Don't buy into the myth that CUs are too small for data analytics
Credit unions have never had more ways to learn about the attitudes, concerns and priorities of their members.
We’ve entered an era of data-driven financial services – from omnichannel engagement with customers to multi-platform digital banking, financial institutions have access to data across a vast array of customer touchpoints. And with the availability of ever-more sophisticated and accessible data analysis tools, they’re capable of extracting a great deal of meaningful insights from that information.
Note the word “accessible.” Because credit unions often lack the technical resources of much larger financial institutions, they often assume that this prevents them from developing a rigorous data analysis platform.
According to a 2018 survey of executives in the credit union industry conducted by Best Innovation Group and OnApproach, 45% of credit unions don’t have a data strategy in place. Meanwhile, the report notes: “Our research indicates that almost all competitors (large banks, regionals, Fintechs and Big Techs) have invested heavily in data, analytics, decisioning and machine learning.”
There are many stubborn myths that prevent credit unions from developing comprehensive data management strategies. They tell themselves they’re too small, have too many financial constraints and can’t compete with large financial institutions. But none of these excuses are sustainable at a time when data collection and analysis is easier and more important than ever before. This is especially true when industry partnerships, so natural to the collaborative nature of the credit union movement, are available to fulfill this need as well.
There are ways credit unions can start taking advantage of all the data at their disposal to learn about their members and provide them with the best financial products, services and experiences possible.
Make data transparent and accessible
It doesn’t matter how much information you collect and analyze – if it isn’t available to all key stakeholders, it isn’t going to do you any good. Data-conscious financial institutions often emphasize the importance of extracting “actionable” insights from consumer data, but what does that actually mean? It means these insights can be put into practice in the form of improved member services and products, which can’t happen if data isn’t available and intelligible to every relevant department and team.
This process starts with a clear delineation of roles and responsibilities. The idea that data management and analysis should be confined to IT teams and other tech professionals is long gone – everyone from branch managers to member services representatives need access to as much data as possible to do their jobs. This is where data analytics platforms provided by companies like Qualtrics can be invaluable – by centralizing data on a single company-wide dashboard, they ensure that your data management strategy is cohesive and transparent.
An article from Credit Union Journal cites the CEO of OnApproach, Paul Ablack, who says credit unions should “build and share applications across the user community.” He’s right: An integrated approach to data management requires consistent and open communication across the company, and this should be a core focus of your data strategy.
Make accountability a top priority
A rigorous data collection and analysis platform allows credit unions to establish specific goals, as well as metrics for determining whether or not they’ve been met. The crucial concept here is accountability: When departments and teams are working toward precise targets for member retention and satisfaction, they’ll have objective indicators of success and failure. This doesn’t just help credit unions identify which parts of the company are struggling – it also helps them repeat successes and reward employees who are doing a good job.
Credit union executives who responded to the Best Innovation Group/OnApproach survey cited “improved member service” as one of the top goals of their data strategies. This is no surprise: High-quality, personalized experiences are what set credit unions apart from larger and more impersonal financial institutions, and consumers are increasingly intolerant of bad service and experiences.
According to a 2018 PwC report: “Even if people love your company or product, in the U.S. 59% will walk away after several bad experiences, 17% after just one bad experience.” No matter how loyal your members are, these numbers should concern you.
The only way to ensure that the member experience is positive at every stage of the member journey (as well as overall, which isn’t necessarily the same thing) is to collect data at every touchpoint. When you have this data, you can determine which customer-facing initiatives are working and which ones aren’t. Then you can have evidence-based discussions about how to hold employees accountable and improve your products and services.
Don’t wait to develop a data management strategy
According to the Best Innovation Group/OnApproach survey, 55% of credit union executives say they won’t be able to “fully implement” their data strategies for three to five years. More than half of credit unions said they would spend less than $100,000 on data analytics in 2018, while one-third of them said they hadn’t invested in analytic tools at all over the preceding 12 months.
The credit unions that fail to invest in data management will inevitably be outpaced by their competitors, and the longer they wait, the wider the gap will become. As the authors of the survey explain: “Credit [u]nions are likely behind the curve and falling farther behind each day that they have not articulated a strategy and begun to execute on it.” The process of developing a strategy can begin immediately, which should make the situation all the more urgent for credit unions that haven’t started.
A proper data management platform isn’t just comprised of data collection and analysis tools – it requires a series of cultural and operational shifts. From clearer and more consistent communication across the company to the development of a results-oriented culture, effective data management rests on a foundation of accountability and transparency.
While it’s essential to determine which technical resources you’ll use, these cultural changes are every bit as important. And they don’t require a team of IT professionals or sophisticated analytics instruments – they just require a willingness to get started.