US20160119689A1 - Systems and Methods for Planning and Executing an Advertising Campaign Targeting TV Viewers and Digital Media Viewers Across Formats and Screen Types - Google Patents

Systems and Methods for Planning and Executing an Advertising Campaign Targeting TV Viewers and Digital Media Viewers Across Formats and Screen Types Download PDF

Info

Publication number
US20160119689A1
US20160119689A1 US14/923,153 US201514923153A US2016119689A1 US 20160119689 A1 US20160119689 A1 US 20160119689A1 US 201514923153 A US201514923153 A US 201514923153A US 2016119689 A1 US2016119689 A1 US 2016119689A1
Authority
US
United States
Prior art keywords
viewers
advertisements
mps
listed
watching
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US14/923,153
Inventor
Alexander R. Hood
Jason LOPATECKI
Justin K. Sung
David Innes-Gawn
John M. Trenkle
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Adobe Inc
Original Assignee
Tubemogul Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tubemogul Inc filed Critical Tubemogul Inc
Priority to US14/923,153 priority Critical patent/US20160119689A1/en
Assigned to TUBEMOGUL, INC. reassignment TUBEMOGUL, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: INNES-GAWN, DAVID, HOOD, ALEXANDER R., LOPATECKI, JASON, SUNG, JUSTIN K., TRENKLE, JOHN M.
Publication of US20160119689A1 publication Critical patent/US20160119689A1/en
Assigned to ADOBE SYSTEMS INCORPORATED reassignment ADOBE SYSTEMS INCORPORATED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: TUBEMOGUL, INC.
Assigned to ADOBE INC. reassignment ADOBE INC. CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: ADOBE SYSTEMS INCORPORATED
Abandoned legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0242—Determining effectiveness of advertisements
    • G06Q30/0244—Optimization
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
    • H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
    • H04N21/25866—Management of end-user data
    • H04N21/25891—Management of end-user data being end-user preferences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
    • H04N21/44204—Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
    • H04N21/81—Monomedia components thereof
    • H04N21/812—Monomedia components thereof involving advertisement data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
    • H04N21/251—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/252—Processing of multiple end-users' preferences to derive collaborative data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
    • H04N21/254—Management at additional data server, e.g. shopping server, rights management server
    • H04N21/2543—Billing, e.g. for subscription services
    • H04N21/2547—Third Party Billing, e.g. billing of advertiser
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/4508—Management of client data or end-user data
    • H04N21/4532—Management of client data or end-user data involving end-user characteristics, e.g. viewer profile, preferences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
    • H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
    • H04N21/84—Generation or processing of descriptive data, e.g. content descriptors

