. This latter approach has an advantage in that it only requires an advertiser to keep a count of the number of airings in each media asset pattern. It does not require a panel or viewing activity in order to provide a fatigue estimate.

Another method is to use the number of historical airings in each media asset pattern and compare it to the phone response from that same media asset pattern. FIGS. 2A, 2B, and 2C depict response per impression for phone responders to television advertisements versus a number of repeat airings in the same station-day-hour. The response per impression decreases as a function of the log of the number of repeat airings.

Another method is to use the number of airings in media asset pattern and compare it to the web response from that media asset pattern. As the airing count increases, the web response should decrease. A Fatigue function can then be estimated and used to estimate the effect of fatigue (or of airing in the same media asset pattern).

Fatigue can also be estimated by examining set top box conversion rate versus number of exposures to an individual person. Set top box conversion rate can be calculated as the number of persons who converted (known buyers as provided by an advertiser) divided by the number of persons in the population. It may then be possible to count the converters/viewers for persons who have had 1 exposure, 2 exposures, 3 exposures and so on. FIGS. 2A and 2B depict, for two different products, person-level conversions per advertisement view. As indicated by FIGS. 2A and 2B, conversion rate declines as a function of the log of airings. FIG. 2C depicts phone calls per million impressions in response to an embedded phone number in a TV advertisement observed after placing the advertisement in the same station-day-hour 1, 2, 3, . . . , 20+ times. As indicated by FIG. 20, the number of phone calls may decline relative to a log of the number of previous airings.

Fatigue with Airing Counts and Co-Viewing:

An airing count for media A (m) may be calculated as a count of known airings placed into media slot m. This airing count, however, may fail to take into consideration co-viewing activity. For example, an advertisement may have been run ten times on, e.g., the Military Channel's “Greatest Tank Battles.” A media buyer may wish to run the advertisement on the Military Channel's “Top 10 Aircraft,” which has had zero airings. The media buyer may have assumed such a run would avoid a decline in the advertisement's performance. However, the media buyer may be under-estimating the effective frequency.

For example, Military Channel viewers may be considered highly “insular” in their viewing habits. Thus, by airing the media buyer's advertisement in Greatest Tank Battles ten times, the media buyer may have effectively hit much of the same audience that would be viewing Top 10 Aircraft. Therefore, calculating the frequency of advertisement viewing that incorporates knowledge of co-viewing probabilities may be an important consideration.

Given knowledge of co-viewing probabilities, the probability that viewers will not have observed the advertisement may be calculated. The co-viewing probabilities may be calculated from, for example, set top box data. Thus, an effective airing rate may be represented by the following formula:

$A^{*}\left(m_j\right)=\max A\left(m_i\right)·\mathrm{Pr}\left(m_i,m_j\right)$

In order to account for the impact of Fatigue, expected response per impression, rpi_Ω, may be equal to the number of buyers per impression (targeting score) divided by a function of the log of airings (a number of repeat exposures), as indicated by the formula below. Thus, a targeting function may include an effect of repeat exposures.

${\mathrm{rpi}}_{\Omega }\left(\stackrel{\_}{P},M\right)=\frac{R_{\Omega }\left(\stackrel{\_}{P},M_i\right)}{F\left(M_i\right)}=\frac{B\left(M_i\right)/I\left(M_i\right)}{a*\ln \left(A\left(m_j\right)+1\right)}$

Table 41, below, shows how Fatigue is combined with an RPI function to provide a measure of Fatigue-adjusted performance. In this case, the fatigue function is log (airingcount), and adjusted performance is RPI/log (airingcount). This can be used by media buyers to prioritize buying programs for an upcoming television campaign. This also has the effect of “intelligently” taking into account the programs where a mature TV campaign has been displayed before, and will automatically shift away from those previously purchased programs.

