The Decision Maker

Why Loan Analytics are Key to Your Credit Union's Profitability

Posted by Dan Price on Jul 30, 2019 11:51:00 AM

One of the most common questions I hear thrown around is, “what is the purpose of analytics?” It’s a great question, but that’s because the answer isn’t as straightforward as it might seem.

The purpose of analytics is to solve complex problems with many variables. Analytics provide a multifaceted view of data sets. Each different view of those data sets gives a slightly different understanding of the data.

However, that answer doesn’t specify which data is being used, or for what. Here’s why that matters:

The Purpose of Analytics Changes Depending per Application

Different business units will use analytics differently. In credit unions, you may have marketers, compliance teams, loan officers, and many more all looking to get something different from an analytics solution.

In that sense, there is no exact purpose of analytics. The purpose is decided by the people who require, gather, work with, and utilize data.

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Topics: Business Intelligence, Data-Driven, Lending

The Credit Union Data Analytics Journey: 4 Steps to Success

Posted by Cassidy Cochrum on Jul 23, 2019 12:07:49 PM

Data analytics is no longer available only to massive technology corporations and big banks. In fact, with access to enough data, even smaller credit unions can join the fun. With several data analytics platforms that are accessible and affordable, credit unions of nearly any size can start taking advantage of integrated access to data. Plus, it’s surprisingly easy to start your own credit union data analytics journey.

At first, it might seem outlandish. Can you really keep up with the Amazons, Googles, Netflixes, and Facebooks of the world? Maybe not at their global level, but in many cases, credit unions have more data about each individual member than Amazon has about each of its customers. Well, sort of. But you can definitely catch up to the Wells Fargos and the JP Morgan Chases out there.

So, how does it happen? How does it all start?

Step One: Find a Project

If you want to establish your own credit union data analytics program, your first step is to find a project. Your project can’t be vague! It’s not enough to choose “use analytics” or “become more data-driven” as a goal. Analytics itself isn’t a goal—it’s the tool you use to accomplish one.

When you choose a goal or project, choose one that addresses a need at your credit union. Some examples that credit unions have had success with are:

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Topics: Business Intelligence, Data-Driven

Data: The Key to a Successful Card Portfolio

Posted by Ann Farrell on Jun 25, 2019 1:17:41 PM

Understanding your members’ behavior gives you the opportunity to serve them effectively, and in turn, increases your bottom line. Unfortunately, it is not uncommon for credit unions to overlook existing cardholders as a significant opportunity to help stimulate portfolio growth and increase profitability. With the use of data, you can identify trends that will help you to ensure that you are offering the right incentives, rewards, and services that will not only retain your existing cardholders, but also attract new prospects.

What can data do for you?

Data can open the door to product and service opportunities that your credit union did not offer in the past. Also, by utilizing data from your card portfolios, as well as home and auto loan applications, you will have a vivid picture of each member that will help you to create unique member experiences. In the end, you can feel confident that you are offering a competitive card product and doing what is required to stay “top of wallet.” With rich data you can:  

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Topics: Marketing, Membership, Data-Driven, Lending

Why Using Data to Understand Membership Trends is Important

Posted by Stephanie Hainje on Jun 20, 2019 1:28:10 PM

Many credit unions are struggling to retain members and capture the wave of increased credit union membership the industry is experiencing despite paying out a record level of membership dividends, helping members affected by the government shut down earlier this year, maintaining lower rates and fees, and providing stellar member experience.

5M new members joined credit unions Y/Y September 2017 to September 2018, but why aren’t consumers flocking to more credit unions in larger numbers? There are nearly 5,000 credit unions with assets of $500M or less, and some think these are the most vulnerable credit unions who may not survive to serve their membership.  Year over year, credit unions with less than $50M in assets (58% of all U.S. credit unions) have reported negative membership growth, while the top 552 credit unions with assets above $500M have experienced strong membership growth.

If your credit union membership isn’t growing, dig in and determine why. How many new memberships were opened in 2018 versus the number of closed memberships?  There is a lot you can do with your membership trends from data you already have. You can:

  1. Identify your most profitable members and apply strategies to shift low profitable members to highly profitable.
  2. Examine the behavior of long term members. What are your member acquisition products? What other products and services have been added throughout their membership? How many products and services do long term members have with your credit union? Are members using the digital products you offer?
  3. Analyze member attrition over the last 3-5 years and create predictive models to decrease member attrition. What segment of membership has the highest attrition? Do they have similar products? What products do long term members have that short-term members do not? What is the average length of membership and what do you want it to be? Does a change in address to a zip-code more than 25 miles from your credit union trigger a closed membership?

