The Decision Maker

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

Using Data to Create a Unique Member Experience

Posted by Erika Hill on May 7, 2019 10:59:00 AM

Enhancing member experience has been the subject of many blogs and white papers, and there is a reason why this topic is so popular. In fact, there are three good reasons why this topic is especially relevant now.

  1. Competition- Competition among financial institutions, challenger and online banks is getting fierce. Banks and online financial institutions like Marcus and Ally need your members’ deposits to fund their loan activity, and are offering higher returns for their business. Also, the huge credit card issuers want to put their cards in your members’ wallets, and are enticing them to do so with sign-up bonuses. Plus, every financial institution wants your best members’ loan activity on their income sheets.
  2. Technology– Technology has become affordable for credit unions of nearly any size. The technology to harvest data to drive decision making, segment your members, create targeted offers, and get a real-time view on each member, is much more accessible.
  3. Expectations- Your youngest members as well as the next generation that could become your newest members, demand a unique experience. They, like other customers (retail customers and e-commerce shoppers), all want a customized, digital experience. If it’s not fully digital, they want an experience that has some digital component. How do we create that experience?

The Answer is Technology

Of the three factors that make this topic especially relevant, technology is the one that drives everything else. Creating a personalized, positive experience that differs for each member relies heavily on data. This includes collecting, normalizing, and combining data from multiple sources to create a unique view of each member. This view is then used during the narrow window of opportunity you have while the member is in the branch, on the phone, in an online chat session, or engaged in online or mobile banking. This unique member-centric view is also used to determine which of your members should receive a special offer, which member should get fees waived, which members deserve bonus interest on money market accounts and other business decisions that help to enhance the member experience.

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Topics: Marketing, Membership, Data Analytics

The CCPA and GDPR: How These Emerging Privacy Laws will Impact the Credit Union Industry: Part II

Posted by Alex Beversdorf on Apr 9, 2019 11:02:00 AM

How GDPR and California Consumer Privacy Will Impact Credit Unions in 2019...

In the first part of this blog, we discussed technology regulation and updates regarding the legislation. With that covered, Part II will focus on what it means for your credit union and how you can prepare for the changes. 

How the CCPA Will Change the Competitive Landscape in the US

The CCPA won’t apply to all companies but will apply to a great majority, especially if one of these three thresholds are met:

  • Gross annual revenues in excess of $25 million
  • Buys, receives, sells or shares the PII of 50,000 or more consumers, households or devices for the business’s commercial purpose
  • 50% or more of the businesses annual revenue comes from selling consumer’s PII

If any of the above conditions are met, the marketers of the effected company have a great deal of work to do. Especially if they have no business tactic or strategy in place to organize all of their customer specific data. To comply with the CCPA, marketers must be able to organize and develop an efficient data scheme that compiles all of their consumer data. Consumers have the right to:

  • Know what PII is being collected regarding them
  • Know whether that is being sold and to whom
  • Say no to the sale of their PII
  • Access their own PII
  • Equal service and pay from the company, even if they exercise their own privacy rights and it requires more work to be done on the side of the business
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Topics: Data Integration, Membership, Digital, Data Ownership

The CCPA and GDPR: How These Emerging Privacy Laws will Impact the Credit Union Industry: Part I

Posted by Alex Beversdorf on Mar 28, 2019 11:54:00 AM

How GDPR and California Consumer Privacy Will Impact Credit Unions in 2019...

We are in the era of digital transformation. A time where data is being collected at exponentially growing rates all around us. A wide variety of businesses and institutions, including credit unions, have been collecting personal data on their customers/members for quite some time now. How can you receive any benefit from all of this available data? The answer lies in the use of Big Data Analytics. Big Data is used to help analyze extremely large data sets to identify patterns, trends and associations in human behavior. This method of analyzing data is very versatile and proving to become one of the most sought-after tools of today.

This has made the overall collection of consumers data much easier and more widespread. This is also commonly referred to as Personally Identifiable Information (PII) and the influx of access to it has raised some concerns. Common questions include: What data is being collected? How has it been collected? What is it being used for? Which third parties have access to it? And, how much control do we have over our own data?

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Topics: Membership, Digital, Data Ownership

Leveraging Data to Create Exceptional Member Experiences at Ideal Credit Union [VIDEO]

Posted by Mark Portz on Apr 17, 2018 1:02:00 PM

 

MnCUN Interviews: Ideal CU and OnApproach Work Together to Leverage Data Analytics' Potential... from CUbroadcast on Vimeo.

At the Minnesota Credit Union Network (MnCUN) Annual Conference, Paul Ablack, CEO, OnApproach and Alisha Johnson, Executive Vice President of Operations for Ideal Credit Union, joined Mike Lawson, Host of CUbroadcast, to discuss data access, member profitability, member engagement, data lakes, timely and targeted marketing, chatbots, real-time analytics, and credit union collaboration.  

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Topics: Video, Membership, Analytic Data Model, Case Study

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

Building Member Risk Scores with Data Analytics

Posted by Nate Wentzlaff on Aug 12, 2015 10:59:00 AM

Many credit unions rely solely on FICO scores for their lending decisions.  Data analytics now gives them the ability to develop their own forward-looking member risk scores.

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Topics: Big Data, Membership, Data Analytics

6 Steps to Develop Member-Centric Models

Posted by Nate Wentzlaff on Jul 22, 2015 11:11:08 AM

As financial technologies become more complex, credit unions must design their business and data models around the member.

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Topics: Big Data, Membership, Analytic Data Model, Data Analytics, Collaboration

Contact Analytics: Going Beyond the Call Center

Posted by Nate Wentzlaff on Jun 10, 2015 12:00:00 PM

As digital strategies continue to proliferate throughout the credit union industry, the contact center has become essential.

Calling a company about an issue can be a miserable experience.  Being transferred to four departments and having to explain an issue, not to mention basic account information, four different times will frustrate the most patient consumers.  These experiences have given the call center a bad image in the minds of consumers.  Stereotypes of call center agents who do not speak fluent English failing to understand a problem have been burned into the American culture.  However, the necessity for remotely assisting consumers has never been greater.  Redesigning the call center into a contact center will enable credit unions to give their members excellent service.   It will also empower credit unions to continue learning about their members through every interaction.

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Topics: Membership, Analytic Data Model, Data Analytics