TABLE 41
								tratio/log
call	hour-	am/						(airing
letters	name	pm	Airings	Tratio	impressions	cpm	cost	count)
ADSM	0	am	37	0.564208	1145554	5.558632	6391.333	0.108305
ADSM	1	am	57	0.55924	923339	3.226842	2977.753	0.095877
ADSM	2	am	88	0.564928	972358	3.41012	3349.129	0.087458
ADSM	3	am	137	0.534411	957852	3.483876	3373.266	0.07529
ADSM	4	am	166	0.522296	716889	3.143587	2263.309	0.070819
ADSM	5	am	179	0.442389	687426	3.136466	2155.338	0.059113
ADSM	9	pm	5	0.480302	791513	5.660395	4426.286	0.206855
ADSM	10	pm	4	0.485575	1311788	5.656115	7436.57	0.242787
ADSM	11	pm	12	0.483474	1815042	6.161579	11099.01	0.134862
BET	0	am	10	0.060438	296540	3.358933	1007.307	0.018194
BET	1	am	78	0.102505	219530	2.760971	610.3875	0.016308
BET	2	am	134	0.107418	201678	2.667251	537.9659	0.015202
BET	3	am	32	0.086856	142240	2.844085	403.199	0.017371
BET	3	pm	2	0.129267	181132	3.519042	637.411	0.129267
BET	4	pm	2	0.204869	222712	3.529425	786.0287	0.204869
BET	5	pm	3	0.151973	249874	3.577142	893.7647	0.095884
BET	6	pm	2	0.217091	267509	3.97265	1062.424	0.217091
BET	7	pm	3	0.177545	297013	4.070511	1208.747	0.112018
BET	11	pm	11	0.068216	345731	4.062014	1392.424	0.019719
BRAV	2	am	5	0.145075	168055	3.94912	663.8968	0.062481
BRAV	3	am	8	0.144072	136435	3.727342	506.8012	0.048024
BRAV	3	pm	1	0.090924	145610	4.157167	605.325	Undefined
								(1 airing)
BRAV	4	pm	3	0.122715	159740	4.049478	646.7862	0.077424
BRAV	5	pm	3	0.134994	181434	4.093817	742.8897	0.085172
BRAV	6	pm	3	0.117722	200048	3.843167	768.537	0.074275
CENT	0	am	36	0.245966	62115	1.662892	103.1727	0.047576
CENT	1	am	33	0.245826	59733	1.531975	91.2917	0.048732
CENT	2	am	31	0.207932	41280	1.467235	60.5249	0.041971
CENT	3	am	31	0.229982	36776	1.440374	52.9547	0.046422
CENT	4	am	20	0.222849	30514	1.39141	42.2301	0.051562
CENT	5	am	20	0.184917	23950	1.400903	33.4143	0.042786
CENT	11	pm	25	0.164801	65806	1.764107	115.5742	0.035488
CMT	6	am	18	0.20047	73361	2.520763	186.593	0.048075
CMT	7	am	16	0.218006	102085	2.193221	223.7244	0.054502
CMT	8	am	8	0.227641	123642	2.115917	264.2453	0.07588
COM	0	am	20	0.410145	392617	6.513513	2550.5	0.094899
COM	1	am	14	0.331025	332366	4.131354	1370.202	0.086944
COM	2	am	145	0.376298	277418	4.007862	1108.234	0.05241
COM	3	am	156	0.363574	230472	3.979667	916.0395	0.049904
COM	4	am	136	0.364425	185923	3.930438	730.9726	0.051418
COM	5	am	20	0.229064	129064	5.5102	711.1725	0.053
COM	6	am	16	0.177085	110900	5.5102	611.0832	0.044271
COM	7	am	14	0.224238	100927	5.5102	556.1299	0.058896
COM	8	am	18	0.271266	108295	5.444142	587.769	0.065053
COM	9	am	2	0.402209	198479	5.2101	1033.623	0.402209
COM	10	am	3	0.296853	193197	4.436206	876.8231	0.187293
COM	11	am	10	0.327457	188749	4.329612	836.7699	0.098574
COM	12	pm	8	0.368493	270155	4.998707	1359.306	0.122831
COM	1	pm	12	0.348506	249638	4.738493	1205.486	0.097213
COM	2	pm	12	0.382865	248630	4.785818	1209.36	0.106798
COM	3	pm	5	0.362077	246482	4.711765	1180.274	0.155938
COM	4	pm	10	0.377976	307463	5.119644	1580.774	0.113782
COM	5	pm	11	0.386921	335880	5.137939	1730.79	0.111845
COM	6	pm	1	0.394461	331391	5.562533	1843.374	Undefined
								(1 airing)
COM	7	pm	3	0.282617	350736	6.095108	2137.75	0.178312
COM	8	pm	5	0.384966	410102	6.689685	2740.605	0.165796
COM	9	pm	3	0.415279	514233	7.1104	3649.757	0.262012
COM	10	pm	6	0.430023	571611	6.352411	3543.82	0.166356
COM	11	pm	18	0.228992	556102	5.722678	3127.78	0.054915
ENN	0	am	24	0.463165	104615	2.565523	267.0332	0.101018
ENN	1	am	21	0.457394	84365	2.198805	183.5872	0.104135
ENN	2	am	27	0.43184	68699	2.173538	148.8815	0.09082
ENN	3	am	104	0.442095	64265	2.172132	139.0443	0.06598
ENN	4	am	106	0.437103	63509	2.170626	137.339	0.064968
ENN	5	am	117	0.399013	60459	2.170165	130.3098	0.058077
ENN	6	am	23	0.386637	57647	2.423775	139.5885	0.085472
ENN	7	am	21	0.413862	61580	2.479913	152.7453	0.094224
ENN	8	am	24	0.404247	62590	2.471786	155.2974	0.088168
ENN	9	am	54	0.403584	59435	2.483862	147.8544	0.070129
ENN	10	am	87	0.335361	68374	2.679908	182.6642	0.052051
ENN	11	am	88	0.328514	70588	2.69034	189.5588	0.050858
ENN	12	pm	57	0.343214	72278	2.652158	191.3165	0.058841
ENN	1	pm	54	0.371394	68743	2.697822	184.9772	0.064535
ENN	2	pm	43	0.360984	69688	2.655957	184.66	0.066525
ENN	3	pm	4	0.426069	112379	2.657375	298.5223	0.213034
ENN	4	pm	5	0.430771	135576	2.675355	362.6836	0.185523
ENN	5	pm	4	0.429995	150685	2.650113	398.9407	0.214998
ENN	6	pm	12	0.402658	138315	2.659052	367.2279	0.112319
ENN	7	pm	6	0.399056	156899	2.69296	422.2595	0.154376
ENN	8	pm	8	0.391363	150196	2.917338	437.9088	0.130454
ENN	9	pm	3	0.395908	141146	2.99225	420.7986	0.24979
ENN	10	pm	3	0.402219	150783	2.909567	438.7097	0.253772
ENN	11	pm	11	0.430644	120833	2.589411	311.1658	0.124484

