All Aboard! A Cross-Agency Mission Toward Cross-Platform Audience-Based Video
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Implementing an audience-based cross-platform video strategy across a large media holding company is a long journey – and one that Omnicom Media Group has recently embarked upon. OMG’s advanced TV platform—a component of Omnicom’s Omni people-based precision marketing and insights platform—ties together audience-based TV planning, buying and measurement capabilities across data-driven linear TV, household-addressable and IP-delivered OTT platforms. Jonathan will share the vision, trajectory and some early lessons learned on the early part of this journey.
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All Aboard! A Cross-Agency Mission Toward Cross-Platform Audience-Based Video
- 1. 1 All Aboard! A Cross-Agency Mission Toward Cross-Platform Audience-Based Video Planning & Measurement MediaPost TV & Video Insider Summit February 25, 2019 Jonathan Steuer Chief Research Officer, Omnicom Media Group jonathan.steuer@omnicommediagroup.com
- 2. Here’s where we’re headed… • TV in transition • The “Unreachables” today • Stacking the future • Picking a platform • Data at the core • Platform components • Early case studies 2 Agenda
- 3. Technology advancements and device proliferation are changing TV distribution models 3 New Distribution Fragmentation Tech & Data
- 4. Content attract humans TV is slowly moving beyond broad demography, evolving how we both target and measure video delivery (Past into Present) (Present into Future) Humans targeted across content Audiences attracted by content Audiences aggregated based on data Ads tied to content Content and ads separable Measured by Nielsen panel proxy Measured by actual delivery (challenge: translate delivery to impressions) Content-First Audience-First Measurement against age/gender demos Measured against precise targets 4
- 5. “The Unreachables” According to OMG primary research, Millenials & Gen-Xers are consuming 30 hours of video content per week, but only 10 hours of this viewership is captured in the Nielsen linear TV currency 47% of respondents reported zero viewership of Nielsen-measured linear TV–hence are “unreachable” from the viewpoint of Nielsen-measured TV reach Source: Hearts & Science and OMG RISE Primary Research Unreachables Research, 2017 5 TV with antenna TV with cable or satellite TV with a streaming device Smart TV app Desktop/ Laptop Smartphone (browser) Smartphone (app) Tablet Video game console Live TV Traditional TV: 10 Hours (What today’s currency of TV measurement is now equipped to capture) TV content on other devices: 5 hours DVR Cable/ Satellite TV Streaming Video Streaming content across all devices: 15 hoursShort form videos Hours of Video Consumption in a Typical Week
- 6. Isolating the the “Unreachables” shows the real declines in linear TV Linear TV viewing declines are 2-3x steeper among younger viewers than A35+ — 5% annual declines are only the P18-49 average! 6 31.67 24.46 30.63 35.92 23.20 13.89 20.06 29.90 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 P18-49 P18-24 P25-34 P35-49 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18 -27% Source: Nielsen National TV Toolbox Linear TV Viewership Trend 2012/13 – 2017/18 (Average Hours per Week) -43% -35% -17%
- 7. Check the stack: Live viewership is migrating to OTT 7 • Total viewership of linear content is across devices is relatively flat (-2% on NBC, shown) • “Stacking” measurement by network is not syndicated by Nielsen–TCR is for networks only (requests out to other networks for their data) • “Fluidity” and ADUs taken in digital are on the table • NBCU’s C-flight represents an attempt to measure and steps in the right direction • But we need to change the way we plan, buy and measure
- 8. 8 What is Omni? Omni is Omnicom’s people-based precision marketing and insights platform, designed to identify and define personalized consumer experiences at scale, in order to drive superior business outcomes for clients. Omni transforms the way our teams work, collaborate and deliver value by providing a single-view of the consumer from insights development to audience building, channel planning, content inspiration, and message distribution. © 2018 Omnicom Group Inc. Proprietary and Confidential.
- 9. C L I E N T T E C H / D A T A Audience Apps Content Inspiration Consumer Journey Channel Planner Digital Content TV Content Performance Reporting Attribution Content Engine Content Automation A G I L E I D Workflow Omni Advanced TV is one of many applications within Omni © 2018 Omnicom Group Inc. Proprietary and Confidential.
