The Decision Maker

Are Credit Unions Missing Out On a Growth Opportunity?

Posted by Ann Farrell on Aug 6, 2019 11:52:00 AM

Credit unions see an average of 7% year-over-year growth in their credit card portfolio revenue. That’s not bad, but it’s also not great. The issue isn’t necessarily that credit unions don’t have strategies in place to increase their card portfolio. They do. There are marketing initiatives, competitive rates, rewards features, and so much more. Nevertheless, more can be done. And, if you want your credit union to capture top-of-wallet revenue, more has to be done. The following five strategies are tried and true, and have helped over a hundred credit unions grow their credit card portfolios by an average of 19% per year. Fair warning: these are easier said than done.

Credit Line Increases

On average, about 60% of a credit union’s cardholder accounts qualify for a credit increase. If you haven’t run a credit line increase in a few years, that percentage is even higher. That presents a lot of opportunity!

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Topics: Business Intelligence, Credit Unions, Membership, Lending

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 Use Cases for Credit Unions: Chapter 2

Posted by Lou Grilli on Jun 6, 2019 10:09:00 AM

Getting Out from Behind The Curve

Chapter 1 of this series considered the importance in establishing a specific goal to solve using data analytics and proving the ROI in order to justify automation and decisioning using business intelligence in a credit union. Chapter 1 also highlighted two real use cases of success credit unions have had using data analytics to solve real-world problems. According to a recent study conducted by Best Innovation Group (BIG) and OnApproach (now Trellance), 45 percent of credit unions don’t currently have a strategy in place, and those that do have a strategy still say it will take three to five years to implement. Credit unions that aren’t making the most of data analytics today could be in even bigger trouble if an economic downturn occurs, as some economists are forecasting. “As we go forward there will be a significant performance difference between those that have invested and those that have not,” says Kirk Kordeleski, senior managing partner at BIG. “We think any downturn in the economy will highlight the advantage that data-oriented FIs will have over their competitors.”

How Much Will It Cost

The survey revealed that more than half of the 85 credit unions surveyed have budgets in place for data analytics. Of those, one-third plan to spend more than $200,000, the other two-thirds plan to spend between $50k to $200k. In addition, credit unions need to consider on-going costs. A rough rule of thumb is that a CU with $500 million in assets should budget between $150,000 and $300,000 per year for three years to cover software/hardware, analytic applications, and strategy. Smaller credit unions can find some savings by relying on a CUSO to provide the analytics and associated services.

The following paragraphs are real use cases that credit unions have shown to prove out their investments.

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Topics: Business Intelligence, Credit Unions, Data

Unlock the Data in your Reports

Posted by Lou Grilli on May 16, 2019 11:07:36 AM

Simple Steps To Make More Informed Decisions And Enhance Member Experience

There is a lot of buzz about big data and data analytics, but all the data in the world does no good unless it is utilized. Credit unions are behind retail and online companies in using data to make informed decisions. For example, if your forms, such as a HELOC application, do not have the fields for name, address, etc. already filled in for your members, that indicates that you are probably not using your data. You should already know this information about your members. Save them the hassle and give them the option of updating if necessary.

There’s so much more data than was available in the past that can be collected and used for purposes that can benefit the member. There are also better tools than previously available to aggregate the data to help decision makers. These two factors are bringing a wave of data analytics to credit unions. More importantly, it’s a matter of survival. Credit unions must take advantage of these opportunities to guide their sales initiatives. For example, the data can help you to decide who to target, like a new member who joined the credit union to access an auto loan, should be offered your credit union-branded credit card to maintain a sticky relationship. Or, who not to target for a specific product, like a member who already has your credit card but accessed a new loan, should not be sent another offer for the same credit card. Rich data helps you to determine who your target is for specific products and services, which helps to enhance your member experience.

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Topics: Reporting and Analytics, Business Intelligence, Data Visualization

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 Future of Analytics: Predictive Analytics

Posted by Austin Wentzlaff on Apr 22, 2015 12:24:00 PM

The promise of business intelligence and Big Data/Analytics has been around for years.  Companies have been making claims that data-driven decision-making will revolutionize organizations but have failed to fully deliver.  It is true that descriptive analytics (reporting) is necessary and valuable but in order to create real value (Return on Investment) for data analytics, organizations must think about the future.  In order to achieve real value from Big Data/Analytics, organizations must execute predictive analytics.

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Topics: Business Intelligence, Big Data, Data Analytics

Credit Unions and Big Data/Analytics [VIDEO]

Posted by Austin Wentzlaff on Mar 16, 2015 12:13:00 PM



 

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

The Next Big Idea for the Credit Union Industry

Posted by Paul Ablack on Feb 23, 2015 11:11:57 AM

The credit union industry is on the cusp of significant challenges with the potential to disrupt the financial services landscape as we know it. Big Data and Analytics is driving a new breed of competitor into what has been a very traditional marketplace. The industry will need to envision and build out the “Next Big Idea” for credit unions to stay competitive and successfully navigate the next 10 years.

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Topics: Business Intelligence, Big Data, Credit Unions, Data Integration, Data Pool, CUSO, Analytic Data Model, Collaboration

How Credit Unions Can Leverage Big Data [Video]

Posted by Austin Wentzlaff on Feb 5, 2015 11:30:00 AM

 


 OnApproach's Founder and CEO Paul Ablack discusses today's evolution of Big Data and how credit unions can benefit from this increasingly refined information to provide more specific products and services for enhanced value.

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Topics: Reporting and Analytics, Business Intelligence, Big Data, Credit Unions, Data Integration, Marketing, Data Pool, Video, Analytic Data Model, Data Analytics, Collaboration, Podcast, Digital, Lending