MADISON, Wis.—One expert insists that Big Data doesn’t have to mean Big Bucks.
A modest investment in data analytics can have a far-reaching impact, including in the critical area of member “churn,” and especially for a credit union that has just begun its digital transformation, says Tim Peterson, president of AdvantEdge Analytics, part of CUNA Mutual Group.
“As tech spending goes, data analytics represents a relatively inexpensive option, and the upside is simply huge,” he said. “All the credit unions I talk to these days see data and analytics as the central component to their strategy, as key drivers for enabling their growth, improving the member experience, and operational efficiencies.”
Peterson said AdvantEdge Analytics has seen numerous credit unions with strong growth in assets, but in many cases over 50% of member growth is negated by membership attrition or member value reduction.
“In other words, there’s no reason to wait. If done thoughtfully, a small-scale data analytics investment can deliver large-scale benefits and lay the foundation for future efforts,” he said.
The insights are being shared as part of a CUToday.info series on what to do with unexpected budget surpluses, such as the payment from the NCUSIF many credit unions will receive this year.
Right Direction
In many ways, limiting the scope of the CU’s data analytics initiative is actually a step in the right direction, Peterson said.
“Many data analytic initiatives run into problems, because they take a page out of the Field of Dreams playbook: if you build it, they will come,” he said. “In other words, organizations will spend several months, sometimes years, working to build out a data foundation and bring all the data together, and then hope the business will find a use for their project. That’s a losing proposition—and an expensive one at that.”
A “value-driven” use-case approach, on the other hand, provides a more focused and cost-effective alternative, Peterson said.
“Essentially, it’s as simple as this: first find the business value and then use it to shape your data strategy,” he explained.
To identify the value, a credit union should start with its specific business questions, not with technology or data.
“You don’t need to solve the credit union’s biggest problems. Just start with any value-driven proposition,” Peterson said. “For example, how can you use your credit union data to reduce churn, attract new members, increase share of wallet with existing members, or convert indirect members into more full-service members? Answering any one of these questions will help you identify a clear source of business value.”
With greater focus, the credit union only goes after the data it needs to solve the problem, thereby increasing speed to value, Peterson said.
Advanced Analytics
For advanced analytics, the credit union should formulate predictive models around the questions.
“The churn reduction model, for example, could address the member attrition problem. After you collect the data, the model can help identify members most likely to leave the credit union,” Peterson said. “Which members are decreasing their engagement with the credit union? Who has recently stopped their direct deposits? How would you rank them in terms of priorities? Once you have compiled all those members, you could turn them over to marketing to create campaigns to win them back.”
The same sort of processes would apply to other predictive models, he said.
“One model might help you accelerate growth by converting indirect members into active, participating accounts. Another might find new sources of growth by increasing upsell and cross-sell opportunities,” Peterson explained.
Peterson termed the ROI for these predictive models “impressive.”
“One of our credit union customers, for example, increased the conversion of indirect members by five to six percentage points on top of their existing conversion rate, for additional revenue between $1-$1.5 million per year,” he said.
The company’s churn reduction solution helps credit unions keep and grow their membership base, a critical, board-level priority, asserted Peterson.
“By identifying which credit union members are most likely to leave or reduce their relationship, our analytics solution equips the credit union’s call center to engage customers more deeply,” he said. “The results have been very encouraging. Using our predictive models to contact high-risk members of one credit union, we reduced churn and value reduction rates by 30% compared to the control group–thus driving annual impact of $0.8-1.4 million in revenue and creating an extremely attractive ROI.”
No 'One-Offs'
What Peterson said might be an even greater benefit to these data solutions is that they are not “one-offs.”
“Implementing them from end-to-end essentially takes your credit union through the basic stages of the data analytic process. Simply put: first you organize the data, then you translate or model it to derive insights, and finally, you take action,” Peterson said. “As long as this process is value-driven, then you’ve also put into place a best-practice data analytics strategy. You can then use the organizational knowledge to model future initiatives or take the projects to scale.”
Peterson said that even a modest entry into data analytics can have immediate financial and operational impact, while also setting up the credit union for the future.
“Ultimately, by setting your data analytics journey into motion, you have also begun a larger transformation, in which data drives value, improves operations, and ultimately your member experience,” he said.