- 10. OMG explored the Advanced TV landscape in 2017 10 Network Group Offerings TV network groups enabling audience-based buying across their portfolios Inventory holders optimize inventory themselves and price on a premium basis Challenging to tie platforms together for unified reporting and inventory may be self-serving to fulfil yield Platforms either facilitate agency solutions or provide client-direct solutions for planning, optimization, and buying Quality of inventory, data availability, and business models vary considerably across platforms Programmatic TV solutions frequently bundle remnant inventory Platform-as-a-service model recommended with VideoAmp Advanced TV Platforms
- 11. OMG Advanced TV Platform powered by and TV Viewership data from OMG selected VideoAmp as our development partner to implement Omni Advanced TV 11 Cross-Channel Measurement (Aggregate Reach and Frequency) Plan & Optimize Leverage linear TV and digital data to plan integrated video investments Allocate Optimize integrated video investments with our data and software solutions Measure Understand duplication of audiences between your linear and digital investment Client Audience/Targeting Data from DMP (1st Party, 3rd Party) Data-Driven Linear TV IPTV Household Addressable Broadcast and Cable Network (Local & National) Inventory OTT/FEP/CTV Inventory MVPD Inventory
- 12. The platform combines, cleans, and matches TV viewership data across ACR and set top box data from multiple sources to enhance representativeness and accuracy 12 Technology Provider Offering Data Source Pros Cons Status ACR Technology and Data provider Most Vizio Smart TVs Collects data from a universe of 10MM opt-in TVs Some skews towards higher income, homeowners, and married (statistically correctable) In platform today, including weighting and calibration STB Data provider Collects from STBs in 1.1 million households concentrated in Florida, California and Texas No limitations on data usage. Data includes TV viewership, VOD, and DVR for northeast, west coast, and central Texas Smaller sample of households than the larger cable companies Commingling in progress, available Q3 2019
- 13. Commingled STB and ACR data is unique to our platform 13 Step 1: Merge common fields present in STB and ACR TV viewership Step 2: Determine overlap of STB and ACR households Step 3: Inform algorithms that classify zombie viewing in the STB data feed with ACR data Step 4: Use STB data to understand programming viewership on multiple TVs in the ACR households Step 5: Deduplicate overlapping TVs in households to prevent double counting in analysis
- 14. How Omni Advanced TV works for our clients 14 1st & 3rd Party Data Leverage website, CRM, sales or 3rd party data to create targets Match IDs to TV HH Match targets from online & offline data to TV Households Build Behavioral Indices Create indices based on viewership and online behaviors Plan Against Audiences Create plans to reach audiences in highest concentration most efficiently Measure Performance Analyze media performance to help optimize future campaigns
- 15. Linear Planner • Apply any data asset to linear planning scenarios to optimize for linear upfront planning and negotiation Linear Allocation • Optimize multi-brand level linear allocation API-based TV Transactions • Plan & buy linear scatter TV inventory from sources directly connected to platform Omni Advanced TV will enable more effective planning of video investment via a continuous optimization loop 15 TV Digital Segments • Create digital audiences based on linear TV ads to drive incremental R/F or conquest in desired channels Cross-Screen Measurement • Measure de- duplicated R/F between linear & digital ad exposure
- 16. Enables advanced audience planning and leverages algorithmic optimization based on data ingestion to increase the efficacy and efficiency of upfront planning LinearPlanning 16 TARGET 2 TARGET 1 TARGET 3
- 17. Advanced Audience Targeting Data Manages upfront investments across brand and product portfolios throughout the year, identifying the most efficient distribution against advanced audiences 17 LinearAllocation TV Advertising Investment Brand X Product E Network 1 Network 2 Product F Network 3 Network 4 Product C Network 5 Network 6 Product D Network 1 Network 2 Brand O Network 1 Network 2 Network 3 Product B Network 3 Network 4 Product A Network 1 Network 2 Phase 1 File-based allocation reports by Q4 2018 Phase 2 File-based API ready at end of Q1 2019 Phase 3 Fully-functioning self-serve UI by Q2 2019 Brand Y Brand Z$ $ $ $
- 18. Surfaces available scatter inventory for discovery, and enables planning / execution against advanced audiences with visualized plan performance forecasts across networks and dayparts API-basedTVTransactions 18 Phase 1 - Proof-of-concept testing of forecasting methodologies and normalization of inventory sources Phase 2 - Initial release with file- based plans with Tier 1 networks Phase 3 - Enhanced version incorporating a self-service UI connected to additional inventory sources
- 19. Creates optimal plans to reach audiences at the proper frequency levels by analyzing cross-screen delivery 19 Cross-ScreenMeasurement Duplication 1.0M Linear Reach (HH) 5.5M Digital Reach (HH) 5.0M Increase on-target reach and control frequency across linear and digital households to reduce waste Understand delivery at the network, daypart, and program level Identify investment opportunities across channels 15% of households who saw a digital ad also saw a TV ad 13% of households who saw a TV ad also saw a digital ad
- 20. Measures de-duplicated reach and frequency 20 Cross-ScreenMeasurement Implement Cross- Screen Measurement Report on linear and digital video to understand reach and frequency, where to gain incremental reach, and exclusive reach by partner. Reduce Waste Combine impression, viewership and ad exposure data with household-level brand exposure across all channels and screens in a privacy compliant manner. Optimize Against Effective Reach & Frequencies Analyze unified data set to help determine which audiences, from both a TV and digital perspective, are being either under or over exposed. Improve Strategy & Buying Maximize reach by providing actionable insights to apply against future or immediate campaign investment opportunities across all TV and digital media.
