201406 IASA: Analytics Maturity - Unlocking The Business Impact

IASA 86TH ANNUAL EDUCATIONAL CONFERENCE & BUSINESS SHOW

Analytics Maturity: Unlocking the Business Impact of Analytics

Session 102

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

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?

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 ReportingAd hoc ReportingQuery/Drill Down

AlertsForecastingSimulation

Predictive ModelingDecision Optimization

Optimization w/uncertainty

Adaptive Analysis

Continual Analysis

Unstructured text/video/audio

Enterprise-wide adoptionNew extractions methods

Learn

New Data Sources + Fewer Boundaries = Greater Value

Sour

ces

and

type

s of

dat

a

New format or usage of data

Structured or standardized

Scope of decisionLow High

Multi-modal demand

forecastingIntent-to-buy

trends

Segmentation-based

market impactestimates Price-based

demand forecasting(own & competitors)Sales-based

demand forecasting

* Truthfulness, accuracy or precision, correctness

Big Data in One Slide

Volume Velocity Veracity*Variety

Data at RestTerabytes to exabytes of

existing data to process

Data in MotionStreaming data, milliseconds to

seconds to respond

Data in Many Forms

Structured, unstructured, text,

multimedia

Data in DoubtUncertainty due to data inconsistency& incompleteness,

ambiguities, latency, deception, model approximations

Big Data is Getting Bigger and More Diverse

Uncertainty Arises from Many Sources

Model UncertaintyProcess Uncertainty

Data Uncertainty

John Smith John Smythe

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

Leadership Decisions Moving To Data Driven


Analytics Used Across Wider Variety of Areas


Relative Adoption by LOB


0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

Predictive

Retrospective

Source of Increasing Interest in Analytics

Location Of Analytics Expertise Varies Widely

?

Increase in Analytic Methods Being Used

Analytics Progression

PROACTIVEDECISIONS

REACTIVEDECISIONS

Maturity by Progressiveness

Maturity by Focus

Maturity by Stage Level Effectiveness

Maturity by Level of Integration

Maturity by Utilization Cycle

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

The Analytics Capability Maturity Evolution

Level 5 Analytics Requires Integration and Continuous Enhancement

Analytics Team Effectiveness: Measure Using RATER Model

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

From Reporting to Innovation


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


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

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.

Case Study: Retention StrategiesStep 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

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

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

Case Study: Claims Processing

FNOL EvaluateClaim

CloseClaim

Negotiate / Initiate Services

Predict durationForecast loss reservesOptimize fast track claimsPrioritize resourcesFraudulent scoringLitigation propensity

Identify salvage and subrogation opportunitiesIndicate deviations Reports on 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

Assign Claim

Fast Track Claim

Prioritized investigationFocus on organized fraudMinimize claim paddingReduce false positives

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.

Case Study: Claims Fraud Red Flag Dashboard

June 201236Courtesy of Attensity


Case Study: Life Underwriting via App + Third Party Data

Second child born last yearHigh investment risk toleranceLived in home 2 yearsOwns homeCommuting distance 1 mileReads Design and Travel MagazinesUrban single clusterPremium bank cardGood financial indicatorsActive lifestyle: Run, Bike, Tennis, AerobicsHealth food choicesLittle to no television consumption

Actively pursue for issuance of a preferred policy without requiring fluids or medical records.Use strong retention tactics.

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 yearsLived in same hometown 15 yearsCurrently rentingCommuting distance 45 milesWorks as administrative assistantDivorced with no childrenForeclosure/bankruptcy indicatorsAvid book readerFast food purchaserPurchases diet, weight loss equipmentWalks for healthHigh television consumptionLow regional economic growthLight wine drinker

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.

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 programsMagazinesEmail listsWebsitesGrocery store cardsBook store cardsPublic records

Life Underwriting Savings:Using 3rd Party Data versus Medical Data

Deloitte Predictive Model for Life

Workers Comp already has a track record of using Social Data

Case Study: Social AnalyticsCustomer 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

Case Study: Social Analytics Conversation Sentiment Tracking

Courtesy of Attensity

Case Study: Social Analytics Website Sentiment by LOB


Social Analytics: Overall Sentiment Ratings Dashboard

Case Study: Social Analytics Competitive Sentiment Dashboard


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

Common Barriers to Using Analytics


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”

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. “


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.

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

Retrospective versus Predictive

Questions and Discussion

Thank You For Your Time! Enjoy the Conference

Steven Callahan, CMC®, FFSIwww.linkedin.com/in/stevencallahan

[email protected] Nolan Company

www.renolan.com

www.linkedin.com/in/stevencallahan

mailto:[email protected]

www.renolan.com