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Measurement Solution for Smart TV

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Measurement Solution for Smart TV

  1. 1. © 2016 Karthik Ethirajan, all rights reserved Measurement Solution for Smart TV Karthik Ethirajan October 2016
  2. 2. © 2016 Karthik Ethirajan, all rights reserved 2 Agenda 3. Use Cases 4. Data Flow 5. Market Size 6. Revenue Forecast 7. Go-to-market Approach Product Business Case Strategy
  3. 3. © 2016 Karthik Ethirajan, all rights reserved TV viewing data can be used to more accurately measure popular metrics such as RF and ROAS 3 Reach is the number of unique HHs exposed to an ad. Frequency is the numbers of times it was exposed. ROAS is conversion (purchase amount from incremental sales) over ad spend Reach & Frequency (R/F) Return on Advertisement Spending (ROAS) Enabled by TV Viewing Data Campaign Data Safe Haven Environment Enabled by TV Viewing Data Campaign Data Safe Haven Environment Purchase Data Value Proposition Results are based on actual measurement, and not extrapolated from a small panel
  4. 4. © 2016 Karthik Ethirajan, all rights reserved 4 Data flow for the RF and ROAS use cases Ad Network Data Match • Mobile ID • Timestamp • IP address Target File • Campaign ID • Creative ID • Advertiser • Duration Campaign Data Smart TV / STB • Mobile ID • Timestamp • IP address TV Viewing Data Report Generation R/F Exposure Report • Age | Experian • Gender • HHI Demographic Data Enrichment Partner Data Match Attribution Partner • Foot Traffic | Ninth Decimal • Online | Convertro • Retail PoS | Datalogix • Auto | Polk • Credit Card | Amex Attribution Data Report Generation ROI Attribution Report Safe Haven Partner • PII • Timestamp • SKU • Purchase amount Matched IDs Matched IDs • Double Blind Match | Acxiom / Liveramp
  5. 5. © 2016 Karthik Ethirajan, all rights reserved Measurement TAM is ~4 billion of which TV is an order of magnitude bigger than digital 5 $531B $582B 2015 2017 Global Ad Spend U.S. Ad Spend 34% 34% $69B $72B $183B $197B CAGR 4.7% 3.8% 0% 14% 38% 36% Global Ad Spend by Medium TV Ad Spend $58B $75B 32% 38%Digital Ad Spend TV Portion of TAM • $72B TV ad spend in US • 50% Measured ads • 10% Measurement pricing -------------------------------------- $3.6 billion TAM Digital Portion of TAM • $75B Digital ad spend in US • 5% Cross-screen TV ads • 10% Measurement pricing -------------------------------------- $0.38 billion TAM Source: Zenith Optimedia, eMarketer, BI, Karthik analysis
  6. 6. © 2016 Karthik Ethirajan, all rights reserved Smart TV measurement solution revenue forecast & product pricing guidelines 6 $24 $48 $72 $- $10 $20 $30 $40 $50 $60 $70 $80 Year 1 Year 2 Year 3 Annual Revenue Forecast (in millions)Revenue Model Year 1 Year 2 Year 3 Market TAM (in Millions) $ 4,000 $ 4,000 $ 4,000 Market Share 5% 10% 15% RF Use Case RF Share 80% 80% 80% Partner Rev Share 10% 10% 10% RF Revenue $ 16 $ 32 $ 48 ROI Use Case ROI Share 20% 20% 20% Partner Rev Share 20% 20% 20% RF Revenue $ 8 $ 16 $ 24 Total Revenue Annual Revenue $ 24 $ 48 $ 72 3 Year PV $ 116 Product Pricing Guidelines  All numbers are in millions  Discount rate applied is 10%  TV dominated TAM expected to stay flat  Market share assumption is based on incubating a brand new business  RF will be the predominant use case compared to ROI reports  Partner costs for safe haven and demographic data will be ~5% each  Attribution data cost is higher around 10% Model Assumptions  Pricing should be based on number of addressable markets rather than addressable TV sets  ROI reports are a higher value product and has more price elasticity, and hence should be priced 2X or more than RF reports
  7. 7. © 2016 Karthik Ethirajan, all rights reserved 7 Key success factors and go-to-market recommendations Product Marketing • Higher the penetration of Smart TV or addressable TV sets denser the sample points to chart reach and conversions • Deployment of precise ACR means better quality of data to work with • Measurement platform will require some of the same DMP and data analytics capabilities as that of audience targeting • Go to market with a branded product, deal direct with advertisers, pay data partners on the back end as vendors • Develop agency/DSP relationships as much of the advertisement dollars flow through these demand agents • Clearly articulate product positioning: actual measurements as opposed to panel used by Nielsen Partnerships • Closed loop attribution require partnerships with offline and online data providers to provide a complete view of user journey from discovery to purchase • Data partnerships should be paid based on usage, not rev share • Safe haven partners can help boost confidence with respect to protection of PII data and user privacy

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