Text Analytics can be fun, useful and distracting. It is not just about the tools, but about how to use tools to drive business outcome. In this deck, you will get a sneak peak into some uses of text analytics in Life Insurance Transformation

1. Life Insurance
Transformation
(Using Text Analytics)
Vishwa Kolla
Head of Advanced Analytics,
John Hancock Insurance

2. 2
 Advanced
Analytics CoE,
Maturity Model
 Customer Analytics
(entire value chain)
 Machine Learning
 Scoring Engine
 Optimization
 Simulations
 Forecasting & Time
Series
• 16+ Years
• John Hancock Insurance
• Deloitte Consulting (Industries – Insurance,
Retail, Financial, Technology, Telecom,
Healthcare, Data)
• IBM
• Sun Microsystems
Business
Analytical
(Math, Stats)
Technical
(Programming)
Expertise
Experience
Vishwa Kolla
Head of Advanced Analytics
John Hancock Insurance, Boston
MBA Carnegie Mellon University
MS University of Denver
BS BITS Pilani, India

3. 3
Where How
So
What?
Say we want to analyze
what are folks about in
various conferences …

4. 4
Where How
So
What?
Unigram
Unigram analysis is a
stat. Be cautious – can
lead to a wrong
conclusion if close
attention is not paid

5. 5
Where How
So
What?
Unigram Bigram
A bi-gram analysis
seems more like in the
right direction

6. 6
Where How
So
What?
Unigram Trigram and aboveBigram
A tri-gram analysis
seems more like an
over-kill for this
particular problem.

7. 7
Prospect Acquire Nurture
Always think simple in
3s.
When thinking about
adding value, we can
broadly think of 3 areas.

8. 8
Prospect Acquire Nurture
• Document Clustering /
Classification
• Word Cloud
• Concept Extraction
• NLP
Social Listening When looking to see
who to target, social
listening seems to be a
good direction

9. 9
Prospect Acquire Nurture
• Info Retrieval / OCR
• Document Clustering /
Classification
• Concept Extraction
• NLP
• Web Mining
Information Organization When looking to
improve operations,
information extraction
and structuring is more
important

10. 10
Prospect Acquire Nurture
• Document Clustering /
Classification
• Concept Extraction
• NLP
• Web Mining
Information Understanding
When looking to nurture
existing relationships,
text analytics of
interactions help a ton

11. 11
Where How So
What?
Benchmark
#PASANDIEGO #ODSC+TDWI+MLSUBMIT+DS AUSTIN
An insight is more
powerful when we
benchmark

12. 12
#PASANDIEGO
#DATA SCIENCE CON AUSTIN
#ODSCEAST
#TDWI
#Machine Learning Summit
Where How So
What?
Benchmark 1 vs. all and
1 vs. other

13. 13
https://www.youtube.com/watch?feature=player_embedded&v=XswX1TSQwfQ
So many stories. IoT led
disruption. Shared
Value. Business Model
Disruption. Breathe new
life into Life Insurance

14. 14
• Where should I talk?
• What topics should I
talk about?
• Who should I talk to?
Word
Cloud
Classification Alignment
Inventory EDA Relevance
Monitor
Influencer
Pool
Relevance Partner

15. 15
What are
people talking
about?
Why do we have a
spike in positive
tweets?
Has our NPS
increased since
we launched ? NPS
18.52%

16. • How do I extract
value from Dark
data?
• What APSes are
similar?
• What is the insight?
• Is there a misrep?
• What does my
customer want?
• Is my customer a
Net Promoter?
• How can I nudge?

17. 17
• How do I extract
value from Dark
data?
• What APSes are
similar?
• Is there a misrep?
• What does my
customer want?
• Is my customer a
Net Promoter?
• How can I nudge?

18. 18
NLP
Plotly
Networx
Dplyr TM
LDA
Zygnet
Caret
ReshapeSentR
Shiny R
App

19. Shiny R
App
Web
scrapping
Sentiment
Analysis
Word
clouds
Topic
Modelling
Word
Frequency

20. 20
Where How
So
What?
What is being talked
about in each of the
industries

21. Increased CSAT
21
Increased NPS
Reduced Software Spending
Reduced Services Spending
Always quantify value

22. 22
Means to an End
Can be very distracting
Automate Mundane
Tasks into Packages
Text Analytics = more
than R&D
Use tools to your
advantage

23. 23
Appendix

24. 24
vs
An example of
distraction

25. 25
Jai Singh
Sr. Data Science Consultant
Agota Sakalauskaite
Data Science Co-op
Always give credit
where credit is due