Most engagement strategies are built around reach. The metric that actually predicts lifetime value is trust. And you can't optimize for trust with a campaign.
That distinction is reshaping how leading banks and credit unions think about consumer engagement—away from volume and campaign reach, toward the kind of relationship depth that earns a customer's next product, their next life stage, and their long-term loyalty.
That was the core argument running through a recent MoneyLIVE webcast featuring Rachel Castro, SVP of Business Banking Partnerships and Channel Growth at US Bank; Pam Piligian, CMO at Navy Federal Credit Union; and Andrew Naoum, EVP of Enterprise Solutions at Engine by Gen. The conversation wasn't about AI hype or channel strategy in the abstract. It was about something more concrete: why most engagement strategies are optimizing for the wrong outcomes, what it actually takes to shift from campaign reach to relationship depth, and how the institutions doing it well have had to change how marketing and product work together to get there.
The Problem With "Personalization" as Most Banks Practice It
Personalization has been the stated goal in financial services marketing for a decade. And yet most of it still looks like a first name in the subject line and a product recommendation based on a demographic segment.
The gap between what personalization promises and what it actually delivers was a thread running through the entire session—and the panel was direct about why it persists. As Pam Piligian put it, what once felt like a pleasant surprise to consumers is now simply the baseline expectation. Knowing a member's name isn't personalization. Knowing where they are in their financial life—and showing up with something relevant to that moment—is.
The bar has moved. And most personalization programs haven't moved with it.
Piligian's example was telling: Navy Federal's loyalty program sends different messages to members depending on whether they're accumulating points toward a large aspirational redemption or looking to cash out for everyday expenses. Same product information. Entirely different framing. That's not a small tweak. It requires understanding the goal behind the behavior, not just the behavior itself.
For business banking, the gap between current practice and what's actually needed is even wider. Rachel Castro described the reality facing most small business owners plainly: half fail within two years, and most are operating with less than a month of cash on hand. These aren't customers who have time to parse product comparisons. A bank that can recognize the seasonal cash flow pattern of a particular type of business and offer a bridge before the owner even realizes they need one isn't just useful—it's the kind of partner that earns loyalty that doesn't churn.
That's the standard. Not segments. Not campaigns. Pattern recognition, applied at the moment it matters.
Why It's Hard: The Data Problem Hiding Inside the Personalization Problem
Here's what makes that standard so difficult to reach: most banks don't have the data infrastructure to support it.
Andrew Naoum identified the structural issue clearly. The average consumer has accounts at five or six different financial institutions. Even within a single bank, data is siloed—mortgage doesn't talk to credit card, credit card doesn't talk to deposits. The result is that most engagement strategies are built on a partial, often outdated picture of the customer. You're seeing who they were at the last interaction, not who they are now or where they're heading.
Traditional models—pre-screen mailers, risk-based eligibility filters—were designed to identify creditworthiness, not intent. They tell you whether someone qualifies for a product. They tell you almost nothing about whether that product is relevant to what that person is actually going through.
That's the data problem hiding inside the personalization problem. And until banks solve it, personalization remains a marketing aspiration rather than an operational capability.
The institutions moving fastest are investing in the connective tissue: bank linking, third-party behavioral data, closed-loop attribution that shows not just what a customer did, but what they're likely to need next. AI is increasingly useful here—not as a channel or a content generator, but as the engine that maps fragmented signals into a coherent picture. But the technology only works if the data infrastructure underneath it is actually built to support a real-time view of the customer.
There's also a dimension that often gets skipped in the data conversation: the consumer's willingness to share it in the first place. As Naoum put it, "What is the incentive or what is the value that we can trade with a consumer against them wanting to share their data? What is the reason they're willing to do it? What value do we provide them?" The answer to that question—and the ability to act on it—is increasingly what separates institutions that have a rich, consented data picture from those still working with fragments.
Campaigns vs. Relationships: A Leading Practice Worth Adopting
Once the data foundation is more solid, the next challenge is how you actually use it. And this is where most engagement strategies go wrong in a different way—by treating every touchpoint as an opportunity to sell.
Rachel Castro's approach for how US Bank thinks about communications is worth borrowing. Every outbound message gets categorized as one of three things: informational (a fraud alert, a payment confirmation), educational (here's how chargebacks work for your business type), or a call to action (here's a product that's directly relevant to what you're dealing with right now). Running those three tracks deliberately—not blending them together into a single promotional voice—is what keeps engagement from sliding into noise.
The framing she used is a useful test: a good friend reads the clues. Sometimes they call just to check in. Sometimes they offer help. They don't pitch you every time they see you. A bank that treats every interaction as a sales opportunity isn't acting like a trusted partner. It's acting like a vendor. And customers—particularly business owners who are already overwhelmed—will disengage accordingly.
