The Science of Creating Better Member Experiences with Analytics
Even as more interactions are digitally enabled, consumers have high expectations for personal relationships.
By Shazia Manus, Chief Strategy and Business Development Officer, AdvantEdge Analytics
On its own, data is just that – minuscule bits and bytes of information. When brought together with analytics technology, however, once-disparate data sets generate insights capable of changing lives – and, in some cases, even saving lives.
Take the fertility treatment, CATI, for example. By layering artificial intelligence over the top of massive stores of human health data, CATI helps fertility specialists identify embryos with the greatest chance of survival. The technology has contributed to a nearly 40-percent increase in pregnancy success rates.
Asthmapolis is another example of lifesaving data science at work. The GPS-enabled inhaler identifies asthma trends in a geographic area. That data is then combined with information from the CDC, helping researchers and health agencies predict trouble spots. With those insights, they can develop highly personalized treatment plans for children and adults who suffer life-threatening asthma attacks, and along the way, ease some of the complications for insurance providers and their insured.
These are just two examples of the many analytics use cases popping up in health care, insurance and other data-rich fields, like financial services.
Of course, not every application of data analytics is life altering; some are designed simply to be life improving. Data analytics use cases in financial services, for example, often center around reducing friction or irritation. After all, money matters can be complex and tedious. Those are not the kinds of experiences credit unions and other legacy financial providers can afford to settle for in this era of immediacy and hyper-personalization.
Predictive Models Highlight, Eliminate Friction
The demographic, transaction, behavior and other types of data that live within even the smallest credit unions offer a wealth of opportunity to modernize member service. That’s because we can better serve members by better knowing them.
And, being known is the expectation among nearly every member segment. As more digital brands change the way people live, work and play, credit unions are expected to keep pace... all while maintaining that human touch that makes them distinctive. That’s where data and digital transformation come into play. To provide exceptional digital experiences, we have to have a data competency. We like to say data and digital are two sides of the same coin.
If we study our members the way scientists study their subjects, we can live out the “people helping people” philosophy in much more meaningful ways. That’s how we’ll ultimately drive revenue and growth.
Predictive models, when fed a steady diet of data, are being trained to streamline everything from new account opening to fraud detection. These classic friction points are rooted in protecting the financial institution, and rightly so. With the help of machine learning algorithms, though, we can now protect the credit union while also creating a simple, perhaps even enjoyable, experience for the member or prospective member.
Crafting Strategy with a Scientific Approach
We can also leverage data analytics internally to improve operations, gain efficiencies and free up our human talent for more imaginative work. That’s what IKEA did recently when leaders were exploring a sort of “off-the-wall” idea.
The furniture giant wanted to begin selling a home solar offering but wondered how their existing customers – who had always turned to them for inexpensive, fashion-forward furniture – would respond. To investigate the idea, they hired a team of researchers in the emerging field of applied neuroscience. The data they collected from portable EEG headsets and eye trackers allowed them to take what could otherwise be considered a subjective study and turn it into a scientific one.
Similarly for credit unions, data analytics can turn gut-based decisions into evidence-based ones. The challenge for the credit union industry, however, is to harness analytics for not only the enriching member and staff experiences it can generate, but also for the strategic business intelligence it can provide.
A good data strategy is cyclical. You start with the strategic imperative, the problem or opportunity you most want to solve, and then implement a data strategy specifically around addressing that problem. Then, when your data competency really gets going, you’ll be able to use it to better identify the next strategic imperative. It’s a land and expand approach that leverages momentum to help credit unions realize incredible speed-to-value within their data analytics programs.
Data Strategy Opens Up a Parallel Universe
To be sure, there are challenges to fully leveraging data for the actionable insights it can generate. That’s the world we live in. To evolve, credit unions must work to develop a new world – not one that replaces the world we live in, but one that runs parallel to it.
Building that universe allows credit unions to not only keep their current systems, but also the relationships they have with vendors that help them optimize transactions and maintain pristine records. At the same time, they can leverage cloud-native platforms and an expanding ecosystem of analytics tools to create their own intuitive enterprise. This is a universe where leaders are empowered and enabled to reimagine value, adapt and evolve – ultimately positioning the credit union for growth well into the future.
Transformation is as much about recognizing the future need of members as it is about addressing the pain points of today. Data analytics will help us expand our mindset, moving from a linear, retail way of thinking about the business of money to a platform-based, ecosystem style of holistically serving members.