Big data & analytics for community banks
In the old days, community banks knew their customers better than the big banks. But now, big banks can know their customers better, because big banks have big data. To hold onto their traditional differentiator, serve their customers, and run their business, community banks must begin leveraging their data via advanced analytics.
To leverage their data, first, a community bank should articulate the use cases for analytics and model the opportunity in each use case. Next, the bank should design plans to introduce analytics into their decisions and actions. Finally, the bank should begin implementing these plans to capture these opportunities.
Step 1: Assessment – A community bank can start by understanding the use cases for data and analytics. After identifying opportunities, the bank should assess and compare them. The resulting opportunity map will indicate where a bank should start. The map will be different for each bank, because no two banks have the same clients, products, markets, and expertise in analytics.
In our experience, marketing, pricing, cross-selling, and credit-risk modeling are 4 areas to tend to have meaningful opportunities for analytics. Many banks have captured opportunities from applying advanced analytics to their processes and decisions in these areas.
Step 2: Design – The next step is to design how analytics will be used. What questions will be answered and how will they be answered with facts and logic? How do we measure the opportunity at a more granular level? What are the ripple effects of making decisions and taking actions in this analytical way? How should processes, roles & responsibilities, and technology adjust to using advanced analytics?
The bank must ensure that the design phase ends with specific actionable recommendations. Those recommendations must be grounded in indisputable facts and rigorous analysis, and their impact to the bottom line must be measurable.
Step 3: Implementation – The final step is implementation. Most implementations begin with a few use cases to test and learn and to build quick wins to share with the organization. As time goes by, the scope of the implementation grows, adding use cases, clients, products, markets, and departments.
In the implementation phase, change management is important because it helps the organization embrace and adopt this new way of making decisions and taking actions. Another important element is starting small and applying early learnings to subsequent rollouts. The last stage in implementation is making the changes permanent by embedding analytical behaviors into the culture, processes, and capabilities of the organization.
Analytics – Whether a community bank is just beginning its analytical journey or has a more evolved analytics function, the banks that are agile and deliberate in adopting advanced analytics will see happier customers and healthier bottom lines.