IASA 86 TH ANNUAL EDUCATIONAL CONFERENCE & BUSINESS SHOW Analytics Maturity: Unlocking the Business Impact of Analytics Session 102
Nov 22, 2014
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: A Cross-Functional Solution to Information Overload
Analytics Used Across Wider Variety of Areas
Analytics: A Cross-Functional Solution to Information Overload
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
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
Analytics: A Cross-Functional Solution to Information Overload
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
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
Analytics: A Cross-Functional Solution to Information Overload
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
Courtesy of Attensity
Social Analytics: Overall Sentiment Ratings Dashboard
Case Study: Social Analytics Competitive Sentiment Dashboard
Courtesy of Attensity
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
Analytics: A Cross-Functional Solution to Information Overload
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. “
Analytics: A Cross-Functional Solution to Information Overload
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