7SHARESShareShareSharePrintMailGooglePinterestDiggRedditStumbleuponDeliciousBufferTumblr,Will Thomas Will Thomas leads business development at Blue Orange Digital, a data science agency that empowers credit unions to make better decisions with their data. Will earned an MBA from the … Web: www.blueorange.digital Details Increased consolidation and competition require that credit unions today adopt a new strategy. The winners in this emerging landscape will be institutions that leverage data and prediction across their organization. Data-driven decision making, made possible by the decreased cost of prediction, allows adopting credit unions to highlight their advantages to customers and run more profitably and efficiently. But to get to this promised land, all CU leaders first need to answer 4 key questions:1) What is my data strategy?Does your institution have a 3-year roadmap for how to take advantage of modern analytics? This seems like a tall order. Most CUs don’t have internal knowledge, resources, analysts, and data structures in place. It is hard to find vendors with the data science expertise to solve these problems holistically and offer simple tools to meet specific, ongoing credit union needs. But without a coherent plan to gain insight into your business, the shift won’t happen. Food for thought: JP Morgan spends 11B on tech in a single year.2) Do my systems talk?Legacy systems weren’t designed for predictive analytics. Core, marketing, loan origination, credit card, and operational data usually live in parallel worlds. Just pulling out the data to build a report or send an email marketing blast can require waiting on the one IT guy who knows commands in an arcane programming language. Ouch.A centralized data warehouse can serve as a single source of truth across the organization, eliminate redundant information systems, and simplify access to analytics that increases revenue. The vision for effective data infrastructure is that all data systems from call center to core talk seamlessly and securely. Cloud storage and computing providers like Amazon Web Services have made this accessible even to the smallest institutions.3) Where can I apply Business Intelligence?Machine learning. Optimization. Decision support. There are a lot of names, so what does prediction actually solve? Here are some examples of BI in action:Sales and Marketing Optimization: Run a more effective email campaign by targeting specific members with the cross-selling offers that they are likely to purchase. Financial Products: Dynamically price loans and interest rates to maximize long term revenue by accounting for member acquisition and retention effects.Operational BI: Use teller and hold/wait time analytics to anticipate interactions that could undermine member loyalty.Balance Sheet Assessments: Go beyond core deposit studies with decision support and automation tools that allow for real-time what-if scenarios and guidance on institutional finance decisions.4) How does our leadership team use data?The reason you are in the C-suite is your ability to make tough decisions. But everything doesn’t need to be a gut call. Simple and effective executive reporting is a key step to transforming into a data-driven organization. Automated reporting and easy exploration of the data surrounding a decision help you identify key variables in less time to make better decisions.Visualization tools, custom dashboards, and self-service BI create an environment where complex questions can be answered in hours not weeks. Layering in prediction can automate routine decisions. Multiplying this speed and accuracy across the credit union is the backbone of offering services that make members feel valued in an increasingly complex world. Pulling it all togetherSo what would this approach look like in practice? Let’s take an auto loan.Your credit union applies predictive member segmentation and lead scoring to determine promotion and outreach timing for members likely to purchase a car in the next 3 months. A personalized email campaign is coordinated with onsite member service conversations. Loan pricing is optimized for profitability over the lifetime of the member relationship in order to drive loyalty and increase the deposit base, as well as maximize current revenue. At the end of the campaign, reporting metrics and visualization tools allow easy assessment of how the campaign contributed to the CUs annual goals and improvement for the next campaign.Need help navigating a modern data strategy? Blue Orange is a data science agency focused on helping credit unions make better business decisions with their data. Reach out for a complimentary call about custom solutions to the data challenges that your institution needs to solve.
December 17, 2020