Overview of how experienced insurers are finally unlocking the business value of analytics to strengthen financial results through improved underwriting, better pricing, agent enablement, enhanced risk management, and targeted cost reductions and how analytics maturity and a roadmap increases the odds of success.

1. IASA 86TH ANNUAL EDUCATIONAL CONFERENCE & BUSINESS SHOW
Analytics Maturity: Unlocking the
Business Impact of Analytics
Session 102

2. Analytics Maturity: Unlocking the
Business Impact of Analytics
Session Overview:
§ Analytics are being used to strengthen financial results through improved
underwriting, better pricing, agent enablement, enhanced risk management,
and targeted cost reductions.
§ Learn how experienced insurers are finally unlocking the business value of
analytics by implementing an analytics maturity model.
§ Hear one carrier’s analytics case study.
Session Objectives:
§ Describe an analytics maturity model
§ Identify analytics-enabled opportunities and ROI
§ Describe how one carrier has used analytics and related technologies to
improve business performance

3. Analytics: Using data to make smart
decisions
Data
Historical
Simulated
Text Video,
Images
Audio
§Data inputs
§Reports and
queries on data
§Predictive models
§Answers and
confidence
§Feedback and
learning
Decision point Possible outcomes
3
How are decisions made?
How can they be better informed?
How does business structure impact decision?

4. The Analytics Hierarchy
Extended from: Competing on Analytics, Davenport and Harris, 2007
Report
Decide and Act
Understand and
Predict
Collect and Ingest/Interpret
Traditional
Analytics
New Data
New
Methods
Standard Reporting
Ad hoc Reporting
Query/Drill Down
Alerts
Forecasting
Simulation
Predictive Modeling
Decision Optimization
Optimization w/uncertainty
Adaptive Analysis
Continual Analysis
Unstructured text/video/audio
Enterprise-wide adoption
New extractions methods
Learn

5. New Data Sources + Fewer Boundaries =
Greater Value
Sourcesandtypesofdata
New format or
usage of data
Structured or
standardized
Scope of decisionLow High
Multi-modal
demand
forecasting
Intent-to-buy
trends
Segmentation-
based
market impact
estimates Price-based
demand forecasting
(own & competitors)Sales-based
demand
forecasting

6. * Truthfulness, accuracy or precision, correctness
Big Data in One Slide
Volume Velocity Veracity*Variety
Data at Rest
Terabytes to
exabytes of
existing data to
process
Data in Motion
Streaming data,
milliseconds to
seconds to
respond
Data in Many
Forms
Structured,
unstructured, text,
multimedia
Data in Doubt
Uncertainty due to
data inconsistency
& incompleteness,
ambiguities, latency,
deception, model
approximations

7. Big Data is Getting Bigger and More Diverse

8. Uncertainty Arises from Many Sources
Model Uncertainty
Process
Uncertainty
Data Uncertainty
John Smith John Smythe

9. Key Applications of Analytics
§ Gain deeper, more relevant business insights to inform decisions
§ Bring predictive analysis & regression modeling to entire organization
§ Use analytics to identify and determine options for addressing
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
§ Create a culture that thrives on fact-based decisions versus “gut”
Analytics: A Cross-Functional Solution to Information
Overload

10. Leadership Decisions Moving To Data Driven
Analytics: A Cross-Functional Solution to Information
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11. Analytics Used Across Wider Variety of Areas
Analytics: A Cross-Functional Solution to Information
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12. Relative Adoption by LOB
Analytics: A Cross-
Functional Solution to
Information Overload
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
Predictive
Retrospective

13. Source of Increasing Interest in Analytics

14. Location Of Analytics Expertise Varies
Widely
?

15. Increase in Analytic Methods Being Used

16. Analytics Progression
PROACTIVE
DECISIONS
REACTIVE
DECISIONS

17. Maturity by Progressiveness

18. Maturity by Focus

19. Maturity by Stage Level Effectiveness

20. Maturity by Level of Integration

21. Maturity by Utilization Cycle

22. Whatever Maturity Model is Used: Measure
the Maturity Capability By Function

23. The Analytics Capability Maturity Evolution

24. Level 5 Analytics Requires Integration
and Continuous Enhancement

25. Analytics Team Effectiveness: Measure
Using RATER Model

26. Elements of the RATER Model
The RATER* Model:
1. Reliability –the ability to provide the service you have promised consistently,
accurately, and on time
2. Assurance –the knowledge, skills, and credibility of staff; and their ability to
use this expertise to inspire trust and confidence
3. Tangible –high quality, or appearance of high quality in the physical aspects
of service delivery. Includes documents, presentation, facilities and packaging
4. Empathy –the extent to which analytics area(s) adequately represent the
concern and values of the functions and areas served
5. Responsiveness –the ability to provide effective answers and solutions
quickly or within needed expectations
*Source: Delivering Quality Service…, Zeithamlet al, 1990

27. From Reporting to Innovation
Analytics: A Cross-Functional Solution to Information
Overload

28. Leveraging the Foundations of Wisdom:
The Financial Impact of Business Analytics (© IDC)
IDC Research
showed
tremendous gains
–
Median ROI:
Predictive: 145%
NonPredictive 89%
30%
25%
20%
15%
10%
5%
0%
1-50% 51-100% 101-500% 501-1000% >1,000%
More Informed Decisions Improves ROI
Analytics: A Cross-Functional Solution to Information
Overload

