201208 namic operations: analytics: a cross-functional solution to information overload
DESCRIPTION
Predictive analytics and business intelligence tools and management practices are rapidly being adopted and evolved across the insurance industry. High-profile results are often touted within specific functional areas. Yet there remains a broader ROI that can be achieved through integrated analytical modeling of information from finance, sales, marketing, pricing, underwriting, and claims. Rapid advances in analytics are enabling companies to translate their wealth of enterprise-wide information into cohesive, actionable strategies directly targeting profitable growth.TRANSCRIPT
Analytics: A Cross-Functional Solution to Information Overload
Presented by:
Steve Callahan, CMCPractice Director
Robert E. Nolan Company
Presented to:
2012 NAMIC Operations SeminarCharleston, S.C.
August 23, 2012
© Robert E. Nolan Company | Page 2Analytics: A Cross-Functional Solution to Information Overload
Today’s Discussion
Why Analytics
Recent Survey Results
Case Studies
Final Thoughts
Questions
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Analytics One of Top 5 Technology Topics
Analytics: A Cross-Functional Solution to Information Overload
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How Do the Top 5 Compare Today
Analytics: A Cross-Functional Solution to Information Overload
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Business Results Driven Priorities
Admin / Legacy System Functional, Flexible, Supportable, Reliable
Analytics / BI Optimal Information Driven Decisions
Mobile Computing Service Delivered in Customer’s Hands
Big Data Incorporate All Data into Decisions
Cloud Computing Evaluate Universal Access / Variable Cost
Most Companies Have or Are Addressing Legacy SystemsNext Step is Analyzing Discrete Data and Focusing Decisions
Analytics: A Cross-Functional Solution to Information Overload
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2011 Survey: Most Decisions Rely on Experience and “Gut”
June 20126Robert E Nolan Company Executive Survey, 2011
60%82%
Analytics: A Cross-Functional Solution to Information Overload
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Retrospective Based on Experience versus Predictive
June 20127
MY REAL-TIME ANALYSIS TELLS ME IT’S SMOOTH SAILING
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Leveraging the Foundations of Wisdom:The Financial Impact of Business Analytics (© IDC)
IDC Research showed tremendous gains –
10 Years Ago(2002)
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
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2011 Survey: Leadership Decisions Moving To Data Driven
Analytics: A Cross-Functional Solution to Information Overload
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2011 Survey: Analytics Used Across Wider Variety of Areas
Analytics: A Cross-Functional Solution to Information Overload
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Analytics Capability Maturity Evolution
Tools and data rapidly evolvingContinuous improvement loop
Direct Link to Decision MakingApplied Across the Organization
Advanced Analytics ToolsIntegrated DataLimited Link to Decision Making
QA Standards AppliedBasic Analytical ToolsLimited Data Integration
Basic DataMinimal QA
1
5
4
3
2
Most Companies Here
Analytics: A Cross-Functional Solution to Information Overload
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A Different View: From Reporting to Data Innovation
Analytics: A Cross-Functional Solution to Information Overload
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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
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Top Line Revenue is Improved As Well
Analytics: A Cross-Functional Solution to Information Overload
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
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Summary Key Benefits of Analytics
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
Create a culture that thrives on fact-based decisions versus “gut”
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Yet Companies Struggle to Implement
June 201216
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
Robert E Nolan Company Executive Survey, 2011Analytics: A Cross-Functional Solution to Information Overload
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And the Barriers Are Diverse
June 201217
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, 2011Analytics: A Cross-Functional Solution to Information Overload
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With Opinions Varying Greatly“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
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Common Barriers to Using Analytics
Analytics: A Cross-Functional Solution to Information Overload
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Analytics is One Tool in the Entire Product Life Cycle
FeedbackFeedback
Retroactive Research
Internal and External Data
Acquisition and Cleansing
Conversion and Formatting
Client and Account Centric
Integration
Population Analysis and Segmentation
Predictive Analysis
External Data Acquisition
Exception Handling and
Workflow
Rule and Rate Automation and
Enforcement
Product Development & Assembly
Pattern and Data Analysis
Rule and Rate Production
Roll Out
Product / Forms Creation
and Assembly
Underwriting Rules Design and Modeling
Rule and Rate Model Prediction and Optimization
Rate Design and Modeling
A clear but specific A clear but specific vision enables a vision enables a
manageable project manageable project structure with iterative structure with iterative
deliveries. deliveries.
Iterative Process
ProductionOperations
Analytics: A Cross-Functional Solution to Information Overload
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Improved use of analytics can be organized so that the key plan areas can be developed along three parallel tracks.