Definitions

  • the present invention relates generally to systems and methods for planning and managing advertising campaigns that include both digital media advertising and TV advertising across one or more digital formats and screens.
  • an electronic advertising agency/consolidator operating a demand-side platform receives billions of daily auction opportunities for electronic media impressions from partners like Google®, Yahoo®, etc. These partners operate auctions for ad impressions and then place electronic ads based on auction results.
  • a partner's auction is considered an external auction with respect to a demand-side platform where an internal auction may also be operated to determine which advertisements, also referred to herein as ads, and bids are submitted to the external auction.
  • Each ad impression opportunity includes information parameters about the ad impression—for example, the target website, geolocation of the user, ad size, user cookie, etc, that are used for targeting purposes.
  • the demand side platform then processes hundreds of ads in their system, supplied by advertiser clients along with desired filtering/targeting parameters, against information parameters supplied by the partner, and filters out any ads that do not qualify (for example the ad does not want to target Youtube.com®). For ads that are not removed due to a mismatch with targeting parameters, the demand-side platform then evaluates the corresponding bids that represent how much each client advertiser is willing to pay. The winning bid in the internal auction is then sent to the external auction to compete for the impression opportunity.
  • the electronic advertising agency/consolidator operating a demand-side platform and the advertiser/client may in fact be the same entity—for instance when they comprise a large organization with an internal advertising department capable of acting as a demand-side platform. Also, in such an instance, there may be no internal auction—just a submission to an external auction.
  • Digital media includes any electronic media format other than TV—essentially all digital formats that can be planned and delivered electronically.
  • Digital media locations where an ad may be placed are called Media Properties.
  • a Media Property or MP as described herein represents a specific instance of a media platform for electronically delivering information to a viewer.
  • An MP as referenced herein usually refers to a website or URL on the Internet, however may also refer for example and without limitation to an App ID, a Game ID, or other digital electronic media including for example electronic billboards—small and large.
  • Even digital watches with wireless connectivity are a form of MP.
  • television or TV includes:
  • VOD Video on Demand
  • VOD is further defined as systems that allow users to select and watch/listen to video or audio content when they choose to, rather than having to watch at a specific broadcast time.
  • IPTV technology is often used to bring video on demand to televisions and personal computers.
  • Television VOD systems can either stream content through a set-top box, a computer or other device, allowing viewing in real time, or download it to a device such as a computer, digital video recorder (also called a personal video recorder) or portable media player for viewing at any time.
  • Television programming in general may be therefore viewed on conventional TV sets, or on any digital media viewing device, including without limitation PC/laptops, tablets, smartphones, and even digital watches.
  • a viewer may be either a person, cookie, household, or any group of persons that watch the same programming—regardless of whether or not they watch simultaneously.
  • a planner may have the following needs and goals (written from the perspective of the planner):
  • FIG. 1 shows an overview block diagram describing system components and data flow for a demand side platform according to the invention, with a focus on planning and executing a future cross format campaign.
  • FIG. 2 shows a flow chart describing a process for planning a cross format campaign with emphasis on reaching unexposed TV viewers.
  • FIG. 3 shows a flow chart describing a process for planning a cross format campaign with emphasis on reinforcing viewership.
  • FIG. 4 shows a flow chart describing a process for planning a cross format campaign with emphasis on achieving the lowest cost per impression.
  • FIG. 5 shows a matrix defining different data sets used for cross format advertising campaign planning.
  • FIG. 6 shows how the various data sets of FIG. 5 are linked and analyzed in creating a cross format advertising campaign.
  • FIG. 7 shows one exemplary user interface for the planning stage of a cross format campaign.
  • FIG. 8 shows an exemplary user interface for the planning stage of a cross format campaign where a planner may observe the effect of shifting portions of their TV advertising budget to digital media.
  • FIG. 9 shows an alternative functionality for guiding the split between TV ad spending and digital media ad spending.
  • FIG. 10 shows the user interface for a planning tool according to the invention which allows planners to adjust their campaign and observe projected results in terms of on-target impressions, GRPs, and cost.
  • FIG. 11 shows a planning tool for a cross format platform focused on GRP or CPM results and including advanced (strategic) targeting choices.
  • FIG. 12 shows an additional planning tool for a cross format campaign including a graph indicating incremental reach.
  • FIG. 13 shows an additional planning tool for a cross format campaign where results are shown in terms of CPP (cost per GRP point) and cost for on target impressions.
  • FIG. 14 shows an additional planning tool for cross format campaign including target placements for both TV and digital media.
  • Systems and methods are disclosed for analyzing a fused sample of viewership data to determine a behavior profile of online viewers who watched and/or didn't watch certain TV advertisements, where the TV advertisements are aligned with campaign targeting characteristics desired by an advertiser/client working with a demand side platform. Then, a campaign targeting plan is developed for dividing an advertising budget between digital media and TV impressions.
  • the digital media portion of the campaign profiles Media Properties (MPs) contained in a historical database from past digital advertising campaigns across multiple digital formats and screens, and aligns digital ad placement with MPs having desired targeting characteristics.
  • MPs Media Properties
  • An optimized apportionment is automatically produced between TV and digital media spending based on an advertiser/client's goals of duplicating or not duplicating viewership of an advertisement between TV and digital media, or alternately based on cost alone. Alternately, the apportionment can be guided interactively.
  • FIG. 1 shows an overview block diagram describing system components and data flow for a demand side platform according to the invention, with a focus on planning and executing a future cross format campaign based on 3 rd party TV and digital viewership information combined with historical digital media viewership information compiled over past campaigns by the demand side platform.
  • the overview block diagram 100 for a system according to the invention includes a DSP (demand-side platform) 114 that interacts with advertiser/clients 116 ; supply side partners 102 ; and sources 124 of third party viewership data.
  • the demand-side platform 114 utilizes automation software operating on one or more servers/processors 112 to bid for ad slots 104 to be shown to viewers 108 via media property or TV programming 106 .
  • Online advertising and/or TV programming supply side partner(s) 102 typically provide a sale opportunity or bid request package 110 —typically including ad slot data—to the demand-side platform 114 .
  • the demand-side platform will, in turn and if appropriate, provide a response 118 to supply side partner 102 , that response including a bid and either an advertisement or information describing an advertisement.
  • the decision to bid, and how much to bid is automatically calculated by software running on the one or more servers 112 where targeting and planning information 120 supplied by advertiser/client 116 is processed. Such information may include for example and without limitation: desired on-target impressions; campaign targeting parameters; campaign runtime; campaign budget; maximum bid; and TV campaign data and desired campaign strategy.
  • the DSP provides campaign cost estimates 122 and estimates for results to advertiser/clients 116 .
  • FIG. 2 shows a flow chart describing a process for planning a cross format campaign where an advertiser/client wishes to plan the campaign such that viewers who did not watch certain ads on TV get to see them on digital media, thereby increasing the reach of the campaign—the process referred to as optimization by de-duped reach.
  • a fused data set of viewership data is received that includes both TV viewing and digital media viewing of advertisements, and where for a plurality of specific viewers watching TV advertisements the data set includes digital media viewership data for those specific viewers.
  • a list of proposed or historical TV advertisements is received for a particular advertiser/client.
  • the fused data set is analyzed for each respective TV advertisement to:
  • step S 208 targeting criteria from an advertiser/client is received for a future campaign.
  • step S 210 a historical database containing data from online advertising campaigns is analyzed, and based on the received targeting criteria for the future campaign and the data from online advertising campaigns, a model for the future campaign is produced that includes a proposed budget split between TV and online placement of advertisements.
  • FIG. 3 shows a flow chart describing a process for planning a cross format campaign where an advertiser/client wishes to plan a campaign such that the viewing of certain ads on TV is reinforced by targeting the same viewers on digital media in order to increase the frequency of viewership of those ads—the process referred to as optimization by increased frequency.
  • a fused data set of viewership data is received that includes both TV viewing and digital media viewing of advertisements, and where for a plurality of specific viewers watching TV advertisements the data set includes digital media viewership data for those specific viewers.
  • a list of proposed or historical TV advertisements is received for an advertiser/client.
  • the fused data set is analyzed for each respective TV advertisement to:
  • step S 308 targeting criteria is received from an advertiser/client for a future campaign.
  • step S 310 a historical database containing data from online advertising campaigns is analyzed, and based on the received targeting criteria for the future campaign and the data from online advertising campaigns, a model for the future campaign is produced that includes a proposed budget split between TV and online placement of advertisements.
  • FIG. 4 shows a flow chart describing a process for planning a cross format campaign where an advertiser/client wishes to plan a campaign such that the lowest cost per impression is achieved without regard to whether or not a TV viewer sees the same ad on digital media—the process referred to as optimization by “cheapest to reach”.
  • a fused data set of viewership data is received that includes both TV viewing and digital media viewing of advertisements, and where for a plurality of specific viewers watching TV advertisements the data set includes digital media viewership data for those specific viewers.
  • a list of proposed or historical TV advertisements is received for an advertiser/client.
  • the fused data set is analyzed for each respective TV advertisement to:
  • step 408 targeting criteria is received from an advertiser/client for a future campaign.
  • step S 410 a historical database containing data from online advertising campaigns is analyzed, and based on the received targeting criteria for the future campaign and the data from online advertising campaigns, a model for the future campaign is produced that includes a proposed budget split between TV and online placement of advertisements.
  • the campaign model includes target lists for both TV demographics and MPs targeted for digital media.
  • the lists are combined and sorted to produce an overall target list for a campaign.
  • the combined list can be then sorted by cost efficiency (cost per on-target impression) to produce a prioritized targeting list for the future campaign.
  • TV viewership information used in the process may be related to past advertising campaigns for TV or may alternately be projected TV viewership information.
  • Characteristics of MPs may include without limitation one or more of demographic information; the cost of purchasing impressions on the MP; a historical “reach” for the MP which reflects how frequently impression opportunities arise for that MP. Note that ad campaigns usually have a specified run time, and if a particular MP has a very small reach it may contribute little value to a campaign if impression opportunities for that MP appear infrequently.
  • Optimization for a campaign targeting both TV and digital media can be performed for cost and/or for reach, including optimization for CPMs (Cost per 1,000 on-target impressions) and/or GRPs (Gross Rating Points).
  • 3 rd party viewership data suppliers may supply data indicative of a viewer panel and/or actual viewership data acquired by monitoring a large number of viewers over time.
  • the most prominent supplier of viewership data today in the United States is Nielsen, however other sources of viewership data exist, and without limitation such other sources may include for example Barb in the UK, OZTam in Australia, and Rentrak in the US.
  • the embodiments described herein do not rely on any specific supplier of viewership data, and any specific mention of data packages supplied by, for example Nielsen, are only exemplary.
  • FIG. 5 shows a matrix defining different data sets used for cross format advertising campaign planning.
  • Four data sets 502 - 508 of the five total data sets are examples of those available from third-party viewership data suppliers such as, for example, Nielsen.
  • the National People Meter 502 shows minute by minute TV viewing and when supplied by Nielsen contains for example 55,000 respondents—the majority of whom have Internet access and recording devices for Internet activity.
  • the data set entitled NetView & VideoCensus 504 when supplied by Nielsen contains for example 200,000 respondents with Internet activity tracking.
  • the Fusion data set 506 contains approximately 255,000 respondents where TV and Internet usage is fused and extrapolated.
  • the data set entitled Monitor plus 508 when supplied by Nielsen, shows TV activity with related cost data.
  • Data set 510 supplied by a demand-side platform, provides Internet site (MP) data and related advertising data. For each supplied data set, descriptions for Contents 512 , Sample Size 514 , and Key Elements for the Planner 516 are shown.
  • MP Internet site
  • FIG. 6 shows how the various data sets of FIG. 5 are linked and analyzed in creating a cross format advertising campaign.
  • For linkage number “4” 604 in FIG. 6 sites (MPs) visited by viewers who saw particular TV ads are linked with sites they did NOT visit. This linkage is required for de-duped (un-duplicated) reach. Sites (MPs) unseen by those who saw a TV ad can be used by the DSP to generate a cross format campaign for incremental reach as described herein.
  • FIG. 7 shows one exemplary user interface for the planning stage 702 of a cross format campaign, where an advertiser/client's current plan 704 for TV advertising is analyzed 706 to provide a baseline.
  • the audience 708 for the plan is entered, including Gender 710 , which can be Male, Female or All genders.
  • the targeted age bracket 712 for the TV plan is entered by independently positioning slider nodes 716 and 718 on horizontal age bracketing tool 714 to visually bracket the targeted ages.
  • FIG. 7 includes a histogram graph showing a viewer population distribution 720 that appears under horizontal age bracketing tool 714 , and can be optionally truncated at either or both ends as either or both of slider nodes 716 and 718 are moved to indicate the age distribution within the specifically bracketed ages. Also, as either or both of slider nodes 716 and 718 are moved, the targeted (bracketed) population 712 is automatically updated. At the bottom left, a Discount from Rack Rate 722 can be entered. Timeframe 724 to be analyzed for the plan is entered and a summary of results 726 is automatically presented to the planner. The exemplary and non-limiting summary of FIG. 7 , includes: Reach; Reach (%); gross impressions; GRPs; average frequency; total cost; eCPM; and eCPP.
  • FIG. 8 shows an exemplary user interface for the planning stage of a cross format campaign where a planner on behalf of a client advertiser may observe the effect of shifting portions of their TV advertising budget to digital media, in this figure referred to as online video ads or (OLV).
  • OOV online video ads
  • a planner can manually optimize the campaign by manipulating a slider bar 804 located at the left side of the display shown in FIG. 8 .
