4 minutes
Three reasons why your 2017 strategic plan needs advanced analytics
As we kick off 2017, how much focus will your credit union put on advanced analytics and data-driven decisions?
I recently read a quote from Chuck Fagan, president/CEO of payments CUSO PSCU, a CUES Supplier member, expressing his view on advanced analytics. The quote read: “Without data, you are blind and deaf and in the middle of the freeway.”
Fagan’s metaphor stresses that for credit unions to keep pace and grow in today’s highly competitive landscape, it is important to strategically plan how to turn their complex data into valuable information. To emerge as a market leader, consider these key reasons why analytics should be a top priority in 2017:
Keeping Pace with Competition
Over the last few years, several tier one financial institutions have redirected and reinvested capital into an analytics initiative. In 2014, Wells Fargo hired their first ever chief data officer and employed an army of data analysts and scientists. Through intelligence extracted from customer analytics and data, they aim to make their customers’ lives easier, to enhance the customer experience, to mitigate risk, and to ensure that they are fully compliant with privacy regulations as well as their own privacy policies. Just over a year ago, Fifth Third Bancorp invested in data science and analytic platforms to progress toward developing a pipeline of intelligent commerce solutions.
The rise of advanced analytics has been a transformative change for every organization leveraging its power to identify models and algorithms to lift member service, contribution and loyalty, improve efficiency, quickly solve business challenges, mitigate risk, and to make the next best offers in real time via digital channels. Keeping the competition in the rearview mirror requires gaining a deeper understanding of the organization’s data, and how to transform this information into an asset.
Credit unions hold a key differentiator from the mega banks—local community service and involvement and building member relationships—but the best members of a credit union are the ideal targets of a bank’s advanced analytical model. I submit that credit unions should begin adding a data-driven culture to their business model; this requires that advanced analytics be at the forefront of the 2017 strategic plan. The algorithms and models for action will help deliver highly personalized and tailored services to members that will translate into increased market share, revenue and member loyalty for years to come. Realizing your best members are your competitors’ best targets, it’s crucial to use your data to win and keep them.
Timing
Turning the data generated by members into actionable, comprehensive insights is a big task—and a long journey. The initial steps should include establishing short-term and long-term business goals. Once ready with an effective process to collect, cleanse, compute and consume the data, a solid multi-step process needs to be established to use this complex data to better pinpoint member’s needs and interests or identify business opportunities to extract untapped value.
On average, an analytics implementation can take 120-180 days. But post implementation, the ultimate reward is when the credit union can establish a set of recurring best practices derived by specific actions on specific data segments that deliver the right message, to the right member, through the right channel, at the right time. These statistically predictive results lead to prescriptive actions that can be repeated and further refined over time for ongoing success. While there are many immediate actions that will deliver a return, building prescriptive action models require an average of 18 to 36 months; it is best to take that first step now.
Planning for the Future
In time, credit unions will have the ability to take action on specific data segments based on member contribution, next best product models and algorithms that identify opportunity. Such action lists are an important resource to the frontline teams and should both align with strategic goals and be measured through scorecard or incentive module feedback. Top performers can be recognized and by becoming a learning organization (a concept coined through the work and research of Peter Senge), the credit union can facilitate the learning of its members and continuously transform itself. The culture of the credit union begins to change, strategic initiatives are measured, resources are provided to team members in the form of information and action lists, and performance can be assessed. Data science is the next step in business intelligence and will drive improved member service, retention and contribution.
It is estimated that by 2020 the number of analytics service providers used by financial institutions will quadruple. Budgeting and planning for a robust advanced analytics initiative now will separate the market leaders from those who chose to lag behind. It’s time to embrace one of the greatest technological advancements of the 21st century.
Steven Simpson has 30 years of experience serving community financial institutions. His focus is to deliver an immediate ROI by leveling the advanced analytic playing field for community financial institutions. For more information, visit FinTechDSC.com.
