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CU Rise Analytics

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Householding

Grouping members into the right households is finally fast and easy.

Household-level information about your members is an important factor in developing more effective marketing strategies and outbound communication. But, organizing members into households has been an expensive, time-intensive challenge for credit unions, due to non-standardized name, number and address recording.

CU Rise has developed a Householding algorithm to efficiently group members into their respective households. It analyzes data like last name, address, city, zip and home phone from within your data stores, and does not require any external information. The “fuzzy string” algorithm sorts and segments information quickly and easily, and uses highly advanced matching rules to produce better results.

Householding is a subscription-based service. It is a Windows-based application with a simple and easy-to-use interface. New features are continuously added to improve the accuracy and utility of the results.


Key Features:


  • Pick and choose different types of matching rules
  • Store and track multiple versions of matches
  • No additional external information required
  • Easy-to-use with fast results
  • Export results in csv, tabular or excel formats
  • Compatible with most core centric or third-party data warehouses

Benefits:


  • Not a black box solution allowing you to control household creation criteria,
  • Develop clean household and member lists for more effective member interactions
  • Enhance cross-selling efforts
  • Reduce duplicate efforts for greater budget efficiency
  • Analyze and mitigate risks at the household level
  • Reduce reliance on expensive MCIF systems and vendors