29. Top Line Revenue is Improved As Well
Carriers effectively using predictive analytics achieved:
• 1% improvement in profit margin
• 6% improvement in year on year customer retention
Carriers not fully using predictive analytics:
• Dropped 2% in profit margins
• Decreased 1% in year on year customer retention
Higher on the Capability Maturity Curve = Better Results:
• Top 20% : 27% Year on Year Growth in Revenue
• Middle 50% : 12% Year on Year Growth in Revenue
• Bottom 30% : : 1% Year on Year Growth in Revenue

30. Case Study: Agency Management
60% of customers would switch carriers if so advised by their agent.
(Source: JD Power & Associates)
33%+ of agents are likely to change insurance carriers.
(Source: National Underwriter and Deloitte)
Insurers better manage their agents achieve competitive advantage.
§ 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.
Implemented an agency dashboard based on new KPI’s that were modeled
with a predictive analytics tool.

31. Case Study: Retention Strategies
Step 1: Determine Life time Value
31
Time of
Purchase
Demographics
-Loses
predictive
value over time
as relevance is
superseded by
inforce
behaviors
Customer
behavior shifts
focus from current
to future value
Predictive
Analysis
Current
Value
Future
Value
Post Purchase
Activity –
Increases in
predictive
value over
time as
behavioral
patterns
develop

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

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

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

35. Case Study: Claims Processing
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 by 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 metrics.
Claims using 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.

36. Case Study: Claims Fraud Red Flag
Dashboard
June 2012
36Courtesy of Attensity
Analytics: A Cross-Functional Solution to Information
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37. Case Study: Life Underwriting via App +
Third Party Data
Second child born last year
High investment risk tolerance
Lived in home 2 years
Owns home
Commuting distance 1 mile
Reads Design and Travel Magazines
Urban single cluster
Premium bank card
Good financial indicators
Active lifestyle: Run, Bike, Tennis,
Aerobics
Health food choices
Little to no television consumption
Actively pursue
for issuance of
a preferred
policy without
requiring fluids
or medical
records.
Use strong
retention
tactics.

38. Case Study: Life Underwriting via App +
Third Party Data
Do not send
offers. Do not
pursue
aggressive
retention
strategies. If
applies, pursue
additional
medical
records and
tests.
Current residence four years
Lived in same hometown 15 years
Currently renting
Commuting distance 45 miles
Works as administrative assistant
Divorced with no children
Foreclosure/bankruptcy indicators
Avid book reader
Fast food purchaser
Purchases diet, weight loss equipment
Walks for health
High television consumption
Low regional economic growth
Light wine drinker

39. Case Study: Life Underwriting
Analytics and Non Intrusive Data
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.
In a test over 30,000 applicants:
• Behavioral and lifestyle factors provided 37% of the risk
assessment influence
• These factors performed as well as additional, more intrusive
medical tests and fluids.
Third party marketing datasets used to develop predictive models:
• Include over 3,000 fields of data,
• Contain no PHI,
• Are not subject to FCRA requirements, and
• Do not require signature authority.
The match rate with insured’s is typically around 95% based only on
name and address.

40. Sources of Third Party Data Pervasive
Survey Data:
• Self-reported information
• Contains many lifestyle elements
Basic demographics
• Age, sex, # & ages of kids, marital status
• Occupation categories, education level
Financial information
• Income, net worth, savings, investments
• Home value, mortgage value, CC info
Lifestyle data
• Activity: Running, golf, tennis, biking, hiking
• Inactivity: TV, PC’s, video games, casinos
• Other: Diet, weight-loss, gardening, health foods,
pets
Rewards programs
Magazines
Email lists
Websites
Grocery store cards
Book store cards
Public records

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

42. Workers Comp already has a track
record of using Social Data

43. Case Study: 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 experience
customers are having on
the site
§ Improve brand image and
emphasize business
legitimacy

44. Case Study: Social Analytics
Conversation Sentiment Tracking
Courtesy of Attensity

45. Case Study: Social Analytics Website
Sentiment by LOB
Courtesy of Attensity

46. Social Analytics: Overall Sentiment
Ratings Dashboard

47. Case Study: Social Analytics Competitive
Sentiment Dashboard
Courtesy of Attensity

48. Yet Companies Struggle to Implement
48
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 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

49. Common Barriers to Using Analytics
Analytics: A Cross-Functional Solution to Information
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50. Comments on Barriers Are Diverse
Survey Comments on Barriers to Growth in Use of Analytics
“Resistance comes from most experienced, those requiring 100% accuracy”
“Access to critical data 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 drives decisions”

51. With Some Skepticism Still There
“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. “
Analytics: A Cross-Functional Solution to Information
Overload

52. 3 Guidelines to Implementing Analytics
1. Have executive sponsored roadmap clearly outlining:
§ 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, proficient with analytics.
§ Every person does not have to meet both qualification.
§ A team can be used with some business and some analytics experts.

53. And Keep Your Eyes On Legal Landscape
§ Stored Communications Act
• Fourth Amendment
• Enacted on 10/21/1986
• Requires insurers to tell policyholders if an action detrimental to
them is taken as a result of the collection of electronic data.
§ Case Law Precedent
• Roman v. Steelcase
• Copes v. State Farm
• Largent v. Reed

54. Retrospective versus Predictive

55. Questions and Discussion
Thank You For Your Time!
Enjoy the Conference
Steven Callahan, CMC®, FFSI
www.linkedin.com/in/stevencallahan
Steve_Callahan@renolan.com
The Nolan Company
www.renolan.com