80% of executives believe they understand consumers’ emotions, but only 15% of consumers agree. Emotion AI can bridge this gap by providing a deeper, richer understanding of your audience and their interactions and experiences with your brand. Through eye tracking, facial coding, brainwave mapping, and automatic speech recognition, Emotion AI can measure emotions at your fingertips, adding accuracy, agility, and actionability to your insights. For more description please visit https://entropiktech.com/

1. Innovation Trends in
Media and Shopper
Research

2. Emerging Research Methods
Source: GRIT Report, 2020
Mobile First
Research
Data &
Analytics
Neuroscience
& Biometrics
Emotion AI
Research
Gamification
Covid-19 acted
catalyst of change, to make
mobile-centric
data collection the most
emerging trend in the last
two years. Mobile first
surveys, mobile qualitative
interviews and mobile
ethnography are some of the
notable methods adopted
recently.
Data and Analytics providers
saw a 13.9% increase in
their revenue in 2020. Text
Analytics, Social Media
Analytics and Big Data
Analytics continued to be
some of the most emerging
trends, help researchers
primarily with quantitative
studies and a bit of
qualitative as well.
Although Applied
Neuroscience and Biometric
Responses fall under the
category of Emerging
Methods, they are seeing a
decline in adoption every
year. The decline could
reflect the challenges of
availability, speed, scale and
cost that both methods pose.
Emotion AI, a culmination of
Eye Tracking, Facial Coding,
Voice AI and EEG Brainwave
Mapping, is one of the
hottest methods to emerge in
2020. Computer vision-
based Emotion AI methods
piqued special interest owing
to their accuracy &
scalability.
2020 was the year for
Research Gamification to
finally take off as an
emerging method that saw
widespread adoption.
Gamification of research can
be a rewarding user
experience during times of
emotional turmoil and
distraction, without the tech
requirements of VR.

3. Traditional Quantitative and Qualitative
Consumer Insights relies on what
consumers say and not what they feel
Traditional Consumer Research is…
• Biased
• Fails to answer “Why”
• Lacks Actionability
• Logistically hard to scale
• Lacks Benchmarks
Drawbacks of Traditional Methods

4. Facial Coding
Using Computer Vision &
advanced Machine Learning
and applied with Facial Action
Coding System (FACS),
computers are taught to
recognize facial expressions
and associate them with
correct emotions.
Introducing Emotion AI
Eye Tracking
A combination of three
paradigms (fixation, pursuit
and natural gaze) is used to
capture the user's gaze in a
natural, remote environment
and generate a highly
accurate and reliable heat
map, gaze plots and AOI
charts.
Brainwave Mapping
EEG headsets turn your
computer into a brain activity
monitor. The headsets safely
measure brainwave signals and
monitor the attention levels of
individuals to different sensorial
stimuli like taste, touch and
smell.
Voice AI
Automatic Speech Recognition
is made possible with the help
of Voice AI technology to
automatically transcribe voice
recordings and recognize the
emotional state of a person
through voice tonality and
sentiment analysis.

5. The 3As of Emotion AI
Accuracy Agility Actionability
Rather than relying on stated
responses alone, Emotion AI
empowers researchers to accurately
measure emotional and
subconscious responses. Powered
by AI and Machine Learning
algorithms, technologies like Facial
Coding and Eye Tracking can
accurately predict verbal and non-
verbal cues with more than 90%
accuracy.
Computer vision based Facial Coding
and Eye Tracking technologies make it
easy for researchers to measure
respondent emotions within 48 hours
i.e., 4X faster than traditional
methods. Also, since the end-to-end
process is DIY and online, there are
no logistical difficulties like travel,
respondent availability and associated
costs to delay research.
The emotional and subconscious
responses are quantified and readily
available as downloadable data on the
Emotion AI platform. Decision makers
can immediately act upon these
Emotion Insights without spending
additional time on drawing inferences
and conclusions. The actionable
nature of Emotion Insights helps
brands achieve up to 34% spend ROI.