Table 42, as shown below, depicts cases where a target score may be calculated by combining an airing count with a targeting ratio, such as “tratio/airing count”

TABLE 42
call	program						tratio/
letters	name	Airings	tratio	impressions	cpm	cost	airingcount
ADSM	YPFIGTH	1	0.555362	1056716	3.0864	3261.448	0.555362
ADSM	Delocated	1	0.550869	1142827	5.58115	6378.289	0.550869
ADSM	Black	1	0.536211	825215	3.977575	3282.355	0.536211
	Dynamite
ADSM	Swords,	1	0.533966	775532	3.129133	2426.743	0.533966
	Knives,
	Very
	Sharp
	Objects
	and
	Cutlery
ADSM	IGPX	1	0.526385	562121	3.0582	1719.078	0.526385
TNNK	Kenan & Kel	1	0.523345	71010	1.48275	105.2901	0.523345
ADSM	Stroker	1	0.516485	808075	3.503175	2830.828	0.516485
	and Hoop
TNNK	NICKMOM	1	0.516058	91757	1.379367	126.5665	0.516058
	NIGHT OUT
ADSM	Ghost In	1	0.515655	1387	Undefined	Undefined	0.515655
	The Shell				(low	(low
					impressions)	impressions)
ADSM	Fat Guy	1	0.505052	825215	2.861175	2361.085	0.505052
	Stuck in
	Internet
MTV	American	1	0.497064	234956	4.083667	959.482	0.497064
	Pie
	Presents:
	Beta
	House
ADSM	Saul of	1	0.492845	808075	2.928625	2366.549	0.492845
	the Mole
	Men

FIGS. 7A and 7B depict pseudo code in which queries count historical airings by station-day-hour, and count a number of airings in a program, respectively.

Rotation Scoring:

Television media buyers often buy blocks of time on networks called “rotations.” In one embodiment of the present disclosure, these rotations are scored by the system. The rotation can be a media asset pattern instance with wildcards, or any collection of airings.

In one embodiment of the present disclosure, the system takes a “rotation” to be scored, e.g., Seattle-CNN-6 pm-9 pm, and then “explodes” this airing into each possible airing or media instance where the ad could be placed within that rotation, eg. “Seattle-CNN-6 pm-Out Front with ErinB”, “Seattle-CNN-7 pm-AC360”, “Seattle-8 pm-Piers Morgan.” These individual airings or media instances are then scored by the Scoring Service.

In one embodiment, the system assumes equal probability of the ad appearing in any of the underlying media instances.

In another embodiment, the system assumes “worst case” insertion in which it selects the underlying media instance with lowest impressions, highest CPM, lowest tratio or the like.

In another embodiment the system attempts to estimate the placement biases of the network and may distribute the airings based on the media instances with the lowest household impressions.

After scoring the underlying media instances for impressions, response per impression (tratio), buyers per impression and other scores generated by Scoring Service, the system then re-aggregates these media instances to create a final score for the rotation. In one embodiment, the system assumes equal probability and averages the underlying scores. In another embodiment, the system assumes “worst-case” insertion and so selects the media instance with the lowest impressions, highest CPM, lowest tratio or the like, and reports that back as the insertion solution for the rotation. Figure below (“Automated Media Scoring”) shows a flow-chart showing how the rotation is exploded, scored, and then each of the underlying scores put back together into a rotation score.

Table 43, below, depicts exemplary Media Asset Pattern Types matched for one airing, in which all providers are not necessarily able to carry cost, imps, etc., and where threshold drops out features if too little data exists.