- 22. At-scale linear TV optimization against advanced audiences leads to substantial savings opportunities © 2019 Omnicom Group Inc. Proprietary and Confidential. 22 CaseStudy:LinearPlanner Challenge Strategy A large OMG client challenged their Omnicom Media Group media agency to provide maximum ROI on their 18/19 upfront commitment via an audience-based linear planning solution activating both 1st party and granular TV viewership data. OMG generated a fully optimized plan based on the clients 1st-party data while still addressing marketplace realities by: • Calculating average ratings / indices for network, dayparts, and programming against each 1st- party segment • Scoring and ranking networks, dayparts, and programming against the indices • Combining the scores with marketplace intelligence to deliver against certain network commitments and investment parameters • Applying client’s actual TV rates for improved accuracy and to account for budget realities Using the Advanced TV Planner, Omnicom mapped the client’s 1st-party custom segments against granular TV viewership data to optimize the client’s broadcast and cable network schedule. The linear TV plan held constant Nielsen GRP goals while optimizing program, network, and daypart selections against over-indexing audience compositions of the custom segmentation to deliver greater media efficiency. Execution Results $6.8MM +5% Savings Proposed scenario offers ability to deliver $6.8MM in cash savings while over-delivering against baseline GRP goals Opportunity to remove 5% of total budget while still over-delivering A18-49 GRPs, optimizing plan on client’s 1st-party segments vs. baseline, and funding implementation of data NextSteps 1. Activate Cross-Screen Measurement to monitor exposed audiences across linear and digital to understand true reach and frequency. 2. Implement Linear Allocation to re-distribute owned inventory across product lines via MediaOcean integration to maximize delivery against desired 1st- party custom audience targets.
- 23. Appendix 23 CaseStudy:LinearPlanner Broadcast Prime Cable Audience 1 Audience 2 Audience 3 Audience 4 Audience 5 Audience 6 A18-49 GRPs Spend Baseline Plan (GRPs) 195.6 197.9 201.4 199.3 194.2 190.7 953.7 $50,225,052 Optimized Plan (GRPs) 238.3 241.6 240.7 242.9 236.5 235.8 985.5 $50,175,949 Index 122 122 120 122 122 124 103 100 Audience 1 Audience 2 Audience 3 Audience 4 Audience 5 Audience 6 A18-49 GRPs Spend Baseline Plan (GRPs) 728.2 738.5 761.5 697.4 724.7 729.4 3147.0 $43,345,000 Optimized Plan (GRPs) 922.3 914.3 1000.5 897.4 892.0 918.9 2973.0 $43,478,421 Index 127 124 131 129 123 126 94 100
- 24. API-based inventory connectivity enables planning and execution against advanced audiences across platforms, networks and dayparts CaseStudy:API-basedTVTransactionPipes Future (in progress) Linear Network FEP / OTT MVPDs/vMVPDs/TV Anywhere Phase 1 Proof-of-concept testing of forecasting methodologies and normalization of inventory sources Phase 2 Initial release with file-based plans with Tier 1 networks Phase 3 Enhanced version incorporating a self- service UI connected to additional inventory sources
- 25. OMG is moving! • Platform implementation continues • Investment partnerships include data and inventory access • Widespread training happening now, in preparation for Upfronts • Client education and ability to compare methods is critical for adoption • Many / most OMG clients will touch the platform in next 2 months (a parallel process for this year) • The normal way in 2020-21? 25 TheJourneyInProgress
- 26. Thanks! Jonathan Steuer Chief Research Officer, Omnicom Media Group jonathan.steuer@omnicommediagroup.com 26
Editor's Notes
- Cord-Cutting Snowballs: U.S. Pay-TV Ops Shed 3.2M Subscribers in 2018 – Variety https://variety.com/2019/biz/news/cord-cutting-2018-accelerate-us-pay-tv-subscribers-1203138404/ In response to technological changes, TV distribution models have changed massively in recent years. Audiences are now highly fragmented across devices and inventory sources, necessitating new data and technology to plan, target, and measure TV delivery, efficiency, and performance. While these changes have challenged traditional linear TV models, the altered industry dynamics have presented an opportunity to leverage new data and technology to bring an audience-based approach to TV.
