Products & Solutions / Customer Analytics for Insurance

SAS® Customer Analytics for Insurance

Lower acquisition costs, improve retention rates and increase wallet share

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.

Benefits

  • Create a single view of the customer.
  • Increase premium revenue.
  • Uncover new sales opportunities, and increase wallet share.
  • Improve retention rates.
  • Reduce marketing costs.

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Features

  • Insurance data model
  • Data management
  • Comprehensive, easy-to-use reporting and business intelligence
  • Data mining and predictive modeling

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How SAS® Is Different

  • Only SAS gathers all customer data into a single version of the truth by pulling together data from all touch points and distribution channels across the organization, then automatically validating the data as part of the data integration process so you can be confident in its integrity throughout the organization.
  • SAS helps you jump-start your analytic capabilities with insurance-specific logical and physical data models that let you:
    • Reduce costs and implementation time with prebuilt customer data marts and predictive models.
    • Accurately forecast customer behavior using SAS' powerful predictive analytics.
    • Analyze data for trends to segment markets, determine customer value and calculate retention scores that provide early-warning indicators when a key customer's behavior is about to change.
  • Only SAS provides prebuilt processes and analytic techniques with a ready-to-deploy architecture specifically designed to address a wide range of insurance business challenges, which boosts the productivity and self-sufficiency of your business analysts and subject matter experts.

Benefits

  • Create a single view of the customer. Consolidate all customer data into one place regardless of source, automatically cleanse the data and transform it to provide a complete picture of the entire customer relationship.
  • Increase premium revenue. Understand interrelationships among key rating variables to improve rating and pricing strategies.
  • Uncover new sales opportunities, and increase wallet share. Identify potential cross-sell/up-sell prospects using predictive analytics – like decision trees – to forecast expected customer behavior.
  • Improve retention rates. Predict customer behavior using detailed analytics, such as cluster analysis, to gain insight into the major factors that influence customer retention.
  • Reduce marketing costs. Connect policy offers to the right customers using predictive analytic techniques based on demographic, geographic and behavioral data across the organization.

Features

Insurance data model
  • A comprehensive, scalable insurance data model serves as a single version of the truth for an enterprise data warehouse that covers all key insurance subject areas.
  • A comprehensive dictionary describes insurance data elements.
  • Includes a complete mapping of physical data structures to business terms.
  • Includes both logical and physical data models – e.g., ERwin data models and SAS metadata.
  • Can be deployed in multiple databases, including SAS, Oracle, Teradata and DB2.
  • Business data definitions are consistent with global insurance data standards, such as ACORD.
  • Supports a variety of business issues, including Solvency II.
Data management
  • An enterprise data management environment meets all your data management needs.
  • Wizards enable you to access source systems, create target structures, import and export metadata, and build and execute data ETL process flows.
  • A dedicated GUI lets you profile data and identify and repair source system issues, while retaining the business rules for later use in ETL processes.
  • Includes real-time services for cleansing and augmenting data in real time.
  • Enables enterprise connectivity to data sources – AS/400, ODBC, IBM DB2/ UDB, Informix, Microsoft Access, Excel, SQL Server, Netezza, Oracle, Sybase, SAS, Teradata and more.
  • Supports unstructured and semistructured data.
  • Data quality is embedded into batch, near-time and real-time processes.
  • Data cleansing is done in native languages, with specific language awareness and localizations for more than 20 worldwide regions.
  • Out-of-the-box standardization rules conform data to corporate standards, and you can build customized rules for special situations.
  • An interactive GUI enables data stewards to profile operational data and monitor ongoing data activities.
  • Customized and reusable data quality business rules can be accessed directly within process job flows.
  • Data can be migrated or synchronized between database structures, enterprise applications, mainframe legacy files, text, XML, message queues and a host of other sources.
  • Data can be linked across sources for real-time access and analysis.
Comprehensive, easy-to-use reporting and business intelligence
  • A Web-based, interactive reporting interface enables business users to define and run their own reports.
  • Includes query capabilities for all levels of users across multiple BI interfaces.
  • You can slice and dice multidimensional data using a special slicer dimension and by applying filters on any level of a hierarchy.
  • Critical first-alert, call-to-action dashboards let you monitor performance results.
  • Dynamic business visualization tools enable interactive data exploration, visual queries and more.
  • SAS capabilities for data access, reporting and analytics can be accessed directly from Microsoft Office, including Word, Excel and PowerPoint.
  • Wizard driven report creation within Microsoft Office tools.
Data mining and predictive modeling
  • A powerful set of interactive data preparation tools optimally address missing values, filter outliers and develop segmentation rules.
  • An unmatched suite of predictive and descriptive modeling algorithms include decision trees, neural networks, memory-based reasoning, hierarchical clustering, linear and logistic regression, associations, market basket analysis, sequence and Web path analysis, and more.
  • Includes the ability to combine model predictions to form a potentially stronger solution (e.g., averaging, voting and maximum).
  • Model evaluation capabilities let you compare multiple models in a single framework for all data sources.

Ready to learn more?

Call us at 1-800-727-0025 (US and Canada) or request more information.