TABLE 43
	Media-
	Asset-
	Pattern-	Media	Source-				Station-	Market-
Airing-	Type-	Asset-	Segment-			Impres-	Master-	Master-		Source-	Thresh-
ID	ID	PatternKey	Key	TRatio	Cost	sions	ID	ID	MAPID	ViewPct	old	CPM
5.51	1	Affiliate	110401	−0.18217	256.0009	25650	8	169	43986			9.9805
E+08		ABC
5.51	4	ABC-M-	110401	0.481338	114455.6	5253207	8	169	31			21.7878
E+08		Su-8p-12a
5.51	5	Sun-6-	110401	−0.13388	1723.303	164671	8	169	25793			10.4651
E+08		9PM
5.51	7	6-9PM	110401	−0.32743	1253.23	117448	8	169	22047			10.6705
E+08
5.51	14	Affiliate	110401	−0.23746	923.0455	50996	8	169	44087			18.1004
E+08		ABC-Su-
		8 pm
5.51	20	Affiliate	110401	−0.07661		88361606	8	169	53041		318794
E+08		ABC
5.51	21	Affiliate	110401	0.042776		26512168	8	169	53095		284698
E+08		ABC-M-
		Su-8p-12a
5.51	25	Once	110401	0.124556		387761	8	169	73472		74484
E+08		Upon a
		Time
5.51	27	Affiliate	110401	0.316849		3674320	8	169	2277882		2.19
E+08		ABC									E+11
5.51	28	Once	110401	0.473722		4246937	8	169	16363861		7.39
E+08		Upon a									E+08
		Time
5.51	30	6-9PM	110401	−0.05157		281602	8	169	88135		4.89
E+08											E+11
5.51	31	ABC-M-	110401	0.496692		5566739	8	169	15112871		5.53
E+08		Su-8p-12a									E+10
5.51	32	ABC-Su-	110401	0.454103		6098754	8	169	2276936		2.15
E+08		8 pm									E+09
5.51	37	Affiliate	110401			3448279	8	169	14087011
E+08		ABC
5.51	45	ABC-Su-	110401				8	169	15067083	0.022112	10119
E+08		8 pm
5.51	46	ABC	110401				8	169	15087374	0.018926	930227
E+08
5.51	47	ABC-	110401				8	169	17079498	0.010732	1907
E+08		Once
		Upon a
		Time
5.51	51	Affiliate	110401	0.532664	90152.72	5572002	8	169	15258889			16.1796
E+08		ABC-Su-
		8 pm
5.51	52	Affiliate	110401	0.288911	30555.57	3050742	8	169	15294285			10.0158
E+08		ABC
5.51	53	Affiliate	110401	0.750027	91973.99	7139718	8	169	16834703			12.882
E+08		ABC-
		ONCE
		UPON A
		TIME
5.51	55	Once	110401	0.77264	579.818	69058	8	169	16822042			8.396
E+08		Upon a
		Time
5.51	57	ABC	110401			12746	8	169	15347783
E+08
5.51	58	ABC-Su-	110401			11616	8	169	15347265
E+08		8 pm
5.51	59	ABC-	110401	0.716187	77804.28	5826263	8	169	16315316			13.3541
E+08		Once
		Upon a
		Time
5.51	60	ABC-	110401	0.38292		4389879	8	169	16065334
E+08		Once
		Upon a
		Time
5.51	65	ABC-	110401	0.716187	84081.14	6261621	8	169	16085946			13.428
E+08		Once
		Upon a
		Time
5.51	74	ABC-Su-	110401	0.119195		6657065	8	169	23966510		8.25
E+08		8 pm-Q1									E+08
5.51	75	Once	110401	0.46353		3910833	8	169	24137635
E+08		Upon a
		Time-Q1
5.51	76	Once	110401	0.746346	457.458	63879	8	169	24216555			7.1613
E+08		Upon a
		Time-Q1
5.51	78	Weekend-	110401	0.891909	80280.79	5944897	8	169	24298243			13.5041
E+08		Prime-
		ABC-
		Once
		Upon a
		Time
5.51	81	ABC-	110401	0.371263		420098	8	169	24430573		16803933
E+08		Once
		Upon a
		Time
5.51	82	Affiliate	110401	0.159728	86607.6	2.38	8	169	24462105			15.2042
E+08		ABC-Su-				E+10
		8 pm
5.51	83	Affiliate	110401	0.229127	73853.27	1.84	8	169	24476315			12.9693
E+08		ABC-				E+10
		Once
		Upon a
		Time
5.51	84	ABC-	110401				8	169	24481814	0.002729
E+08		Once
		Upon a
		Time
5.51	85	ABC-Su-	110401				8	169	24499779	0.002328
E+08		8 pm
5.51	86	ABC-	110401	0.446768		3812924	8	169	24819778		2.59
E+08		Once									E+08
		Upon a
		Time-
		Q12013
5.51	87	ABC-Su-	110401	−0.38981		5558337	8	169	24923543		2.89
E+08		8 pm-									E+08
		Q12013

TABLE 44
Pre-computed Media Asset Patterns and scores-Maptype 69
sourcesegmentkey	MediaAssetPatternKey	mediaassetpatterntypeid	wpi
110401	SOAP-Su-3 pm	69	0.00322
110401	COM-Tu-1 pm	69	0.003025
110401	DFH-Tu-11 am	69	0.002895
110401	DFH-W-2 pm	69	0.00273
110401	DFH-M-7 am	69	0.002596
110401	COM-W-1 pm	69	0.002539
110401	DFH-M-1 pm	69	0.002291
110401	COM-Th-1 pm	69	0.002148
110401	COM-Tu-12 pm	69	0.00211
110401	DFH-Th-3 pm	69	0.00206