- As a result of the declines in traditional TV viewership, new approaches are needed. In particular, the TV industry needs to migrate from planning against broad demography to an audience-based approach. This will enable teams to reach target audiences across all the content they view, to aggregate those audiences based on data, and to measure delivery against those audiences rather than to rely on content or a broad demographic as a proxy for an audience. In order to enable audience-based TV, new datasets with more granularity and identifiers that can be matched to 1st party, 2nd party, and 3rd party datasets are needed. Examples of this type of viewership data include Smart TV data collected via ACR (automatic content recognition) technology and set top box data. Here providers such as Inscape (for Smart TV ACR) and Frontier (for Set Top Box data) can be leveraged to power advanced audience solutions.
- According to OMG Rise primary research, adults 18-49 – the core demographic that linear TV is transacted on – are now consuming 30 hours of video content per week, but only 10 of those hours are captured in the Nielsen currency. 5 hours is spent watching TV content on devices not measured by Nielsen, and 15 hours are spent viewing streaming content across various devices.
- These changes in viewership behavior will continue to exacerbate the TV measurement problem as consumers migrate to viewing via OTT sources rather than traditional TV. The declines in viewership are more drastic among viewers under the age of 35, with viewership in the 18-24 bracket declining 43% and viewership in the 25-34 bracket declining 35%.
- Omni is Omnicom’s people-based precision marketing and insights platform, designed to identify and define personalized consumer experiences at scale, in order to drive superior business outcomes for clients. Omni transforms the way our teams work, collaborate and deliver value by providing a single-view of the consumer from insights development to audience building, channel planning, content inspiration, and message distribution.
- Advanced TV Solutions: As the TV market has evolved, the marketplace has produced two types of solutions. The first type is network group solutions. These are offering from TV network ownership groups and their consortiums. At this point, most of the major networks have their own advanced TV offerings, but the capabilities vary considerably. Most are still powered by Nielsen and have only panel 3rd party datasets such as MRI. The most prominent of these solutions is OpenAP a consortium of Fox, Viacom, Turner, and NBCU. However, there are major drawbacks to all of these solutions. Primarily, none of them provide a holistic view of the market, they only optimize within their participant networks, which severely limits the value of their optimization. In addition, the methodology of optimization underlying these tools is opaque, with no insight into which inventory is selected. In other words, as an agency, we are unable to tell whether spots are assigned to increase reach against an audience we care about or if it has been assigned to improve the network’s yield. The other set of solutions comes from Advanced TV platforms. There are numerous providers in the space that broadly fit into two categories. Platforms that essentially operate as ad networks (e.g. Adobe/Tube Mogul and Simulmedia), rolling up remnant inventory and selling it non-transparently as premium inventory and platforms that operate as agency partners under a platform-as-a-service model (VideoAmp and 4C Insights). The former are not recommended (obviously), while we have an internal solution powered by one of the latter.
- Omnicom’s Advanced TV Solution: Omnicom has developed an integrated media, data, and software solution, powered by VideoAmp, to enable holistic planning, buying, and measurement across TV and video distribution channels. The platform is built upon granular household-level TV viewership data matched to strategic targets from either the client’s DMP, the Omni Audience Builder, or other sources via the Agile ID. In day-to-day operation, the tool provides strategy, marketing science, and investment teams with a point-and-click interface to evaluate and optimize the performance of a client’s 1st and 3rd party audiences across channels in the video ecosystem.
