Keys to successful measurement of digital media including cookieless tracking, verification and cross-device attribution.

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Media Measurement Success
Steve Latham, Head of Analytics
June 2017

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Re-Introducing Flashtalking

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Known for
data-driven
ads…

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We’ve expanded focus on data-driven insights
Flashtalking Analytics
Data and Analytics Suite
• Unification of data across channels
• Algorithmic insights for optimizing
media and creative
• Seamless integration with 3rd parties
• Easy to onboard
• No conflicts of interest

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With seamless ecosystem integration
Flashtalking Analytics
DSP VERIFICATION
DIRECTINTEGRATIONS
SEARCH TAG MANAGMENT
ATTRIBUTION
5DMP
DIRECTINTEGRATIONS
IDENTITY
Passing data between platforms without loss or friction

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Our Integrated Platform…
CREATE ACTIVATE MEASURE
Campaign Manager and
PowerFeeds Activate data in
real-time to drive creative
experience
fTrack, Connected Logs,
Attribution & Integrations:
Industry’s best analytic toolset for
optimizing performance
Creative Manager and
Ad Builder
Create / organize assets
across screens and formats
…for data driven, programmatic advertisers

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Analytics Clients and Case Studies
CPG
37%
Increase in
display media
efficiency
AUTO
136%
Lift in leads
with a 45%
drop in CPA
TRAVEL
55%
Increase in
ROI for online
bookings
RETAIL
25%
Budget saved
by reducing
wasted spend

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Today’s topic
Advertisers have made big investments in Digital Media
• Digital is surpassing TV with $77bn in global spend
We’re good at using data to find customers!
…At least our media vendors tell us so
…and big investments in Ad-Tech
• DSPs, DMPs, Verification, Identity
Yet.. Most still use antiquated performance metrics
• CTRs, last-touch and last-click (circa 1994) are still the norm
$77bn
2017 Spend
TopGrossingNon-animatedfilmof1994

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So how do we know if our investments
are really paying off? ?
What we’ll cover:
• Fractional attribution
• Cookieless tracking
• Cross-device
• Verification (fraud, viewability, brand safety)
…and how to tie them all together

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1. Keys to Successful Attribution

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The customer journey is increasingly complex
Fractional Attribution
…Highlighting the need for accurate attribution

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The problem with “old” attribution
Last-touch (100%)
• Penalizes upper funnel ads
• Rewards low-funnel ads
• Encourages bad behavior (cookie bombing)
Even weighted
• Gives credit to every ad – regardless of impact
• Penalizes quality ads and rewards cookie bombing
First touch (100%)
• Rewards ads that may not have been noticed
• Penalizes middle and lower funnel
Time Decay
• Rewards most recent ads – regardless of impact
• Penalizes upper funnel ads
Display Imp: #1
Paid
Visit
#1
Display Imp: #4 Display Imp: #3
Display Imp: #2
Paid
Visit
#2
Non-
Paid
Visit
Display Imp: #5 Conversion

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Bad attribution can do more harm than good
Fractional Attribution
30-50% of
Rules-based
Signals are
wrong

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Solution: algorithmic fractional attribution
Best practice
• Unify customer journey (across platforms)
• Validated, machine learning models to measure
contribution of each touchpoint
• Award fractional credit to each channel, placement,
creative and keyword
• Apply those insights to optimize spend
Benefits:
• Accurate insights: cross-channel / full-funnel
• Higher returns from digital media
Display Imp: #1
Paid
Visit
#1
Display Imp: #4 Display Imp: #3
Display Imp: #2
Paid
Visit
#2
Non-
Paid
Visit
Display Imp: #5 Conversion

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5 Keys to Successful Attribution
FRACTIONAL ATTRIBUTION
1. Unified data
2. Validated models
3. Intuitive reporting
4. Actionable recommendations
5. Ecosystem friendly

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Requirement #1: Unified data via ad server log files
FRACTIONAL ATTRIBUTION
Desktop
Impression
CONVERSION PATH ASSEMBLY
Non-Paid
Click
Conversion
Desktop Click
In-App
Impression
Desktop
Impression
In-App
Impression
Mobile Web
Impression
In-App
Impression
VISUALIZATION
DATA INPUTS
In-App Ads
Desktop Ads
Mobile Ads
Conversions
AD SERVER
ALGORITHMIC
MODELING
No redundant tagging.
No discrepancies.
Better data.
Better insights.

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Data Unification
…needs to happen at the
event or impression level.