TABLE 45
Pre-computed Media Asset Patterns and scores-Maptype 60
Source		Mediaasset
segmentkey	MediaAsset PatternKey	pattern type ID	correlation
110401-NC--3	STYL-Chances Are	60	0.826812
110401-NC--3	WE-Notting Hill	60	0.822194
110401-NC--3	STYL-Christian Siriano:	60	0.813051
	Having a Moment
110401-NC--3	BRAV-Pretty Woman	60	0.812836
110401-NC--3	BRAV-Proof of Life	60	0.808653
110401-NC--3	STYL-Fashion Police:	60	0.808165
	Academy Awards
110401-NC--3	STYL-Project Runway	60	0.806292
110401-NC--3	E!-Sabrina	60	0.805671
110401-NC--3	LIFE-After the Runway	60	0.804079
110401-NC--3	E!-Countdown to the Red	60	0.803744
	Carpet: The Golden Globe
	Awards

TABLE 46
Pre-computed Media Asset Patterns and scores-Maptype 32
Source	MediaAsset
segmentkey	PatternKey	mediaassetpatterntypeid	correlation
110401	E!-Sa-6 am	32	0.771822
110401	STYL-W-2 am	32	0.770532
110401	E!-W-4 am	32	0.76947
110401	E!-Tu-12 am	32	0.769332
110401	E!-Su-6 am	32	0.769055
110401	BRAV-F-7 am	32	0.768813
110401	E!-Tu-8 pm	32	0.767945
110401	STYL-W-3 am	32	0.76748
110401	E!-W-2 am	32	0.76741
110401	STYL-Su-1 am	32	0.7674

Table 47, below, depicts Scoring Service Output records (examples). The records below show some examples of television airings and scored response per impression (tratio), CPM, Impressions and so on.

TABLE 47
			SourceSegmentKey		AiringUniversal
JobID	ProductID	ProductName	(“Target”)	SourceSegmentDesc	ID	CreateDate
33	10107	Art.Com	110401	Art.Com	234266	Aug. 7, 2020
						13 11:20
33	10107	Art.Com	110401	Art.Com	247336	Aug. 7, 2020
						13 11:20
33	10107	Art.Com	110401	Art.Com	245484	Aug. 7, 2020
						13 11:20
33	10107	Art.Com	110401	Art.Com	248284	Aug. 7, 2020
						13 11:20
33	10107	Art.Com	110401-NC--1	Art.Com	248313	Aug. 7, 2020
				Cluster 1		13 11:20
33	10107	Art.Com	110401	Art.Com	216881	Aug. 7, 2020
						13 11:20
33	10107	Art.Com	110401	Art.Com	216897	Aug. 7, 2020
						13 11:20

Table 48, below, depicts dimensions (e.g., Network ID, Program ID, Day of Week, etc.), as well as that dual feed airing may have multiple airing events (i.e., different airing dates.)

TABLE 48
					AirDate_	AirDate_
MarketID	NetworkID	ProgramID	DayOfWeek	HourOfDay	Local	UTC	Callletters
169	16	334	4	23	Apr. 24, 2013	Apr. 24, 2013	BBCA
					11:42 PM	6:42 PM
169	11	359	3	17	May 21, 2013	May 21, 2013	AMC
					5:02 PM	12:02 PM
169	894	427	7	22	May 18, 2013	May 18, 2013	WE
					10:17 PM	5:17 PM
169	11	512	5	12	May 23, 2013	May 23, 2013	AMC
					12:28 PM	7:28 AM
169	11	512	5	13	May 23, 2013	May 23, 2013	AMC
					1:06 PM	8:06 AM
169	20	587	3	8	Mar. 19, 2013	Mar. 19, 2013	BRAV
					8:45 AM	3:45 AM
169	20	587	3	9	Mar. 19, 2013	Mar. 19, 2013	BRAV
					9:33 AM	4:33 AM

TABLE 49A
Program	Media		Impres-
Name	Market	tRatio	sions	Cost	CPM
Resident	NATIONAL	0.13501	66862	171.7975	2.569433
Evil
The	NATIONAL	−0.23812	423659	1022.501	2.4135
Scorpion
King
Titanic	NATIONAL	0.028158	199198	792.9408	3.980667
As Good as	NATIONAL	−0.07006	252962	616.1395	2.4357
it Gets
As Good as	NATIONAL	−0.06727	285029	689.1526	2.417833
it Gets
Inside the	NATIONAL	0.326143	115778	395.7726	3.418375
Actors
Studio
Inside the	NATIONAL	0.341543	126991	425.9437	3.354125
Actors
Studio

TABLE 49B
				Match	SDH	Program
Program				Error	Airings	Airings
Name	BPI	RPI	WPI	Code	By Date	By Date
Resident Evil	0.013668		0.021814	1	1	0
The Scorpion	0.0015		0.021814	1	2	0
King
Titanic	0.002871		0.021814	0	1	0
As Good	0.00097		0.021814	1	5	2
as it Gets
As Good	0.00101		0.021814	1	5	3
as it Gets
Inside the	0.013363		0.021814	0	3	1
Actors
Studio
Inside the	0.013331		0.021814	0	1	2
Actors
Studio

FIG. 14 depicts an example of a sample scored output text file.

