This session was delivered on Apr 20th 2020 at the Rutgers Business School. This was a Guest Lecture for the "AI in Marketing" class. Session Brief: Today, customers interact with brands continuously, either intentionally or indirectly. They do so on a number of channels, and leave a variety of digital footprints. Unfortunately, enterprises miss out on this opportunity to understand and connect with the customers. This session will show how brands can leverage data and technology to understand their customers. Brands can harvest both structured and unstructured data from diverse channels. With the help of data analytics, they can build an integrated view of the customer. By overlaying the content with context, they can map every conversation on the customer journey. By stitching all these insights together, brands can use storytelling to help drive the right business decisions.

1. 1
HOW BRANDS CAN USE AI
FOR ACTIONABLE
CUSTOMER INTELLIGENCE
Ganes KesariGuest Lecture, ”AI in Marketing”

2. 2
STORYTELLANALYZELISTEN UNDERSTAND

3. 3
INTRODUCTION
Ganes Kesari
Co-founder & Head of Analytics
“Simplify Data Science for all”100+ Clients
Insights as Stories
@kesaritweets Help start, apply and adopt Data Science

4. 4
LET’S START WITH A
STORY!
Image by svgsilh

5. 5
CUSTOMER EXPERIENCE IS
BROKEN TODAY
MYOPIC TRANSACTIONAL UNSCIENTIFICSILOED INDIFFERENT

6. 6
ERRORS USING INADEQUATE DATA
ARE MUCH LESS THAN USING NO
DATA AT ALL
C H A R L E S B A B B A G E
“

7. 7
4 STEPS
TO
TRANSFORM YOUR
CUSTOMER EXPERIENCE

8. 8
L I S T E N
E V E R Y W H E R E
1
4 STEPS TO TRANSFORM YOUR CX

9. 9

10. 10
L O O K O U T F O R 3
T Y P E S O F F E E D B A C K
Review websites, social
media
INDIRECT
Website clickstream
data, contact center
INFERRED
Voice of Customer
surveys, Interviews
DIRECT
Source: Gartner Market Guide for Voice-of-the-Customer Solutions

11. 11
LINKGOOGLE SUGGEST: INDIA’S RELIGIONS

12. 12
GOOGLE SUGGEST: US’ RELIGIONS LINK

13. 13
WITH COVID-19, THE PRIORITIES HAVE CHANGED

14. 14Source: Bain & Co - Ample Data, often free, can predict consumer behaviour through the Covid-19 crisis
PUBLIC DATA CAN HELP YOU UNDERSTAND CONSUMERS
https://trends.google.com/trends/

15. 15
R E M E M B E R ,
D A T A B E F O R E A I !

16. 16
A N A L Y Z E
T H E
C U S T O M E R
S I G N A L S
2
4 STEPS TO TRANSFORM YOUR CX

17. 17
WHAT DO WE KNOW ABOUT YOU BASED
ON YOUR FACEBOOK LIKES?
Source: University of Cambridge

18. 18
AI CAN UNDERSTAND PICTURES
Source: Gramener crowd counting solution

19. 19
AI CAN HELP WITH SOCIAL DISTANCING!
Source: LandingAI – AI to help monitor social distancing in the work place

20. 20
…BUT, IT’S NOT REALLY THAT SMART!
Gifs from Giphy: Gif1, Gif2 AI Adversarial examples: Natural - ImageNet on Synced Review; Attack – MIT CSAI on Design News

21. 21
T H E K E Y I S A S K I N G T H E
R I G H T Q U E S T I O N S
Approach to data Benefits
AI / ML models with recommendations What actions will help me convert my
detractors into promoters?
Simple ML models What will be my promoter score
next quarter?
Statistics What led to lower
satisfaction in EMEA?
Simple summaries
Did I improve on
customer satisfaction?

22. 22
U N D E R S T A N D
Y O U R
C U S T O M E R S
3
4 STEPS TO TRANSFORM YOUR CX

23. 23
S A M P L E T H I S C U S T O M E R
F E E D B A C K F R O M A V O C S U R V E Y
“I loved the product features and super-quick onboarding, but the great experience
did not continue while using your product. Your support teams have been helpful,
but I’m not sure whether I’ll buy again.”

24. 24
T A G T H E C O N T E N T T O
J O U R N E Y S T E P S
Identify Customize Use Product
Deal with IssuesReorder
“I loved the product features and super-quick
onboarding, but the great experience did not
continue while using your product. Your
support teams have been helpful, but I’m not
sure whether I’ll buy again.”
“I loved the product features and super-quick onboarding, but the great experience
did not continue while using your product. Your support teams have been helpful,
but I’m not sure whether I’ll buy again.”

