Customer Analytics for Banking turns raw data into insight that you can use to manage marketing strategy intelligently and increase customer retention. Customer analytics for banking 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.
- 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.
- Banking data model. 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.