FIG. 15 depicts another example of a sample scored output text file, including sample scored output (JSON). Imps, Price, C1TR, C2TR, C3TR, TR refer to “Impressions predicted”, “CPM predicted”, “Cluster 1 tratio”, “Cluster 2 tratio”, “Cluster 3 tratio”, “tratio overall”. The system is designed to score multiple targets at once for response per impression-hence the above showing the 3 clusters plus overall score.

FIG. 16 depicts another example JSON output from the scoring service showing a media instance being scored.

Table 50, below, depicts an example cardinality of different media asset pattern types that may be used by the system. In one embodiment there are approximately 18,642,000 pre-computed media asset patterns being used to estimate the response per impression, impressions, CPM and other aspects of a television airing.

TABLE 50
mediaassetpatterntypeid Number of instances
1                       2,336
2                       5,599
3                       39
4                       16,352
5                       56
6                       7
7                       8
8                       213
9                       59
10                      59
11                      211
12                      812
13                      812
14                      20,664
15                      214
18                      52,467
20                      241
21                      1,711
22                      21,840
24                      15,219
25                      30,164
27                      229
28                      49,667
29                      7
30                      8
31                      1,603
32                      38,472
33                      210
34                      42,299
35                      46,549
36                      4,021,971
37                      146
38                      13,205
39                      1,406
40                      40,006
42                      3,774,960
45                      21,359
46                      128
47                      15,903
49                      933
50                      31,900
51                      18,332
52                      120
53                      34,586
54                      231
55                      13,152
57                      288
58                      15,877
59                      28,018
60                      91,958
61                      160,403
62                      959
63                      225,348
65                      1,000
66                      999
68                      633
69                      1,706
70                      46
71                      802
72                      7,361
73                      3,760
74                      155,478
75                      143,722
76                      35,851
77                      5,000
78                      4,992
80                      73,223
81                      21,968
82                      18,231
83                      17,065
84                      19,506
85                      35,815
86                      273,070
87                      385,134
89                      8,107,070
90                      234,800
91                      139,347
93                      14,758
98                      81,908
105                     106
106                     156

Table 51, below, depicts an example of trained weights (wexpert) applied to each media asset pattern type. These weights are evaluated multiplied by normalized ad effectiveness scores and combined to estimate the response per impression target. Cadaline is a one-variable linear model. Cadaline_test is the model applied on a hold-out set. % is the percentage of airings where this media asset pattern type is present (non-missing). The weights below are from weightid=20.

TABLE 51
			cadaline
Variable	w	cadaline	test	wexpert	wadaline	%
m-1-Distributor	0.17	0.00	−0.53	0	0	92%
m-2-Program	0.78	0.16	0.18	0	1.43	4%
m-4-Distrib.-Rot.	0.21	0	−0.38	0	0	67%
m-5-Day-Hour	0.25	0.12	0.30	0.09	0.14	100%
m-6-Day of Week	0.40	0.07	0.17	0	0.06	100%
m-7-Hour of Day	0.48	0.12	0.27	0	0.10	100%
m-14-SDH	0.51	0.16	0.27	0.33	0.30	64%
m-20-STB Station	0.62	0	−0.02	0	0	100%
m-21-STB Station-Rot.	0.63	0	0.12	0	0	100%
m-22-STB SDH	0.99	0	0.23	0	0	100%
m-25-STB Program	0.36	0	−0.31	0	0	31%
m-27-STBHead Station	0.28	0.23	−0.07	0	0.26	39%
m-28-STBHead Program	0.68	0.04	−0.08	0	0.49	9%
m-29-STBHead Day	0.51	0	0.04	0	0	100%
m-30-STBHead	0.28	0	−0.12	0	0	100%
Hour of Day
m-31-STBHead S-Rot.	0.58	0.30	0.25	0.12	0.37	39%
m-32-STBHead SDH	0.82	0.24	0.23	0	0.21	39%
m-37-Telesales Nat S	0.94	0.49	0.43	0	0.19	39%
m-38-Telesales Nat SDH	0.44	0.06	−0.04	0	0.06	22%
m-39-Telesales Loc S	0.42	0.31	0.30	0.38	1.03	96%
m-40-Telesales Loc SDH	1.00	0.35	0.32	0	1.46	61%
SVPct45-STBDevice SDH	0.61	0.15	0.19	0.00	0.02	100%
SVPct46-STBDevice S	0.94	0.17	0.20	0.08	0.03	100%

Table 52, below, depicts yet another set of weights from an earlier model (weightid=3).

