© 2016 Karthik Ethirajan, all rights reserved

Measurement Solution for Smart TV Karthik Ethirajan October 2016

© 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

© 2016 Karthik Ethirajan, all rights reserved

TV viewing data can be used to more accurately measure

popular metrics such as RF and ROAS

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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

© 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

© 2016 Karthik Ethirajan, all rights reserved

Measurement TAM is ~4 billion of which TV is an order of

magnitude bigger than digital

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$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

© 2016 Karthik Ethirajan, all rights reserved

Smart TV measurement solution revenue forecast &

product pricing guidelines

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$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

© 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