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Connected Decision Making to Drive Sustainable Growth

June 23, 2026

By Tim Keith

All community bankers need to answer four fundamental strategic questions when it comes to relationship banking: 

  1. What type of customer do I want to serve? 
  2. How do I engage those customers, bring them onboard and capture their wallet?
  3. How do I manage the evolving risk associated with that relationship? 
  4. How do I understand and manage the profitability of those relationships across various accounts in dynamic economic cycles?

Bankers often make these decisions in disconnected silos. A big reason for this is that the data supporting those decisions is frequently fragmented across the organization. What would change if we were able to make them in a connected rather than disconnected way?

Start With a Concrete Problem
Take sustainable deposit funding, which is the most pressing challenge at most institutions right now. Rates have come off their cycle peak, but competition for core deposits has not normalized the way many CEOs expected. Fintechs and digital-first challengers are pulling transaction balances in ways that do not show up cleanly in traditional attrition metrics. The institutions managing this well are not leading with rate, but with data about who to talk to and what to say.

A connected, data-informed approach to deposit funding begins before the customer ever walks in the door. Life-stage data from your trade area can identify prospects with material deposit capacity in segments where you have historically done well. That distinction matters. You are not marketing to everyone with money; you are marketing to the households most likely to become the right kind of customer for your institution. More targeted selection, with greater frequency across channels, consistently produces better results than broad rate-based campaigns.

Once those prospects convert, the relationship requires intentional management. Most institutions treat new account onboarding as an operational process rather than a revenue-building window. The first 90 days determine whether a new household becomes a primary banking relationship or a rate-chasing, single-product account. A communication track built on what you already know about that household can systematically expand wallet share without ever leading with rate.

The Checking Account as Analytical Anchor
Once a household is fully onboarded, checking account balance becomes one of the most powerful and underused signals in your data. Outside of a Fair Isaac Corp. (FICO) score, checking balance is more predictive of financial capacity than almost any other variable available to a community bank. If I maintain $25,000 in my checking account, I am going to pay my home equity line of credit (HELOC) on time every month. That balance reflects not just wealth but financial behavior and commitment. It is a tangible, risk-mitigating factor and a growth signal. It tells you, at the household level, where the opportunities for high-performing cross-sell actually are.

Where Call Report Data Fits In
An artificial intelligence (AI) assisted view of your call report, benchmarked against a custom peer group, can surface diagnostics that are genuinely actionable. Consider two scenarios. If your cost of funds is above peer despite certificate of deposit (CD) penetration below the peer norm, that may indicate that the CDs you are selling are overpriced. Conversely, if your cost of funds is below peer but your loan-to-deposit ratio is elevated and money market savings penetration is below norm, a reasonably priced money market offer targeted to the right existing households may be exactly what your balance sheet needs. Peer context is what makes the difference between data and a decision.

The Data Integration Challenge
The decisions described above draw on a layered set of inputs: trade area life-stage data, new account records, campaign tracking, loan origination data, call report and peer group benchmarks, household-level product penetration and balance data, and normative comparisons that tell you what is typical for institutions like yours. Each source is available in some form at most community banks, but they live in different systems and are maintained by different teams with different update cycles.

Community banks often struggle to bring these sources together due to IT capacity limitations and lack of support from core providers. The institutions that close this gap typically do it through partnerships that integrate data infrastructure and analytical expertise rather than treating them as separate projects. Not all partnerships deliver on that, and the difference shows up in whether the resulting data connections actually inform decisions across growth, credit and profitability functions together.

For most institutions, asking, “How many of those four decisions are currently being made from the same data picture, by teams who can see each other’s context?” points to the opportunity.

Tim Keith serves as Chief Executive Officer at OptimaFI, bringing more than 30 years of experience in banking, marketing, and growth strategy. He co-founded Infusion (now part of OptimaFI) in 2007 and continues to work with C-suite leaders at banks and credit unions — both large and small — to translate household data into actionable growth strategies and measurable marketing performance. Before founding Infusion, Tim served as Senior Strategist at Acxiom Corporation, advising executives at Bank of America, Wells Fargo, SunTrust, and HSBC. A frequent speaker at industry conferences and contributor to financial publications, he previously held senior marketing and retail leadership roles at First Horizon and AmSouth Bank (now Regions). Tim holds an MBA and a Bachelor of Science in Marketing from Mississippi College.

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