The Trellance Data Blog

Amazon Established a Strong, Loyal Customer Base... Credit Unions can too with their Members

Posted by Kim Carlstrom on Sep 8, 2014 6:56:30 AM

According to the whitepaper “Amazon vs Borders: A Lesson for Credit Unions”, written by Founder and CEO of OnApproach, Paul Ablack, Amazon has thrived on fostering loyal and intimate relationships with its customers. These relationships are the cornerstone of Amazon’s success. With utilization of data analytics, Amazon has information readily available to pursue and enrich these relationships.

As stated in the white paper, “Amazon sold its first book online in 1995. It was a new entrant to a very mature market and within 10 years became a leader in a market that saw the demise and eventual bankruptcy of a formidable competitor, Borders Books.”

Amazon’s success is attributed to:

  • Building their business around a core data strategy.
  • Integrating all of their business processes into a single repository and then churning out analytics to drive efficiency and effectiveness across the enterprise.
  • Committing to use data to run every aspect of their operations.
  • Investing in data analytics technology.
  • Collecting and storing data in their data warehouse from every click and transaction made on their site.
  • Applying valuable analytics to the collected data to create intimate “relationships” with customers.
  • Personalizing customer relationships by providing helpful reviews and suggestions on what others like them were purchasing.

This same analytics driven business strategy can be effectively applied to credit unions.

Integrating Data

Credit unions have a wealth of information about their members and the way they spend and save their money. Unfortunately, for most credit unions, this information is currently contained in separate data sources. To tap into the full potential of the data, the credit union must first integrate the data from their separate data sources into a data warehouse.

Analyzing the Data

Once all member transactional data is in a single repository, a data warehouse, then the credit union can analyze it to build relationships with the member base and to attract and retain younger members. As mentioned in the white paper “currently, less than 5% of the credit union industry has embraced data and analytics as a core foundational element of their growth strategy.” Today credit unions sit on a mountain of data that gives them a unique advantage over any looming competitors. Harnessing the power of the data is necessary to take a leadership position in the industry.

Enriching Relationships with Members

With the data warehouse and analytics, credit unions can apply innovative applications to enhance their relationship with members. For instance, it is possible to provide information to the front line staff that allows them to recommend the next best product to members based on “basket” analysis that identifies complementary products.

Most credit unions invest heavily in mobile technology to allow members the convenience of interacting virtually. With data analytics, a much more powerful mobile application can be created. This application could allow members to set a reserve in their accounts and give daily updates on their balance remaining until the next paycheck deposit. Before making a purchase decision at a store, a member could consult with their credit union application and see the impact of their purchase decision on their average daily allowance for the rest of the month.

The possibilities of enriching member relationships are countless when all of the member transaction data is integrated into a single repository. Data integration allows the credit union to better know its members and better meet their needs, which will ultimately lead to increasing member satisfaction and retention. Inevitably resulting in reduced risks and improved financial performance.

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Article written by: Kim Carlstrom, Marketing Manager at OnApproach

Topics: Business Intelligence, Big Data, Credit Unions, Data Integration, Analytic Data Model, Data Analytics