SAS Customer Analytics for Insurance delivers specific analytical techniques to help you understand and drive decisions related to customer profitability. The solution enables you to segment customers according to a multitude of variables – including demographics, geographics, claims history and other behavioral attributes – to create more meaningful and targeted marketing programs that lead to improved retention rates.

1. SAS CUSTOMER ANALYTICS FOR INSURANCE
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2. CHALLENGES
ISSUE IMPACT
Increased marketing
No single view of customer costs
Ineffective customer segmentation Rising acquisition costs
Decreased premium
Inability to predict customer behavior revenue
Inability to improve wallet share Lower retention rates
Multiple distribution channels Wasted marketing spend
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3. SAS CUSTOMER ANALYTICS FOR INSURANCE
The solution provides an integrated environment to develop, deploy and
monitor customer analytics models.
SAS® Customer Analytics for Insurance
Business Analytics Industry IP
Framework
 Data integration technologies  Insurance data model – logical and
physical
 Data quality tools
 Solution data marts for customer
 Business intelligence segmentation, cross sell, up sell
technologies and retention
 Analytical technologies  Pre-built data management jobs
 Analytical model templates
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4. SAS CUSTOMER ANALYTICS FOR INSURANCE
Insurance
Operational
Systems Analytics
Predictive Executive
modeling, data Dashboards
mining
Policy
Analytical Data
Data Marts
Insurance
Integration Segmentation
Claims Data
& Data Retention
Model Cross-sell
Quality
Up-sell
Billing Products Reinsurance
(P&C & Life) Reports
BI & Business,
Policies Marketing Reporting regulatory
Claims Risk Factors Data reporting
Marts Model
Customers Accounting
Sales & validation
… …
Marketing
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5. SAS INSURANCE
SAS CUSTOMER ANALYTICS FOR INSURANCE
DATA MODEL
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6. SAS INSURANCE
SAS CUSTOMER ANALYTICS FOR INSURANCE
DATA MODEL
• Single version of the truth
• A warehouse for granular, historical and integrated data
• Comprehensive coverage to support a variety of analytical
applications
• Approx. 440 Tables and 6,300 Attributes
• Model supports both P&C and Life Insurance
• Both logical and physical data model
• Erwin data models
• SAS metadata
• DDL scripts for database environments (DB2, Oracle, Teradata)
• Mapping of data items to business terms
• Aligns with global data standards like ACORD and GDV
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7. DATA MANAGEMENT SAS CUSTOMER ANALYTICS FOR INSURANCE
• Enterprise data management environment
• ETL technologies
• Data profiling capabilities
• Enterprise connectivity to data sources
• SAS, SQL, DB2, Access, Excel, Oracle, Teradata......
• Data quality business rules
• Support for unstructured and semi-structured data
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

8. ANALYTICS SAS CUSTOMER ANALYTICS FOR INSURANCE
• Analyze data for trends to segment markets, determine
customer value and calculate retention scores
• Powerful set of interactive data preparation tools
• Suite of predictive modeling techniques
• Decision trees
• Neural networks
• Hierarchical clustering
• Linear & logistic regression
• Market basket analysis
• Model comparion evaluation
• Pre-built Insurance specific analytical models including:
• Segmentation
• Retention
• Cross-sell
• Up-sell
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9. CUSTOMER
SAS CUSTOMER ANALYTICS FOR INSURANCE
SEGMENTATION
• Customer Segmentation enables insurers to identify
homogeneous groups within the customer base.
• A behavioral segmentation will consider past customer
behavior and will predict future segment assignment.
• Customer segments provide a strategic view for identifying
over arching patterns and help:
• Price more effectively
• Understand potential profitability
• Focus attention to higher value segments
• Develop tactics to improve value segments
• Retain and serve the customers better
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10. CUSTOMER
SAS CUSTOMER ANALYTICS FOR INSURANCE
RETENTION
• Two key activities
• Scoring customers on likelihood of lapsing
• Acting on this knowledge
• Using this output to communicate with the customer
BEFORE lapse
• Passing information out to agent and incentivising action
• Campaign Management – mail, telephone, email etc.
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11. CROSS-SELL &
SAS CUSTOMER ANALYTICS FOR INSURANCE
UP-SELL
• Know customer’s propensity to buy more policies/benefits
• Know which policies are preferred by customers and why
• Know what your customers are likely to buy next
• Enhance profitability by selling to known customers
• Make best offers
• Retain customers longer
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

12. REPORTING SAS CUSTOMER ANALYTICS FOR INSURANCE
• Empower users to make better business decisions faster
• Web-based, interactive reporting interface
• Query capabilities across multiple BI interfaces
• Slice and dice multidimensional data
• Critical first-alert, call-to-action dashboards for performance
results
• Dynamic business visualization tools
• Microsoft Office integration
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13. WHY SAS? KEY BENEFITS
• Creation of a single view of customer
• Consistent, accurate, verifiable and up-to-date information
• Access to the data you need, when you need it
• Improve retention rates
• Uncover new sales opportunities and increase wallet share
C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .

14. WHY SAS? LOWER COST OF OWNERSHIP
• Insurance data model
• Jump start reporting capabilities with insurance specific logical &
physical data models
• Superior data management capabilities
• Single version of the truth
• Improved data quality
• Award winning business intelligence technology
• Portal framework for scorecarding & dashboards
• Access to online reports with drill-down capabilities
• Powerful predictive analytical capabilities
• Reduce costs and implementation time with pre-built customer data
marts and predictive models
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15. CUSTOMER STORY MAX NEW YORK LIFE (INDIA)
Business Problem
• Accurate data warehouse
• Increase customer retention
Customer Quote
• Improve cross-sell sales
In the first quarter after
implementing SAS, sales
to existing customers Solution
jumped to more than 20 • SAS Customer analytics for Insurance
percent
Nagaiyan Karthikeyan,
Head of Business
Results
Intelligence and • Increase cross-sell sales opportunities by nearly 300%
Analytics
• 40 percent improvement in premium revenue
• Reduced sales expenses through shortened sales cycle
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16. MORE
INFORMATION
• Contact information:
Stuart Rose, SAS Global Insurance Marketing Director
e-mail: Stuart.rose@sas.com
Blog: Analytic Insurer
Twitter: @stuartdrose
• Research:
State of Customer Insights in Insurance
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17. THANK YOU
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