25. 25
L E T ’ S N O W A S K T H E
Q U E S T I O N S
Approach to data Benefits
AI / ML models with recommendations What actions will help me convert my
detractors into promoters?
Simple ML models What will be my promoter score
next quarter?
Statistics What led to lower
satisfaction in EMEA?
Simple summaries
Did I improve on
customer satisfaction?

26. 26
INTEGRATE THE FEEDBACK
SIGNALS
“Your summer collection
didn’t interest me”
Store Survey
“Drop in market share by
2.5% last month”
Market Report
“Disappointed with Brand
‘A’. Anyone still buying?”
Social Media
“Brand ‘B’ has more
‘vibrant’ colors than you”
Competitive Survey
Our summer collection didn’t work. We los 2.5% market share
with a projected revenue dip. We must improve our product.
“
“Predicted dip in Monthly
revenue by 11%”
Financials

27. 27
P R E S E N T
I N S I G H T S A S
S T O R I E S
4
4 STEPS TO TRANSFORM YOUR CX

28. 28
O V E R 5 0 % O F D A T A S C I E N C E
P R O J E C T S N E V E R G E T
D E P L O Y E D .
B A D S T O R Y T E L L I N G I S A K E Y
R E A S O N F O R T H I S F A I L U R E .
G A R T N E R
Gartner: “How to use Storytelling to sell your Data science projects”, Apr 25 ‘19
“

29. 29Gartner: “How to use Storytelling to sell your Data science projects”, Apr 25 ‘19

30. 30
VISUALIZATION: CONVERT NUMBERS TO
PICTURES
Gartner: “How to use Storytelling to sell your Data science projects”, Apr 25 ‘19

31. 31
ADD CONTEXT & NARRATIVE TO BUILD THE STORY
Sales grew 40% in 2018, despite competitive product launches
Gartner: “How to use Storytelling to sell your Data science projects”, Apr 25 ‘19

32. 32
STORYTELLING CHANGES IN
CUSTOMER SATISFACTION
Hig
h
ImpactonSatisfaction
Low
High impact CX
Negative Positive
Low HighCustomer Sentiment
Q2’20
Identify
BuyQ2’20
Service
Q2’20
Reorder
Q2’20
Use
Q2’20
Customize
Improve on ‘Service’
Maintain ‘Identify’ & ‘Buy’
‘Customize’ is less importantQ2’20

33. 33
A T E C H M A J O R T A P S
I N T O C U S T O M E R
I N T E L L I G E N C E
C A S E S T U D Y
A leading US-based technology leader needed help with
setting up a customer experience practice driven by AI
Source: Gramener case study

34. 34
Business Challenge
• A leading computer
technology company
wanted to improve customer
satisfaction and revenue.
• The intent was to
understand what drove the
CSAT scores and
engagement levels
Approach
• Gramener collected data
from multiple sources. The
questions asked of the data
determined the text
analytics and ML techniques.
• By contextualizing insights,
customer intent was
identified. The findings were
presented as data stories to
drive actions and behavior.
Benefits
• Gramener identified the top
most themes, topics and
factors that drive customer
ratings and feedback.
• Recommendations were
presented as automated,
visual dashboards.
• The solution delivered $50M
incremental revenue from
satisfied customers
INTEGRATED VOC
ANALYTICS
Source: Gramener case study

35. 35
INTEGRATED VOC ANALYTICS – DETAILED METHODOLOGY
01
02
03 04
05
06
Sentiment Analysis
Identify aspect-based sentiment for
the feedback
Theme Mapping
Group the the keywords into relevant
themes
Impact Analysis
Find how the themes & sentiments
drive the customer satisfaction
Topic Modeling
Identify the top themes of interest for
customers
Ranks estimation
Rank the themes based on degree
of impact to recommend actions
Tokenization
Tokenize the customer comments
into keywords
Source: Gramener case study

36. 36
R E C A P : 4 S T E P S T O
T R A N S F O R M Y O U R C X
• Direct, Indirect, Inferred
• Data before AI
LISTEN
1
2 • Analyze all data types
• Ask the right questions
ANALYZE
4
STORY-TELL
• Visualize the insights
• Drive actions with stories
3
UNDERSTAND
• Understand their Journey
• Roll-up for the headline

37. 37
T H A N K Y O U !
G E T I N T O U C H
@kesaritweetsgramener.com @kesari