#ARFAxS

Quality Attention

Adam Heimlich

President

Parsec Media

Erik Nylen

Head of Data Science

Parsec Media

The Google Maps Approach to AI

• Routes depend on several things• Google uses AI to predict which route it thinks is best, then—if

you take that route—uses the data from tracking you to update its AI and decision making.

• One critical component that they measure and take into account is time.

Google Maps Takeaways

• With only a speedometer—and no odometer—how can one measure distance travelled?

Let’s Pretend Cars Lack Odometers

Taking samples over a known, measured period of time, we can take a sum over time to tell us something very valuable — which is how far we’ve come, an important datum in the model.

Taking samples over a known, measured period of time, we can take a sum over time to tell us something very valuable — which is how far we’ve come, an important datum in the model.

0

20

40

60

80

100

120

0 1 2 3

Spe

ed in

MPH

Time in Seconds

How Is This AI?

• Google Maps• Facebook Image Recognition• Parsec Attention Prediction

AI & the Ladder Of Causation

Image from Judea Pearl, The Book of Why

We’re here

The Incoming Data

URL

Keywords

User

Device type

IP address

View percent

Refresh rate

Clicks(x-y)

Accelerometer(α,β,γ)

Swipes(x-y)

Taps(x-y)

MRC viewable

100% viewable

75% viewable

50% viewable

25% viewable

First pixel

Publisher

Ad copy

What We Used For Model v0

User

View percent

100% viewable

50% viewable

First pixel

How We Implemented the AI

Input Model Output

Attention Model

Awareness Model

Awareness Model

time0,time50,…,time100

time0,time50,…,time100

timefuture

awarenesscurrent

timefutureawarenessfuture

What Is the Minimum Effective Dose?

Optimum Frequency…

-10%

-5%

0%

5%

10%

15%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Bra

nd L

ift

Time (sec) or Freq (imp)

Individual Brand Lift Likelihood (%)

freq t_0 t_100

Optimum Frequency

Optimum Attention…

-10%

-5%

0%

5%

10%

15%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Bra

nd L

ift

Time (sec) or Freq (imp)

Individual Brand Lift Likelihood (%)

freq t_100

Optimum Attention

Frequency Is Blind to Attention

-10%

-5%

0%

5%

10%

15%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Bra

nd L

ift

Time (sec) or Freq (imp)

Individual Brand Lift Likelihood (%)

freq t_100

Or here…

Frequency hereAttention could be here…

AI Application

-10%

-5%

0%

5%

10%

15%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Bra

nd L

ift

Time (sec) or Freq (imp)

Individual Brand Lift Likelihood (%)

freq t_100

Predict time

Frequency

Looking at Both Variables

Low Frequency Mid Frequency High Frequency

Low Attention

Medium Attention

High Attention

Frequency & Attention Working Together

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.4 1.45 1.5 1.55 1.6 1.65 1.7 1.75 1.8 1.85 1.9 1.95 2

Rea

ch

Distribution of Prediction Audience Awareness

Attention Index (1 is Optimal)

Underexposed Optimally exposed Over exposed