This deck was used in the guest lecture delivered by Ganes at the Rutgers Business School, New Jersey, on July 7th, 2021. These slides were supplemented with a live business case that was used for the class discussion.
2. 2
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
8. 8
G O A L S A U D I E N C E A C T I O N S
Goals: Why am I
creating this?
Who are my users and
what do they want?
What actions should I
enable for them?
9. “Is purpose defined in sufficient detail?”
“ Show the manufacturing delays. ”
“ Show the manufacturing delays:
Ø over the last 12 months,
Ø across different products,
Ø by stages of production
Ø and highlight the bottom 10%. ”
vs
10. “Is my coverage complete?”
What’s excluded...
…is often more important than
what’s included
12. 12
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
19. 19
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.”
20. 20
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 Issues
Reorder
“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.”
21. 21
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?
22. 22
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
24. 24
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
“
25. 25
Gartner: “How to use Storytelling to sell your Data science projects”, Apr 25 ‘19
27. 27
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
28. 28
STORYTELLING CHANGES IN
CUSTOMER SATISFACTION
Hig
h
Impact
on
Satisfaction
Low
High impact CX
Negative Positive
Low High
Customer Sentiment
Q2’20
Identify
Buy
Q2’20
Service
Q2’20
Reorder
Q2’20
Use
Q2’20
Customize
Improve on ‘Service’
Maintain ‘Identify’ & ‘Buy’
‘Customize’ is less important
Q2’20
29. 29
RECAP: 4 STEPS TO ACTIONABLE
CUSTOMER INTELLIGENCE
• Define customer persona
• Ask the right questions
UNDERSTAND
1
2 • Direct, Indirect, Inferred
• Analyze all data types
COLLECT
4
STORY-TELL
• Visualize the insights
• Drive actions with stories
3
ANALYZE
• Understand their Journey
• Roll-up for the headline