Why Most Financial Institutions Still Can’t Achieve Data-Driven Marketing’s Full Potential
By Nicole Volpe, Contributor at The Financial Brand
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It happens with even the most basic campaigns. A bank sets out to launch a promotion for a new card or account, aiming to capture a market opportunity. The campaign includes offer variants such as “earn up to 4.9% APY” or “get up to $500,” driven by dynamic rules that require variable disclosures and fulfillment across multiple channels. But to get it done, the institution must circulate the disclosures to a series of internal approvers via email. Eligibility rules must be entered by hand, and fulfillment itself depends on manual coordination across teams. By the time it all comes together, the opportunity has passed.
It’s a disappointing scenario, especially for banks and credit unions that have spent heavily on advanced analytics, AI models, and customer data platforms. These systems are designed to bring intelligence to the surface — identifying high-value accountholders, predicting attrition, or simulating the impact of an interest rate or other pricing change. But with the rise of data-driven insights, the institutions are frozen in place, unable to take advantage of this information because of the complexity of managing and tracking offers.
The problem is an “insight-execution disconnect,” and rapid advances in customer data analytics are making it harder for institutions to ignore, says Steve Dobrenski, Head of Sales at Naehas, a vertical SaaS company purpose-built for financial services. The company provides an execution framework that helps institutions deliver personalized products and offers with increased speed and compliance. Dobrenski sees firsthand how disparate teams, constrained workflows, and legacy processes are holding institutions back from turning insights and recommendations into customer interactions at scale.
Want more insights like these? Check out Naehas’ content hub: More Speed, More Value: Bring Personalized Offers to Market Faster.
Doing Business in Alternate Universes
To understand the nature of the disconnect, and its importance for banks’ and credit unions’ competitive success, it helps to look at how fintechs do business. In fact, fintechs’ ability to translate data and insights into execution is a major reason they’ve taken so much market share from traditional institutions over the past few decades. With business models rooted in recommendation- and behavior-driven customer experiences, fintechs are natively equipped to deliver exactly what banks and credit unions are now struggling to achieve.
According to Dobrenski, fintechs and traditional institutions today compete on starkly different playing fields. Fintechs can respond in real time to deepen engagement and increase revenue and profitability, and in doing so, they continue to elevate consumers’ expectations for what a financial relationship can and should feel like. Banks, in contrast, are swimming in data but lack the ability to effectively execute on it. Moreover, they must also reckon with consumers who are increasingly accustomed to — and even demand — timely, relevant offers.
Fintechs bring their products and services to market through well-unified customer experiences and modern core systems, using cloud based architecture. A challenger bank can waive overdraft fees based on the overall relationship status of a client and their real-time behavior. A digital lender can adjust pricing and update offers mid-campaign without restarting an entire approval cycle. A B2B-focused fintech can launch usage-based rewards with disclosures embedded directly into the experience, staying compliant without slowing speed to market.
Traditional financial institutions, with their long-time legacy cores, see something completely different. They have no shortage of strategic ideas — many of them well-defined in strategic and compliance terms — but they are challenged to connect ideas and insights to delivery. A typical campaign from planning to the offers delivered across channels can take 100 days with 50+ steps in the process.
Many banks, for example, have set up relationship-based pricing policies but can’t flexibly execute against them with heavy manual intervention. Behavioral onboarding is another missed opportunity: institutions know how they define primacy and recognize the behaviors that signal it, but resulting offer sequences don’t make it off the whiteboard and to market in time to be effective.
Similar execution gaps affect other priorities. Compliance teams lack the governed, modular system they need to deliver personalized terms at scale, and when markets shift, corresponding campaign adjustments often trigger new end-to-end approval cycles. Cross-channel visibility is also inconsistent: marketing may launch an offer that branches never see, while call centers can’t confirm eligibility for confused customers who were preapproved in another channel. A bank trying to build loyalty with customers through personalized offers can instead irritate them.
The lack of centralization also creates regulatory risk: With no structured way to trace an offer from origination through fulfillment, compliance is left to manually reconstruct audit trails — more of an exercise in advanced forensics than governance.
What’s Missing: ‘Governed Execution’
Some institutions are beginning to close the gap by centralizing such work. In proof-of-concept projects, banks have created what Naehas calls a “governed execution” layer where offers, rules, and disclosures are managed using a single unified data model, according to Dobrenski. Critically, such an approach does not require replacement or duplication of an institution’s enterprise platforms. It integrates existing platforms using tokenized data, enabling automated updates across offers and linking product changes directly to disclosures.
It’s tempting to believe that artificial intelligence — by bringing increased power and speed to segmentation and personalization — can bridge the gap. But banks that go this route are likely to find that AI actually increases the degree of difficulty, says Dobrenski. Where an institution might once have seven offer variants to execute against, AI might generate 27 — far beyond what most institutions can manage. AI models may be able to instantly surface complex optimized opportunity trees, but without a semantic data model for financial services, an institution can’t act on them.
The AI option can also, paradoxically, increase the sense of disconnect across functions and operating units: marketers pour resources into data science teams and advanced models, only to put their colleagues in IT in the unhappy position of having to hit the brakes. “They very quickly find out they can’t execute what AI is asking them to do,” says Dobrenski.
But if better analytics has the unintended consequence of pushing execution further out of reach, a governed execution layer can allow banks to absorb the complexity that AI surfaces. It provides financial institutions with the capacity to process dozens or even hundreds of variant offers — while maintaining control, speed, and compliance. With it, institutions can finally connect the intelligence they’ve invested in to the market outcomes they’ve promised.
Ask Yourself These Questions
A bank or credit union strategist — confronting their institution’s inability to bring a robust data-driven marketing approach to market — may be inclined to assume the problem lies in the data: its timeliness, quality, or comprehensiveness. But it may be architecture that’s lagging, and an architecture-based solution may ultimately provide more leverage.
A good first step is to explore where execution breaks down within your organization. Start with a review of each component of the process: product, pricing, offers, and disclosures.
Can you trace how decisions move through your institution? Can you adjust them quickly when conditions change? Can you prove, to regulators or auditors, how a specific offer was configured and fulfilled? How does each go-forward decision happen, and where do they all come together to drive an action?
If your organization has no process step or stack application that enables it to stage and govern go-to-market decisions, that’s a strong signal that an execution layer is the missing piece. The institutions that bridge the insight-execution disconnect will define what personalization means in banking over the next decade.
