SAS® Customer Analytics for Banking
Lower acquisition costs, improve retention rates and increase wallet share
SAS Customer Analytics for Banking turns raw data into insight that you can use to manage marketing strategy intelligently and increase customer retention. Our integrated software infrastructure enables business users to analyze complex customer behavior hidden in large volumes of historical data. You can then use that information to answer critical business questions, such as which customers are likely to try a new product and which are likely to leave the bank entirely.
Benefits
- Create a single view of the customer.
- Uncover new sales opportunities, and increase wallet share.
- Improve retention rates.
- Reduce marketing costs.
Features
- Banking data model
- Superior data management
- Comprehensive, easy-to-use reporting and business intelligence
- Data mining and predictive modeling
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 banking-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 banking 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 in one place regardless of source, automatically cleanse the data and transform it to provide a complete picture of the entire customer relationship.
- 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 offers to the right customers using predictive analytic techniques based on demographic, geographic and behavioral data across the organization.
Features
- Banking data model
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- A comprehensive, scalable banking data model serves as a single version of the truth for an enterprise data warehouse that covers all key banking subject areas.
- A comprehensive dictionary that describes thousands of banking 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, Microsoft SQL Server, Teradata and DB2.
- Business data definitions are consistent with global banking data standards.
- Supports a variety of business issues.
- Superior data management
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- 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 data cleansing and augmentation services, and can be customized for real-time processes.
- Enables enterprise connectivity to data sources – ODBC, IBM DB2/UDB, Microsoft Access and Excel, Microsoft SQL Server, Netezza, Oracle, Sybase, SAS, Teradata, core banking systems and more.
- Supports unstructured and semi-structured data.
- Data quality is embedded into all 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 that 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
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- 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
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- A powerful set of interactive data preparation tools optimally addresses missing values, filters outliers and develops segmentation rules.
- An unmatched suite of predictive and descriptive modeling algorithms includes 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.
Screenshots
Ready to learn more?
Call us at 1-800-727-0025 (US and Canada) or request more information.
