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Using Data Analytics to Create Customer Loyalty
Thursday, January 14, 2016 6:25 AM

How to Leverage the Concept of Big Data to Better Understand Your Members’ Needs

The concept of “big data” has been gaining momentum since the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition as the three Vs1:

  • Data Volume. E-commerce channels increase the depth/breadth of data available about a transaction. As enterprises come to see information as a tangible asset, they become reluctant to discard it.
  • Data Velocity. E-commerce has increased point-of-interaction speed and the pace data is used to support interactions.
  • Data Variety. No greater barrier to effective data management exists than the variety of incompatible data formats, non-aligned data structures, and inconsistent data semantics.

One needs only to look to Amazon.com to realize the implications of big data on e-commerce. Amazon has no face-to-face customer interactions, yet Amazon set a new standard for retail commerce. Think about it, you can get almost anything from Amazon. Amazon not only tracks the products you buy, it tracks the products you browse and makes cross-selling recommendations based upon what other people who shopped for similar items actually purchased. Purchases are fulfilled from a variety of suppliers, fulfillment and shipping are prompt and accurate, and delivery tracking is readily available through any internet-connected device. Amazon even accumulates and publishes customer feedback ratings on products and the purchasing experience.

According to Wikipedia, “big data” is a broad term for data sets so large or complex (structured or unstructured data) that traditional data processing applications are inadequate. Amazon and many other companies created sophisticated enterprise data processing infrastructure to leverage information (in the form of data) to know their customers, enhance the service experience, and create customer loyalty.

By now you may be thinking, “Credit unions are nothing like Amazon. We don’t deal in huge data sets, and even if we did, we don’t have the computing capacity to do what Amazon does.”  And you would be right.

Most credit unions do not have big data as it is typically defined, but if you have a member’s primary share draft/checking account, you have access to knowledge about how they spend their money. That information could be invaluable to building a lasting member relationship. Data analytics, even if it is not big data, could therefore be a critical component to future credit union success.

As consumer financial services preferences migrate away from face-to-face transactions toward online and mobile delivery, members will increasingly compare credit union services with e-commerce benchmarks like Amazon.com. A satisfying mobile experience may be the key determinant in Millennials’ (currently ages 15 to 34) selection of a financial services provider. And it might be the primary determinant for Generation Z (born after Y2K) as they become mainstream consumers of financial services. Who among you is not interested in attracting younger members?

With this in mind, Credit Union Resources has been exploring the tools and competencies credit unions need to leverage data analytics. Our research suggests there are no “one size fits all” solutions, but there is a host of opportunities for credit unions, large and small, to use data analytics to better understand and fulfill members’ needs.

Over the next several weeks, we will publish a series of articles and blogs authored by some of the premier credit union data analytics providers to help you understand the basic tools, techniques, costs, and benefits of leveraging data analytics to manage the business of the credit union and match products and services to member needs. The initiative will culminate with a panel discussion with industry experts at the Cornerstone League’s Annual Meeting in April. Our hope is that the information to follow will help you determine if and how data analytics might help you reach your goals.

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[1] Application Delivery Strategies, META Group, Doug Laney, February 6, 2001.