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Emotion Mining™What Really Moves Us?
C H I C A G O • D E T R O I T • F R A N K F U R T • S H A N G H A I • B E I J I N G
Agenda Understanding Emotions
The Tool
The Results
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Understanding Emotions
It’s More Than Buzz; It’s Science
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Turning Starbucks Around With EmotionsHoward Schultz re-appointed as Starbucks CEO.
◦ Commits to laser focus on customer experiences‒ New espresso machines are too tall, automated
‒ Coffee aroma lost by bagging and burnt sandwiches
‒ Merchandizing is sterile and boring
Customers’ emotional experiences drove the turnaround strategy.
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Great CEOs Empathize With Customers
Steve Wynn on the resort business: All of the razzmatazz and jazz we hear about facilities and everything else doesn't amount to a hill of beans.
It's customer experience that determines the longevity and endurance of these enterprises.
Steve Jobs on how technology fits into Apple products: The hardest thing is: how does that fit in to a cohesive, larger vision, that’s going to allow you to sell $8B, $10B of product a year?
And, one of the things I’ve always found is that you’ve got to start with the customer experience and work backwards to technology.
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Great Products Reflect Empathy
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Emotions Drive Our ExperiencesThe brain has two systems of thinking.
◦ System 1: Fast, automatic, frequent, emotional, stereotypic, subconscious
◦ System 2: Slow, effortful, infrequent, logical, calculating, conscious
Most customer experience is in System 1.◦ Experts say 95% of cognitive activity is subconscious
◦ Walking into a Wynn casino or Starbucks
◦ Paying the premium for an Apple product
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The Big Prize: Understanding EmotionsCan a market research firm supplement C-suite intuition?
◦ Can customer emotions be quantified? ‒ Can the emotional response to a Blackberry be compared to an iPhone?‒ Could Shultz’s insights be derived using quantitative methods?
◦ Could such quantitative insights be relied upon?
◦ If so, what would be the value of such a solution? ‒ Invest in what customers love‒ Eliminate what they don’t
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The Tool
Emotions Are Expressed With Words
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We acquire language at an early age.◦ Capable of verbalizing feelings by the age of 4
Emotion Mining™ discovers the emotions hidden in words.
◦ The Emotion Mining dictionary maps 4K+ emotion words to 32 channels
Cloud-based Survey
Unique Emotion Mining™ web-based tool drills down into how respondents “feel”
Three Sources of Words
Existing Data
Text from previous research, company reports and/or news can be used
Web Scraping
Content from online sources is scraped and analyzed
Active Passive
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Start with a Simple Topic“How does ______ make you feel?”
◦ Tons of options for how a <topic> makes you feel: brand, product feature, market trend, competitor, problem, etc.
Stimulus can provide context before the respondent starts the exercise.
Follow-up (and screening) questions get closer to the right insights – enable rich segmentation.
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Respondents complete an online survey to uncover emotions.
Unique Survey Process
Respondents provide emotions (words) related to stated topic/questions – “How do you feel?”
Respondents elaborate on the meaning of emotional responses – attempts to rationalize emotions.
Each emotional response is measured for current ambient “temperament” or mood (apart from the topic).
Free Association Exercise
Ambient Mood & Subconscious Intensity
Emotion Clarity
Conscious Intensity
Each emotional response is measured for baseline intensity relevant to topic.
Conscious Emotion Mapping
Conscious and Subconscious Emotion Mining
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Algorithms Map Words to 32 ChannelsEach channel represents a type of emotion – based on a combination of 4 channel properties.
◦ Enjoyment◦ Interest◦ Commitment◦ Passion
Separates conscious from subconscious emotions.
