Presentation in conjunction with American Family on implementing and using analytics within the insurance industry. Presentation includes summary of usage survey within industry, common uses and approaches, and an implementation approach that leverages Six Sigma and Lean Manufacturing to put analytics in place.

1. The Analytics “Gold Rush”:
Mountains of Data, Hidden Profits

2. Agenda
Presenter: Steven Callahan, Robert E Nolan Company
§ Analytics Update
§ Techdecisions / Nolan Survey Results
§ Analytics Application Examples
Presenter: Alan Rault, American Family
§ From Lead to Gold: A Practical Approach to Get the Best
ROI from Data Analytics
Shared by both presenters
§ Question and Answer Session
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3. ANALYTICS UPDATE
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4. Enough Already!
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5. May 2011 Bloomberg Research Study Key Findings
§ Business analytics is still in the “emerging stage”
§ Organizations are proceeding cautiously in their adoption of analytics
§ Intuition based on business experience is still the driving factor in
decision making
§ Companies look to analytics to solve big issues, with primary focus on
improving the bottom line
§ Data quality, acquisition, and integration is the #1 challenge in the
adoption and use of business analytics
§ Many organizations lack the proper analytical talent and know how to
effectively apply the results – to move from insights to action
§ Culture plays a critical role in or barrier to the effective use of business
analytics
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6. Why Bother?
Both predictive and
nonpredictive projects
yielded high median ROI,
145% and 89%,
respectively per IDC.
According to the Aberdeen
Group, predictive analytics
carriers achieved a 1%
improvement in profit
margin and improved by
6% in year on year
customer retention.
Those who had not yet
adopted predictive
analytics dropped 2% in
profit margins and
decreased 1% in year on
year customer retention.
Leveraging the Foundations of Wisdom:: The Financial Impact of Business Analytics., Copyright © 2002, IDC
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7. Top Line Benefits Touted for Analytics
Business analytics enable organizations to be able to:
§ Gain deeper, more relevant business insights to inform decision making
§ Bring predictive analysis and regression modeling to entire organization
§ Use analytics to identify and determine options for industry challenges
– Be prepared to respond to significant business challenges as they emerge
§ 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 anecdotal
– Achieve more consistent, objective and prospective business decisions
§ Effectively and proactively manage risks
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8. The Analytics Capability Maturity Evolution
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9. March 2012 Page 9

