Increase Membership with Analytics

Leverage advanced data visualization tools to better understand your members and increase membership with analytics.  By combining your credit union member information and external postal code level demographic data.  Maps can be created that color code each postal code based on the concentration of members.  For example, in the graph below postal code 78222 has 40 active members.  In addition, there are 3,685 households that earn under $50K dollars.  There are also 2,440 households that earned between $50K and $100K.  As well as 1,096 households that earn over $150K.   There is also information available for each postal code that lets you know the number of people that rent vs buy their homes, how many cars people have, average number of children and age in each household.  By using this information you can market to your existing customers like never before.  In addition, you can better understand the demographics of a postal code to know how to market to add new members.

Do you have products that help new college graduates?

Are you trying to help existing members know what products are available to them?

Looking to add new branches or ATMS?

Credit Union Membership Power BI

See where you members live and how to engage to better serve them

 

These projects provide great value and typically can be completed in 3 months.  Start helping your members get more from their credit union by better understanding your members.

 

Predictive Analytics Presentation Summary – Increase Membership and Revenue with Predictive Analytics

5 Strategies to Grow Credit Union Membership – CU Times

 

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