LoyaltyKindnessAcceptance
Interest
JoyPride
ConfidenceEnergy
DreadDismay
FearHesitation
SorrowEmbarrassment
AcceptanceInterest
AmazementAdmiration
AttractionTrust
SerenityWorth
ContentmentSecurity
ContemptAnger
RejectionDisinterest
DiscomfortDisreputeDiscontent
Insecurity
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Property #1: EnjoymentPleasant Unpleasant
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Offerings should be pleasant. Problems typically are unpleasant.◦ Per Michael Skok’s Value Proposition
Framework, a good offering must have…‒ High gain => Unpleasant problem‒ Low pain* => Pleasant offering
*Refers to pain of adoption, which could be a topic in itself Pleasant in green. Unpleasant in red.
Property #1: Enjoyment
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Property #2: InterestInward Outward
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Inward in red. Outward in green.
Property #2: Interest
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Inward emotions relate the topic to internal things.
Outward emotions relate the topic to external things.
◦ Inward offerings are valuable, ubiquitous and internalized by the customer
◦ Outward offerings should be luxurious, exclusive and in need of customer loyalty‒ How does <product feature> make you feel?‒ How does <trend/competitor> make you feel?
Property #3: CommitmentActive Passive
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Property #3: CommitmentActive emotions indicate a need to control.
Passive emotions indicate a consent or coercion to give up control.
◦ Passive offerings should be habit forming and easy to use
◦ Active offerings should be feature-rich and customizable
◦ Active problems are blatant needs, and passive problems could be latent ‒ Relates to Michael Skok’s BLAC analysis
Mild Intense
Active
Passive
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Property #4: PassionMild
(Mundane < Common)Intense
(Extreme < Sublime)
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Property #4: PassionPassion ranks the emotional intensity within a channel group.
◦ There are 4 levels of intensity for each channel group
◦ Intensity is good within “ideal” channel groups for specific topics
◦ Similarly, a mild level of passion is a good thing in “non-ideal” channel groups‒ e.g., the Insecurity channel ranks fourth as the
mildest level of passion, which is far better (less intense) than Discomfort
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Results
◦ Analysts chart the ideal channel groups that will benefit the client and work to get inside the mind of the respondent
◦ This exercise helps reduce hindsight bias and forces analysts to seek the reasons why results mismatch expectations
Find the IdealFind the ideal channel groups before beginning analysis.
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Bar chart showing conscious and subconscious responses by channel
Dials showing ◦ Enjoyment◦ Interest◦ Commitment◦ Passion
Word cloud based upon verbatims entered by respondents
Database linking verbatims to specific emotional channels
Core Reports
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Emotion Analysis
Filter by segments
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Subconscious intensity in gold (above 0)
Conscious intensity in grey (below 0)
Engagement Metrics◦ Average CI % across four
channel properties‒ Enjoyment = Pleasant/Unpleasant‒ Interest = Inward/Outward‒ Commitment = Active/Passive‒ Passion = Mild/Intense
◦ Filter by segments◦ Filter by conscious or
subconscious
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Word Cloud◦ Word cloud of words in verbatims◦ Filter by segments◦ Filter by conscious or
subconscious◦ Filter by emotional channel
properties
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Verbatim Data◦ Searchable◦ Filter by
segments◦ Sorted by
intensity◦ Filter by C/SC &
channel properties
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Compare Apples-to-Apples
Can compare a topic across time with similar respondents. ◦ Check the pulse of the topic, such as “driving a Volkswagen”◦ Understand the impact of events, such as the emissions scandal
Can also compare two topics within the same category.◦ Apple iPhone 6+ vs. Nexus 6P◦ American auto industry vs. American retail industry◦ Gas powered vs. electric
Compare results across time and within the same category.
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• Non-linguistic measures• Additional dimensions• Crowd-sourced dictionary• Synonyms and variants• Transcript analysis
• Result validity• Probabilistic methods• Correlation detection• Dictionary reliability• Study reliability
• Pulse tracking• Emotion repository• Respondent pools• Visualization techniques• Best practices on topics
Three Pillars to Trust the Results
Emotional measurement
Law of Large Numbers
Wisdom of Crowds
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Jim DurkinPartner/PresidentDirect: [email protected]
Chuck BeanPartner/CMODirect: [email protected]?