  • the position of the bar affects both the reach and cost of the campaign, and as the slider bar is manipulated the resultant parameters shown on the right side of the screen in FIG. 8 are automatically calculated and change accordingly to display the calculated parameters.
  • a choice is automatically made for reach 808 with respect to proportions of OLV 810 versus TV Only 812 .
  • a choice for CPM 814 is automatically made with respect to a percentage shift towards OLV.
  • parameters 816 guide the plan for this advertising placement that was set in other screens are shown and include the brand; the demographic age/gender parameters; and the timeframe.
  • results are automatically updated by underlying processes according to embodiments of the invention. For instance, a new percentage for unduplicated reach 818 is displayed as well as a change in average frequency 820 . Estimated numbers of impressions 822 are updated along with financial savings 824 resulting from the plan.
  • a summary 826 for the plan including parameters 726 that were shown with respect to FIG. 7 . For FIG. 8 however, these projected results are shown separately with respect to a TV Schedule 828 ; and Online Video Ad Schedule 830 ; and a Combined Schedule 832 .
  • FIG. 9 shows an alternative functionality for guiding the split between TV ad spending and digital media ad spending. Similar to FIG. 8 , but with a different graphical appearance, choices for automatic optimization by target 802 is shown in the upper left corner of the user interface display of FIG. 9 . Similar to FIG. 8 , the basic strategy is chosen by the planner to be one of “cheapest to reach”; “did not see my TV ad”; and “did see my TV ad”. Below targeting choice 802 , a radial dial mechanism for budget adjustment 902 is presented at the left whereby the planner can choose the percentages for the split between TV and Online Video.
  • a unique fan shaped graphical input mechanism 906 is presented to the planner where by dragging node 904 left or right around the arc of fan 906 , a budget shift can be defined between TV and Online Video. Alternately, a shift percentage 908 may be entered, or a dollar amount 910 can be entered to define the shift.
  • resultant projected parameters are automatically calculated and displayed at the right along with example online placements.
  • prominent boxes are shown with parameter changes to help guide the planner including: a New Incremental Reach 912 ; a Change in Frequency 914 ; a Change in Impressions 916 ; and Per CPM Savings 918 .
  • a graph 920 showing a percentage reached of the population with respect to the percentage split between OLV and TV. Specific graphs for TV-Only 922 , OLV+TV 924 , and OLV-Only 926 are shown along with an overall CPM savings 928 .
  • a result summary 930 which is similar to summary 826 of FIG. 8 , and also a summary 932 of online placements characterized by viewer interest area—including for example and without limitation: men's lifestyle; sports; automotive; and fitness. Included for each of these viewer interest areas is either a projected cost as shown in the display example, or alternately projected impressions. Which of cost or impressions are shown for the placement breakdown at the lower right of FIG. 9 is controlled by selector 934 .
  • FIG. 10 shows the user interface for a planning tool which allows planners to adjust their campaign and observe automatically projected results in terms of on-target impressions, GRPs, and cost.
  • This tool was previously shown and described in co-pending U.S. Patent Application Publication No. 2014-0278912 entitled: “Systems and Methods for Predicting and Pricing of Gross Rating Point scores by Modeling Viewer Data”.
  • cross format planning tools such as those shown in FIGS. 7, 8, and 9
  • the user interface shown in FIG. 10 now incorporates data and planning for a cross format campaign targeting both TV advertising and digital media advertising.
  • an information bar 1002 that contains: the name of the placement; a mechanism for choosing the campaign and editing options; the particular planner's email address; and a search tool.
  • selectors for planning according to CPM 1004 or GRP 1006 are selectors for planning according to CPM 1004 or GRP 1006 .
  • an audience targeting mechanism similar to that previously shown in FIG. 7 , and includes selection 702 for age and gender, and in particular includes horizontal selector bar 718 with individually positioned slider nodes 716 and 718 that are manipulated by the planner to bracket the lower and upper bounds respectively of the targeted age bracket.
  • a graphical histogram 720 of the age distribution bracketed by selector bar 718 is also shown.
  • Placement parameters are entered here such as for example and without limitation a GRP goal 1008 that may be entered as well as a Frequency Cap 1010 .
  • additional placement parameters such as budgeting controls 1012 , including dates 1014 and a budget 1018 .
  • a planner may further select particular dayparts to address by clicking the daypart selector 1016 which brings up a user interface facility for daypart selection.
  • An inventory tier 1020 to be utilized is chosen at the bottom left.
  • a planner may choose one or more classifications or segmentations of MPs to be addressed by a campaign, herein labeled “Inventory Tiers” 1020 .
  • available classifications include tiers ranging from MPs that are very well-recognized to those that are less recognizable.
  • Tier 1 can represent sites a user's friends and family would recognize.
  • Tier 2 can represent MPs with lower brand recognition or awareness.
  • Tier 3 can represent brand-safe sites that are niche or reach.
  • “Brand safe” refers to MPs where there is nothing controversial such as guns, sex, violence, or alcohol, etc.
  • “Niche” means there is a smaller audience, such as for instance a site for vegan cooks.
  • “Reach” sites are MPs with huge audiences and many demographics such as YouTube, CBS, etc. Reach sites provide broad exposure, however with little specific brand alignment.
  • Forecasted results including at the top projected CPP 1022 ; projected On Target CPM and % 1024 ; and a GRP Goal and Expected value 1026 .
  • a mechanism is also included to Revert Changes 1028 .
  • a graph 1030 of projected GRP results is shown with respect to budget, and an annunciator 1032 is shown positioned on the graph in response to a value entered as budget 1018 .
  • Projected total impressions 1034 are shown at the bottom right along with projected total GRPs 1036 and projected Uniques 1038 .
  • FIG. 11 shows a planning tool for a cross format platform focused on GRP or CPM results and including advanced/strategic targeting choices that go beyond simple age and gender to include viewer characteristics such as for example and without limitation: income; marital status; education; presence of children; homeownership; and ethnicity. Also included are exemplary choices for advanced targeting of buyer behavior—which could be either modeled or actual behavior—in this instance targeting a specific product or service that a shopper might buy and also targeting a frequency for their purchase of that product or service. Also included are choices of different target screen formats for display platforms including platform variations for both TV and digital media, ensuring that planners using the tool per FIG. 11 have the ability to optimize for de-duplication or reach across all formats and screen types.
  • a facility 1102 for loading previous media plans includes a date range 1104 and a timeframe specification 1106 that provides for specification of quarters or a full year.
  • a facility for budget entry 1108 is provided for planning a new campaign with a specified date range 1110 and budget 1112 .
  • campaign goals are specified that include advanced/strategic targeting 1114 ; age bracket targeting 1128 ; and shopper targeting 1130 .
  • Advanced/strategic targeting 1114 includes targeting for: income 1116 ; marital status 1118 ; education 1120 ; presence of children 1122 ; home ownership 1124 ; and ethnicity 1126 .
  • Age bracket targeting 1128 utilizes a horizontal age bracketing tool 714 as previously shown in FIG.
  • Age bracketing tool 714 is linked with histogram graph 720 showing a viewer population distribution that appears under horizontal age bracketing tool 714 , and can be optionally truncated at either or both ends as either or both of slider nodes 716 and 718 are moved.
  • Shopper targeting 1130 shows for this example targeting shoppers who buy toothpaste 1132 , however this is exemplary and drop-down arrows allow editing to target a wide variety of products and services. Frequency 1134 for a viewer's purchase of a product or service can also be targeted.
  • At the bottom of the user interface display of FIG. 11 is the ability to select different cross screen formats 1136 for targeting.
  • An exemplary list of screen types shown for targeting includes without limitation: TV; Addressable TV; Online video; VOD; Mobile; Tablet; and Network extension.
  • FIG. 12 shows an exemplary user interface for an additional planning tool for a cross format campaign, where a planner may view/edit 1202 a plan.
  • An editable strategy summary 1204 is shown in the upper left, including date range 1206 , budget 1208 , and targeting criteria similar to those shown in FIG. 11 , including gender targeting 1210 and age bracket targeting 1128 , plus advanced/strategic targeting including for example and without limitation: income 1214 ; marital status 1216 ; education 1218 ; presence of children 1220 ; home ownership 1222 ; and ethnicity 1224 .
  • a graph 1238 showing % Reach with respect to Budget, and with the results broken down with separate subgraphs for different display platform types including: TV; online; mobile; and VOD.
  • the Incremental Reach from Digital 1242 has been automatically calculated and is specifically shown at the point on the graph corresponding to the specified budget amount.
  • An annunciator balloon 1240 is shown aligned with the specified budget number on the horizontal axis of the graph, and includes within annunciator balloon 1240 a display of the specified budget number and the average CPM.
  • a prioritized list of placement targets 1226 is shown at the lower left that resulted from the process shown in FIG. 2 for a campaign where the overriding strategy is incremental reach via non-duplication between TV and digital media.
  • a “Combined” list 1228 is shown, however the list can be shown specifically focused on any of the following formats by selecting the appropriate tab: TV; Addressable TV; Online video; VOD; Mobile; Tablet; and Network extension.
  • the displayed placement list 1228 can be sorted by spend or sorted by format 1236 .
  • Network names are displayed 1230 with logo and editable checkboxes for inclusion/exclusion.
  • Horizontal bar graphs 1232 are included to visually indicate relative spending, and forecasted dollar spend 1234 is additionally shown.
  • a graph 1244 of relative media format mix is shown with horizontal bars for each format type including a percentage for each format as well as CPMs and CPPs. To the right of the media mix graph 1244 are prominent boxes indicating average CPM 1246 and average CPP 1248 .
  • FIG. 13 shows an additional planning tool for a cross format campaign.
  • the results are shown in terms of CPP (cost per GRP point) and cost for on target impressions.
  • prioritized lists of placement targets are also shown, where a different prioritized list has been established for each of a plurality of display platform types.
  • a strategy summary for a cross format plan that was previously shown as part of the user interface of FIG. 11 .
  • Date range 1110 and budget 1112 are shown along with a targeting summary 1114 including conventional age/gender demographic targeting as well as advanced/strategic targeting for: income; marital status; education; presence of children; homeownership; and ethnicity.
  • a targeting summary 1114 including conventional age/gender demographic targeting as well as advanced/strategic targeting for: income; marital status; education; presence of children; homeownership; and ethnicity.
  • FIG. 13 is a graph 1316 showing GRPs versus budget with a specified budget 1320 indicated by a horizontal dotted line, and annunciator balloon 1318 shown vertically above the specified budget location on the horizontal axis, where the annunciator balloon additionally indicates the budget number.
  • At the right of graph 1316 are prominent boxes displaying: CPP 1322 ; On target CPM and % 1324 ; and GRPs 1326 .
  • a placement summary 1304 similar to summary 1226 of FIG. 12 , however here the tab for Mobile 1302 has been selected, and therefore placements 1304 shown below indicate placement specifically planned for viewing on mobile devices.
  • a logo 1306 is typically displayed next to a network name, and for networks where reservations are appropriate or required for placement a button marked “Reserve” 1312 is shown.
  • some network categories such as 1308 have subnetworks 1310 where placements may be individually planned, and an arrow icon at the left of the network name allows expansion below to reveal available subnetworks 1310 , in this example ESPN.com and FOXSports.com.
  • Most of the parameters shown on the user interface of FIG. 13 that guide placements for an advertising campaign are editable, and in addition to editing individual fields, sites can be added to the placement list by selecting button 1314 .
  • FIG. 14 shows an additional tool for reviewing a cross format campaign prior to execution 1402 , includes target placements for both TV and digital media, and including campaign start and end dates, and projected costs.
  • a button 1440 is provided to move from this tool to a tool as shown for example in FIG. 10 from where a final view of the projected campaign can be examined followed by executing the campaign.
  • a TV Reservation List 1404 At the top of the exemplary user-interface display of FIG. 14 is a TV Reservation List 1404 , displaying a summary of networks that have been added to the reservation list where reservations are appropriate or required for placement on a particular network.
  • placement names are shown 1408 , including projected GRPs, network name, and an annunciator icon that includes the particular type of placement, such as TV or OLV.
  • Date ranges 1410 are shown for each placement along with network name and logo 1412 . Also shown for each reserved placement are: projected CPM 1414 ; projected impressions 1416 ; and status 1418 .
  • a tool icon 1420 is shown that allows the planner to observe and edit options for the particular reserved placement.
  • a “Send” button 1422 is provided to send the request for placement reservation as well as a master button 1424 that enables the planner to send all reservation requests at once.
  • the bottom of the exemplary user-interface display of FIG. 14 is a summary of Online Placements 1406 .
  • the Placement name is shown along with: Date ranges 1434 ; projected CPMs 1436 ; Impressions 1438 ; and projected Cost 1442 .
  • an arrow annunciator icon such as that shown for the Sports category 1426 can be clicked, displaying additional detail on specific network placements falling within the selected (Sports) category.
  • ESPN.com shown as a Mobile placement 1428
  • FOXSports.com shown as an Online placement 1430 .
  • Daypart information 1432 is then viewable for both the ESPN and FOXSports placements.
  • DSP Demand Side Platform
  • the entire process with respect to online advertisements requires an intimate usage of the Internet for the Demand Side Platform to communicate with: supply-side platforms; advertising exchanges; advertising networks; and attribution partners.
  • the DSP must receive bid request packages, place bids, and supply the ads themselves in milliseconds via the Internet. Then, after a campaign has run, the DSP automatically receives attribution data from attribution partners.
  • the preceding description names only some of the automated processes and actions involved in implementing the invention as claimed.
  • the claims reflect a computerized process since, at this time, computing resources have evolved to include “Cloud-based” computing as described above in the Background section. As such, it is also impossible to predict where (physically) the claimed processes will be executed and/or if they will be distributed across multiple machines. It is also impossible to predict the specific ownership of machines whereupon the claimed processes will be executed, and therefore against whom the claims would protect against should the claims instead have been written as system claims as opposed to the method claims attached hereto.
  • At least certain principles of the invention can be implemented as hardware, firmware, software or any combination thereof.
  • the software is preferably implemented as an application program tangibly embodied on a program storage unit, a non-transitory machine readable medium, or a non-transitory machine-readable storage medium that can be in a form of a digital circuit, an analog circuit, a magnetic medium, or combination thereof.
  • the application program may be uploaded to, and executed by one or more machines comprising any suitable architecture.
  • the various processes and functions described herein may be either part of microinstruction code or part of one or more application programs, or any combination thereof, which may be executed by one or more CPUs, whether or not such machine(s) or processor(s) are explicitly shown.
  • various other peripheral units may be connected to machine platforms such as one or more data storage units and printing units.