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Requirement #2: Validated Attribution Models
FRACTIONAL ATTRIBUTION
Split the data into two representative groups: Training and Holdout
Run model(s) against the Training group
Validate the outputs against Holdout group to determine the predictive accuracy
Machine Learning Validation
Models must be validated. Validate predictions against a holdout group

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Requirement #3: Intuitive reporting
FRACTIONAL ATTRIBUTION

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Requirement #4: Actionable recommendations
FRACTIONAL ATTRIBUTION

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Fractional Attribution
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FRACTIONAL ATTRIBUTION
Dynamic Ad Serving
Cookieless tracking
Ad Server
Programmatic
Creative
Data and Reporting
Standard Ad Serving
Verification & Fraud
Cross-Device
Offline Sales
DMP Segments
Connected Logs
Requirement #5: Ecosystem friendly (frictionless)

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It’s time to revisit how Creative is measured

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Introducing Creative Analytics
Problem: traditional metrics are flawed
• Click-through rates are misleading
• Last-touch favors low funnel (penalizes upper funnel)
• Bias exists among placements and audiences
Solution: Creative Analytics
• Control for media and audience bias
• Analyze Creatives using algorithmic
models

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Inside Creative Analytics
1. Remove underperforming placements via attribution modeling
2. Cluster Users based on complexity of their paths (via machine learning)
3. Analyze creatives within each cluster based on probability of converting
Data Driven approach to measuring creative performance

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In Closing…
Multi-touch attribution is no longer a luxury – it’s a necessity
Benefits
• Better insights
• Superior performance
• Less friction
• Higher ROE: Return On Effort
Keys to Success
• Unified data
• Validated models
• Intuitive reporting
• Actionable recommendations
• Ecosystem friendly

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Cookieless Tracking

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To date, ad-tech platforms have relied
heavily on cookies to track user engagement.
0%
Of ad servers can set
a browser cookie in
an app.
30-50%
Of all cookies are
routinely deleted
30%
Of devices block
or delete cookies
This is great, EXCEPT …

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Solution: Cookieless Tracking (via privacy-friendly probabilistic IDs)
Sample Non PII
Signals:
> hashed IP
> browser and OS
settings
> timezone, geo
> screen size,
clock, pixel
ratio
> Installed
plugins
> CPU speed
> Canvas data
How we do it:
We create unique signature based
on 50+ non PII signals
1. Tag fires on device when:
• An add loaded on a page or in an app
environment.
• Or a spotlight loaded on a page or in an
app Environment
2. Unique data signature generated from signals
and validated by the fTrack brain.
fTrack by Flashtalking
Accuracy
validated
against
deterministic
truth set.

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Cross-Device

30. MARKETERS’ CHALLENGES
§ Consumer engagement is increasingly
fragmented across devices and channels
§ Brands don’t know which media investments
drive conversions
§ Traditional performance metrics are
becoming less and less reliable

31. CROSS-DEVICE METHODOLOGIES
DETERMINISTIC* PROBABILISTIC
Benefits Limitations
• Highly precise
• A trusted
methodology
• Limited scale
• Data use
restrictions (walled
gardens)
Benefits Limitations
• Scale is
achievable
• Non-PII data
usable across
entire online
ecosystem
• Can be less
precise
• Analytics &
attribution can be
more complex

32. HOW WE BUILD THE DEVICE GRAPH
REMOTE ADDRESS: 171.161.199.100:443
DATE: Mon, 22 Aug 2016 22:04:14 GMT
COOKIE: TLTSID=57C69D30688410680F1F08119DD1
HOST: www.hotels.com
REFERRER: https://www.google.com/
A sighting is when we see a
device come into the graph,
either through a bid request,
page request on your site, or
some other action
SIGHTING

33. HOW WE BUILD THE DEVICE GRAPH
A device is a particular
browser (e.g. Chrome, Firefox)
or mobile ID, such as Google
AAID or Apple IDFA
DEVICE
TPC:AAA
AAM:ZZZ
BAC:123
BDC:789
FLA:555
IDFA:456

34. HOW THE DEVICE GRAPH IS BUILT
An edge is a probabilistic
assumption that two devices
are connected to the same
user or household
EDGE
TPC:AAA
AAM:ZZZ
BAC:123
BDC:789
FLA:555
IDFA:456
EDGE

35. A cluster is a probabilistic
assessment of all the
devices that belong to a user
or household
CLUSTER
HOW THE DEVICE GRAPH IS BUILT

