This document summarizes key points from a presentation on applied analytics. It discusses how analytics can provide business insights, predictive analysis, and more accurate decision-making. It then presents several case studies, including using analytics to score prospects, manage agents, improve pricing with loss-based models, develop retention strategies, and detect fraud. It concludes with guidelines for implementing analytics, emphasizing having an executive roadmap, using comprehensive data, and engaging talented staff.

1. June 2012 1

2. Applied Analytics: Converting
Mountains of Data into Nuggets of Gold
Session 408 Tuesday June 5, 2012 11 AM

3. Session Objectives
Learn how some insurers have found creative new ways to
make use of analytics, with the goal of turning information into
management action.
Hear one carrier’s analytics case study and learn how to use
an analytics roadmap to effectively assess and capture the
benefits of analytics company-wide.
After attending this session, audience members will be able to:
§ Describe a roadmap for applying analytics
§ Evaluate the suitability of analytics for various functional areas
§ Describe how one carrier has used analytics and related technologies to
improve business performance
June 2012 3

4. Top Line Benefits of Analytics
Business analytics enable organizations to be able to:
§ Gain deeper, more relevant business insights to inform decisions
§ Bring predictive analysis and regression modeling to entire organization
§ Use analytics to identify and determine options for industry challenges
§ Effectively and proactively manage risks
§ Strengthen data governance at each level of the organization
§ Reduce costs through more accurate, data-driven decision-making
§ Use analytic capabilities and outcomes for change management efforts
§ Create a culture that thrives on fact-based decisions versus “gut”
Impact on decision-making is one of the key reasons……
June 2012 4

5. Top Reasons for Adopting Analytics
June 2012 5

6. Survey Says….AKA “gut feel”
Future Projections (Predictive) 2% 41% 24% 33%
Historical Data 2% 25% 36% 36%
Collaborative Consensus 7% 28% 43% 22%
Group Dynamics 2% 34% 39% 24% 1%
82% 60%
Experience 7% 32% 55% 5%
Intuition 5% 31% 38% 25%
0% 20% 40% 60% 80% 100%
Not at all Some Typical/Common Almost Always Exclusively
June 2012 Robert E Nolan Company Executive Survey, 2011 6

7. Analytical Companies Perform Better
27% year over year growth
A difference of
26% in year on
year growth
1% year over year growth
June 2012 7

8. Yet Companies Struggle to Implement
Most frequent reasons companies struggle with analytic initiatives:
• Too much management, not enough leadership
• Limited support and buy-in at multiple levels within the organization
• No guiding purpose or vision for people to rally around
• Overemphasis on technology implementation/success criteria
• Business benefits are too fuzzy to articulate and communicate clearly
• No consistent communication or messaging to stakeholders
• Poor identification of stakeholders and influencing factors
• Compensation structures and incentives not aligned
Survey Comments on Barriers to Growth in Use of Analytics
“Resistance comes from most experienced, those requiring 100% accuracy”
“Access to critical data that is not captured in the system but is on paper”
“Getting away from tribalism, managing by anecdote and subjective decisions”
“Availability of resources and the money necessary to do it right”
“Data is spread all over and difficult to integrate or consolidate”
“Privacy will become a major issue as external data sources drive decisions”
June 2012 Robert E Nolan Company Executive Survey, 2011 8

9. And Opinions Vary Greatly
(2011 Nolan Analytics Survey Comments)
“The importance placed on analytics will grow, however there will be a
disproportionate reliance placed on results, until management learns that
garbage in/garbage out continues to cast its shadow.“
“It really doesn’t matter as most data currently produced comprises the
basis for most uses necessary. Advanced techniques do not therefore
produce ‘advanced’ data - the numbers are the numbers no matter how
produced. Indeed, give me a room full of ladies in green eyeshades
and Marchant calculators and maybe a punch card reader or two and I
could be perfectly happy with managing the business, no matter how
complex.“
“Those companies that do not embrace technology and analytics will
be left behind in the dust of those companies that do. “

10. Retrospective versus Predictive
June 2012 10

11. Case Study A: 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 11

12. Case Study B: Agency Management
60% of customers would switch carriers if advised to do so by their agent.
(Source: JD Power & Associates)
33%+ of agents are likely to change insurance carriers.
(Source: National Underwriter and Deloitte)
Insurers that better manage their agents achieve competitive advantage.
§ New customers have high acquisition expense, retaining one is more profitable.
§ New agents have high acquisition expenses and pose a greater risk of inferior
retention rates, resulting in lower profits.
§ Monitoring effectiveness of agents provide early warning that an agent may be
about to leave, triggering action and market differentiation.
§ Predictive scorecards tie traditional features like traffic lights and speedometers to
powerful analytics.
§ Dashboard visuals provided at-a-glance access to the current status of new KPIs, with
automatic alerts for underperforming objectives and strategies.
Case Study B implemented an agency dashboard based on new KPI’s that
were modeled with a predictive analytics tool.
June 2012 12