- Viewership Data: There are 3 types of TV viewership data available in the market – panels, set top boxes, and ACR providers. Ideally we would have a dataset that merges all three sources of TV data, but today this is not available. Panels recruit individuals record their TV viewing habits through paper diaries, meters, or cameras in their households. Typically, panels will have a recruitment process to ensure that there are enough individuals in a given area or demographic. This allows the data provider to weight their data in a statistically stable way to be representative of the US. However, many panels are relatively small which leads to the data being heavily weighted making it difficult to understand local and niche program viewership. Most panels limit how much data processing and manipulation can be done by their licensees, which forces users of the data to rely on the vendor to correctly analyze the data on their behalf. Examples of panels include Nielsen and ComScore. Set top box data is collected by multi-channel video programming distributors (MVPDs – e.g. Comcast, Spectrum, AT&T/DirecTV, Verizon Fios). A set-top box is the device that requests from the video server and displays the TV programming for a specific channel and time. It is possible for data to be collected when a STB is turned on and the TV is off, a phenomenon called zombie viewing, which can cause inaccuracies in measurement if not properly accounted for. Depending on the collection methodology of the specific provider, the STB may be able to track linear, VOD, and/or DVR viewership. Automatic Content Recognition (ACR) enables the capture of images and/or audio fingerprints being played on Smart TVs which can then be matched to content or advertising feeds. ACR captures live TV, VOD & DVR playback, and OTT consumption across a number of different viewing sources. The information that is captured from the TVs is passed back to the device manufacturer along with an IP address. This results in the manufacturers having large-scale (the size of their opted-in footprints) event-level viewership data. Within the ACR landscape only Vizio/Inscape licenses their viewership data for agency use, while others take walled-garden approaches (Samsung) or managed service models (SambaTV, Alphonso) with limited transparency into methodology or the underlying data. OMG’s data solution will include Frontier STB data before the end of 2018. Inform ACR Data ACR data tends to only capture one TV in a household. Cable data will be able to inform us on what the other TVs in the households are watching. Flexibility of data Frontier will provide us with data feeds with no conditions attached. We will be able to manipulate and cut the data for any research and client use cases. Spread of subscribers Frontier is not exclusively in urban or rural areas, so we will have a sampling from many geographic areas. Skews in demographics can be corrected for, but niche viewership may be limited depending on request.
- Viewership Data: There are 3 types of TV viewership data available in the market – panels, set top boxes, and ACR providers. Ideally we would have a dataset that merges all three sources of TV data, but today this is not available. Panels recruit individuals record their TV viewing habits through paper diaries, meters, or cameras in their households. Typically, panels will have a recruitment process to ensure that there are enough individuals in a given area or demographic. This allows the data provider to weight their data in a statistically stable way to be representative of the US. However, many panels are relatively small which leads to the data being heavily weighted making it difficult to understand local and niche program viewership. Most panels limit how much data processing and manipulation can be done by their licensees, which forces users of the data to rely on the vendor to correctly analyze the data on their behalf. Examples of panels include Nielsen and ComScore. Set top box data is collected by multi-channel video programming distributors (MVPDs – e.g. Comcast, Spectrum, AT&T/DirecTV, Verizon Fios). A set-top box is the device that requests from the video server and displays the TV programming for a specific channel and time. It is possible for data to be collected when a STB is turned on and the TV is off, a phenomenon called zombie viewing, which can cause inaccuracies in measurement if not properly accounted for. Depending on the collection methodology of the specific provider, the STB may be able to track linear, VOD, and/or DVR viewership. Automatic Content Recognition (ACR) enables the capture of images and/or audio fingerprints being played on Smart TVs which can then be matched to content or advertising feeds. ACR captures live TV, VOD & DVR playback, and OTT consumption across a number of different viewing sources. The information that is captured from the TVs is passed back to the device manufacturer along with an IP address. This results in the manufacturers having large-scale (the size of their opted-in footprints) event-level viewership data. Within the ACR landscape only Vizio/Inscape licenses their viewership data for agency use, while others take walled-garden approaches (Samsung) or managed service models (SambaTV, Alphonso) with limited transparency into methodology or the underlying data. OMG’s data solution will include Frontier STB data before the end of 2018. Inform ACR Data ACR data tends to only capture one TV in a household. Cable data will be able to inform us on what the other TVs in the households are watching. Flexibility of data Frontier will provide us with data feeds with no conditions attached. We will be able to manipulate and cut the data for any research and client use cases. Spread of subscribers Frontier is not exclusively in urban or rural areas, so we will have a sampling from many geographic areas. Skews in demographics can be corrected for, but niche viewership may be limited depending on request.
