Getting real about big data for the “main street” credit union
What’s The Hype About?
The Big Data and Analytics movement says that a 5% uptick in customer loyalty – like compound interest – can add from 25% to 85% in profits. With numbers like that, it’s no wonder that Big Data and the services that go along with it are growing at a 26% CAGR through 2018 – a $41 billion market.
Unlike some management fads that have come and gone, if you pull back the curtain on Big Data, you’ll find math, science and technology.
How does Big Data work?
When you sift through the hype, Big Data promises the opposite of chaos – high tech ways of finding patterns in data that reveal insights not otherwise detectable. Basically, just about every economic outcome you struggle with can be analyzed by algorithms probing reservoirs of data fed from internal and external sources.
In order to find patterns in chaos, you need three things: algorithms, databases and people that know how to use them wisely. Big Data is no magician’s trick. Top talent is scarce, expensive and treated like rock stars. An entry-level data science analyst can earn a starting salary of $110-$120K.
Big Data applications are everywhere and growing. Do a Google search on Big Data with these words – customer, pricing, loyalty, risk, ALM, lifetime value – or review topics at recent conferences of CUNA or BAI Retail Delivery on marketing, credit risk or payment fraud.
The question isn’t whether Big Data is powerful or valuable, Wall Street and Silicon Valley have been pumping billions of dollars into Big Data startups. The question is how a community-oriented credit union can harness and use Big Data to improve performance or prevent unintentional mistakes from turning into regulatory nightmares.
Big Data for Main Street Credit Union?
Just as the CRM movement shifted away from its core concept – customer value – to massive investments in data warehousing, systems integration and sales force automation software, the big money behind Big Data is following a similar playbook.
Thanks to technology advances, companies can store 25x more data per dollar than 10 years ago.
It may be exciting to think what you could do if all the data you ever dreamed was put into one big pot. Ironically, the technology side of Big Data is probably the easiest part. The hard work is 1) figuring out the question Big Data will answer and 2) whether the insight is actionable, affordable and practical for your organization.
Before you begin your Big Data journey, ask yourself these Big Data questions:
- What question can Big Data answer that would be a financial and strategic success? What’s the BIG IDEA you can’t figure out without Big Data?
- Have you tried segmenting your member base by life stage, profitability or key economic behaviors to identify opportunities for improvement? Have you compared your products and prices against your competitive peer group to identify opportunities and gaps?
- If you had perfect information about every possible action and reaction of your members’ behavior tomorrow morning – like the perfect weather forecast – how would you configure your resources, systems and staff differently in order to capitalize on it? Would the cost be justified?
Can Big Data “scale” to the needs and culture of a small credit union? Absolutely YES, but we need to take a radically different approach since Big Data was designed and justified for large applications and large companies by large vendors.
Let’s borrow a page from the entrepreneur’s playbook to innovation.
Collaborate, Innovate, Experiment
For the past year, CUNA and Informa Research Services have been collaborating with individual credit unions, CFO groups and state trade organizations on a practical R&D effort around pricing, margins and competitive positioning.
“This collaborative, deep-dive approach Informa takes with credit union CFOs like me, our state association and other partners makes a lot of sense. I like the spirit of teamwork they bring and their focus on how real work gets done. The way they’ve layered easy-to-use business intelligence tools with financial products and pricing in our markets will be help us show our value to our Members. We take a pragmatic approach to innovation focused on results for our members, not a silver bullet. Informa knows the difference.”
-Alan Althouse, EVP/CFO, TruWest Credit Union
The goal is to provide an 80/20 solution – the tools, methods and processes – used by competitors with scale and resource advantages. There was a bias towards “usability” and practical results with simple, proven approaches. The scope of the effort was based on 200+ hours of interviews with 35 financial institutions in 5 states in 2014. Research findings were openly shared at the national and state league level
Design goals were discussed early and often as the rapid-cycle prototyping process kicked off. CFOs around the country were consulted and provided feedback.
As a result, scale-appropriate applications of Big Data – called “Business Intelligence” – were woven through databases from Informa and other external sources to streamline and simplify decision-making for pricing, marketing and business development applications. Smart calculators and “financial rules” were integrated with dashboards and drop-down menus to empower users with just-in-time insight customized to their competitive situation.
For example, a credit union can compare its market position for any product – deposits products, account and service fees and consumer loans and LOC – against a competitive peer group or state-level indices of banks, mega banks or just credit unions.
A unique relationship view quantifies a credit union’s financial advantage for members segmented into 8-10 “life stages,” such as new professional, young family and empty nesters. These “life stages” can be customized to reflect a credit union’s target market and regional influences.
“We focus on members, margins and prudent AL management — a consistent pattern of funding, risk and pricing decisions based on hard data. The intensive Q&A sessions with Informa dug to the roots of those decisions. We identified how a “lean” Big Data approach could improve those decisions with transparency and speed. Now I can quantify our value to members at the product and relationship level, but also compare strategies for margin improvement from better funding and pricing in one place. I’d love to see our industry adopt a smart framework like this.”
-Sean Bowers, EVP/Chief Information Officer, MECU
Yield curves for FHLB funding and investment were integrated with calculators of duration with varying prepay assumptions. Users can test loan pricing strategies against internal ROA targets and competitive positioning against a peer group or state-level indices. Analytic reporting that compares products, prices, competitors and market rates is performed by drop-down menus like tax software. No programming is required.
This model of collaboration – CUNA, state leagues, credit unions and tech partners – is a smarter approach to innovation that leads to faster results without the risk and cost of traditional DIY methods. It requires trust, experience and egos are parked at the door.
Working together as a team – as partners – is a smarter approach to problem-solving than swinging for the fences by yourself.