10. Business Reports
Rules Dashboards
See Models
next Correlations
Distributed Data Stores Simulations
page*
Select, Scrub, Transform
External Internal
Data ENTERPRISE DATA WAREHOUSE Text
Sources Mining
Extract, Verify, Clean
Marketing Agents Apps UW Info Services CRM Claims
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11. ** Available Third Party Data is Extensive
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:
Survey Data:
• Self-reported information
Rewards programs
• Contains many lifestyle elements
Magazine subscriptions
Basic demographics
Email lists
• Age, sex, number & ages of kids, marital status
Websites
• Occupation categories, education level
Grocery store cards Financial information
Book store cards • Income level, net worth, savings, investments
Public records • Home value, mortgage value, credit card info
Lifestyle data
• Activity: running, golf, tennis, biking, hiking, etc.
• Inactivity: TV, computers, video games, casinos
• Diet, weight-loss, gardening, health foods, pets
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12. SURVEY REVIEW
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13. Composition of Survey Participants
4%
22% 21%
27% 43%
57% 16%
62%
7% 36%
5%
P&C L&A Large (over $1000M) Executive Team
Health Multiline Medium ($500M to $1000M) Middle Management
Other Small (under $500M) Individual Contributor
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14. Source of Increasing Interest in Analytics
Predictive analytics/GLM Bring
11% 19% 59% 11%
Opportunities
Complex Data Arrays Add Value 2%5% 31% 51% 11%
New Tools Make Analyzing Easier 7% 23% 55% 15%
Too Much Info for Old Ways 16% 25% 18% 34% 7%
Information Quality Improved 7% 15% 31% 45% 2%
Quantitative Importance Grown 4% 5% 38% 49% 4%
Nothing New/Just press 18% 27% 35% 20%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Not at all Some Average Amount Consistently Significantly
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15. Leadership Decisions Moving To Data Driven
Future Projections (Predictive) 2% 41% 24% 33%
Historical Data 2% 25% 36% 36%
Collaborative Consensus 7% 28% 43% 22%
Group Dynamics 2% 34% 39% 24% 1%
Experience 7% 32% 55% 5%
Intuition 5% 31% 38% 25%
0% 20% 40% 60% 80% 100%
Not at all Some Typical/Common Almost Always Exclusively
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16. Location Of Analytics Expertise Varies Widely
? Dedicated Shared Team 19% 38% 26% 13% 4%
Centralized Finance or Actuarial 4% 28% 33% 33% 2%
Teams by LOB 16% 33% 30% 19% 2%
Centralized IT 11% 25% 45% 19%
Key Resources By Dept 11% 31% 22% 34% 2%
0% 20% 40% 60% 80% 100%
Not at all Some Typical/Common Almost Always Exclusively
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17. Increase in Analytic Methods Being Used
Predictive Models/Simulations 4% 38% 27% 29% 2%
Text/Content Analysis 25% 35% 31% 9%
Data Mining 5% 26% 32% 32% 5%
Segmentation/Clustering 4% 34% 35% 25% 2%
Dashboards/Scorecards 2%13% 38% 36% 11%
Benchmarks 2% 30% 27% 39% 2%
Trending/Comparisons 7% 33% 53% 7%
Unit Measures/Ratios 4% 20% 35% 37% 4%
0% 20% 40% 60% 80% 100%
Not at all Some Typical/Common Almost Always Exclusively
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18. Across A Wider Variety of Areas
Workforce Management 2% 26% 41% 26% 5%
Loss Control and Fraud 2% 18% 23% 46% 11%
Risk Management 4% 15% 28% 49% 4%
Retention Analysis 2% 16% 30% 43% 9%
Operational Efficiency 2% 18% 30% 48% 2%
Revenue Growth 2% 20% 27% 46% 5%
Channel Management 7% 30% 34% 25% 4%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Not at all Some Typical/Common Almost Always Exclusively
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19. Common Barriers to Using Analytics
Cultural Barriers to Data Sharing 23% 26% 32% 17% 2%
Perceived Costs > Expected Benefits 12% 34% 24% 28% 2%
Lack of Exec Sponsorship 20% 20% 34% 26%
Lack of Business Expertise 19% 37% 35% 9%
Inadequate Tech Resources 4% 35% 39% 20% 2%
Fragmented Data 9% 34% 31% 21% 5%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%
Not at all Some Typical/Common Almost Always Exclusively
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20. Survey Comments on Using Analytics
Areas of Increasing Use of Analytics
§ “Fastest growth will be in claims and in fraud detection, SIU”
§ “Product development / pricing, marketing, and underwriting”
§ “Property / territory grouping and experience rating”
§ “Social media analytics and text mining as sources of data for products and claims”
§ “Profitability modeling on specific segments of market or agents”
§ “Risk profiling by discrete market segments and then different levels of service”
§ “Combining internal/external data, unstructured data, geospatial & self-service analytics.”
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 start to drive decisions”
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21. Analytics: From Reactive to Predictive
* Insurance Customer Retention and Growth, © Copyright IBM Corporation 2010
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22. ANALYTICS APPLICATION EXAMPLES
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23. Agency Compensation
§ Background:
– Large P&C Carrier
– Direct sales through captive agencies
§ Problem:
– Single measure agency bonus program failed to motivate the desired behavior
– Desire to strongly link growth behavior change to compensation
§ Approach:
– Establish comprehensive database of agency data
– Built modeling tool and complete extensive baseline modeling process using historical data
• Established performance ranges, eligibility requirements, and targeted levels of participation
• Modeled top agents to verify soundness
– Determine impact on agencies more and less focused on growth
§ Solution:
– Balanced scorecard approach that incorporated differences in state and product strategies
– Modeling of impact on higher performing agencies
– Product and measure weight factors based on individual state priorities
§ Impact:
– Significant shift in bonus dollars paid to agents best supporting company goals
– Improved alignment with state and market strategies
– More balanced distribution of bonus dollars
– Increased rewards for top half of agency force
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24. Customer Retention
§ Background:
– Lincoln Financial Distributors
• Marketing and retail distribution arm of Lincoln Financial Group
• Annuities, life insurance, long-term care insurance, and investment products
§ Problem:
– Desire to better understand customer base and optimize the acquisition,
development, and retention of its customers
§ Approach:
– Selection of analytics software (SPSS) to segment and understand customer base
and to implement actions to increase retention
§ Solution:
– Identification of opportunities to reach out to customers to strengthen relationships
and create long-term loyalty
– Longer-term use for acquisitions and to identify up-sell and cross-sell opportunities
§ Impact
– Significant change in the way data is viewed and used
– Improved strategic decision making directly tied to business goals
– Maximize customer value by transforming data into important insight
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25. Customer Lifetime Value
§ Background:
– Farmers Insurance, third largest U.S personal lines insurer
§ Problem:
– Utilize analytics tool (SAS) to establish ability to support business units by answering
questions with high level of analytical rigor for more informed decision making
– Desire to understand how to adapt branding, direct mail, agency location and
behavior, and pricing to attract higher lifetime value customers
§ Approach:
– Explore unchartered territory to enable strategy through the use of analytic insight
– Analyze customer base data and determine that the top 20% provided nearly 80% of
revenues
§ Solution:
– Analyze and model customer lifetime loyalty and profitability, determine distribution
and marketing strategies, and identify customer experience investments
§ Impact
– Changes to marketing campaign success measure from number of respondents and
number of individual sales completed, as opposed to a holistic customer view
– Agent performance now tied to the powerful measure of lifetime value
– 14% increase in ROI related to direct marketing efforts
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26. IT Efficiency & Effectiveness
§ Background:
– Regional Financial Services Organization
– 500 EE IT organization, Build and Run functions maintained in-house, 50% resources
§ Problem:
– IT expense % of revenue high relative to peer group, complex admin processes
– < 10% of programmer time spent in coding, testing, and validation
– Significant time logging and tracking time
– Negative perception of IT efficiency and effectiveness by business line customers
§ Approach:
– Activity based costing used on functions performed and baseline resource allocation
– Gather business unit results and align with IT resource and function allocations
§ Solution:
– Develop related baseline customer focused IT performance ratios and metrics
– Identify and reduce or eliminate non-value added work functions
– Develop a business case for program / project management application that would
§ Impact
– Selection of a new PPM package, capacity creation, and ROI within two years
– Gradual elimination of non-value added work and reduced IT expense
– Improved business unit alignment and improved IT accountability
March 2012 Page 26