TABLE 52
Media Asset Pattern Type	cadaline_test	wexpert
m-1-Distributor	−0.27321	0
m-5-Day of Week-Hour of Day	0.218107	0.002726
m-6-Day of Week	0.176983	0.113136
m-7-Hour of Day	0.153618	0.091361
m-14-Distributor-Day-Hour	−0.02842	0.725416
m-20-STBDevice-STB Station	−0.03184	0
m-21-STBDevice-STB Station-Rotation	−0.01012	0
m-22-STBDevice-STB Station-Day-Hour	0.009289	0
m-29-STBHead Day of Week	0.040314	0.045517
m-30-STBHead Hour of Day	−0.06275	0
m-32-STBHead Station-Day-Hour	0.142697	0.000718
m-33-DMA	−0.30986	0.021126

FIG. 17 depicts an exemplary graph of standardized score (x-axis) versus buyers per million impressions (y-axis) for an advertiser whose response per impression function was buyers per million impressions.

FIG. 18 depicts an exemplary graph of a comparison of Media Asset Patterns, showing that the program is more predictive than SDH when only considering non-missing values.

FIG. 19 depicts an exemplary graph depicting that the program is often poorly populated, and that program authority increases the match rate.

FIGS. 20 and 21 depict an exemplary graph showing that program authority is not as predictive as the program, however the increase in match rate offsets the small drop in accuracy.

FIG. 8 is a simplified functional block diagram of a computer that may be configured as client devices, APs, ISPs, and/or servers for executing the methods, according to exemplary an embodiment of the present disclosure. Specifically, in one embodiment, any of the modules, servers, systems, and/or platforms may be an assembly of hardware 800 including, for example, a data communication interface 860 for packet data communication. The platform may also include a central processing unit (“CPU”) 820, in the form of one or more processors, for executing program instructions. The platform typically includes an internal communication bus 810, program storage, and data storage for various data files to be processed and/or communicated by the platform such as ROM 830 and RAM 840, although the system 800 often receives programming and data via network communications 870. The server 800 also may include input and output ports 850 to connect with input and output devices such as keyboards, mice, touchscreens, monitors, displays, etc. Of course, the various server functions may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load. Alternatively, the servers may be implemented by appropriate programming of one computer hardware platform.

Program aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of executable code and/or associated data that is carried on or embodied in a type of machine-readable medium. “Storage” type media include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer of the mobile communication network into the computer platform of a server and/or from a server to the mobile device. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links, or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

While the presently disclosed sharing application, methods, devices, and systems are described with exemplary reference to mobile applications and to transmitting data, it should be appreciated that the presently disclosed embodiments may be applicable to any environment, such as a desktop or laptop computer, an automobile entertainment system, a home entertainment system, etc. Also, the presently disclosed embodiments may be applicable to any type of protocol stack.

With the above described disclosure, it may be possible to target TV ads to maximize well-defined ad response metrics at scale. As described herein, TV targeting may be defined as a well-defined supervised learning problem. Accordingly, the types of ad effectiveness methods that are available may vary, and may each be combined to offset weaknesses in each method. By combining these techniques improvements in TV ad targeting may be realized using present TV systems.

Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import, when used in this application, refer to this application as a whole and not to any particular portions of this application. Where the context permits, words in the above Detailed Description using the singular or plural number may also include the plural or singular number respectively. The word “or,” in reference to a list of two or more items, covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, and any combination of the items in the list.

The above detailed description of examples of the present disclosure is not intended to be exhaustive or to limit the present disclosure to the precise form disclosed above. While specific examples for the present disclosure are described above for illustrative purposes, various equivalent modifications are possible within the scope of the present disclosure, as those skilled in the relevant art will recognize. For example, while processes or blocks are presented in a given order, alternative implementations may perform routines having steps, or employ systems having blocks, in a different order, and some processes or blocks may be deleted, moved, added, subdivided, combined, and/or modified to provide alternative or sub-combinations. Each of these processes or blocks may be implemented in a variety of different ways. Also, while processes or blocks are at times shown as being performed in series, these processes or blocks may instead be performed or implemented in parallel, or may be performed at different times. Further any specific numbers noted herein are only examples: alternative implementations may employ differing values or ranges.

The teachings of the present disclosure provided herein can be applied to other systems, not necessarily the system described above. The elements and acts of the various examples described above can be combined to provide further implementations of the present disclosure. Some alternative implementations of the present disclosure may include not only additional elements to those implementations noted above, but also may include fewer elements.

These and other changes can be made to the present disclosure in light of the above detailed description. While the above description describes certain examples of the present disclosure, and describes the best mode contemplated, no matter how detailed the above appears in text, the present disclosure can be practiced in many ways. Details of the system may vary considerably in its specific implementation, while still being encompassed by the present disclosure disclosed herein. As noted above, particular terminology used when describing certain features or aspects of the present disclosure should not be taken to imply that the terminology is being redefined herein to be restricted to any specific characteristics, features, or aspects of the present disclosure with which that terminology is associated. In general, the terms used in the following claims should not be construed to limit the present disclosure to the specific examples disclosed in the specification, unless the above detailed description section explicitly defines such terms. Accordingly, the actual scope of the present disclosure encompasses not only the disclosed examples, but also all equivalent ways of practicing or implementing the present disclosure.

