Presentation given at the 2012 IASA Annual Conference on the use of social data in the insurance industry. Snapshot survey results and review of case examples.

1. June 2012 1

2. Mining Social Data to make informed
Risk Evaluations
Session 673 Tuesday June 5, 2012 3:30 PM
June 2012 2

3. Survey Results
(Thank you to those who participated)
Does your company have a formal Social Media strategy?
Does your company have a formal Social Media use policy for employees?
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4. Survey Results
(Thank you to those who participated)
How well does your company currently use each of the following?
0 = Not at All, 1 = Fairly Well, 2 = Well, 3 = Very well
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5. Survey Results
What is the main purpose for your company’s website?
What is the main purpose for your company’s use of LinkedIn?
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6. Survey Results
What is the main purpose for your company’s use of YouTube?
Other/Na
Servicing
Leads
Relationships
Advertising
0% 20% 40% 60% 80%
100% of respondents answered “Other/Na” for Pinterest.
What is the main purpose for your company’s use of Blogs?
Other/Na
Servicing
Leads
Relationships
Advertising
June 2012 0% 20% 40% 60% 80% 6

7. Survey Results
What is the main purpose for your company’s Facebook page?
Other/Na
Servicing
Leads
Relationships
Advertising
0% 20% 40% 60% 80%
What is the main purpose for your company’s use of Twitter?
Other/Na
Servicing
Leads
Relationships
Advertising
June 2012 0% 20% 40% 60% 80% 7

8. Survey Results
How are you capturing your online chats or posts?
NOTE: Negative posts / blogs / tweets can be considered to be
“complaints” under insurance department regulations and could
require the same logging and reporting as if written or called in.
June 2012 8

9. Survey Results
Support / Staff availability hours for online presence:
No Presence
20%
Multiple Business
40% Time Zones
20% Local Business Time
Zone
24 Hours
20%
The average number of staff supporting social media strategy
was 1 after discounting an answer of 160 that was probably
total service staff.
June 2012 9

10. Survey Results
Capturing analytics on social media site(s) usage:
Claims
Customer Service
Underwriting
Marketing
0% 20% 40% 60% 80% 100%
Capturing demographics about users of social media site(s):
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11. Survey Results
Tracking ROI for the use of Social Media:
Claims
Customer Service
Underwriting
Marketing
0% 20% 40% 60% 80% 100%
Capturing customer information from social media site(s):
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12. Survey Results
Monitoring company reputation across the internet:
Claims
Customer Service
Underwriting
Marketing
0% 20% 40% 60% 80% 100%
Which departments in your company use information
collected from social media?
Claims
Customer Service
Underwriting
Marketing
0% 20% 40% 60% 80% 100%
June 2012 12

13. Survey Results
How does your company use mobile apps:
Claims
Customer Service
Underwriting
Marketing
0% 20% 40% 60% 80% 100%
100% of Respondents stated they attempt to collect email
addresses from customers.
27% of Respondents stated they attempt to collect Facebook
account name.
June 2012 13

14. LIMRA’s Research on Social Media use
by Insurance Carriers
Currently On or Plan To Be EXPAND BRAND
Tumblr 15% AWARENESS 56%
0%
Companies Expanding Presence
Google+ 45% 70% 65%
0% 59%
60% 56%
YouTube 88% 50% 47%
65% 43%
40%
Twitter 81% 30%
54%
20%
LinkedIn 95% 10%
65%
0%
Facebook 98%
85%
0% 50% 100% 150%
June 2012
2011 2010 14
LIMRA 2012 Life Insurance Conference

15. Social Data (not Social Media and not
Social Networks) comes from all over
Acxiom
Dun & Bradstreet
ISO
LexisNexis
Merckle
MIB
Milliman
Neustar
Polk
Riskmeter
Grocery store rewards programs
Frequent guest and Frequent Flyer programs
Credit Card purchasing
June 2012 15
Online purchasing – books, movies

16. Social Data is constantly evolving
Social Data is Being Added in Immense Volumes daily:
§ 66% of adults and 75% of teens are content creators on the internet
§ 66% of internet users are social networking site users
§ 55% share photos
§ 37% contribute rankings and ratings Social data is more than the
§ 33% create content tags data, it is the data and the
§ 30% share personal creations relationships – that’s what
§ 26% post comments on sites and blogs makes it “social” data, why it
§ 15% have personal websites is complex and unstructured,
§ 15% are content re-mixers and how it differs from simple
§ 14% are bloggers data.
§ 13% use Twitter
§ 6% use location services–9% allow location awareness and 23% use
maps etc. Source: Pew Research
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17. Social Data Quadrant Map for Use
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18. Everyone is getting into Social Data
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19. Social Intelligence –
Insurance Solutions for Social Data
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20. Risk Areas Key:
Prospect, Underwriting and Claims
Recent research indicates that 24
percent of insurance companies
are evaluating using social data
in claims and 26 percent are
evaluating it for underwriting.
Source : SMA
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21. Case Study: Prospect Scoring
Scoring of prospects based on conversion and
Psycho- value, marketing strategy developed to match
graphic
Data
Potential Value
Text High value, High value, High value,
High
Low Medium High
Data conversion, conversion, conversion,
2nd Priority Top Priority Top priority
Predictive Potential
Medium
Good value, Good value, Good value,
Web Analysis Future Low Medium High
Log and Value of conversion, conversion, conversion,
Data Low Priority 2nd Priority Top Priority
Modeling Customer
Low value, Low value, Low value,
Low Medium High
Low
Survey conversion, conversion, conversion,
Data Low Priority Low Priority 2nd Priority
Purchased Low Medium High
Data Propensity to Convert
June 2012 21