Navy Federal applies the same logic from a different angle. Piligian deliberately chose the phrase "next best experience" over "next best action"—because not every experience should involve an action at all. Sometimes the right engagement is educational content. Sometimes it's silence. The credit union pauses non-essential outreach entirely when a member is in a FEMA-designated disaster zone, and the first thing a service rep is trained to say in those situations isn't "how can I help you today?" It's "are you okay?"
Naoum framed the next best action challenge as the holy grail of marketing—and illustrated what actually getting it right looks like in practice. "You spend at Walmart. How about a Walmart credit card that earns you 3% cash back and you'll save $500." That's not a campaign. It's a relevant action derived from real behavior, delivered at the moment it's useful. Most banks have the transaction data to do this. The gap is in the segmentation granularity and the willingness to act on it.
That's a small operational detail. But it's also the kind of detail that separates institutions members stay with for decades from ones they leave when a better rate comes along.
More Personalization Is Not the Goal. Better Judgment Is.
One of the more useful moments in the session came when the panel pushed back on the assumption that more personalization is inherently the right direction.
Naoum's argument was direct: if you've truly understood a customer's context, you should be sending them less, not more. The point of better data and better models isn't to generate more targeted volume—it's to identify the one message or product that's actually relevant and lead with that. Better personalization means fewer, higher-quality interactions. Most banks are still wired to do the opposite.
The constraint the panel kept returning to is what they called "creepiness"—the point at which personalization stops feeling like helpful service and starts feeling like surveillance. Piligian described the tension concretely: what do you do when you can see that a member's savings behavior is inconsistent with their stated financial goals? You have the data. You could say something. But should you? And if so, how?
There's no algorithmic answer to that question. It requires judgment—and judgment requires trust in both directions. Castro's practical approach: test communications with real humans before deploying at scale, use synthetic audiences to refine the model, and treat the rollout of more proactive engagement as something you earn incrementally, not something you flip on all at once.
The institutions getting this right aren't the ones with the most sophisticated models. They're the ones that have built enough trust with their customers to act on what they know—because the customer believes they're doing it in their interest.
Trust Is the Metric That Predicts Everything Else
Beneath all of this—the data infrastructure, the communication frameworks, the personalization models—the panel kept returning to the same underlying variable: trust.
Piligian's formulation is worth keeping: trust is earned in drops and lost in buckets. It accumulates through small, consistent actions over time. And it evaporates fast when something goes wrong—a bot that can't answer a real question, a product push in a moment of financial stress, a message sent to the wrong person at the wrong time.
Naoum made the point that the trajectory of AI in banking is moving in exactly this direction—from support function to action. "It's taking it from a support function where it's pulling up a support page to something now where—do you want me to just get that done for you?" That shift from insight to action is where the real value unlocks. But it only works if the trust foundation is already there. An institution that hasn't earned the right to act on a customer's behalf won't get permission to do so—algorithmically or otherwise.
That framing has direct implications for how marketing and product teams should measure success. Not open rates. Not campaign conversions. The metrics that actually predict lifetime value are the ones that track relationship depth: product breadth per household, engagement frequency, retention over time, and the degree to which a customer is consolidating more of their financial life with you rather than less.
Navy Federal's clearest example of what trust-building looks like in practice came during a government shutdown. Rather than leave members managing delayed paychecks on their own, the credit union launched a quiet payroll protection program—a short-term, interest-free advance for the amount of each member's expected paycheck, no credit check required. It wasn't a revenue initiative. It was a signal: we're on your side when it's hard, not just when it's convenient.
The members overwhelmingly paid the loans back. Because the credit union had extended trust first.
What This Requires Organizationally
None of this happens with a better campaign management tool. The institutions that are actually executing on deeper engagement have had to change how their marketing and product functions relate to each other.
The clearest structural example came from US Bank. Their 2025 Business Essentials launch—a bundled product combining merchant processing and deposits—required treasury management, merchant services, and business banking teams to operate under a single customer-first mandate rather than optimizing separately for their own product metrics. The internal complexity was real. The customer experience was dramatically simpler as a result. The outcome: the Tearsheet 2025 Best Bank for Small Businesses recognition.
That's not a technology story. It's an organizational design story. Building toward relationship depth rather than transactional volume requires breaking down the internal silos that produce fragmented, product-centric outreach in the first place. It requires aligning product and marketing around shared metrics—lifetime value, engagement depth, trust indicators—rather than campaign KPIs that measure reach but not relationship.
Naoum's commercial framing adds another layer: in a marketplace model where a customer who isn't eligible for one of your products can be matched to a relevant partner offering instead, you stay in the relationship rather than losing them. That generates non-interest revenue, keeps engagement intact, and protects the trust account. The goal isn't just to serve customers better in the moment. It's to remain the institution they turn to as their financial needs evolve.
Watch the full MoneyLIVE webcast recording here.