Landscapes

  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Business, Economics & Management (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Marketing (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Graphics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
  • Computing Systems (AREA)

Abstract

Systems and methods are disclosed for analyzing a fused sample of viewership data to determine a behavior profile of online viewers who watched and/or didn't watch certain TV advertisements, where the TV advertisements are aligned with campaign targeting characteristics desired by an advertiser/client working with a demand side platform. Then, a campaign targeting plan is developed for dividing an advertising budget between digital media and TV impressions. The digital media portion of the campaign profiles Media Properties (MPs) contained in a historical database from past digital advertising campaigns across multiple digital formats and screens, and aligns digital ad placement with MPs having desired targeting characteristics. An optimized apportionment is automatically produced between TV and digital media spending based on an advertiser/client's goals of duplicating or not duplicating viewership of an advertisement between TV and digital media, or alternately based on cost alone. Alternately, the apportionment can be guided interactively.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • This application claims the benefit of U.S. Provisional Patent Application No. 62/069,213 filed Oct. 27, 2014.
    • BACKGROUND OF THE INVENTION
  • 1. Field of the Invention
  • The present invention relates generally to systems and methods for planning and managing advertising campaigns that include both digital media advertising and TV advertising across one or more digital formats and screens.
  • 2. Prior Art
  • In the RTB (Real-Time Bidding) environment for electronic media impression auctions, an electronic advertising agency/consolidator operating a demand-side platform receives billions of daily auction opportunities for electronic media impressions from partners like Google®, Yahoo®, etc. These partners operate auctions for ad impressions and then place electronic ads based on auction results. A partner's auction is considered an external auction with respect to a demand-side platform where an internal auction may also be operated to determine which advertisements, also referred to herein as ads, and bids are submitted to the external auction. Each ad impression opportunity includes information parameters about the ad impression—for example, the target website, geolocation of the user, ad size, user cookie, etc, that are used for targeting purposes. The demand side platform then processes hundreds of ads in their system, supplied by advertiser clients along with desired filtering/targeting parameters, against information parameters supplied by the partner, and filters out any ads that do not qualify (for example the ad does not want to target Youtube.com®). For ads that are not removed due to a mismatch with targeting parameters, the demand-side platform then evaluates the corresponding bids that represent how much each client advertiser is willing to pay. The winning bid in the internal auction is then sent to the external auction to compete for the impression opportunity.
  • Note that in some scenarios, the electronic advertising agency/consolidator operating a demand-side platform and the advertiser/client may in fact be the same entity—for instance when they comprise a large organization with an internal advertising department capable of acting as a demand-side platform. Also, in such an instance, there may be no internal auction—just a submission to an external auction.
  • With the ever increasing growth of digital advertising, advertiser/clients are dealing with splitting their ad budget between TV and digital media. Digital media includes any electronic media format other than TV—essentially all digital formats that can be planned and delivered electronically. Digital media locations where an ad may be placed are called Media Properties. A Media Property or MP as described herein represents a specific instance of a media platform for electronically delivering information to a viewer. An MP as referenced herein usually refers to a website or URL on the Internet, however may also refer for example and without limitation to an App ID, a Game ID, or other digital electronic media including for example electronic billboards—small and large. Even digital watches with wireless connectivity are a form of MP.
  • As used in describing the invention as defined herein, television or TV includes:
  • Connected TV;
  • VOD (Video on Demand);
  • Broadcast TV;
  • Cable TV; and
  • TV programming provided online.
  • VOD is further defined as systems that allow users to select and watch/listen to video or audio content when they choose to, rather than having to watch at a specific broadcast time. IPTV technology is often used to bring video on demand to televisions and personal computers. Television VOD systems can either stream content through a set-top box, a computer or other device, allowing viewing in real time, or download it to a device such as a computer, digital video recorder (also called a personal video recorder) or portable media player for viewing at any time.
  • Television programming in general may be therefore viewed on conventional TV sets, or on any digital media viewing device, including without limitation PC/laptops, tablets, smartphones, and even digital watches. A viewer may be either a person, cookie, household, or any group of persons that watch the same programming—regardless of whether or not they watch simultaneously.
  • Current tools for helping advertisers split their budget between TV and digital media only deal with MP categorizations in aggregate, and typically only with respect to demographic characteristics such as age and gender. Methods are needed to more precisely plan and predict cross format ad campaigns where both TV and digital media are targeted in the same campaign. In particular, it would be useful to plan a campaign such that viewers who did not watch certain TV ads get to see them on digital media. It would also be useful at times to reinforce the viewing of certain TV ads by targeting the same viewers on digital media in order to increase the frequency of viewership of those ads.
  • Specific problems faced by a planner at an advertiser/client may include (written from the perspective of the planner):
      • Roughly how much incremental reach can I get by spending a portion of my TV budget on online video?
      • How do I get a controlled amount of additional frequency of impressions against targets that have likely seen my ads on TV?
      • Exactly how much should I spend on online video ads based on various goals: budget, frequency, reach, viewable?
      • How can I execute the digital and television portions of the campaign as easily as possible on an electronic platform, where planning and execution are integrated?
      • Exactly how much of the next campaign's share of budget should I spend on video?
      • How much should I spend on VOD and other avenues that provide incremental reach? How can I execute and track my ad spend across all formats easily?
  • More specifically, a planner may have the following needs and goals (written from the perspective of the planner):
      • 1. As a Planner, I need the latest data to be included in the TV Planning tool so that I can be confident in the results of the tool, by recognizing that the data from my historical TV buy is correct.
      • 2. As a Planner, I need to have access to the correct and up-to-date data from the demand-side platform so that I can compare costs and reach versus my TV buy data historically.
      • 3. As a Planner, I need to view the impact of shifting away some of my historical TV budget to online video advertising (executed via a demand-side platform) so that I can assess the impact of this shift on the unduplicated reach gains I can achieve.
      • 4. As a Planner, I need to be able to easily execute a campaign with a demand-side platform for the online video ad portion based on the hypothetical TV and OLV (digital media) spend that I create in the planner, so that I can save time and reap the benefits around transparency and delivery that the demand-side platform offers.
      • 5. As a Planner, I need to be able to apply a discount to the ‘rack rates’ that are quoted as ‘spend’ in the Nielsen TV data so that discounts I received from networks are included in the analysis, making my hypothetical spend plan more realistic.
      • 6. As a Planner, I need to be able to ‘save’ a draft spending plan that I can access later so that I don't have to set up the analysis again after I've considered the plan over some time.
      • 7. Preferably, the planning tool must be available via a public-facing website so I can easily perform prototype plans.
      • 8. As a Planner, I need to be able to have the planning tool figure out the optimal amount of online video ad spend based on at least three different types of goals given the overall historical TV budget: maximize unduplicated reach at the same budget, maximize frequency at the same budget, and the most cost-effective reach across all available digital and TV formats and screens, so that I can tune my spend optimization to my particular marketing goal.
    DEFINITIONS
      • Planner—a user of the invention including one who operates/uses the user interface. A planner may belong to a DSP organization or to an advertiser/client organization.
      • Media avails or “avails”: Unsold units of time available for broadcasters to sell to advertisers. The number of impressions available for purchase on a daily or monthly basis for a given media property.
      • TV Base Plan: A TV advertising campaign that has already been run and where historical viewer information is available
      • OT CPM: Cost-per-Impression (per 1,000 impressions) for on-target impressions
      • Incremental reach: Viewers reached (by the invention) that were not reached by the TV Base Plan
      • PTV—“Programmatic TV”
      • CPM—cost per 1,000 impressions
      • CPP—cost per (GRP) point
      • eCPM—ad revenue generated per 1,000 impressions
      • eCPP—ad revenue generated per (GRP) point
      • VOD—Video on Demand
      • OLV—Online Video
      • MVPD (Multichannel Video Programming Distributor)
      • Xscreen or Cross-Screen—Campaigns that target multiple diverse screens: TV; Desktop; tablet; smartphone; etc.
      • HH—Household
      • Demo and Demo Targeting—Demographic Segment (Age and Gender)
      • Strategic Targeting—Targeting other than Demographic. Strategic targeting can include without limitation and for example any or all of: buying behavior; income; ethnicity; education; children; home; auto; and pets.
      • Media Property or just “Property”—Any screen type where an advertisement may appear
      • Daypart—Portions of a day where media may be viewed
      • Demand-Side Platform (DSP) An organization that performs an advertising agency function for advertisers/clients to plan and operate advertising campaigns on their behalf. A DSP (such as TubeMogul, Inc.) may develop and operate a proprietary system for planning and executing advertising campaigns, including profiling Viewers and Media Properties. Such as DSP operated system is also a source of data from previous advertising campaigns. Note that a DSP may represent client companies, or alternately be captive to a company or operate as a department within a company.
      • Fusion—A source of 3rd party data (e.g., Nielsen) that merges multiple and diverse datasets of viewership information into a single database.
      • NPN—A source of 3rd party data such as Nielsen's “National People Meter”
      • SSP—Supply Side Partner
      • DSP—Demand Side Platform
      • Sites—Websites
      • DT—Desktop
      • Linear TV—Conventional time-and-channel-based TV
      • KPI—Key Performance Indicator
      • Comscore—a 3rd party provider of data regarding advertising campaigns
      • Nielsen—a 3rd party provider of viewership data and related tools
    BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 shows an overview block diagram describing system components and data flow for a demand side platform according to the invention, with a focus on planning and executing a future cross format campaign.
  • FIG. 2 shows a flow chart describing a process for planning a cross format campaign with emphasis on reaching unexposed TV viewers.
  • FIG. 3 shows a flow chart describing a process for planning a cross format campaign with emphasis on reinforcing viewership.
  • FIG. 4 shows a flow chart describing a process for planning a cross format campaign with emphasis on achieving the lowest cost per impression.
  • FIG. 5 shows a matrix defining different data sets used for cross format advertising campaign planning.
  • FIG. 6 shows how the various data sets of FIG. 5 are linked and analyzed in creating a cross format advertising campaign.
  • FIG. 7 shows one exemplary user interface for the planning stage of a cross format campaign.
  • FIG. 8 shows an exemplary user interface for the planning stage of a cross format campaign where a planner may observe the effect of shifting portions of their TV advertising budget to digital media.
  • FIG. 9 shows an alternative functionality for guiding the split between TV ad spending and digital media ad spending.
  • FIG. 10 shows the user interface for a planning tool according to the invention which allows planners to adjust their campaign and observe projected results in terms of on-target impressions, GRPs, and cost.
  • FIG. 11 shows a planning tool for a cross format platform focused on GRP or CPM results and including advanced (strategic) targeting choices.
  • FIG. 12 shows an additional planning tool for a cross format campaign including a graph indicating incremental reach.
  • FIG. 13 shows an additional planning tool for a cross format campaign where results are shown in terms of CPP (cost per GRP point) and cost for on target impressions.
  • FIG. 14 shows an additional planning tool for cross format campaign including target placements for both TV and digital media.
    •  DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
  • Systems and methods are disclosed for analyzing a fused sample of viewership data to determine a behavior profile of online viewers who watched and/or didn't watch certain TV advertisements, where the TV advertisements are aligned with campaign targeting characteristics desired by an advertiser/client working with a demand side platform. Then, a campaign targeting plan is developed for dividing an advertising budget between digital media and TV impressions. The digital media portion of the campaign profiles Media Properties (MPs) contained in a historical database from past digital advertising campaigns across multiple digital formats and screens, and aligns digital ad placement with MPs having desired targeting characteristics. An optimized apportionment is automatically produced between TV and digital media spending based on an advertiser/client's goals of duplicating or not duplicating viewership of an advertisement between TV and digital media, or alternately based on cost alone. Alternately, the apportionment can be guided interactively.
  • FIG. 1 shows an overview block diagram describing system components and data flow for a demand side platform according to the invention, with a focus on planning and executing a future cross format campaign based on 3rd party TV and digital viewership information combined with historical digital media viewership information compiled over past campaigns by the demand side platform. In FIG. 1, the overview block diagram 100 for a system according to the invention includes a DSP (demand-side platform) 114 that interacts with advertiser/clients 116; supply side partners 102; and sources 124 of third party viewership data. The demand-side platform 114 utilizes automation software operating on one or more servers/processors 112 to bid for ad slots 104 to be shown to viewers 108 via media property or TV programming 106. Online advertising and/or TV programming supply side partner(s) 102 typically provide a sale opportunity or bid request package 110—typically including ad slot data—to the demand-side platform 114. The demand-side platform will, in turn and if appropriate, provide a response 118 to supply side partner 102, that response including a bid and either an advertisement or information describing an advertisement. The decision to bid, and how much to bid, is automatically calculated by software running on the one or more servers 112 where targeting and planning information 120 supplied by advertiser/client 116 is processed. Such information may include for example and without limitation: desired on-target impressions; campaign targeting parameters; campaign runtime; campaign budget; maximum bid; and TV campaign data and desired campaign strategy. During the campaign planning stage, the DSP provides campaign cost estimates 122 and estimates for results to advertiser/clients 116.
  • FIG. 2 shows a flow chart describing a process for planning a cross format campaign where an advertiser/client wishes to plan the campaign such that viewers who did not watch certain ads on TV get to see them on digital media, thereby increasing the reach of the campaign—the process referred to as optimization by de-duped reach. In step S202 a fused data set of viewership data is received that includes both TV viewing and digital media viewing of advertisements, and where for a plurality of specific viewers watching TV advertisements the data set includes digital media viewership data for those specific viewers. In step S204, a list of proposed or historical TV advertisements is received for a particular advertiser/client. In step S206, the fused data set is analyzed for each respective TV advertisement to:
      • (i) identify those viewers in the fused data set who are categorized as NOT WATCHING the TV advertisement;
      • (ii) identify viewer characteristics of the viewers categorized as NOT WATCHING the TV advertisement; and
      • (iii) produce a list of MPs visited by the viewers in the fused data set categorized as NOT WATCHING the TV advertisement, and identify specific characteristics of those MPs;
  • In step S208, targeting criteria from an advertiser/client is received for a future campaign. In step S210, a historical database containing data from online advertising campaigns is analyzed, and based on the received targeting criteria for the future campaign and the data from online advertising campaigns, a model for the future campaign is produced that includes a proposed budget split between TV and online placement of advertisements.
  • FIG. 3 shows a flow chart describing a process for planning a cross format campaign where an advertiser/client wishes to plan a campaign such that the viewing of certain ads on TV is reinforced by targeting the same viewers on digital media in order to increase the frequency of viewership of those ads—the process referred to as optimization by increased frequency. In step S302, a fused data set of viewership data is received that includes both TV viewing and digital media viewing of advertisements, and where for a plurality of specific viewers watching TV advertisements the data set includes digital media viewership data for those specific viewers. In step S304, a list of proposed or historical TV advertisements is received for an advertiser/client. In step 306, the fused data set is analyzed for each respective TV advertisement to:
      • (i) identify those viewers in the fused data set who are categorized as WATCHING the TV advertisement;
      • (ii) identify viewer characteristics of the viewers categorized as WATCHING the TV advertisement; and
      • (iii) produce a list of MPs visited by the viewers in the fused data set categorized as WATCHING the TV advertisement, and identify specific characteristics of those MPs;
  • In step S308, targeting criteria is received from an advertiser/client for a future campaign. In step S310, a historical database containing data from online advertising campaigns is analyzed, and based on the received targeting criteria for the future campaign and the data from online advertising campaigns, a model for the future campaign is produced that includes a proposed budget split between TV and online placement of advertisements.
  • FIG. 4 shows a flow chart describing a process for planning a cross format campaign where an advertiser/client wishes to plan a campaign such that the lowest cost per impression is achieved without regard to whether or not a TV viewer sees the same ad on digital media—the process referred to as optimization by “cheapest to reach”. In step 402, a fused data set of viewership data is received that includes both TV viewing and digital media viewing of advertisements, and where for a plurality of specific viewers watching TV advertisements the data set includes digital media viewership data for those specific viewers. In step 404, a list of proposed or historical TV advertisements is received for an advertiser/client. In step 406, the fused data set is analyzed for each respective TV advertisement to:
      • (i) identify those viewers in the fused data set who are categorized as WATCHING OR NOT WATCHING the TV advertisement;
      • (ii) identify viewer characteristics of the viewers categorized as WATCHING OR NOT WATCHING the TV advertisement; and
      • (iii) produce a list of MPs visited by the viewer in the fused data set categorized as WATCHING OR NOT WATCHING the TV advertisement, and identify specific characteristics of those MPs.
  • In step 408, targeting criteria is received from an advertiser/client for a future campaign. In step S410, a historical database containing data from online advertising campaigns is analyzed, and based on the received targeting criteria for the future campaign and the data from online advertising campaigns, a model for the future campaign is produced that includes a proposed budget split between TV and online placement of advertisements.
  • For the processes of FIGS. 2, 3, and 4, the campaign model includes target lists for both TV demographics and MPs targeted for digital media. The lists are combined and sorted to produce an overall target list for a campaign. The combined list can be then sorted by cost efficiency (cost per on-target impression) to produce a prioritized targeting list for the future campaign.
  • Note that TV viewership information used in the process may be related to past advertising campaigns for TV or may alternately be projected TV viewership information. Characteristics of MPs may include without limitation one or more of demographic information; the cost of purchasing impressions on the MP; a historical “reach” for the MP which reflects how frequently impression opportunities arise for that MP. Note that ad campaigns usually have a specified run time, and if a particular MP has a very small reach it may contribute little value to a campaign if impression opportunities for that MP appear infrequently.
  • Optimization for a campaign targeting both TV and digital media can be performed for cost and/or for reach, including optimization for CPMs (Cost per 1,000 on-target impressions) and/or GRPs (Gross Rating Points).
  • A campaign planner at an advertiser/client needs the latest data to be included in a cross format planning tool so that they can be confident in the results of the tool, by recognizing that the data from their historical TV buy is correct. This is accomplished by:
      • building a historical record of the previous year's data
      • using the planning tool by month, quarter, or year
      • handling updates from a 3rd party (e.g., Nielsen) when they correct data
      • identifying when there are gaps in the datasets, indicating where for some reason all the data is not available
      • acquiring datasets from a 3rd party that tracks viewership equivalent to the following datasets available from Nielsen:
        • National People Meter;
        • Netview & VideoCensus;
        • Fusion; and
        • Monitor Plus
  • Note that 3rd party viewership data suppliers may supply data indicative of a viewer panel and/or actual viewership data acquired by monitoring a large number of viewers over time. The most prominent supplier of viewership data today in the United States is Nielsen, however other sources of viewership data exist, and without limitation such other sources may include for example Barb in the UK, OZTam in Australia, and Rentrak in the US. The embodiments described herein do not rely on any specific supplier of viewership data, and any specific mention of data packages supplied by, for example Nielsen, are only exemplary.