13. Case Study B: Agency Management
Shift away from traditional sales performance metrics like Premium Revenue.
New KPI: Agent Profitability net of cost to serve –calls, e-mails, supplies, etc.
June 2012 13

14. Case Study C: Loss based Pricing
Territory average loss ratios
generate prices that are too high for
some and too low for others.
$812.50
Detailed risk analytics
$438.00
generate more
accurate loss cost
estimates by discrete
$1187.00 segments of business.
Result: More equitable and competitive risk adjusted pricing.
ISO Price Analyzer Tool used for graphics
June 2012 14

15. Case Study D: Retention Strategies
Step 1: Determine Life time Value
Post Purchase
Activity –
Increases in Future
predictive value Value
over time as
behavioral
patterns
develop
Predictive
Analysis
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
June 2012 15

16. Case Study D: Retention Strategies
Step 2: Predict Potential Lapse
Source of Business influences
lapse tendencies based on
channel behaviors
Predictive
Analysis –
Model
Transaction behavior Channel and
influences lapse tendencies Consumer
based on consumer behaviors Behaviors
June 2012 16

17. Case Study D: 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 17

18. Case Study E: Claims Fraud
About 10% of all insurance claims are fraudulent.
Annual fraud losses for P&C industry total $30B in US alone.
§ Need to detect unknown patterns of financial fraud.
§ Keep track of new fraud schemes.
§ Unsure exactly what to look for.
Rules: Captures fraud on known patterns previously used
Ex: Two claims in different time zones within short window
Anomaly Detection: Detect unknown patterns (ind & aggr)
Ex: Statistics (mean, std dev, uni/multivariates, regression)
Advanced Analytics: Detect complex patterns
Ex: Knowledge discovery, data mining, predictive assessment
Social Network Analytics: Determine associative links
Ex: Knowledge discovery via associative link analysis (entity map)
June 2012 SAS Institute
18

19. Automated Fraud Detection Points
Prioritized investigation SIU Re-estimate duration
Focus on organized fraud Reassess loss reserving
Minimize claim padding Prioritize resources
Fraudulent rescoring
Reduce false positives Review litigation propensity
Fraud Referrals Fraud Referrals
FNOL Assign Evaluate Update Close
Claim Claim Claim Claim
Fast Track Claim
Cross-sell options for
satisfied customer
Negotiate / Customer retention program
Predict duration Initiate Services
Forecast loss reserves
Optimize fast track claims
Prioritize resources Identify salvage and subrogation
Fraudulent scoring Initiate opportunities
Litigation propensity Indicate deviations from similar
Settlement claims
June 2012 Reports on claims overrides19

20. Other Brief Claims Examples
Optimized Claims Adjudication process.
§ Using data mining to cluster and group claims by loss characteristics
(such as loss type, location and time of loss, etc.).
§ Claims scored, prioritized and assigned per experience and loss type.
§ Higher quality, more consistent, and faster claims handling.
Adjuster Effectiveness Measurement.
§ Adjusters typically evaluated based on an open/closed claims ratio.
§ Analytics create key performance indicator (KPI) reports based on
customer satisfaction, overridden settlements and other relevant metrics.
Claims involving attorneys often 2X settlement and expenses.
§ Analytics help determine which claims are likely to result in litigation.
§ Assign to senior adjusters to settle sooner and for lower amounts.
June 2012 20

21. One More Claims Example
Analytics help shorten the claims cycle times.
§ Claims cost 40% more if FNOL is delayed by 4 days.
§ Auto accidents take average of 16.2 days to repair and return.
§ Significant expense savings on rental cars, etc. for auto repairs.
Duration and Customer Satisfaction are directly correlated.
854
% of Claimants
828
Overall CSI Index
772
Satisfaction going down
CSI of 854 to 828 to 772
37%
36%
27%
JD Power, 2007
1 wk or less 8-14 2007
JD Power, days Over 2 wks
Analytics drive higher Customer Satisfaction and Lower Costs
June 2012 21

22. 3 Guidelines to Implementing Analytics
All-executive panel agreed on three key guiding principles:
1. Have an executive sponsored roadmap that clearly outlines.
§ What resources will be needed for how long,
§ Where and when predictive analytics will be used,
§ Which tools will be used, and
§ How will success be measured.
2. Use data that is comprehensive, accurate, and current.
§ Not necessarily 100%, some have used only 70%. Must be representative.
3. Staff with talented and engaged people.
§ Completely understand business problem and are proficient with analytics.
§ Every person does not have to meet both qualifications; a team can be
used with some experts on the business and others experts on analytics.
June 2012 22

23. Closing Thoughts
When it comes to leveraging customer analytics, remember
this well-known proverb:
The best time to plant a tree was 20 years ago.
The second best time is today!
June 2012 23

24. Are You Motivated?
June 2012 24

25. Questions
THANK YOU!!
Robert E. Nolan Company
Management Consultants
www.renolan.com
Steven M. Callahan, CMC®
Practice Director
www.linkedin.com/in/stevenmcallahan
June 2012 25