Download - Cracking the Code of Human Behavior
Cracking the Code of Human Behavior
Dan Hill, Ph.D. – President, Sensory LogicAustin, TX; May 7, 2013iMedia 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 . . .
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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
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95% of mental activity is subconscious.
What % of mental activity is subconscious?
Thoughts &Self-reported
FeelingsClaims/Text
Intuitive Emotions
Imagery/SoundsRetail Cues
Product Usage
Persuasion & Loyalty
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SENSE – FEEL – think- DO
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Emotional Brain200 Million Years Old
Sensory Brain500 Million Years Old
Rational Brain100,000 Years Old
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3
1123 1
©Sensory Logic 2012
First Mover Advantage5X Faster
Facial Coding Measures how they feel
Eye Tracking Measures what they see
Verbal Input Suggests why they feel
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Irrational Rationality . . . Limited Data
Need more data points to make the right decision
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Testing Issues
Not Pre-tested Pre-tested0
10
20
30
40
50
60
70
80
71% 44%
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.
standard approach
Scientific Research
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
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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 PortfolioScience 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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22
Facial Coding Humanizes “Big Data”
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Customization
Smart Digital Kiosk
Smart Billboard Targets Women
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Converging Forces
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FROM FLEETING TO ENDURING: PERSONALITY
Part 4
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26
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
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The Elements
30
“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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38
Go Deeper Than Online Behavior or Social Media Chatter
Emotions
Personality Traits Values
Immediate
Lifetime
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And Then You and the Client Can Be (Really) Smiling
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