Global

Big

Data

Confe

rence,

Santa

Clara

March

7-9

Life Insurance

Transformation (Using Text Analytics)

Vishwa Kolla Head of Advanced Analytics,

John Hancock Insurance

Global

Big

Data

Confe

rence,

Santa

Clara

March

7-9

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

Where How So

What?

Say we want to analyze

what are folks about in

various conferences …

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

Where How So

What?

Unigram Bigram

A bi-gram analysis

seems more like in the

right direction

6

Where How So

What?

Unigram Trigram and above Bigram

A tri-gram analysis

seems more like an

over-kill for this

particular problem.

Global

Big

Data

Confe

rence,

Santa

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March

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7

Prospect Acquire Nurture

Always think simple in

3s.

When thinking about

adding value, we can

broadly think of 3 areas.

Global

Big

Data

Confe

rence,

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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

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Big

Data

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rence,

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March

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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

Global

Big

Data

Confe

rence,

Santa

Clara

March

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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

Where How So

What?

Benchmark

#PASANDIEGO #ODSC+TDWI+MLSUBMIT+DS AUSTIN

An insight is more

powerful when we

benchmark

12

#PASANDIEGO

#DATA SCIENCE CON AUSTIN

#ODSCEAST

#TDWI

#Machine Learning Summit

Where How So

What?

Benchmark 1 vs. all and

1 vs. other

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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

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Data

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rence,

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March

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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

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Data

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rence,

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What are

people talking

about?

Why do we have a

spike in positive

tweets?

Has our NPS

increased since

we launched ? NPS

18.52%

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Big

Data

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rence,

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7-9

• 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?

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Data

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rence,

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• 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

NLP

Plotly

Networx

Dplyr TM

LDA

Zygnet

Caret

Reshape SentR

Shiny R

App

Shiny R

App

Web scrapping

Sentiment Analysis

Word clouds

Topic Modelling

Word Frequency

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rence,

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19

20

Where How So

What?

What is being talked

about in each of the

industries

Increased CSAT

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rence,

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21

Increased NPS

Reduced Software Spending

Reduced Services Spending

Always quantify value

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rence,

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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

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23

Appendix

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24

vs

An example of

distraction

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25

Jai Singh Sr. Data Science Consultant

Agota Sakalauskaite Data Science Co-op

Always give credit

where credit is due