The Trellance Data Blog

The Comfort of Data-Driven Analytics Decisions for Your Credit Union

Posted by Nate Wentzlaff on Nov 29, 2018 1:22:22 PM

The Comfort of Data-Driven Analytics Decisions for Your Credit Union

As the next generation begins making financial decisions, credit unions will be able to comfort them with data- and analytics-driven product recommendations.

In the realm of financial institutions, the credit union still offers more than its competition. Whereas credit unions were hampered by limited technological options in the past, new developments in data collection, integrations, and analytics are helping them compete with banks.

Recently, my wife and I were shopping for a mattress. We began the process by “trying out” mattresses by how they felt. My wife thought she preferred firm mattresses, while I thought I preferred soft ones. As we tried mattress after mattress, my wife would ask me, “what do you think about this one,” to which I would usually reply, “It feels pretty good to me.” We became frustrated by our search until we found a mattress store that comforted us with data.

The mattress store (Becker Furniture World) is locally owned with only 8 locations (does this sound familiar to your credit union?). They approached mattress shopping from a data-driven way. By using an analytic data model (developed by Sleep to Live Institute), they are using analytics to aid customers in their mattress investments through data sensors and user input. The data comforted us enough that we decided to purchase one of the mattresses it recommended.

Data Acquisition from Users

When we walked into the Becker Furniture World, it was different than all the other mattress stores. There was a futuristic-looking canopy near the front of the store. We asked a store associate what the machine was and were informed that it collected data from our bodies and sleeping patterns to recommend the best mattresses. Before entering the contraption, we entered in personal data about ourselves using ranges for age, weight, and height, along with other qualitative data including where we currently have pain and our sleeping preferences.

After entering in our personal data, we both laid down on the bed (hooked up to data sensors). This data was then sent to the Sleep to Live’s data pool, and a report was printed for us. The report displayed statistics about us and recommended mattresses throughout the store.

Analytic Data Model

Sleep to Live Institute used their analytic data model to give us a data-driven recommendation for mattresses that would give us the best sleep possible. Utilizing this data, we found the mattress that would meet both our needs. Feeling empowered by the analytics we were finally able to come to a fast decision.

Improving Member Service with Data at Cornerstone Credit Union, Read the Case Study

Data Science

A new era of data analytics is transforming the credit union industry as well. While our sleep health was important to us, we’re just as concerned about our financial health. Strong data analytics and integration are key to providing critical modern services for members.

A positive experience can convert a customer for life and will establish a strong level of trust with their credit union.

Financial Health Problems

Just as my wife and I were initially confused in the investment decision of a new mattress, many credit union members are confused when it comes to making decisions to improve the financial health of their family. They have tried a lot of products by how they “feel” and what they have heard from friends and other unofficial sources. Unfortunately, just like buying a mattress, the decisions made on financial products have consequences and members have to lie in the bed they made (pun intended). Credit unions should be collecting data from members to improve their financial health.

Member-Centric Analytics

When members interact with their credit union, they should be able to enter their financial goals and pains for a personalized output. The good news is, credit unions have the “sensor data” of members’ financial lives, and it is located within the transactions that members have been conducting on a daily basis. By integrating transactional data and member-input data, with the guidance of a member-centric data model, product recommendations can be presented to members in a comforting experience.

Credit union data analytics and data integration are key to providing the kind of member-centric services that are the hallmark of the credit union industry. Everyone should enjoy the kind of comfort that data-driven decisions can give them. If a small, community mattress company has the technology to help me and my family sleep better at night (pun again!), why shouldn’t my credit union have the same?

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Fall 2018 Semi-Annual Credit Union Industry Survey Report

Topics: Big Data, Analytic Data Model, Data-Driven