MTA vs. born out of need to better understand the impact each advertising touchpoint has on outcomes. Programmatic bid optimization was born out of the need for high predictive accuracy, and needs to be able to optimize several bidding decisions per second. And never the twain shall meet- or will they?

1. BRIDGING THE GAP BETWEEN MTA
& PROGRAMMATIC BID OPTIMIZATION
October 17, 2019
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2. 2
SPEAKERS
JOY JOSEPH
Head- Data & Analytic Services
GroupM
TIM SMITH
Senior Director, Business Development
The Trade Desk

3. 3
MTA VS. PROGRAMMATIC
OPPOSITE BANKS OF THE RIVER?
MTA vs. born out of need to better understand the impact each advertising touchpoint has on outcomes-
• More holistic measurement and optimization of campaign execution
• Rich insights around audience characteristics and their responsiveness across a spectrum of media
journeys and creative components
• More explanatory than predictive
• In-flight course correction of campaign investment strategy- often at no more than a weekly cadence
Programmatic bid optimization was born out of the need for high predictive accuracy, and needs to be able
to optimize several bidding decisions per second-
• Deliver outcomes rather than explain outcomes
• High dimensionality- optimize in real-time across multiple bidding decisions per second
• Update models in real-time to increase sensitivity to short-term audience propensity shifts

4. 4
DEFINING THE GAP
Marketing Objective MTA Programmatic Bid
Optimization
Drive campaign goals by identifying opportunities to optimize efficiency of
addressable marketing spend across channels, journey stages, & audience
X
Understand the fractional impact of each touchpoint within the journey on
conversions
X
Cross-channel addressable investment strategy & optimization (channels,
supply-side platforms/publishers, creative, placements)
X
Achieve in-channel campaign performance by optimizing real-time spend
allocation at an impression level
X
Determine optimal bids across ad inventory attributes to drive campaign KPIs X
Identify the most effective paths to conversion (frequency & sequencing) X
Drive reach or target custom audiences with an extremely high level of
predictive accuracy to obtain higher returns on their advertising spend
X

5. 5
A CRASH COURSE IN PROGRAMMATIC BIDDING ALGOS
Bid decisioning algorithms drive a complex process that:
• Translate bidding strategy into quantified bid factors
• Optimize bid factors using real-time predictions on campaign KPIs
• Prioritize across competing internal bids and submit the bid most likely to win
For instance, The Trade Desk evaluates 10 Million Impressions per second.
For each of these impressions, The Trade Desk uses a unique bid factoring methodology that offer traders
advanced optimization capabilities to narrow targeting and pay the right price by using multiple triggers including
time of day, device, ad format and recency.
Basically, traders input a bidding strategy based on brand goals and the algorithm tests and optimizes it.
However, bidding strategies are set at the aggregate audience segment level rather than user-level, resulting in
lower specificity in bidding vs. true user-level bid optimization.

6. 6
USER-LEVEL BIDS VS. SEGMENT BIDDING
Segment 1
Bid Multiplier = 0.9
Segment 2
Bid Multiplier = 1.1
Segment 3
Segment 3
1.3
1.1
1.2
1.0
1.4

7. 7
WE NEEDED…
A real-time user level scoring system to enhance
programmatic bid optimization algorithms.

8. CUSTOMER BID SCORING
Use log level data to develop and apply scores for each individual prospect to attain maximum expressiveness
Browsing
Behavior
Geography /
Temperature
Time & DOW
Page Visits
POTENTIAL INPUTS
Showed significant interest
in new product through
landing page visits
Browsed recipe sites
that use relevant
products
Lives in a geo that experienced
incredibly warm temperatures
3.0 2.0 1.5

9. 9
THE SOLVE…
Develop an MTA with DMP
audience, Adserver log and
DSP log data
Calibrate Model to both
Client vendor MTA and
DSP bid logic
Generate User-level
propensity scores against
audience user-level data
Transform propensity
scores into DSP base bids
Push user-level base bids
into DSP bidding API.
Test lifts and optimize
using DSP Algo
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6 5
3
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10. 10
VALIDATION METHODOLOGY

11. 11
RESULTS- FASTEST PATH TO CONVERSION,
LOWEST CPA

12. 12
THANK YOU!

13. Confidential and proprietary. All rights reserved. Distribution without permission is prohibited.