The data equivalent of “nirvana” might be having affordable access to all the business intelligence you need to predict the optimum experience for an individual member, streamline operations, or solve business problems.

That’s the promise of data analytics, which uses mathematical algorithms to translate information into insights and forecast future behavior. Credit unions are partnering with vendors to shorten the data development timeline and take advantage of the right solutions for their situation.

A switch in perspective

Credit unions start moving toward “data nirvana” when they stop looking at data through the rear-view mirror that shows where they’ve already been, says Shazia Manus, strategy and business development officer for AdvantEdge Analytics, a CUNA Strategic Services alliance provider. Data analytics shifts their focus to looking through the windshield to see what’s ahead.

Manus says credit unions that engage in this process are transforming their business model by gaining the ability to contextualize and analyze data and insights. That lets them deliver personalized offers to members using a range of digital channels.

It also helps them identify member groups that hold the potential for deeper relationships. An example is discovering that millennials want a simplified route to a mortgage when buying their first home.

“We have to be more anticipatory when we offer experiences through the digital channel,” Manus says. “The only way to do that is through understanding the consumer journey and their mindset.  Data analytics makes it possible to gain that information.”

‘We have to be more anticipatory when we offer experiences through the digital channel.’

Shazia Manus

A CUSO approach

OnApproach, a credit union service organization (CUSO), has helped credit unions become data-driven since 2009, and provides a “collaborative analytics ecosystem” to credit unions with assets ranging from $40 million to more than $4 billion, says Austin J. Wentzlaff, vice president, business development.

“Credit unions are largely trying to solve the same problems,” he says. The CUSO’s approach minimizes the need to repeatedly “reinvent the wheel” at each credit union. This means credit unions shouldn’t have to build the same integrations, reports, and models as their peers.

“The credit union community working together has exponentially greater resources available than a single organization,” Wentzlaff says. “OnApproach’s ecosystem aims to facilitate simplified collaboration around analytics to make credit unions, as a whole, more competitive in today’s financial services arena.”

Costs vary based on size. A credit union with assets from $500 million to $1 billion can start using data analytics through OnApproach for an initial baseline fee of $130,000 plus an annual fee of $40,000. That fee covers prebuilt reports and dashboards, as well as installation of forecasting tools and prebuilt predictive models.

“This is a major advancement from the traditional data warehousing methods, which would require credit unions to spend $1 million to $3 million over several years in hopes of building an isolated system that would have to then be maintained by the credit union,” Wentzlaff says.

Offering a scalable industry-standard platform allows OnApproach to condense a credit union’s implementation timeline to three months, Wentzlaff says. In parallel, credit unions need to understand what they want to do with data and evolve the culture so employees will use it.

“If you don’t have the culture that accepts data-driven decision-making and accepts that data should be part of everything we do, the technology will be harder to adopt,” Wentzlaff says.

Credit unions that overcome that hurdle by getting organization-wide buy-in reinforce their data-driven culture with data analytics that becomes pervasive across the organization, he says.

Overcoming ‘disparate systems’

Ideal Credit Union in Woodbury, Minn., began investing in data analytics to overcome the barriers of disparate systems that would otherwise keep information about its 50,000 members in separate silos.

“We wanted a solution where we could combine all these disparate systems into one data warehouse for reporting and data analytics,” says Dennis R. Bauer, executive vice president/chief financial officer of the $730 million asset credit union.

A data warehouse would have been “prohibitively expensive” from both a tools and talent perspective if the credit union tackled the project alone, Bauer says. Once the data was accessible, Bauer had an “oh wow” moment as he began to use analytics software from OnApproach to analyze member behavior.

Ideal began investing in third-party solutions that tap into its data warehouse to gather insights and generate reports that help convert insights into action. Examples include:

• Giving members the ability to lower their loan payments with CU RateReset, a CUNA Strategic Services alliance provider. Bauer says most members are eligible for the online service, which lets them use a seamless connection from the credit union’s website to pick a loan and complete a simple refinance agreement that extends the life of the loan to lower their payment. Data analytics identifies eligible members for personalized loan offers, which means no credit check is needed.
• Allowing branches to track referrals through Raddon’s “Lead Builder” solution. Staff can use OnApproach’s M360 solution to monitor their incentives based on current referrals.
• Using BankBI’s cloud-based solution to allow the credit union to look at financial information, brand, and product production from any device, any time anywhere at a summary level or drill down into specific branches and/or products.
• Using a solution from 2020 Analytics, a CUNA Strategic Services alliance provider, to comply with the Current Expected Credit Loss (CECL) accounting rule change governing the calculation for loan losses.
• Developing marketing campaigns for loans or deposits based on information about members’ transactions.

Bauer says staff throughout the credit union are involved in using data analytics, with two team members assigned to data analytics full-time.

His advice for credit unions just starting a data warehouse project aimed at maximizing data analytics? “Grab all the data you can, even if you don’t know what you’ll do with it yet.”

Next week: Part II of our series on data analytics looks at how to invest in an analytics solution to remain competitive.