In today’s data-driven world, there is more possibility than ever for credit unions to leverage the personal relationships with members that have always given them an edge over mega banks. And harnessing their data via advances analytics can provide CUs with the deep membership understanding they need to enrich such relationships.
It is a good time to be a credit union. In the last year, credit union membership grew by 2.9 percent, adding 2.85 million new members. It was the largest increase in more than 25 years.(1) Making the depositor happy with exceptional member service and competitive rates gives credit unions a major competitive advantage over mega banks. But now there is an even greater opportunity to shine in a crowded marketplace.
Today, in the midst of a data-driven environment, credit unions have a huge advantage over other financial institutions: personal relationships. Using advanced data analytics couple with a strong community focus and deep member relationships, credit unions have the power to better understand and respond to member needs by offering more tailored products and services across different channels. Credit unions that harness the value of their data are gaining a deeper understanding of their membership’s vital trends and purchasing influences and behavior. This newfound knowledge is leading to improved financial performance, reduced risk, enriched relationships and greater member loyalty.
A recent study from Celent found that up to 40 percent of bank executives interviewed desired to be data driven. (2) For those institutions eager to explore the benefits, 69 percent believe data analytics can improve sales results and customer relationships. In a recent American Banker survey, 36 percent of bank executives said they were looking to sign new contracts with providers of analytics services. (3) It is clear that financial institutions see the tremendous value and the competitive advantage the right advanced analytics program offers.
Is your credit union ready to leverage data analytics?
Those data-centric credit unions that are adopting predictive analytics are turning detailed information on each member into more comprehensive insights. The result? They have gained a foothold in the market by offering fewer but more relevant products to their members. Plus, these credit unions are strengthening relationships, improving retention, offering even better service, driving new business and boosting revenue. It’s a win-win that needs to be part of the overall strategic direction.
TRANSFORMING YOUR BIG DATA INTO SMALL, ACTIONABLE DATA
It’s estimated that more than 2.5 quintillion bytes of data are created every day. The data your credit union produces can be a goldmine of knowledge and foresight to the marketing team once it is converted into actionable data. Data analytics enable you to interpret signals to better understand what is trending by identifying large-scale patterns of behavior. then you can develop “what if?” scenarios and statistical models to forecast likely outcomes. In other words, when your institution is looking to launch a new product or service or to open a new branch, you will have the insight and information to make more informed decisions rather than guessing or relying on historical trends. The data can further reveal member demographics, geographic factors, response rates to marketing campaigns and how likely different customer segments will be to visit a branch, buy a product or be perfect candidates for one of your credit union’s services.
Collecting and utilizing the data and then transforming it into small, actionable, decision-making data creates the opportunity to harvest rich sources of information. Your credit union can build profiles supporting compelling marketing and member acquisition campaigns and offers. Data analytics can assist marketing to respond to specific clues about what products a member may use or need across his or her lifetime at the credit union.
DATA ANALYTICS- PLAYING A PIVOTAL ROLE IN STRENGTHENING RELATIONSHIPS
Credit unions can accelerate past the competition in developing and deepening member relationships with advanced data analytics. It starts with building a better member experience. By tracking and measuring important indicators in a member’s life, career, family status, income and geography, you can segment members into easily targeted groups. From there, you can develop refined marketing campaigns that create relevant, personalized messages and experiences. This shows the members you care and are listening to their needs. Data analytics can improve the member experience and strengthen relationship by:
IMPROVING RETENTION MATTERS
Member retention is just as important as member acquisition. So the same enthusiasm and data-mining strategies that go into gaining new members should go into improving relationships with existing members. Credit unions that use data analytics to build relationships with members can positively influence retention rates. High member satisfaction levels lead to greater loyalty and referral business. However, all members are not created equal and some relationships are worth more than others. That’s why it’s vital to identify the most loyal and profitable members who can drive revenue at your credit union. Enter a solid advanced analytics program that can arm you with information to:
BETTER INSIGHTS WITH BETTER DATA
Most credit unions have vast amounts of data about their members, but they have trouble sorting through it all to make meaningful decisions. By investing in the right advanced analytics tools and data science consultants, in combination with deep member relationships, your credit union can compete and rise above the large, more analytically advanced financial institutions. A solid analytics program gives you the power to uncover valuable insights while recommending prescriptive actions. By having a 360-degree view of your member portrait, personalized communications, and enriched service and experience, you can maximize relationships and your community impact, and you can increase member loyalty for years to come. It’s not rocket science, it’s data science.
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