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 . . .

6

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7

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

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

2

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

10

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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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14

How Can “Big Data” Be Bigger,

As In More On Target?

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The Future of Marketing

15

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

17

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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

24

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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

37

• 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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