Big data is inherently democratic. Credit unions just need to know how to slice, dice, and segment … or hire someone else who does.
That was a key message during the CULytics Summit, where credit unions and vendors have gathered to commune about the realities and possibilities of data analytics.
Among the recurring themes was the inherent leveling power of data analytics that credit unions can leverage to compete and serve.
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As an industry, we need to get over the mindset that we’re not big enough to do this, said Joel Hartzler, senior stewardship and development director at Filene Research Institute, during a Wednesday morning panel discussion. There’s a lot of help out there to give credit unions a more complete picture.
That’s good because a Filene study of 30 credit unions showed a desire to use big data but a lack of internal skill, regardless of size. That gap has created analytically impaired institutions, according to Hartzler.
Along with finding the people who have the skills to help credit unions mine data and craft products, many credit unions also must overcome a baked-in reluctance to favor some members over others.
A lot of us find ourselves fighting muscle memory, said panel moderator Dale Davaz, research and development strategist for Spokane Teachers Credit Union ($2.4B, Liberty Lake, WA). We’re democratic institutions. One for all. All for one. But there’s really no such thing as an average member. There are real differences.
A pithy take on that perspective came from Brian Ley of Alpharank, a San Francisco startup that uses APIs to turn payments data into human influence graphs.
Think of it as a cell and an organism, Ley said.You want the organism to do well, and that means sometimes you have to focus on the cell.
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But organisms, including member-owned financial cooperatives, need nourishment, too.
Go for the low-hanging fruit, advised Paul Ablack, president and CEO of OnApproach, a data integration and reporting CUSO based in Plymouth, MN.
Those include ACH and credit card data to target candidates for use and limit promotions. Engaging indirect borrowers is another well of data, whereas more advanced analytics include spotting when a member might be encountering challenges or life cycle events that could be an opportune time for counseling or cross-selling.
There also were warnings.
You can get lost in the data if you let it, said Matt Maguire, the new chief data officer for CO-OP Financial Services. The best strategies solve member problems, and they’re specific and measurable.
The array of data sources available can be overwhelming, prompting another panelist to suggest not being everything to everybody.
We’re focusing on key segments, said Steve Simpson, vice president of strategy and innovation at Suncoast Credit Union ($8.7B, Tampa, FL). Which, by the way, might not be the same segments we want to focus on 10 years from now.
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Whether a credit union goes the DIY route or hires out, the time to start is now. Competition and consumer expectations won’t get any easier to navigate.
Finally, a couple of longtime credit union technology leaders advised conference attendees to not get bogged down in the definitions.
Analytics is analyzing data of all types, said conference attendee Heather Moshier, the former CIO of San Diego County Credit Union ($8.3B, San Diego, CA) who recently became director of technology consulting for CU Engage. Find a way to sort through your data on your various systems, centralize it, and implement an analytics strategy.
Moshier said analytics with executive sponsorship should be top of mind for all credit unions.
Start your discovery phase, she said. Work on developing an 18- to 36-month data analytics strategy plan, continue to stay educated on the various use cases of analytics, and keep analytics top of mind at your credit union.
That’s what’s happening at ESL Federal Credit Union ($6.2B, Rochester, NY), which is four years into a project with consultant Paul Kautza of StarSoft Solutions that began with creating a 150-page roadmap laying out how to use 200-plus data sources and many millions of recorded transactions to address daily business problems such as risk mitigation.
Kautza, who attended the conference with ESL data warehouse manager and business systems analyst Dan Gates, said he’s worked with the New York credit union to develop the internal skills to get the most of out that vast data store.
Kautza stressed that credit unions not lose sight of the long-term goal of big data.
This is not about the next shiny object, Kautza said. The data warehouse is not dead. You just have to learn how to drive and then you can take the car wherever you want to go.
That pragmatism is shared by Jeff Johnson, SVP and CIO at BCU ($2.9B, Vernon Hills, IL), who served as one of the judges in the 2018 CULytics Analytics Challenge, won by SAFE Credit Union ($2.7B, Folsom, CA) for a member loyalty optimization project that resulted in sharp increases in both new card account openings and cardholder spend.
Johnson considers data to be a utility, like electricity.
Our data warehouse, and more importantly our analysts embedded in the business, are leveraging data on a daily basis to run our business, said Johnson, a member emeritus of the CUNA Technology Council and its liaison to the CUFX integrating software standards project.
He also prefers to use member intelligence instead of analytics.
The rest of the world is moving toward better use of intelligence, Johnson said. In the younger generations, there is an expectation that financial institutions, retail providers, and so on should know exactly what these consumers want. We need to be there.