6. Benefits of Emotion AI
Faster than traditional
consumer research, reducing
TAT of accurate
consumer insights by 4X
Accurate and
quantifiable human
experience Insights
with tangible ROI
Unbiased and real
consumer insights that go
beyond stated or
inarticulate responses
Online qual and quant
research with the help of
computer vision and online
tester panel
Rational
Drivers
Emotional
Drivers
Engaging
Experiences
+
Spend ROI &
Customer Loyalty
&

7. Emotion AI in Media Research
Video/Advertising Long Form Content Print/Static Media
TV Commercials
Bumper Ads
Contextual Ads
Digital/Social Media Ads
YouTube Videos
Trailers/Promos
Music Videos
TV Show Episodes
Pilot Episodes
Broadcasting
Newspaper Ads
Billboard Ads
Digital/Social Media Ads
Posters
Direct Mail

8. Media Insights – Facial Coding
Scene/Segment
Level Insights
Sec by Sec Emotion Insights Overall Emotional Engagement

9. Media Insights – Eye Tracking
Heatmaps Transparency Plots Gaze Plots
AOI Charts

10. Client: Multinational American Mass Media and Entertainment Conglomerate
Business Need:
• Gather insights to take Marketing Decisions to launch their new TV show
• Measure audience responses to a 30s and another 85s launch promo
Study Objective:
• Analysis of the 30s launch promo and the 85s launch promo
• Benchmark efficacy of both the promos against competition promos
• Identify creative led scope of improvements
• Determine efficacy of creative elements
Audience: A monadic panel of 150 people | Aged 15-35 | Urban
Technologies Used: Eye Tracking and Brainwave Mapping
Impact:
• 34% lift in Marketing Spend ROI. By releasing the 30s launch promo which
was more visually appealing and emotionally engaging to the audience.
• 400% increase in Creative Efficacy. By identifying areas of creative led
improvements and evaluating placement efficacy of creative elements.
Media Insights Case Study

11. Emotion AI in Shopper Research
In-store Shopper Research Online Shopper Research
Virtual Shopping
Virtual Store Walkthrough
Package Testing
Planogram/Shelf Testing
Point of Sale Testing
Recorded User Session
Path to Purchase
Product Placement
Ad/Banner Placement
Product Listing Analysis

12. Shopper Insights – Facial Coding
Purchase Intent
Sec by Sec Retail Experience
Overall Emotional Engagement

13. Shopper Insights- Eye Tracking
Planogram Transparency Plots
Packaging Heatmaps
Packaging AOI
Planogram Heatmaps

14. In-Store Shopper Case Study
Client: Multinational American Brand of Men’s Personal Care Products
Challenges:
• Crowded men’s beauty market with incumbent players
• Accurate understanding of its new consumer type
Business Need:
• Analyze pack design efficacy to launch its new product lines
• Understand SEA market which is dominated by local players
Audience: Men aged 20 – 39 years, new or inactive user
Technologies Used: Facial Coding and Eye Tracking
Impact:
• 42% increase in Package Pick-up Rate. By identifying the most
efficient pack design, client products stood out in a new market.
• 23% increase in In-store Conversions. Understanding the
consumer type and optimizing packaging translated in retail sales.

15. Online Shopper Insights
Placement/Listing Insights
Path to Purchase Insights
Ad/Banner Insights

16. Online Shopper Case Study
Client: Multinational British Consumer Goods Company
Challenges:
• Moving from retail to online stores as a response to Covid-19
• Optimizing online presence for its Household Cleaning products
Business Need:
• Lift Discoverability & Consideration through product placement and listing
• Evaluate product placement efficacy and optimize online buyer journey
Audience: Men & women aged 21-40 | 50 urban and 50 sub-urban
Technologies Used: Facial Coding, Eye Tracking and Screen Recording
Impact:
• 16% increase in Online Conversions. By identifying the most optimal
position to place their products for easy discovery and consideration.
• 63% savings in Customer Acquisition Costs. By understanding online
consumer behavior towards cleaning products to refine ads/marketing.

17. Emotion Insights – Entropik Approach
Facial Coding Metrics
Attention = Visual Appeal (0-100)
Engagement = Comprehension (0-100)
+ve Emotions = Happiness, Surprise
-ve Emotions = Anger, Fear, Sadness, Contempt
Eye Tracking Metrics
Time to Discover = Area of Interest (AOI)
Earned Attention = Focus Levels on AOI
% People Watched = Out of Total Testers
Exposure Time Duration = Time Spent on AOI
56%
Benchmarking Data By Creative Type
15%
26%
2% 1%
Videos 50,000+
Banners 8,000+
Images 14,000+
Websites 1,200+
Mock-ups 800+
Mobile Apps 110+
Benchmarking By Comparison

18. Emotion AI enabled online Consumer Insights Platform to deliver real, quick & meaningful insights

19. Have Questions?
Please drop an email at info@entropiktech.com
www.entropiktech.com