Claims

1. A computer-implemented method comprising: generating, by a server, a model to predict advertisement effectiveness for a pairing of an advertisement and a media placement based on a first advertisement effectiveness measure;combining the first advertisement effectiveness measure with a historical airing count to predict the advertisement effectiveness;converting the predicted advertisement effectiveness into native units including buyers per million and phone responses per million; andplacing the advertisement within the media placement based on the first advertisement effectiveness measure.

2. The method of claim 1, further comprising: adjusting a value of the first advertisement effectiveness measure for the pairing of the advertisement and the media placement to a default value of the first advertisement effectiveness measure if a number of impressions for the pairing is below the minimum participation threshold for the first advertisement effectiveness measure, the first advertisement effectiveness measure including one or more of phone responses, demographic similarity, set top box buyers, and web responses.

3. The method of claim 1, wherein the model is further generated based a number of previously placed airings of the advertisement in the media placement, the number of previously placed airings being estimated based on the historical airing count and co-viewing probabilities from set top box data.

4. The method of claim 1, wherein a weight for each media placement is based on one or more of a count of persons, and other data sufficiency statistics.

5. The method of claim 3, wherein the model disregards a second advertisement effectiveness measure for a second particular pairing of an advertisement and a media asset if a number of impressions for the second particular pairing of the advertisement and the media asset is below a minimum participation threshold for the second advertisement effectiveness measure.

6. The method of claim 3, further comprising: applying the model to a plurality of media assets for a particular advertisement to assist in selection of one or more of the plurality of media assets for airing the advertisement.

7. The method of claim 1, wherein the media placement is identified as having at least a predetermined number of observed viewers over an expected number of viewers for a predetermined time and a predetermined network.

8. The method of claim 1, further comprising: creating, by the server, a media placement of same-time-last-week; andcalculating, by the server, an advertisement effectiveness measure for the media placement.

9. The method of claim 1, wherein the media placements include one or more of station, program, station-program, station-day-hour, station-day-hour-program-market.

10. The method of claim 1, wherein each advertisement effectiveness measure is standardized so that media placements are directly comparable with each other.

11. The method of claim 10, wherein the standardized advertisement effectiveness measures predict a standardized advertisement effectiveness.

12. The method of claim 11, wherein the predicted standardized advertisement effectiveness is converted into the native units including the buyers per million and the phone responses per million.

13. A system for generating a model to predict advertisement effectiveness, the system comprising: a data storage device that stores instructions for generating a model to predict advertisement effectiveness; anda processor configured to execute the instructions to perform a method including: generating, by a server, a model to predict advertisement effectiveness for a pairing of an advertisement and a media placement based on a first advertisement effectiveness measure;combining the first advertisement effectiveness measure with a historical airing count to predict the advertisement effectiveness;converting the predicted advertisement effectiveness into native units including buyers per million and phone responses per million; andplacing the advertisement within the media placement based on the first advertisement effectiveness measure.

14. The system of claim 13, the method further comprising: adjusting a value of the first advertisement effectiveness measure for the pairing of the advertisement and the media placement to a default value of the first advertisement effectiveness measure if a number of impressions for the pairing is below the minimum participation threshold for the first advertisement effectiveness measure, the first advertisement effectiveness measure including one or more of phone responses, demographic similarity, set top box buyers, and web responses.

15. The system of claim 13, wherein the model is further generated based on a number of previously placed airings of the advertisement in the media placement, the number of previously placed airings being estimated based on the historical airing count and co-viewing probabilities from set top box data.

16. The system of claim 15, wherein a weight for each media placement is based on one or more of a count of persons, and other data sufficiency statistics.

17. The system of claim 15, wherein the model disregards a second advertisement effectiveness measure for a second particular pairing of an advertisement and a media asset if a number of impressions for the second particular pairing of the advertisement and the media asset is below a minimum participation threshold for the second advertisement effectiveness measure.

18. A non-transitory machine-readable medium storing instructions that, when executed by a computing system, causes the computing system to perform a method comprising: generating, by a server, a model to predict advertisement effectiveness for a pairing of an advertisement and a media placement based on a first advertisement effectiveness measure;combining the first advertisement effectiveness measure with a historical airing count to predict the advertisement effectiveness;converting the predicted advertisement effectiveness into native units including buyers per million and phone responses per million; andplacing the advertisement within the media placement based on the first advertisement effectiveness measure.

19. The non-transitory machine-readable medium of claim 18, further comprising: adjusting a value of the first advertisement effectiveness measure for the pairing of the advertisement and the media placement to a default value of the first advertisement effectiveness measure if a number of impressions for the pairing is below the minimum participation threshold for the first advertisement effectiveness measure, the first advertisement effectiveness measure including one or more of phone responses, demographic similarity, set top box buyers, and web responses.

20. The non-transitory machine-readable medium of claim 18, wherein the model is further generated based a number of previously placed airings of the advertisement in the media placement, the number of previously placed airings being estimated based on the historical airing count and co-viewing probabilities from set top box data.