36. A graph is a map of all
devices and clusters.
GRAPH
HOW THE DEVICE GRAPH IS BUILT

37. DEVICE GRAPH BENEFITS
§ Holistic view of each user
§ Unified customer journey
§ Better data for more accurate insights
ENHANCED INSIGHTS

38. ><
In-AppIn-App Mobile Web Search
Cross-device attribution
Using device graphs to unify the customer journey
Pre-roll video Dynamic Creative
Static
Attribute credit where it’s due

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39
Cross-device attribution case study
Key Insights
• 38% of visitor IDs were cookie rejecters
• 42% of converters were matched to Tapad device graph
• 61% of matched converting IDs were bridged (2.0 per User)
• Unifying data resulted in a 35% lift in Display ROI*
• Deeper insight into vendor performance (not all benefited)
• 30% searched for flights on more than 1 device
38%
Reject
Cookies
42%
Match Rate
61%
Bridge Rate
Key Takeaway
• Cross-device + Cookieless are key to accurate insights
2.0
IDs per User
35%
Display ROI
Lift*
*Overall lift was 9% on 26% of converters. When extrapolated to 100% of converters, lift would be 35%
Enabled by Tapad Device Graph + Cookieless Tracking by Flashtalking
30%
> 1 Device

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Fraud, Viewability, Brand Safety

41. State of Consumer Exposure

42. U.S. display snapshot
42
Optimized: use of ad fraud prevention technology
Non-optimized: no use of ad fraud prevention technology

43. How frequently am I serving ads to my users?
43

44. How many ads are my users actually seeing?
44

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Marrying Verification and Attribution
Objectives
• Extend value of viewability and fraud detection
• Enhance accuracy of attribution models
Approach: Use IAS outputs to cleanse conversion path data
• Remove non-viewable ads
• Remove non-human traffic
• Model cleaner data for better insights

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Verification Case Study:
Some saw
10%-40%
Fewer Actions
Some saw
15%-50%
More Actions
3.3 Million
Non-
Viewable
Ads
1,400 Non-
Human
Visitors
SITES CONVERTERS
Segment Name Without IAS With IAS Change %
Thomson Mobile FY2016 V2 D9 - Tripadvisor 100 85 -15%
Thomson Travel Content Always On - Tripadvisor 3,275 3,190 -3%
Thomson BAU Retargeting Always On - Media IQ 2,562 2,513 -2%
Thomson BAU Prospecting Always On - AOL 332 301 -9%
Thomson BAU Prospecting Always On - Captify 170 139 -18%
Thomson Mobile FY2016 V2 D9 - Quantcast 33 29 -12%
Thomson Mobile FY2016 V2 D9 - Criteo 409 398 -3%
Thomson BAU Prospecting Always On - Quantcast 134 117 -13%
Thomson BAU Prospecting Always On - Rocket Fuel 63 46 -27%
Thomson Travel Content Always On - TravelSupermarket 243 232 -5%
Thomson Travel Content Always On - Skyscanner 251 218 -13%
Thomson Travel Content Always On - Holiday Watchdog 10 15 50%
Thomson Cruises September 16 - AOL 58 56 -3%
Thomson Cruise MoL Partnership Sep 2016 - Mail Online 19 18 -5%
Thomson Travel Content Always On - Kayak 41 48 17%
Thomson Travel Content Always On - Holiday Weather 49 50 2%
Thomson Cruises September 16 - Cruise Critic 23 21 -9%
Thomson BAU Prospecting Always On - Holiday Weather 23 19 -17%
Thomson Mobile FY2016 V2 D9 - Skyscanner 20 22 10%
Thomson Longhaul AOL Partnership Sep 2016 - AOL 5 3 -40%
Thomson Cruises September 16 - Time Inc 3 4 33%
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More
Accurate
Insights
Enhanced Attribution via IAS Verification

47. 3 Exposures
3 Exposures
3 Exposures
47

48. 3 Exposures
3 Exposures
3 Exposures
1 30Seconds
18
1 30
6
1 30
13
Seconds
Seconds
48

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What’s Next?
Incorporating time-based exposure data in Attribution models
Display Imp: 2
secs
Paid
Visit
#1
Display Imp:
15 secs
Display Imp: 3
secs
Display Imp: 4
secs
Paid
Visit
#2
Non-
Paid
Visit
Display Imp: 1
sec
Conversion
• Further enhance accuracy of
attribution models
• No work required by advertisers

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Thank You