June 2012 1

Applied Analytics: Converting Mountains of Data into Nuggets of GoldSession 408 Tuesday June 5, 2012 11 AM

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

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……

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Top Reasons for Adopting Analytics

June 2012 5

5%

2%

7%

2%

2%

31%

7%

34%

28%

25%

41%

38%

32%

39%

43%

36%

24%

25%

55%

24%

22%

36%

33%

5%

1%

0% 20% 40% 60% 80% 100%

Intuition

Experience

Group Dynamics

Collaborative Consensus

Historical Data

Future Projections (Predictive)

Not at all Some Typical/Common Almost Always Exclusively

Survey Says….AKA “gut feel”

June 2012 6Robert E Nolan Company Executive Survey, 2011

60%82%

Analytical Companies Perform Better

June 2012 7

A difference of 26% in year on year growth

27% year over year growth

1% year over year growth

Yet Companies Struggle to Implement

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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”

Robert E Nolan Company Executive Survey, 2011

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. “

Retrospective versus Predictive

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Case Study A: Prospect Scoring

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PredictiveAnalysis

and Modeling

Low Medium HighPropensity to Convert

High value,Low

conversion, 2nd Priority

High value, Medium

conversion, Top Priority

High value, High

conversion, Top priority

Good value, Low

conversion, Low Priority

Good value, Medium

conversion, 2nd Priority

Good value, High

conversion, Top Priority

Low value, Low

conversion,Low Priority

Low value, Medium

conversion, Low Priority

Low value, High

conversion, 2nd Priority

Potential Value

Low

Med

ium

H

igh

Potential Future Value of Customer

Scoring of prospects based on conversion and value, marketing strategy developed to match

Survey Data

Web LogData

TextData

Purchased Data

Psycho-graphic Data

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.

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Case Study B: Agency Management

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Shift away from traditional sales performance metrics like Premium Revenue.New KPI: Agent Profitability net of cost to serve –calls, e-mails, supplies, etc.

Case Study C: Loss based Pricing

Result: More equitable and competitive risk adjusted pricing.

June 2012 14

$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

Case Study D: Retention StrategiesStep 1: Determine Life time Value

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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

Case Study D: Retention Strategies Step 2: Predict Potential Lapse

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Predictive Analysis –

Model Channel and

Consumer Behaviors

Source of Business influences lapse tendencies based on channel behaviors

Transaction behavior influences lapse tendencies based on consumer behaviors

Case Study D: Retention StrategiesStep 3: Develop Strategy Matrix

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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

Case Study E: Claims Fraud

June 2012 18SAS Institute

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 usedEx: 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 patternsEx: Knowledge discovery, data mining, predictive assessment

Social Network Analytics: Determine associative linksEx: Knowledge discovery via associative link analysis (entity map)

Automated Fraud Detection Points

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FNOL EvaluateClaim

CloseClaim

Negotiate / Initiate ServicesPredict duration

Forecast loss reservesOptimize fast track claimsPrioritize resourcesFraudulent scoringLitigation propensity

Prioritized investigationFocus on organized fraudMinimize claim paddingReduce false positives

Identify salvage and subrogation opportunitiesIndicate deviations from similar claimsReports on claims overrides

InitiateSettlement

SIU

Update Claim

Fraud Referrals Fraud Referrals

Re-estimate durationReassess loss reservingPrioritize resourcesFraudulent rescoringReview litigation propensity

Cross-sell options for satisfied customerCustomer retention program

Assign Claim

Fast Track Claim

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.

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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.

Analytics drive higher Customer Satisfaction and Lower Costs

One More Claims Example

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JD Power, 2007

27%36%

37%

772

854828

Over 2 wks8-14 days1 wk or less

% o

f Cla

iman

ts

Ove

rall

CSI

Inde

x

JD Power, 2007

Satisfaction going downCSI of 854 to 828 to 772

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.

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June 2012 23

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!

Closing Thoughts

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Are You Motivated?

Questions

June 2012 25

THANK YOU!!Robert E. Nolan CompanyManagement Consultants

www.renolan.com

Steven M. Callahan, CMC®

Practice Directorwww.linkedin.com/in/stevenmcallahan