1. Cracking the Code of Human
Behavior
Dan Hill, Ph.D. – President, Sensory Logic
Austin, TX; May 7, 2013
iMedia Agency Summit
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WHAT’S BIG DATA’S TRUE SCOPE?
Part 1

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3
How Big Is
“Big Data”?

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Big Volume - Sources
– Information captured by
sensors
4
• Extremely large datasets generated
from technology practices, such as:
– Social media activity
reports
– Mobile phone call
detail records
– Operational technology
– Web server logs
– Internet clickstream data
– Streaming sources
BIG DATA

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5
How Small Is
“Big Data”?
How Small Is “Big
Data”?

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Irrational Rationality . . .
6

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How Is “Big
Data” Big & Small
At The Same
Time?

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Moving Beyond Won’t or Can’t Say
8
95% of mental activity is
subconscious.
What % of mental activity is
subconscious?
Thoughts &
Self-reported
Feelings
Claims/Text
Intuitive
Emotions
Imagery/Sounds
Retail Cues
Product Usage
Persuasion &
Loyalty

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SENSE – FEEL – think- DO
9
©Sensory Logic 2012

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Irrational Rationality . . . Limited Data
Need more data points to make the right decision
10

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Testing Issues
71% 44%
0
10
20
30
40
50
60
70
80
Not Pre-tested Pre-tested
Effectiveness Success Rate %
“Cases with favorable pre-testing results
did significantly worse in market
than those that were not tested.”
Binet and Field,
Marketing in the Era of Accountability, 2007

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Step Closer… Step Ahead.
sensory emotive
non-verbal
conscious
verbal
rational
subconscious
rational
verbal
conscious
Traditional Economics Behavioral Economicsvs.

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QUANTIFYING EMOTIONS:
FACIAL CODING
Part 2

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How Can “Big
Data” Be
Bigger, As In
More On Target?

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The Future of Marketing
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Information
Talking Points
On-Message On-Emotion
Feeling Points
Satisfaction
20th Century 21st Century

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Mona Lisa
16

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Mona Lisa
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History of Facial Coding
1872
• Scientific Theory:
Charles Darwin
– Universal
– Spontaneous
– Abundant
• Theory Refined:
Paul Ekman, Ph.D.
– 43 facial muscles,
express universal core
emotions
• Business Inventor:
Dan Hill, Ph.D.
– Pioneer in using facial
coding to create
emotional metrics
– U.S. Patent Portfolio
Science Psychology Business
1965 1998

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History of Facial Coding
2005
2009-2011
Which tool will have the most transformative
impact on MR?
“The reviewers felt that neuroscience
suggests that neurological methods
(fMRI)and facial coding are best suited
to assess the emotional valence of
viewer reactions”
- The ARF NeuroStandards
Collaboration Project
2011
2010

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Sales Correlation to Super Bowl TV Spots
Electroencephalography (EEG)
Facial Coding
USA Today .0003
EEG .232
Facial Coding .6112
Seventeen automotive TV spots were
analyzed over the past 3 years.
Rating

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FROM IDENTITY TO EMOTION
RECOGNITION
Part 3

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Facial Coding
Humanizes
“Big Data”

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Customization
Smart Digital KioskSmart Billboard
Targets Women

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Converging Forces
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FROM FLEETING TO ENDURING:
PERSONALITY
Part 4

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Big, Deep Data:
Facial Coding &
Big 5 Factor

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The Problem
In general, “evidence indicates
that demographic
measures, outside of
education, are not an accurate
predictor of consumer behavior.”
Sales/Marketing Management (11/09)
Only 15% of major companies
surveyed derive real value from
creating segmentation typology.
(Marakon 2006)
Only 6% of marketers have
excellent knowledge of
customers, 51% have fair to little
knowledge.
(CMO Council 2008)

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Solution: Big 5 Traits Model
“Surprisingly, most marketers have
no idea how well the Big 5 can
predict consumer behavior. The Big
5 predict attitudes, values, self-
concepts, and motivations.”

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Myers-Briggs Inadequate
29

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The Elements
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“The fifth element is mud.”
-Napoleon Bonaparte

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Male Version: Big 5 Examples
Openness Conscientiousness Extraversion
Agreeableness Neuroticism

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Traits by State
• States that vote
Democratic tend to be
higher on openness and
extraversion, vs.
conscientiousness for
Republicans
Neuroticism Openness
Extraversion
Source: Gosling, Snoop: What Your Stuff Says About You

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White vs. Wheat Bread

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Whole Wheat Supporters
0%
6%
0%
0%
6%
12%
10%
0%
55%
12%
0% 50% 100%
True Smile
Robust Smile
Weak Smile
Micro Smile
Surprise
Skeptical
Dislike
Sadness
Frustration
Anxiety
Emotional Profile
3 – Healthy Whole Wheaters 4 – Sophisticated Whole Wheaters
12%
88%
0%
36%
9%
0%
0%
0%
21%
9%
18%
6%
0% 50% 100%
True Smile
Robust Smile
Weak Smile
Micro Smile
Surprise
Skeptical
Dislike
Sadness
Frustration
Anxiety
Emotional Profile
45%
55%
The Healthy Whole Wheaters are most notable for having 2x more frustration than any other segment.
Sophisticated Whole Wheaters, in contrast, are all about enjoyment.

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White Bread Supporters
0%
0%
13%
0%
0%
7%
23%
21%
29%
7%
0% 50% 100%
True Smile
Robust Smile
Weak Smile
Micro Smile
Surprise
Skeptical
Dislike
Sadness
Frustration
Anxiety
Emotional Profile
13%
87%
1 – White Bread Traditionalists 2 – White Bread Neutralists
0%
4%
15%
0%
1%
4%
39%
5%
27%
6%
0% 50% 100%
True Smile
Robust Smile
Weak Smile
Micro Smile
Surprise
Skeptical
Dislike
Sadness
Frustration
Anxiety
Emotional Profile
20%
80%
Traditionalists exhibit 2x more sadness than any of the other three segments. Meanwhile, the Neutralists
are notable for showing 1/3rd more dislike than any other segment.

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SUMMARY
Part 5

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Big Data: To Be Successful . . .
• Walmart’s transaction
databases – 2.5
petrabytes
• 40 billion Facebook
photos
• Amount of digital
information increases 10x
every 5 years
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• Walmart’s transaction
databases – 2.5
petrabytes
• 40 billion Facebook
photos
• Amount of digital
information increases 10x
every 5 years

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Go Deeper Than Online Behavior or Social Media Chatter
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Emotions
Personality
Traits
Values
Immediate
Lifetime

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And Then You and the Client Can Be (Really) Smiling
True Fake Fake Fake True
Fake True Fake True Fake
Fake Fake True True True