22. The Addition of Social Data
to Score Prospects
Hobbies and Extreme Sports
Relationships
Psycho- Activities and Calendar
graphic
Travel Comments
Data
Home Repair / Construction Updates
Personal Family Updates
Web
Log GPS Coordinates of daily trips
Data Tweets on political and organizational affiliations
Blog comments – what blob as well as content
Religious and community affiliations
June 2012 22

23. Case Study: Target Retention Strategies
Step 1: Determine Life time Value
Post Purchase
Activity – Future
Increases in Value
predictive
value over time
as behavioral
patterns
develop –
Integrate Predictive
Social data Analysis
here Customer behavior
shifts focus from
Time of Purchase current to future value
Demographics -
Loses predictive Current
value over time Value
as relevance is
superseded by
inforce behaviors
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24. Case Study: Target Retention Strategies
Step 2: Predict Potential Lapse
Source of Business influences
lapse tendencies based on Web
channel behaviors Log
Data
Predictive
Analysis –
Model
Transaction behavior Channel and
influences lapse tendencies Consumer Supplement with
based on consumer behaviors Behaviors
Social data
June 2012 24

25. Case Study: Target Retention Strategies
Step 3: Develop Strategy Matrix
Match effort to risk
and value –
• High value low
risk gets medium
effort, save money
on retaining low
risk customers
• Low value
customers get low
cost efforts across
the board
• Targeted high
efforts on high
value / high risk
June 2012 25

26. Case Study:
Life Underwriting via App + Social Data
Second child born last year Actively
High investment risk tolerance pursue for
Lived in home 2 years
Owns home issuance of a
Commuting distance 1 mile preferred
Reads Design and Travel Magazines policy without
Urban single cluster requiring
Premium bank card fluids or
Good financial indicators medical
Active lifestyle: Run, Bike, Tennis, records.
Aerobics
Health food choices Use strong
Little to no television consumption retention
tactics.
Life UW using a GLM predictive model to assess risk:
§ Use info on app plus social data, No fluids or files
§ Integrate 3rd party publicly available information.
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27. Case Study:
Life Underwriting via App + Social Data
Current residence four years
Lived in same hometown 15 years
Do not send
Currently renting offers. Do not
Commuting distance 45 miles pursue
Works as administrative assistant aggressive
Divorced with no children retention
Foreclosure/bankruptcy indicators
strategies. If
Avid book reader
Fast food purchaser applies,
Purchases diet, weight loss equipment pursue
Walks for health additional
High television consumption medical
Low regional economic growth records and
Light wine drinker
tests.
In a test over 30,000 applicants, behavioral and lifestyle factors
provided 37% of the risk assessment influence and performed
as well as additional, more intrusive medical tests and fluids.
June 2012 27

28. Types of third party marketing data
June 2012 Deloitte Predictive Model for Life 28

29. Life Underwriting Savings:
Using 3rd Party Data versus Medical Data
Deloitte Predictive Model for Life
June 2012 29

30. Workers Comp already has a track
record of using Social Data
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31. Claims Analytics:
Fraud Red Flag Dashboard
June 2012 Courtesy of Attensity 31

32. Social Analytics:
Customer Engagement Dashboard
§ Automatically monitor
social conversations
§ Filter out irrelevant
posts
§ Analyze posts to
extract key insights
§ Engage customers
with proactive
outreach
§ Improve the
experience customers
are having on the site
§ Improve brand image
and emphasize the
legitimacy of business
June 2012 Courtesy of Attensity 32

33. Social Analytics:
Conversation Sentiment Tracking
June 2012 Courtesy of Attensity 33

34. Social Analytics:
Website Sentiment by LOB
June 2012 Courtesy of Attensity 34

35. Social Analytics:
Overall Sentiment Ratings Dashboard
June 2012 Courtesy of Attensity 35

36. Social Analytics:
Competitive Sentiment Dashboard
June 2012 Courtesy of Attensity 36

37. Contact Information
Robert E. Nolan Company
Management Consultants
www.renolan.com
Steven M. Callahan, CMC®
Practice Director
www.linkedin.com/in/stevenmcallahan
June 2012 37