Current Data Evaluation
Data Cleansing and Alignment
Market Segmentation Analysis
External Data Aggregation
Trend Analysis Tools and Modeling
Product Development
Product Development
Modeling and Analytic Tools
Rules Engines
Rating Engines
Product Assembly
Process Integration and Management
Performance and Scalability
Data Evaluation
and Analysis
Data Evaluation
and Analysis
Production Processing Integration
Production Processing Integration
Rates and Rules Integration
Predictive Analysis Models
Workflow and Exception Process
Legacy Integration
“Dashboard” Development
Transforming Product Development
Analytics: A Cross-Functional Solution to Information Overload
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Case Study A: Prospect Scoring
PredictiveAnalysis
and Modeling
Low Medium High Propensity 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
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
Analytics: A Cross-Functional Solution to Information Overload
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Case Study B: 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 customers have high acquisition costs, retaining one 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.
Implemented an agency dashboard based on new KPI’s that were modeled with a predictive analytics tool.
June 201223Analytics: A Cross-Functional Solution to Information Overload
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Case Study B: Agency Management
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Case Study C: Loss based Pricing
Result: More equitable and competitive risk adjusted pricing.
25
$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
Analytics: A Cross-Functional Solution to Information Overload
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Case Study D: Retention StrategiesStep 1: Determine Life time Value
26
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
Analytics: A Cross-Functional Solution to Information Overload
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Case Study D: Retention Strategies Step 2: Predict Potential Lapse
27
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
Analytics: A Cross-Functional Solution to Information Overload
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Case Study D: Retention StrategiesStep 3: Develop Strategy Matrix
28
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
Analytics: A Cross-Functional Solution to Information Overload
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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 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)
Analytics: A Cross-Functional Solution to Information Overload
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Automated Fraud Detection Points
30
FNOL EvaluateClaim
CloseClaim
Negotiate / Initiate
Services
Predict durationForecast loss reservesOptimize fast track claimsPrioritize resourcesFraudulent scoringLitigation propensity
Prioritized investigationFocus on organized fraudMinimize claim paddingReduce false positives
Identify salvage and subrogation opportunitiesIndicate deviations Reports on overrides
InitiateSettleme
nt
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
Analytics: A Cross-Functional Solution to Information Overload
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Claims Analytics:Fraud Red Flag Dashboard
June 201231Courtesy of AttensityAnalytics: A Cross-Functional Solution to Information Overload
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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 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.
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Case Study G:Life Underwriting via App + Social Data
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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.
Life UW using a GLM predictive model to assess risk:Use info on app plus social data, No fluids or filesIntegrate 3rd party publicly available information.
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Case Study:Life Underwriting via App + Social Data
Do not send offers. Do not pursue aggressive retention strategies. If applies, pursue additional medical records and tests.
In a test over 30,000 applicants, behavioral and lifestyle factors provided 37% of the risk assessment influence and performed as well as additional, more intrusive medical tests and fluids.
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
Analytics: A Cross-Functional Solution to Information Overload
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Types of third party marketing data
35Deloitte Predictive Model for Life
Analytics: A Cross-Functional Solution to Information Overload
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Life Underwriting Savings:Using 3rd Party Data versus Medical Data
36Deloitte Predictive Model for LifeAnalytics: A Cross-Functional Solution to Information Overload
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Workers Comp already has a track record of using Social Data
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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
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Social Analytics: Conversation Sentiment Tracking
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Social Analytics:Website Sentiment by LOB
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Available Third Party Data is Extensive
Survey Data:•Self-reported information •Contains many lifestyle elementsBasic demographics•Age, sex, number & ages of kids, marital status•Occupation categories, education levelFinancial information•Income level, net worth, savings, investments•Home value, mortgage value, credit card infoLifestyle data•Activity: running, golf, tennis, biking, hiking, etc. •Inactivity: TV, computers, video games, casinos •Diet, weight-loss, gardening, health foods, pets
Third party marketing datasets are often used to develop the predictive models, they 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. Third party marketing data includes:
Rewards programsMagazine subscriptionsEmail listsWebsitesGrocery store cardsBook store cardsPublic records
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Social Analytics:Overall Sentiment Ratings Dashboard
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Social Analytics:Competitive Sentiment Dashboard
June 201243Courtesy of Attensity
Analytics: A Cross-Functional Solution to Information Overload
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Closing Notes: Bloomberg Qualitative Research Findings
Analytics rapidly advancing past “emerging stage” Organizations proceeding cautiously in adoption Business experience driving factor in decision making Analytics for big issues, focus on improving bottom line
Key adoption challenges:– Data quality, acquisition, integration – Many carriers lack proper analytical talent – Culture critical– Executive sponsorship key– Start small, work big
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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.
§ Staff with talented and engaged people. 1. Completely understand business problem, proficient with analytics. 2. Every person does not have to meet both qualification.3. A team can be used with some business and some analytics experts.
June 201245Analytics: A Cross-Functional Solution to Information Overload
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Questions?
Thank You...
Steve Callahan, CMCPractice Director
[email protected]/in/stevenmcallahan
@stevenmcallahan(206) 619-7740