  • FIG. 5 shows a matrix defining different data sets used for cross format advertising campaign planning. Four data sets 502-508 of the five total data sets are examples of those available from third-party viewership data suppliers such as, for example, Nielsen. As shown in FIG. 5, the National People Meter 502 shows minute by minute TV viewing and when supplied by Nielsen contains for example 55,000 respondents—the majority of whom have Internet access and recording devices for Internet activity. The data set entitled NetView & VideoCensus 504 when supplied by Nielsen contains for example 200,000 respondents with Internet activity tracking. The Fusion data set 506 contains approximately 255,000 respondents where TV and Internet usage is fused and extrapolated. The data set entitled Monitor plus 508, when supplied by Nielsen, shows TV activity with related cost data. Data set 510, supplied by a demand-side platform, provides Internet site (MP) data and related advertising data. For each supplied data set, descriptions for Contents 512, Sample Size 514, and Key Elements for the Planner 516 are shown.
  • FIG. 6 shows how the various data sets of FIG. 5 are linked and analyzed in creating a cross format advertising campaign. Note in particular for linkage number “3” 602 that demographic characteristics of those viewers tracked for Internet and TV usage are linked to TV and online video ad data. For linkage number “4” 604 in FIG. 6, sites (MPs) visited by viewers who saw particular TV ads are linked with sites they did NOT visit. This linkage is required for de-duped (un-duplicated) reach. Sites (MPs) unseen by those who saw a TV ad can be used by the DSP to generate a cross format campaign for incremental reach as described herein.
  • FIG. 7 shows one exemplary user interface for the planning stage 702 of a cross format campaign, where an advertiser/client's current plan 704 for TV advertising is analyzed 706 to provide a baseline. The audience 708 for the plan is entered, including Gender 710, which can be Male, Female or All genders. The targeted age bracket 712 for the TV plan is entered by independently positioning slider nodes 716 and 718 on horizontal age bracketing tool 714 to visually bracket the targeted ages. A functionality of the user interface of FIG. 7 includes a histogram graph showing a viewer population distribution 720 that appears under horizontal age bracketing tool 714, and can be optionally truncated at either or both ends as either or both of slider nodes 716 and 718 are moved to indicate the age distribution within the specifically bracketed ages. Also, as either or both of slider nodes 716 and 718 are moved, the targeted (bracketed) population 712 is automatically updated. At the bottom left, a Discount from Rack Rate 722 can be entered. Timeframe 724 to be analyzed for the plan is entered and a summary of results 726 is automatically presented to the planner. The exemplary and non-limiting summary of FIG. 7, includes: Reach; Reach (%); gross impressions; GRPs; average frequency; total cost; eCPM; and eCPP.
  • FIG. 8 shows an exemplary user interface for the planning stage of a cross format campaign where a planner on behalf of a client advertiser may observe the effect of shifting portions of their TV advertising budget to digital media, in this figure referred to as online video ads or (OLV). Here, in the box 802 at the upper left, the planner Optimizes by Target by choosing from three basic strategies for the cross format campaign that can be automatically calculated according to the invention based on the data sets supplied:
      • (i) “Are the cheapest to reach”—where the campaign combines TV and digital media without concern for whether or not digital media viewers in the fused data set saw or did not see ads on TV;
      • (ii) “Didn't see my ad on TV”—where the strategy is focused on de-duplication in order to increase the reach of the combined campaign; and
      • (iii) “Saw my ad on TV”—where the strategy is focused on reinforcing TV viewership with online impressions in order to increase the frequency of the combined campaign.
  • Alternately, a planner can manually optimize the campaign by manipulating a slider bar 804 located at the left side of the display shown in FIG. 8. The position of the bar affects both the reach and cost of the campaign, and as the slider bar is manipulated the resultant parameters shown on the right side of the screen in FIG. 8 are automatically calculated and change accordingly to display the calculated parameters. As slider bar 804 is moved horizontally, a choice is automatically made for reach 808 with respect to proportions of OLV 810 versus TV Only 812. Also with respect to impressions, a choice for CPM 814 is automatically made with respect to a percentage shift towards OLV.
  • On the right-hand side of the user interface display of FIG. 8, parameters 816 guide the plan for this advertising placement that was set in other screens are shown and include the brand; the demographic age/gender parameters; and the timeframe. As key parameters on the left hand side of FIG. 8 with respect to shifting between TV and OLV are changed by a planner utilizing this user interface, results are automatically updated by underlying processes according to embodiments of the invention. For instance, a new percentage for unduplicated reach 818 is displayed as well as a change in average frequency 820. Estimated numbers of impressions 822 are updated along with financial savings 824 resulting from the plan. Finally, at the bottom right of FIG. 8 is a summary 826 for the plan, including parameters 726 that were shown with respect to FIG. 7. For FIG. 8 however, these projected results are shown separately with respect to a TV Schedule 828; and Online Video Ad Schedule 830; and a Combined Schedule 832.
  • FIG. 9 shows an alternative functionality for guiding the split between TV ad spending and digital media ad spending. Similar to FIG. 8, but with a different graphical appearance, choices for automatic optimization by target 802 is shown in the upper left corner of the user interface display of FIG. 9. Similar to FIG. 8, the basic strategy is chosen by the planner to be one of “cheapest to reach”; “did not see my TV ad”; and “did see my TV ad”. Below targeting choice 802, a radial dial mechanism for budget adjustment 902 is presented at the left whereby the planner can choose the percentages for the split between TV and Online Video. A unique fan shaped graphical input mechanism 906 is presented to the planner where by dragging node 904 left or right around the arc of fan 906, a budget shift can be defined between TV and Online Video. Alternately, a shift percentage 908 may be entered, or a dollar amount 910 can be entered to define the shift.
  • As fan dial 906 is rotated, resultant projected parameters are automatically calculated and displayed at the right along with example online placements. At the top right of the display, prominent boxes are shown with parameter changes to help guide the planner including: a New Incremental Reach 912; a Change in Frequency 914; a Change in Impressions 916; and Per CPM Savings 918. At the center-right of the display is a graph 920 showing a percentage reached of the population with respect to the percentage split between OLV and TV. Specific graphs for TV-Only 922, OLV+TV 924, and OLV-Only 926 are shown along with an overall CPM savings 928.
  • At the bottom of the user interface display of FIG. 9 is a result summary 930, which is similar to summary 826 of FIG. 8, and also a summary 932 of online placements characterized by viewer interest area—including for example and without limitation: men's lifestyle; sports; automotive; and fitness. Included for each of these viewer interest areas is either a projected cost as shown in the display example, or alternately projected impressions. Which of cost or impressions are shown for the placement breakdown at the lower right of FIG. 9 is controlled by selector 934.
  • FIG. 10 shows the user interface for a planning tool which allows planners to adjust their campaign and observe automatically projected results in terms of on-target impressions, GRPs, and cost. This tool was previously shown and described in co-pending U.S. Patent Application Publication No. 2014-0278912 entitled: “Systems and Methods for Predicting and Pricing of Gross Rating Point scores by Modeling Viewer Data”. When used in conjunction with cross format planning tools such as those shown in FIGS. 7, 8, and 9, the user interface shown in FIG. 10 now incorporates data and planning for a cross format campaign targeting both TV advertising and digital media advertising.
  • At the top of the exemplary user interface display of FIG. 10 is an information bar 1002 that contains: the name of the placement; a mechanism for choosing the campaign and editing options; the particular planner's email address; and a search tool. At the top left side are selectors for planning according to CPM 1004 or GRP 1006. Under these selectors is an audience targeting mechanism similar to that previously shown in FIG. 7, and includes selection 702 for age and gender, and in particular includes horizontal selector bar 718 with individually positioned slider nodes 716 and 718 that are manipulated by the planner to bracket the lower and upper bounds respectively of the targeted age bracket. As with FIG. 7, a graphical histogram 720 of the age distribution bracketed by selector bar 718 is also shown. Placement parameters are entered here such as for example and without limitation a GRP goal 1008 that may be entered as well as a Frequency Cap 1010. At the bottom left of the user interface display of FIG. 10 are additional placement parameters such as budgeting controls 1012, including dates 1014 and a budget 1018. A planner may further select particular dayparts to address by clicking the daypart selector 1016 which brings up a user interface facility for daypart selection. An inventory tier 1020 to be utilized is chosen at the bottom left. A planner may choose one or more classifications or segmentations of MPs to be addressed by a campaign, herein labeled “Inventory Tiers” 1020. For this non-limiting example, available classifications include tiers ranging from MPs that are very well-recognized to those that are less recognizable. For example, Tier 1 can represent sites a user's friends and family would recognize. Tier 2 can represent MPs with lower brand recognition or awareness. Tier 3 can represent brand-safe sites that are niche or reach. “Brand safe” refers to MPs where there is nothing controversial such as guns, sex, violence, or alcohol, etc. “Niche” means there is a smaller audience, such as for instance a site for vegan cooks. “Reach” sites are MPs with huge audiences and many demographics such as YouTube, CBS, etc. Reach sites provide broad exposure, however with little specific brand alignment.
  • On the right-hand side of the user interface display of FIG. 10 are forecasted results including at the top projected CPP 1022; projected On Target CPM and % 1024; and a GRP Goal and Expected value 1026. A mechanism is also included to Revert Changes 1028. A graph 1030 of projected GRP results is shown with respect to budget, and an annunciator 1032 is shown positioned on the graph in response to a value entered as budget 1018. Projected total impressions 1034 are shown at the bottom right along with projected total GRPs 1036 and projected Uniques 1038.
  • FIG. 11 shows a planning tool for a cross format platform focused on GRP or CPM results and including advanced/strategic targeting choices that go beyond simple age and gender to include viewer characteristics such as for example and without limitation: income; marital status; education; presence of children; homeownership; and ethnicity. Also included are exemplary choices for advanced targeting of buyer behavior—which could be either modeled or actual behavior—in this instance targeting a specific product or service that a shopper might buy and also targeting a frequency for their purchase of that product or service. Also included are choices of different target screen formats for display platforms including platform variations for both TV and digital media, ensuring that planners using the tool per FIG. 11 have the ability to optimize for de-duplication or reach across all formats and screen types.
  • At the top of FIG. 11, a facility 1102 for loading previous media plans is provided and includes a date range 1104 and a timeframe specification 1106 that provides for specification of quarters or a full year. Below that, a facility for budget entry 1108 is provided for planning a new campaign with a specified date range 1110 and budget 1112. Next, campaign goals are specified that include advanced/strategic targeting 1114; age bracket targeting 1128; and shopper targeting 1130. Advanced/strategic targeting 1114 includes targeting for: income 1116; marital status 1118; education 1120; presence of children 1122; home ownership 1124; and ethnicity 1126. Age bracket targeting 1128 utilizes a horizontal age bracketing tool 714 as previously shown in FIG. 7, with independently positioned slider nodes 716 and 718 to visually bracket the targeted ages. Age bracketing tool 714 is linked with histogram graph 720 showing a viewer population distribution that appears under horizontal age bracketing tool 714, and can be optionally truncated at either or both ends as either or both of slider nodes 716 and 718 are moved. Shopper targeting 1130 shows for this example targeting shoppers who buy toothpaste 1132, however this is exemplary and drop-down arrows allow editing to target a wide variety of products and services. Frequency 1134 for a viewer's purchase of a product or service can also be targeted. At the bottom of the user interface display of FIG. 11 is the ability to select different cross screen formats 1136 for targeting. An exemplary list of screen types shown for targeting includes without limitation: TV; Addressable TV; Online video; VOD; Mobile; Tablet; and Network extension.
  • FIG. 12 shows an exemplary user interface for an additional planning tool for a cross format campaign, where a planner may view/edit 1202 a plan. An editable strategy summary 1204 is shown in the upper left, including date range 1206, budget 1208, and targeting criteria similar to those shown in FIG. 11, including gender targeting 1210 and age bracket targeting 1128, plus advanced/strategic targeting including for example and without limitation: income 1214; marital status 1216; education 1218; presence of children 1220; home ownership 1222; and ethnicity 1224.
  • At the top right of the user interface display of FIG. 12 is a graph 1238 showing % Reach with respect to Budget, and with the results broken down with separate subgraphs for different display platform types including: TV; online; mobile; and VOD. The Incremental Reach from Digital 1242 has been automatically calculated and is specifically shown at the point on the graph corresponding to the specified budget amount. An annunciator balloon 1240 is shown aligned with the specified budget number on the horizontal axis of the graph, and includes within annunciator balloon 1240 a display of the specified budget number and the average CPM.
  • A prioritized list of placement targets 1226 is shown at the lower left that resulted from the process shown in FIG. 2 for a campaign where the overriding strategy is incremental reach via non-duplication between TV and digital media. Here a “Combined” list 1228 is shown, however the list can be shown specifically focused on any of the following formats by selecting the appropriate tab: TV; Addressable TV; Online video; VOD; Mobile; Tablet; and Network extension. The displayed placement list 1228 can be sorted by spend or sorted by format 1236. Network names are displayed 1230 with logo and editable checkboxes for inclusion/exclusion. Horizontal bar graphs 1232 are included to visually indicate relative spending, and forecasted dollar spend 1234 is additionally shown. At the bottom center of the user interface display, a graph 1244 of relative media format mix is shown with horizontal bars for each format type including a percentage for each format as well as CPMs and CPPs. To the right of the media mix graph 1244 are prominent boxes indicating average CPM 1246 and average CPP 1248.
  • FIG. 13 shows an additional planning tool for a cross format campaign. Here, the results are shown in terms of CPP (cost per GRP point) and cost for on target impressions. Also shown are prioritized lists of placement targets, where a different prioritized list has been established for each of a plurality of display platform types. At the upper left of the user interface screen of FIG. 13 is a strategy summary for a cross format plan that was previously shown as part of the user interface of FIG. 11. Date range 1110 and budget 1112 are shown along with a targeting summary 1114 including conventional age/gender demographic targeting as well as advanced/strategic targeting for: income; marital status; education; presence of children; homeownership; and ethnicity. At the top right of the user interface display of FIG. 13 is a graph 1316 showing GRPs versus budget with a specified budget 1320 indicated by a horizontal dotted line, and annunciator balloon 1318 shown vertically above the specified budget location on the horizontal axis, where the annunciator balloon additionally indicates the budget number. At the right of graph 1316 are prominent boxes displaying: CPP 1322; On target CPM and % 1324; and GRPs 1326.
  • At the bottom of the user-interface display of FIG. 13 is a placement summary 1304 similar to summary 1226 of FIG. 12, however here the tab for Mobile 1302 has been selected, and therefore placements 1304 shown below indicate placement specifically planned for viewing on mobile devices. For each entry, a logo 1306 is typically displayed next to a network name, and for networks where reservations are appropriate or required for placement a button marked “Reserve” 1312 is shown. Note that some network categories such as 1308 have subnetworks 1310 where placements may be individually planned, and an arrow icon at the left of the network name allows expansion below to reveal available subnetworks 1310, in this example ESPN.com and FOXSports.com. Most of the parameters shown on the user interface of FIG. 13 that guide placements for an advertising campaign are editable, and in addition to editing individual fields, sites can be added to the placement list by selecting button 1314.
  • FIG. 14 shows an additional tool for reviewing a cross format campaign prior to execution 1402, includes target placements for both TV and digital media, and including campaign start and end dates, and projected costs. A button 1440 is provided to move from this tool to a tool as shown for example in FIG. 10 from where a final view of the projected campaign can be examined followed by executing the campaign. At the top of the exemplary user-interface display of FIG. 14 is a TV Reservation List 1404, displaying a summary of networks that have been added to the reservation list where reservations are appropriate or required for placement on a particular network. At the left of Reservation List 1404, placement names are shown 1408, including projected GRPs, network name, and an annunciator icon that includes the particular type of placement, such as TV or OLV. Date ranges 1410 are shown for each placement along with network name and logo 1412. Also shown for each reserved placement are: projected CPM 1414; projected impressions 1416; and status 1418. For each reserved placement, a tool icon 1420 is shown that allows the planner to observe and edit options for the particular reserved placement. Also, for each reserved placement a “Send” button 1422 is provided to send the request for placement reservation as well as a master button 1424 that enables the planner to send all reservation requests at once.
  • The bottom of the exemplary user-interface display of FIG. 14 is a summary of Online Placements 1406. Here, for each placement, the Placement name is shown along with: Date ranges 1434; projected CPMs 1436; Impressions 1438; and projected Cost 1442. When a planner wishes to see more detail for a given placement such as network and daypart information 1432, an arrow annunciator icon such as that shown for the Sports category 1426 can be clicked, displaying additional detail on specific network placements falling within the selected (Sports) category. In this example, there are two sub-placements within the Sports placement category: ESPN.com shown as a Mobile placement 1428 and FOXSports.com shown as an Online placement 1430. Note that Daypart information 1432 is then viewable for both the ESPN and FOXSports placements.
  • The processes performed as described herein are implemented as engines (sequential machines) running on one more processors, wherein the engines:
      • receive input from a user/planner through a realtime user interface;
      • access various databases;
      • compute estimated campaign results in realtime;
      • report those estimated results to the user/planner through the realtime user interface;
      • enable and control the execution of the advertising campaign; and
      • report to the user/planner the results of the actual advertising campaign.
  • Note that the quantity and complexity of the data and tasks involved in operating the invention make implementation of the invention impossible without the aid of one or more sequential machines—typically sequential processes operating on the one or more processors referred to above—and also use of a hardware communications infrastructure—typically the Internet. During the analysis and actionable processes involved, millions of data elements must be considered and without using a machine as part of the invention, implementation of the claimed processes would not be possible. Typically a Demand Side Platform (DSP) MUST both analyze and respond to an ad slot opportunity in less than 100 milliseconds. Performing the claimed processes with “pencil and paper” is impossible for many reasons, as is performing the process without the Internet. In fact, the entire process with respect to online advertisements requires an intimate usage of the Internet for the Demand Side Platform to communicate with: supply-side platforms; advertising exchanges; advertising networks; and attribution partners. The DSP must receive bid request packages, place bids, and supply the ads themselves in milliseconds via the Internet. Then, after a campaign has run, the DSP automatically receives attribution data from attribution partners. The preceding description names only some of the automated processes and actions involved in implementing the invention as claimed.
  • The claims reflect a computerized process since, at this time, computing resources have evolved to include “Cloud-based” computing as described above in the Background section. As such, it is also impossible to predict where (physically) the claimed processes will be executed and/or if they will be distributed across multiple machines. It is also impossible to predict the specific ownership of machines whereupon the claimed processes will be executed, and therefore against whom the claims would protect against should the claims instead have been written as system claims as opposed to the method claims attached hereto.
  • The foregoing detailed description has set forth a few of the many forms that the invention can take. It is intended that the foregoing detailed description be understood as an illustration of selected forms that the invention can take and not as a limitation to the definition of the invention. It is only the claims, including all equivalents that are intended to define the scope of this invention.
  • At least certain principles of the invention can be implemented as hardware, firmware, software or any combination thereof. Moreover, the software is preferably implemented as an application program tangibly embodied on a program storage unit, a non-transitory machine readable medium, or a non-transitory machine-readable storage medium that can be in a form of a digital circuit, an analog circuit, a magnetic medium, or combination thereof. The application program may be uploaded to, and executed by one or more machines comprising any suitable architecture. The various processes and functions described herein may be either part of microinstruction code or part of one or more application programs, or any combination thereof, which may be executed by one or more CPUs, whether or not such machine(s) or processor(s) are explicitly shown. In addition, various other peripheral units may be connected to machine platforms such as one or more data storage units and printing units.