Additionally, credit unions need an internal membership champion who is continuously focused on membership numbers.  Do your employees know how many members you have?  Do they know what your membership growth goals are?  You have one, right?  Have employees been trained on how to retain a member and informed of the conversations to have at account opening, and throughout a membership life-cycle to continue developing member relationships?

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Topics: Reporting and Analytics, Membership, Data-Driven

Data Use Cases for Credit Unions: Chapter 1

Posted by Lou Grilli on Apr 29, 2019 11:53:00 AM

Data Use Cases is an ongoing series showcasing real use cases of success that credit unions have had using data analytics to solve real world problems. Data analytics was once the sole domain of giant tech companies – Amazon’s suggestions “If you bought that you might like this”; Facebook’s algorithms that determine which of your friend’s posts you most want to see on your timeline; Google’s ability to propagate data about you so that when you search for something like “hotels in San Francisco” you start seeing ads for restaurants in San Francisco on other sites. With the proliferation of data across multiple systems, the increase in computing power at a decreasing price, and tools to extract and harness data, the science of data analytics to create solutions to business problems, also known as business intelligence, is being increasingly used by credit unions to make better decisions. And it’s not just the biggest credit unions introducing business intelligence through data analytics to their staff. Credit unions with under $500 million in assets are realizing that use cases for data analytics drive ROI, better member experiences, and increased product penetration across their member base. Almost ironically, it is the smaller credit unions that absolutely need to embrace the use of data analytics – they are the ones that need to remain competitive or be merged out of existence.

Data Just For Data’s Sake – NOT!

It’s important to keep in mind that no company, regardless of what industry, invests in data analytics just for the technology. The cost of the tools, the hardware (or more commonly, the cost of cloud storage), investment in staff such as business analysts and possibly a data scientist, consulting services to help get started, can represent not just a significant up-front investment, but an on-going cost that must be justified. The justification comes in the form of use cases – individual examples of data-driven decision-making that makes a difference in how members are rewarded, or sold-to, or what products are offered, or just making a member feel more connected to their credit union through targeted, meaningful campaigns. In fact, for a credit union that’s just embarking on the data analytics journey, the best way to start is with the end in mind. Pick a single use case, a single vexing problem to solve, ideally one that has a fairly high payback if solved correctly. There are many articles that talk about the intangible benefits of business intelligence. But credit unions, especially their CFOs, want to see a return on their investment. The following paragraphs are a few real use cases that credit unions have shown to prove out their investments.

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Topics: Business Intelligence, Data Analytics, Data-Driven

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

Posted by Nate Wentzlaff on Nov 29, 2018 12:22:22 PM

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.

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

7 Ways to Make the Most of Credit Union Member Data

Posted by Steven D. Simpson and Paul Ablack on May 16, 2017 11:01:00 AM

Leaders and business intelligence at credit unions are putting a tremendous focus on ways to use advanced data analytics to identify trends, detect patterns and glean other valuable findings from the sea of information available to them. Without question, member data is valuable. But the greatest value lies in the ability to empower each line of business to achieve strategic initiatives and performance goals. When this empowerment is coupled with improving member service, a proven, repeatable best practice results.

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Topics: Membership, Data Analytics, Leadership, Data-Driven

Looking to the Future of Data Warehousing

Posted by Mark Portz on Jan 30, 2017 11:01:00 AM

In the fourth Data Analytics Series BIGcast, From Questions to Answers: Becoming a Data-Driven Organization, John Best speaks with Brewster Knowlton of The Knowlton Group about data-driven decisions, data warehousing and successful data integrations.

The Six Characteristics of a Data-Driven Organization

According to Brewster, there are six characteristics to determine whether your organization is really data-driven:

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Topics: Analytic Data Model, Data-Driven, Podcast

8 Steps to Make Data Analytics Work for You

Posted by Peter Keers, PMP on Jan 17, 2017 11:01:00 AM

Credit unions interested in advancing their data analytics efforts will find a wealth of information in a recent article in the McKinsey Quarterly. Simply entitled, “Making Data Analytics Work for You – Instead of the Other Way Around” (Mayhew, Salah, and Williams), the article provides an easy to follow list of steps for any organization to get the most out of their investment in data analytics.

The authors emphasize that improving corporate performance is the only meaningful reason for organizations to pursue data analytics. As a result, they state two important principles:

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Topics: Data Analytics, Data, Data-Driven

The Comfort of Data-Driven Decisions

Posted by Nate Wentzlaff on Dec 13, 2016 11:02:00 AM

 

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

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”, in which I would usually reply, “It feels pretty good to me”. We became frustrated by a complicated search for a large budget item 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.

Read More

Topics: Data Analytics, Data-Driven