27. From Lead to Gold:
A Practical Approach to Get the Best
ROI from Data Analytics
Alan Rault
Strategic Business Process Management
March 28, 2012
March 2012 Page 27

28. Agenda
Background
Problem
Take Aways Definition
Implementation Measure
Plan Plan
Analytics
Plan
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29. Problem Definition
§ What is the business performance problem/gap that you are
trying to understand/solve (outputs)?
§ Is it measureable? Is the measure(s) valid to business and
considered reliable? Subjective vs. objective?
§ What is in or out of scope?
§ What are the potential factors that could influence your current
performance?
If you can’t figure out the factors,
STEEP is at your call
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30. What is STEEP?
Social Technology Economic
• Customer Wants/ • Support • Incentives
Needs • Systems • Products
Environment Political
• Market • Company Culture
• Competitors • Regulatory
• Demographics
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31. Measurement Plan
1. Determine the best means to collect the output data
– Survey, Data Mining, Internal/External Research
2. From the input factors identified, determine the extreme
range for each
– From that range, determine low, mid, and high level
3. Determine the number of samples to collect
– Ideally want at least three replicates per factor/level
Sales
Demographics Competetion Y1 Y2 Y3
-1 -1
-1 0
-1 1
0 -1
0 0
0 1
1 -1
Balance is key 1
1
0
1
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32. What About Data Contamination?
Unavoidable…
§ Define go/no-go thresholds up front
§ Build in validation checks
– Use MSA Rules: look for bias
§ Screen outliers
May have to eliminate 50% of the data
– account for this in your sample size
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33. Analytics Plan
§ High Level:
– Aggregate the performance data by factor to determine any
correlation
– How does it compare to industry benchmarks?
– How does it compare from historical data (any trends)?
§ Identify Key Factors and Interactions (Competitive
Advantage):
– Moderating factors
– Mediating factors
“All Models are Wrong,
but some are Useful” – G.E. Box
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34. Implementation Plan
§ Test the Model:
– Go into the field – confirm the results
– Is the model useful?
§ Pilot Some of the Changes:
– Compare the control (current state) to the pilot
– Compare pilot to predicted results
§ If Results are Positive, Roll Out the Change
Trust, but Verify!
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35. Takeaways
1. Have you done your homework? Potential to eliminate
inconsequential factors by leveraging results from previous
internal/external studies. Less factors = faster data collection and
less expense.
2. Measurement Plan – Pilots of the planned collection instrument can
identify errors, or confusion areas. Usability professionals are great
to test validity.
3. Smell Test – If your results, or conclusions from the model seem
odd, there may be something off in the calculations or data itself.
Look before you leap
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36. Questions?
Problem
Definition
Take Aways Measure Plan
Implementation Analytics
Plan Plan
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37. Contact Information
Robert E. Nolan Company
Management Consultants
(800) 248-3742
www.renolan.com
Steve Callahan
Practice Director
(206) 619-7740
steve_callahan@renolan.com
Access Nolan’s analytics information page at
www.renolan.com/analytics
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