Claims (30)

What is claimed is:
1. A computerized method for planning an advertising campaign targeting both TV and online impressions, whereby one or more processors perform the computerized method, comprising:
receiving a fused data set of viewership data that includes TV viewing and digital media viewing of advertisements, and where for a plurality of viewers watching TV advertisements the fused data set includes digital media viewership data for those viewers;
receiving a list of TV advertisements for an advertiser/client;
analyzing the fused data set to:
for each of the listed TV advertisements, identify those viewers in the fused data set who are categorized as not watching the respective listed TV advertisement;
identify viewer characteristics of the viewers categorized as not watching the respective listed TV advertisement, and
produce a list of MPs visited by the viewers in the fused data set categorized as not watching the respective listed TV advertisement, and identify specific characteristics of those MPs;
receiving targeting criteria from the advertiser/client for a future campaign; and
analyzing a historical database containing data from digital media advertising campaigns, and based on the received targeting criteria for the future campaign and the data from digital media advertising campaigns, producing a model for the future campaign that includes a proposed budget split between TV and digital media placement of advertisements.
2. The computerized method of claim 1 wherein the categorization of viewers not watching the listed TV advertisements includes viewers who did not watch the listed TV advertisements in the past.
3. The computerized method of claim 1 wherein the categorization of viewers not watching the listed TV advertisements includes one or both of viewers who did not watch the listed TV advertisements in the past or viewers who are projected to not watch the listed TV advertisements in the future.
4. The computerized method of claim 1 wherein the specific characteristics of the MPs in the list of MPs includes at least demographic characteristics.
5. The computerized method of claim 1 wherein the specific characteristics of the MPs in the list of MPs includes at least characteristics related to cost of purchasing impressions on each MP.
6. The computerized method of claim 1 wherein the specific characteristics of the MPs in the list of MPs includes at least reach characteristics.
7. The computerized method of claim 1 wherein the model for the future campaign is optimized for at least one of cost or reach.
8. The computerized method of claim 1 further comprising generating a target list of MPs visited by the viewers in the fused data set categorized as not watching the listed TV advertisements, and that are described in the historical database with respect to:
a demographic profile of each MP;
a historical cost of purchasing impressions on each MP; or
a historical reach of each MP.
9. The computerized method of claim 8 further comprising generating a combined target list for the future campaign by:
creating a combined list of TV demographic targets combined with the target list of MPs; and
sorting the combined list with respect to cost efficiency for on-target impressions.
10. The computerized method of claim 9 further comprising executing the future campaign by initially targeting advertising opportunities in the combined list that have a lowest cost for on-target impressions.
11. A computerized method for planning an advertising campaign targeting both TV and online impressions, whereby one or more processors perform the computerized method comprising:
receiving a fused data set of viewership data that includes TV viewing and digital media viewing of advertisements, and where for a plurality of viewers watching TV advertisements the fused data set includes digital media viewership data for those viewers;
receiving a list of TV advertisements for an advertiser/client;
analyzing the fused data set to:
for each of the listed TV advertisements, identify those viewers in the fused data set who are categorized as watching the respective listed TV advertisement;
identify viewer characteristics of the viewers categorized as watching the respective listed TV advertisement, and
produce a list of MPs visited by the viewers in the fused data set categorized as watching the respective listed TV advertisement, and identify specific characteristics of those MPs;
receiving targeting criteria from the advertiser/client for a future campaign; and
analyzing a historical database containing data from digital media advertising campaigns, and based on the received targeting criteria for the future campaign and the data from digital media advertising campaigns, producing a model for the future campaign that includes a proposed budget split between TV and digital media placement of advertisements.
12. The computerized method of claim 11 wherein the categorization of viewers watching the listed TV advertisements includes viewers who watched the listed TV advertisements in the past.
13. The computerized method of claim 11 wherein the categorization of viewers watching the listed TV advertisements includes one or both of viewers who watched the listed TV advertisements in the past and viewers who are projected to watch the listed TV advertisements in the future.
14. The computerized method of claim 11 wherein the specific characteristics of the MPs in the list of MPs visited by the viewers in the fused data set categorized as watching the listed TV advertisements include at least demographic characteristics.
15. The computerized method of claim 11 wherein the specific characteristics of the MPs in the list of MPs visited by the viewers in the fused data set categorized as watching the listed TV advertisements include at least characteristics related to cost of purchasing impressions on each MP.
16. The computerized method of claim 11 wherein the specific characteristics of the MPs in the list of MPs visited by the viewers in the fused data set categorized as watching the listed TV advertisements include at least reach characteristics.
17. The computerized method of claim 11 wherein the model for the future campaign is optimized for at least one of cost and reach.
18. The computerized method of claim 11 further comprising generating a target list of MPs visited by the viewers in the fused data set categorized as watching the listed TV advertisements, and that are described in the historical database with respect to:
a demographic profile of each MP;
a historical cost of purchasing impressions on each MP; or
a historical reach of each MP.
19. The computerized method of claim 18 further comprising generating a combined target list for the future campaign by:
creating a combined list of TV demographic targets combined with the target list of MPs; and
sorting the combined list with respect to cost efficiency for on-target impressions.
20. The computerized method of claim 19 further comprising executing the future campaign by initially targeting advertising opportunities in the combined list that have a lowest cost for on-target impressions.
21. A computerized method for planning an advertising campaign targeting both TV and online impressions, whereby one or more processors perform the computerized method comprising:
receiving a fused data set of viewership data that includes TV viewing and digital media viewing of advertisements, and where for a plurality of viewers watching TV advertisements the fused data set includes digital media viewership data for those viewers;
receiving a list of TV advertisements for an advertiser/client;
analyzing the fused data set to:
for each of the listed TV advertisements, identify those viewers in the fused data set who are categorized as watching or not watching the respective listed TV advertisement;
identify viewer characteristics of the viewers categorized as watching or not watching the respective listed TV advertisement, and
produce a list of MPs visited by the viewers in the fused data set categorized as watching or not watching the respective listed TV advertisement, and identify specific characteristics of those MPs;
receiving targeting criteria from the advertiser/client for a future campaign; and
analyzing a historical database containing data from digital media advertising campaigns, and based on the received targeting criteria for the future campaign and the data from digital media advertising campaigns, producing a model for the future campaign that includes a proposed budget split between TV and digital media placement of advertisements.
22. The computerized method of claim 21 wherein the categorization of viewers watching or not watching the listed TV advertisements includes viewers who watched or did not watch the listed TV advertisements in the past.
23. The computerized method of claim 21 wherein the categorization of viewers watching the listed TV advertisements includes one or both of viewers who watched or did not watch the listed TV advertisements in the past and viewers who are projected to watch or not watch the listed TV advertisements in the future.
24. The computerized method of claim 21 wherein the specific characteristics of the MPs in the list of MPs visited by the viewers in the fused data set include at least demographic characteristics.
25. The computerized method of claim 21 wherein the specific characteristics of the MPs in the list of MPs visited by the viewers in the fused data set include at least characteristics related to cost of purchasing impressions on each MP.
26. The computerized method of claim 21 wherein the specific characteristics of the MPs in the list of MPs visited by the viewers in the fused data set include at least reach characteristics.
27. The computerized method of claim 21 wherein the model for the future campaign is optimized for at least one of cost and reach.
28. The computerized method of claim 21 further comprising generating a target list of MPs visited by the viewers in the fused data set categorized as watching or not watching the listed TV advertisements, and that are described in the historical database with respect to:
a demographic profile of each MP;
a historical cost of purchasing impressions on each MP; and
a historical reach of each MP.
29. The computerized method of claim 28 further comprising generating a combined target list for the future campaign by a computerized method further comprising:
creating a combined list of TV demographic targets combined with the target list of MPs; and
sorting the combined list with respect to cost efficiency for on-target impressions.
30. The computerized method of claim 29 further comprising executing the future campaign by initially targeting advertising opportunities in the combined list that have a lowest cost for on-target impressions.
US14/923,153 2014-10-27 2015-10-26 Systems and Methods for Planning and Executing an Advertising Campaign Targeting TV Viewers and Digital Media Viewers Across Formats and Screen Types Abandoned US20160119689A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US14/923,153 US20160119689A1 (en) 2014-10-27 2015-10-26 Systems and Methods for Planning and Executing an Advertising Campaign Targeting TV Viewers and Digital Media Viewers Across Formats and Screen Types

