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SAS CUSTOMER ANALYTICS FOR INSURANCE

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Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CHALLENGES

No single view of customer Increased marketing

costs

ISSUE IMPACT

Ineffective customer segmentation Rising acquisition costs

Inability to predict customer behavior Decreased premium

revenue

Inability to improve wallet share Lower retention rates

Multiple distribution channels Wasted marketing spend

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

SAS CUSTOMER ANALYTICS FOR INSURANCE

Data integration technologies

Data quality tools

Business intelligence technologies

Analytical technologies

Insurance data model – logical and physical

Solution data marts for customer segmentation, cross sell, up sell and retention

Pre-built data management jobs

Analytical model templates

Business Analytics

Framework Industry IP

SAS® Customer Analytics for Insurance

The solution provides an integrated environment to develop, deploy and

monitor customer analytics models.

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

Data

Integration

& Data

Quality

Insurance

Data

Model

Analytical Data

Marts Segmentation

Retention

Cross-sell

Up-sell

Analytics Predictive

modeling, data

mining

BI &

Reporting

Data

Marts

Executive

Dashboards

Reports Business,

regulatory

reporting

Model

validation

Insurance

Operational

Systems

Policy

Sales &

Marketing

Billing

Claims

Products

(P&C & Life)

Reinsurance

Policies Marketing

Claims Risk Factors

Customers Accounting

… …

SAS CUSTOMER ANALYTICS FOR INSURANCE

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SAS INSURANCE

DATA MODEL SAS CUSTOMER ANALYTICS FOR INSURANCE

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

SAS INSURANCE

DATA MODEL SAS CUSTOMER ANALYTICS FOR INSURANCE

• 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

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

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

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

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

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER

SEGMENTATION SAS CUSTOMER ANALYTICS FOR INSURANCE

• 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

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

SAS CUSTOMER ANALYTICS FOR INSURANCE CUSTOMER

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.

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CROSS-SELL &

UP-SELL SAS CUSTOMER ANALYTICS FOR INSURANCE

• 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

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

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

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

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

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

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

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER STORY MAX NEW YORK LIFE (INDIA)

Customer Quote

In the first quarter after implementing SAS, sales to existing customers jumped to more than 20 percent

Nagaiyan Karthikeyan, Head of Business Intelligence and Analytics

Business Problem

• Accurate data warehouse

• Increase customer retention

• Improve cross-sell sales

Results

• Increase cross-sell sales opportunities by nearly 300%

• 40 percent improvement in premium revenue

• Reduced sales expenses through shortened sales cycle

Solution

• SAS Customer analytics for Insurance

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

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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