- Optimization of Linear TV Plans: Once a client’s desired 1st and 3rd party audiences have been identified, these audiences are matched to the viewership data in the platform via the Agile ID. In the platform, the strategy, marketing science, and investment teams collaborate to create scenario plans against the strategic audiences for each of the brands. Since the majority of linear TV is still transacted offline using Nielsen ratings as the currency, the plan outputs provide a side-by-side comparison of the Nielsen target and the strategic audience enabling investment teams to gain an asymmetrical advantage in negotiation with the network. The platform optimizes delivery concentrations down to the program level based on viewing and other behaviors captured in the audience definitions, maximizing gains in efficiency and effectiveness. After a client has committed to inventory from the networks (during the upfronts and/or in the scatter market), the platform has direct API connectivity to the client’s inventory in MediaOcean to allocate across the brand portfolio. Inventory is allocated according to concentrations of the strategic audience targets for each of the client’s brands as well as delivery of Nielsen targets. Once units have been allocated, the finalized plans for each brand are loaded back into MediaOcean for record-keeping and billing purposes.
- Linear TV Upfront Planner Apply any data asset to linear planning scenarios to optimize for linear upfront planning and negotiation Linear Allocation Optimize multi-brand level linear allocation Linear Scatter TV Buying Plan & buy linear scatter TV inventory from sources directly connected to platform Cross-Screen Measurement Measure de-duplicated R/F between linear & digital ad exposure TV Digital Segments Create digital audiences based on linear TV ads to drive incremental R/F or conquest in desired channels Identify past success and future opportunity Through layering advanced audiences on historical buys to establish target baselines Build better upfront plans and smarter scatter plans Through greater workflow efficiency and audience insights Allocate and activate on linear and digital with cross-screen intelligence Through incremental reach discovery and digital extensions of TV plans
- Cross-Platform Video Optimization & Performance Measurement: The same targets used for linear TV targeting can also be pushed directly to OTT, FEP, addressable VOD and digital platforms that enable such granular targeting. Linear TV delivery information against strategic targets can be used to create digital targets that optimize for incremental reach beyond the audiences delivered via linear TV. Household-level TV delivery data is also combined with device-level digital platform delivery data to create unified reach and frequency reporting across all video sources. Such combined delivery data enables the ability to develop advanced performance metrics.
- Custom 1st party DMP segments for planning identified Map custom MRI segments to TV viewership data at the airings level Planning Segments Driven Based on Combination of Clusters from Actual Purchase Behavior Build custom network indexing across 3 segments: McCafe, Breakfast, Classics, Value Utilize Custom indexing plus other factors for optimizations Run allocator taking only GRP goals into account. System allocates inventory to brands in flight to tie back to their GRP goals. Take snapshot of the results Manually tweak allocations based on vendors that over index on estimated segment ratings and then on segment composition. Take snapshot of new results Inventory allocation is communicated to vendor for air Brand lift can be identified in strategic demand spaces First, optimize towards segment rating indexes that are higher than the network average index Second, optimize towards segment % Comp Indexes that are higher than the network average index Methodology Calculated average ratings / indexes for network, dayparts and or programming against each CRM segments Scored and ranked networks, dayparts and programming against the indexes Applied McDonald’s actual CPMs for accuracy Utilized both CRM indexing and actual costs to develop the recommended plans Proposed scenario offers ability to deliver $6.8MM in cash value while over-indexing GRPs Opportunity to remove 5% of total budget while still over-delivering A1849 GRPs and optimizing plan on McDonalds segments vs. baseline
- OMG Advanced TV Platform Get ready for Upfront planning Prepare to more effectively plan and allocate your client’s cross-platform video investment via The OMG Advanced TV Platform, powered by VideoAmp, a component of Omni that Leverages linear TV and digital data to plan, optimize and measure integrated video investments and Uncovers duplication of audiences to prompt more effective allocation decisions in order to Move beyond broad demos by using audience data to better plan video investments and fuel true cross-channel measurement against KPIs.