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201462069213P 2014-10-27 2014-10-27
US14/923,153 US20160119689A1 (en) 2014-10-27 2015-10-26 Systems and Methods for Planning and Executing an Advertising Campaign Targeting TV Viewers and Digital Media Viewers Across Formats and Screen Types

Publications (1)

Publication Number Publication Date
US20160119689A1 true US20160119689A1 (en) 2016-04-28

Family

ID=55792318

Family Applications (2)

Application Number Title Priority Date Filing Date
US14/923,183 Active 2037-03-03 US10185971B2 (en) 2014-10-27 2015-10-26 Systems and methods for planning and executing an advertising campaign targeting TV viewers and digital media viewers across formats and screen types
US14/923,153 Abandoned US20160119689A1 (en) 2014-10-27 2015-10-26 Systems and Methods for Planning and Executing an Advertising Campaign Targeting TV Viewers and Digital Media Viewers Across Formats and Screen Types

Family Applications Before (1)

Application Number Title Priority Date Filing Date
US14/923,183 Active 2037-03-03 US10185971B2 (en) 2014-10-27 2015-10-26 Systems and methods for planning and executing an advertising campaign targeting TV viewers and digital media viewers across formats and screen types

Country Status (1)

Country Link
US (2) US10185971B2 (en)

Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160150280A1 (en) * 2014-11-24 2016-05-26 The Nielsen Company (Us), Llc. Methods and apparatus to project ratings for future broadcasts of media
CN106604136A (en) * 2016-12-22 2017-04-26 中国传媒大学 TV program ratings accurate calculation method and TV program ratings accurate calculation structural system
US9681159B2 (en) * 2015-07-27 2017-06-13 Adobe Systems Incorporated Creating audience segments for campaigns
WO2018022144A1 (en) * 2015-07-24 2018-02-01 Videoamp, Inc. Programmatic tv advertising placement using cross-screen consumer data
US9980011B2 (en) 2015-07-24 2018-05-22 Videoamp, Inc. Sequential delivery of advertising content across media devices
US10085074B2 (en) 2014-10-27 2018-09-25 Adobe Systems Incorporated Systems and methods for planning, executing, and reporting a strategic advertising campaign for television
US10085073B2 (en) 2015-07-24 2018-09-25 Videoamp, Inc. Targeting TV advertising slots based on consumer online behavior
US10136174B2 (en) 2015-07-24 2018-11-20 Videoamp, Inc. Programmatic TV advertising placement using cross-screen consumer data
US10185971B2 (en) 2014-10-27 2019-01-22 Adobe Systems Incorporated Systems and methods for planning and executing an advertising campaign targeting TV viewers and digital media viewers across formats and screen types
US20190289363A1 (en) * 2015-11-30 2019-09-19 The Nielsen Company (Us), Llc Methods and apparatus to estimate deduplicated total audiences in cross-platform media campaigns
US10445765B1 (en) * 2015-10-02 2019-10-15 Adobe Inc. System and method for executing an advertising campaign that incrementally reaches unexposed target viewers
US10555050B2 (en) 2015-07-24 2020-02-04 Videoamp, Inc. Cross-screen measurement accuracy in advertising performance
US10580455B2 (en) * 2016-06-20 2020-03-03 Scripps Networks Interactive, Inc. Non-linear program planner, preparation, and delivery system
US10728625B2 (en) 2017-10-04 2020-07-28 AMC Network Entertainment LLC Graphical user interface for graphically displaying television viewership data
US10812870B2 (en) 2016-01-14 2020-10-20 Videoamp, Inc. Yield optimization of cross-screen advertising placement
US10949873B2 (en) * 2015-10-02 2021-03-16 Adobe Inc. System and method for executing an advertising campaign that incrementally reaches unexposed target viewers
US11109105B2 (en) * 2019-01-11 2021-08-31 Sharp Nec Display Solutions, Ltd. Graphical user interface for insights on viewing of media content
US20220230205A1 (en) * 2021-01-15 2022-07-21 Xandr Inc. Cross-media targetable content recommender system for audience extension in content delivery systems
US11699168B2 (en) * 2021-04-22 2023-07-11 Wavefront Software, LLC System and method for aggregating advertising and viewership data
US20230376994A1 (en) * 2022-05-23 2023-11-23 Oracle International Corporation Cross-Platform Resource Optimization

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10257572B2 (en) 2017-01-03 2019-04-09 Bliss Point Media, Inc. Optimization of broadcast event effectiveness
US11368752B2 (en) 2017-01-03 2022-06-21 Bliss Point Media, Inc. Optimization of broadcast event effectiveness
WO2019070880A1 (en) 2017-10-04 2019-04-11 AMC Network Entertainment LLC Analysis of television viewership data for creating electronic content schedules
US11265602B2 (en) * 2018-04-12 2022-03-01 Rovi Guides, Inc. Systems and methods for evaluating a promotional campaign
US11856156B2 (en) 2021-04-16 2023-12-26 Cross Screen Media, LLC Method and artificial intelligence system for developing enhanced plans related to messaging across cross-screen media platforms
US11893607B1 (en) * 2021-05-10 2024-02-06 Jelli, LLC Exposing demand side platforms mechanism for broadcast radio
CN113256346B (en) * 2021-06-21 2022-06-10 广州市丰申网络科技有限公司 Advertisement delivery account information learning method and device and computer equipment
CN113760121B (en) * 2021-07-17 2022-06-14 深圳康显壹视界智能科技有限公司 Intelligent multi-interface controlled liquid crystal screen splicing system
CN116340396B (en) * 2023-05-30 2023-08-04 浙江出海数字技术有限公司 Multisource big data fusion processing system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080271070A1 (en) * 2007-04-27 2008-10-30 Navic Systems, Inc. Negotiated access to promotional insertion opportunity
US20090006145A1 (en) * 2007-06-27 2009-01-01 Google Inc. Booking Advertising Campaigns
US20120084813A1 (en) * 2006-10-17 2012-04-05 Google Inc. Targeted video advertising

Family Cites Families (38)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5600364A (en) 1992-12-09 1997-02-04 Discovery Communications, Inc. Network controller for cable television delivery systems
US6286005B1 (en) 1998-03-11 2001-09-04 Cannon Holdings, L.L.C. Method and apparatus for analyzing data and advertising optimization
EP1204909A4 (en) 1999-07-27 2002-11-05 Directrep Inc Method and system for selling and purchasing media advertising over a distributed communication network
US7870578B2 (en) 2000-04-07 2011-01-11 Visible World, Inc. Systems and methods for managing and distributing media content
US7730509B2 (en) 2001-06-08 2010-06-01 Invidi Technologies Corporation Asset delivery reporting in a broadcast network
SE526544C2 (en) 2003-02-18 2005-10-04 Kianoush Namvar Administrative signal transmission system
US9118812B2 (en) 2003-08-01 2015-08-25 Advertising.Com Llc Audience server
US8768766B2 (en) 2005-03-07 2014-07-01 Turn Inc. Enhanced online advertising system
US8131574B2 (en) 2005-09-29 2012-03-06 Amadeus S.A.S. Air travel system and method for planning roundtrip flights using one way combinable fares
US20070157228A1 (en) 2005-12-30 2007-07-05 Jason Bayer Advertising with video ad creatives
US8037506B2 (en) 2006-03-03 2011-10-11 Verimatrix, Inc. Movie studio-based network distribution system and method
JP5649303B2 (en) 2006-03-30 2015-01-07 エスアールアイ インターナショナルＳＲＩ Ｉｎｔｅｒｎａｔｉｏｎａｌ Method and apparatus for annotating media streams
CA2654866C (en) 2006-06-12 2016-09-27 Invidi Technologies Corporation System and method for auctioning avails
US20080022301A1 (en) 2006-06-20 2008-01-24 Stavros Aloizos Placing television commercials into available slots on multiple television stations
US8468561B2 (en) 2006-08-09 2013-06-18 Google Inc. Preemptible station inventory
US7743394B2 (en) 2007-04-03 2010-06-22 Google Inc. Log processing of channel tunes and channel tune times generated from a television processing device
EP2179348A4 (en) * 2007-07-13 2011-04-20 Spot Runner Inc Methods and systems for performing media searches, media creation and for secure distribution of media
US20090077579A1 (en) 2007-09-14 2009-03-19 Att Knowledge Ventures L.P. System and method for estimating an effectivity index for targeted advertising data in a communitcation system
US8522270B2 (en) 2007-10-16 2013-08-27 Mukesh Chatter System for and method of automatic optimizing quantitative business objectives of sellers (advertisers) with synergistic pricing, promotions and advertisements, while simultaneously minimizing expenditure discovery and optimizing allocation of advertising channels that optimize such objectives
US20090197582A1 (en) * 2008-02-01 2009-08-06 Lewis Robert C Platform for mobile advertising and microtargeting of promotions
US20090248478A1 (en) 2008-03-26 2009-10-01 Google Inc. Frequency-Aware Spot Selection for Content Campaigns
US8112301B2 (en) 2008-04-14 2012-02-07 Tra, Inc. Using consumer purchase behavior for television targeting
EP2269334A4 (en) * 2008-04-17 2013-02-20 Arbitron Inc Cross-media interactivity metrics
US9612995B2 (en) 2008-09-17 2017-04-04 Adobe Systems Incorporated Video viewer targeting based on preference similarity
CA2764442A1 (en) 2009-06-03 2010-12-09 Visible World Inc. Targeting television advertisements based on automatic optimization of demographic information
US9612716B2 (en) * 2009-11-04 2017-04-04 International Business Machines Corporation Enhanced slider bar system
WO2011094734A2 (en) 2010-02-01 2011-08-04 Jumptap, Inc. Integrated advertising system
US8789092B2 (en) 2010-08-12 2014-07-22 Lucid Commerce, Inc. Targeting television ads using keywords
US8490125B2 (en) 2010-12-22 2013-07-16 General Instrument Corporation Video content navigation with revenue maximization
EP2801063A4 (en) 2012-01-06 2015-08-05 David S Kidder System and method for managing advertising intelligence and customer relations management data
US9621446B2 (en) 2012-10-26 2017-04-11 Comscore, Inc. Combining measurements based on beacon data
US20140195334A1 (en) 2013-01-10 2014-07-10 United Video Properties, Inc. Systems and methods for optimizing data driven media placement
US11120467B2 (en) 2013-03-13 2021-09-14 Adobe Inc. Systems and methods for predicting and pricing of gross rating point scores by modeling viewer data
US8874652B1 (en) 2013-03-15 2014-10-28 Comscore, Inc. Multi-platform overlap estimation
US9635404B2 (en) 2013-04-24 2017-04-25 The Nielsen Company (Us), Llc Methods and apparatus to correlate census measurement data with panel data
US10478114B2 (en) * 2013-09-11 2019-11-19 Maxell, Ltd. Brain dysfunction assessment method, brain dysfunction assessment device, and program thereof
US9501783B2 (en) 2014-10-27 2016-11-22 Tubemogul, Inc. Systems and methods for planning, executing, and reporting a strategic advertising campaign for television
US10185971B2 (en) 2014-10-27 2019-01-22 Adobe Systems Incorporated Systems and methods for planning and executing an advertising campaign targeting TV viewers and digital media viewers across formats and screen types

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120084813A1 (en) * 2006-10-17 2012-04-05 Google Inc. Targeted video advertising
US20080271070A1 (en) * 2007-04-27 2008-10-30 Navic Systems, Inc. Negotiated access to promotional insertion opportunity
US20090006145A1 (en) * 2007-06-27 2009-01-01 Google Inc. Booking Advertising Campaigns

Cited By (43)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10250951B2 (en) 2014-10-27 2019-04-02 Adobe Inc. Systems and methods for planning, executing, and reporting a strategic advertising campaign for television
US10085074B2 (en) 2014-10-27 2018-09-25 Adobe Systems Incorporated Systems and methods for planning, executing, and reporting a strategic advertising campaign for television
US10531163B2 (en) 2014-10-27 2020-01-07 Adobe Inc. Planning and executing a strategic advertising campaign
US10185971B2 (en) 2014-10-27 2019-01-22 Adobe Systems Incorporated Systems and methods for planning and executing an advertising campaign targeting TV viewers and digital media viewers across formats and screen types
US11989746B2 (en) * 2014-11-24 2024-05-21 The Nielsen Company (Us), Llc Methods and apparatus to project ratings for future broadcasts of media
US20230325858A1 (en) * 2014-11-24 2023-10-12 The Nielsen Company (Us), Llc Methods and apparatus to project ratings for future broadcasts of media
US11657413B2 (en) * 2014-11-24 2023-05-23 The Nielsen Company (Us), Llc Methods and apparatus to project ratings for future broadcasts of media
US20160150280A1 (en) * 2014-11-24 2016-05-26 The Nielsen Company (Us), Llc. Methods and apparatus to project ratings for future broadcasts of media
US20210174381A1 (en) * 2014-11-24 2021-06-10 The Nielsen Company (Us), Llc Methods and apparatus to project ratings for future broadcasts of media
US10867308B2 (en) 2014-11-24 2020-12-15 The Nielsen Company (Us), Llc Methods and apparatus to project ratings for future broadcasts of media
US11425441B2 (en) 2015-07-24 2022-08-23 Videoamp, Inc. Programmatic TV advertising placement using cross-screen consumer data
US10085073B2 (en) 2015-07-24 2018-09-25 Videoamp, Inc. Targeting TV advertising slots based on consumer online behavior
US9980011B2 (en) 2015-07-24 2018-05-22 Videoamp, Inc. Sequential delivery of advertising content across media devices
US9980010B2 (en) 2015-07-24 2018-05-22 Videoamp, Inc. Cross-screen optimization of advertising placement
US20210105541A1 (en) * 2015-07-24 2021-04-08 Videoamp, Inc. Yield optimization of cross-screen advertising placement
US10136174B2 (en) 2015-07-24 2018-11-20 Videoamp, Inc. Programmatic TV advertising placement using cross-screen consumer data
US10555050B2 (en) 2015-07-24 2020-02-04 Videoamp, Inc. Cross-screen measurement accuracy in advertising performance
WO2018022144A1 (en) * 2015-07-24 2018-02-01 Videoamp, Inc. Programmatic tv advertising placement using cross-screen consumer data
US10667020B2 (en) 2015-07-24 2020-05-26 Videoamp, Inc. Cross-screen optimization of advertising placement
US11856272B2 (en) 2015-07-24 2023-12-26 Videoamp, Inc. Targeting TV advertising slots based on consumer online behavior
US9681159B2 (en) * 2015-07-27 2017-06-13 Adobe Systems Incorporated Creating audience segments for campaigns
US10405013B2 (en) 2015-07-27 2019-09-03 Adobe Inc. Creating audience segments for campaigns
US10949873B2 (en) * 2015-10-02 2021-03-16 Adobe Inc. System and method for executing an advertising campaign that incrementally reaches unexposed target viewers
US10445766B1 (en) * 2015-10-02 2019-10-15 Adobe Inc. System and method for executing an advertising campaign that incrementally reaches unexposed target viewers
US10445765B1 (en) * 2015-10-02 2019-10-15 Adobe Inc. System and method for executing an advertising campaign that incrementally reaches unexposed target viewers
US11818429B2 (en) * 2015-11-30 2023-11-14 The Nielsen Company (Us), Llc Methods and apparatus to estimate deduplicated total audiences in cross-platform media campaigns
US11012746B2 (en) * 2015-11-30 2021-05-18 The Nielsen Company (Us), Llc Methods and apparatus to estimate deduplicated total audiences in cross-platform media campaigns
US20210274248A1 (en) * 2015-11-30 2021-09-02 The Nielsen Company (Us), Llc Methods and apparatus to estimate deduplicated total audiences in cross-platform media campaigns
US20230137912A1 (en) * 2015-11-30 2023-05-04 The Nielsen Company (Us), Llc Methods and apparatus to estimate deduplicated total audiences in cross-platform media campaigns
US20190289363A1 (en) * 2015-11-30 2019-09-19 The Nielsen Company (Us), Llc Methods and apparatus to estimate deduplicated total audiences in cross-platform media campaigns
US11558667B2 (en) * 2015-11-30 2023-01-17 The Nielsen Company (Us), Llc Methods and apparatus to estimate deduplicated total audiences in cross-platform media campaigns
US10812870B2 (en) 2016-01-14 2020-10-20 Videoamp, Inc. Yield optimization of cross-screen advertising placement
US10580455B2 (en) * 2016-06-20 2020-03-03 Scripps Networks Interactive, Inc. Non-linear program planner, preparation, and delivery system
US10923153B2 (en) 2016-06-20 2021-02-16 Scripps Networks Interactive, Inc. Non-linear program planner, preparation, and delivery system
CN106604136A (en) * 2016-12-22 2017-04-26 中国传媒大学 TV program ratings accurate calculation method and TV program ratings accurate calculation structural system
US10728625B2 (en) 2017-10-04 2020-07-28 AMC Network Entertainment LLC Graphical user interface for graphically displaying television viewership data
US11109105B2 (en) * 2019-01-11 2021-08-31 Sharp Nec Display Solutions, Ltd. Graphical user interface for insights on viewing of media content
US11831954B2 (en) 2019-01-11 2023-11-28 Sharp Nec Display Solutions, Ltd. System for targeted display of content
US11617013B2 (en) 2019-01-11 2023-03-28 Sharp Nec Display Solutions, Ltd. Graphical user interface for insights on viewing of media content
US20220230205A1 (en) * 2021-01-15 2022-07-21 Xandr Inc. Cross-media targetable content recommender system for audience extension in content delivery systems
US12014399B2 (en) * 2021-01-15 2024-06-18 Microsoft Technology Licensing, Llc Cross-media targetable content recommender system for audience extension in content delivery systems
US11699168B2 (en) * 2021-04-22 2023-07-11 Wavefront Software, LLC System and method for aggregating advertising and viewership data
US20230376994A1 (en) * 2022-05-23 2023-11-23 Oracle International Corporation Cross-Platform Resource Optimization

Also Published As

Publication number Publication date
US10185971B2 (en) 2019-01-22
US20160117720A1 (en) 2016-04-28

Similar Documents

Publication Publication Date Title
US10185971B2 (en) Systems and methods for planning and executing an advertising campaign targeting TV viewers and digital media viewers across formats and screen types
US10531163B2 (en) Planning and executing a strategic advertising campaign
US11589135B2 (en) Automated allocation of media campaign assets to time and program in digital media delivery systems
CN108476334B (en) Cross-screen optimization of advertisement placement
US11651389B1 (en) Programmatic advertising platform
US9147198B2 (en) Systems and methods for providing an interface for data driven media placement
US20160189198A1 (en) Automated media campaign management system
US20120158461A1 (en) Content management and advertisement management
US20140046777A1 (en) Methods and systems for using consumer aliases and identifiers
CN108140199A (en) Dynamic video advertisement is replaced
JP2014525109A (en) Cross-media attribution model for marketing resource allocation
EP2727065A1 (en) Creation and usage of synthetic user identifiers within an advertisement placement facility
WO2014127455A1 (en) System, method and computer program for providing qualitative ad bidding
AU2021257902A1 (en) Methods and systems for using consumer aliases and identifiers
US10445765B1 (en) System and method for executing an advertising campaign that incrementally reaches unexposed target viewers
US10949873B2 (en) System and method for executing an advertising campaign that incrementally reaches unexposed target viewers
US20060248014A1 (en) Method and system for scheduling tracking, adjudicating appointments and claims in a health services environmentand broadcasters
Scott Jr The Impact of Programmatic Advertising on Local Broadcast Television
Schelp et al. Linear Sales Automation and Data Insight As a Step Towards Programmatic TV

Legal Events

Date Code Title Description
AS Assignment

Owner name: TUBEMOGUL, INC., CALIFORNIA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:HOOD, ALEXANDER R.;LOPATECKI, JASON;SUNG, JUSTIN K.;AND OTHERS;SIGNING DATES FROM 20151207 TO 20151208;REEL/FRAME:037537/0559

AS Assignment

Owner name: ADOBE SYSTEMS INCORPORATED, CALIFORNIA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:TUBEMOGUL, INC.;REEL/FRAME:041024/0872

Effective date: 20170113

STCV Information on status: appeal procedure

Free format text: ON APPEAL -- AWAITING DECISION BY THE BOARD OF APPEALS

AS Assignment

Owner name: ADOBE INC., CALIFORNIA

Free format text: CHANGE OF NAME;ASSIGNOR:ADOBE SYSTEMS INCORPORATED;REEL/FRAME:047688/0635

Effective date: 20181008

STCV Information on status: appeal procedure

Free format text: BOARD OF APPEALS DECISION RENDERED

STCB Information on status: application discontinuation

Free format text: ABANDONED -- AFTER EXAMINER'S ANSWER OR BOARD OF APPEALS DECISION