Reliance on Single-Source Performance Data Is Killing Your Bank’s Marketing ROI
By Kristopher Lazzaretti, president of data solutions at Deluxe
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In the race for customer growth, financial institutions have put their marketing programs under the microscope for detailed examination. When these campaigns underperform, marketers tend to look in familiar places to find the cause: poor timing, the creative missed the mark, a competitor had a better rate or cash incentive offer. These are all reasonable places to start; however, they’re often not the real drivers.
The more fundamental issue is often the data itself, specifically incomplete, outdated or wrong campaign data. No creative refresh, incentive adjustment or timing tweak will compensate for acting on a partial picture of a consumer or small business. Yet, for most banks, a partial picture is the only thing their external data partners can deliver.
Need to Know:
- Underperforming bank marketing campaigns are typically driven by incomplete, outdated or inaccurate data rather than flawed creative, timing or incentive offers.
- Relying on a single data provider creates critical blind spots, as no single vendor can capture the complete range of life events, intent signals and financial patterns needed to identify all profitable marketing opportunities.
- Leveraging super aggregation helps banks gain critical advantages in coverage, speed, accuracy and attribute depth, ultimately improving marketing materiality by three to four times or more for certain data types.
Single-source data for marketing campaigns creates a structural problem that hides in plain sight.
Most banks source third-party marketing data from a single source. The appeal is straightforward: one vendor, one contract and one feed. However, customer and prospect intelligence doesn’t work that way. Life events, behavioral signals, spending patterns and business financials don’t live in one place. They’re distributed across hundreds of sources, and no single third-party source captures them all.
The results are predictable:
- Banks relying on single-source data miss prospects their competitors can see, act on intent signals that have already lost their value, and build AI models and predictive tools on structurally incomplete foundations.
- Sophisticated analytics don’t matter much if the underlying data is thin.
- Single-source data simply can’t capture all the meaningful consumer and small businesses insights required to run effective marketing campaigns.
Bottom line: Super aggregation addresses this directly by gathering, consolidating and organizing information from various sources to build richer, more complete views of consumers and small businesses. This isn’t always a straightforward task. It requires reconciling duplicates and resolving datapoint conflicts through AI-powered entity resolution. The resulting deep profiles unlock insights for better decisions about prospect acquisition and customer engagement, management, upselling and cross-selling. The difference is moving from a narrow snapshot to something closer to a complete picture.
Super aggregation provides better performance of coverage, speed, accuracy, and depth of attributes and proprietary data
Financial institutions that super aggregate get a leg up on their competition by taking advantage of the four key attributes super aggregation offers.
1. Coverage: Every data provider has blind spots, or consumers and small businesses that appear in other sources but not in theirs. When a bank relies on a single provider, those blind spots become the bank’s, causing marketers to miss prospect records and customer data enrichment opportunities. A super aggregated approach helps close that gap, ensuring financial institutions aren’t systematically ignoring their market’s full potential. For some data types, coverage can improve marketing materiality by three to four times or more. Only with a complete data picture can organizations capture the full profitable marketing opportunities available.
2. Speed: Real-time or near real-time decision-making across prospect marketing and customer engagement and cross-sell is now the gold standard. Trigger-based marketing (marketing to a business opening, a consumer relocating into the footprint or a customer milestone life event, for example) is only as valuable as it’s timely. Our research shows the conversion value of trigger signals declines by more than 30% one week following the event and continues falling thereafter. Banks relying on single-source providers are beholden to when their provider identifies the trigger, which may be weeks later than others. Super aggregation, with its overlapping source coverage, captures data changes in real time, allowing marketers to act at the moment of highest intent.
3. Accuracy: Strategic decision-making in marketing campaigns requires the most accurate data. Accuracy improves when data points from multiple sources can be used to corroborate insights and sharpen analytics. Consider the challenge of estimating a small business prospect’s size. A single data source might offer a rough revenue estimate, but that’s an approximation at best. Combining sales data with merchant processing volume, trade credit activity and publicly reported lending information produces a materially more reliable picture. This translates directly into better decisions related to which businesses to approach, with which products and at what points in their financial lifecycles.
4. Depth of attributes and proprietary data: Fully understanding customers and prospects, such as their demographics, interests, online and offline shopping patterns, realty data, and marketing channel preferences requires drawing from many different sources. And this basic profiling information can be further enriched with high-value proprietary data, such as psychographic drivers, discretionary spending and cash flow patterns. Super aggregators have done the integration work, pulling these inputs into an accessible, actionable data set. The operational benefit is real: banks avoid managing a fragmented ecosystem of vendor relationships and data feeds. But, the marketing benefit is even more significant, including richer segmentation, sharper targeting and campaigns that reach the right people with messages relevant to them.
What stronger data reveals about improving conversion rates, increasing account value, and creating a more durable marketing advantage
The performance gap between single-source and super-aggregated data isn’t theoretical. In a recent head-to-head test at a top 10 bank in the U.S.:
- Campaigns built on super-aggregated data produced two to four times higher conversion rates for small business marketing than those built on single-source data.
- Average balances were also higher for accounts acquired from the campaigns built on super-aggregated data.
- Higher conversion rates and larger average balances more than offset super-aggregated data’s incremental expense, yielding better costs per account, faster paybacks and higher ROIs.
In another test focused on trigger-based marketing for consumer checking acquisition, single-source intent signals lagged multi-source signals by three to five weeks, resulting in wasted marketing dollars and lost revenue. Sub-optimal data yielded sub-optimal results.
The case in practice: Adopting super aggregation delivers marketing campaign efficiency and effectiveness
The throughline is worth stating: the AI models, predictive analytics platforms and personalization engines that banks invest in heavily will only perform as well as the data they’re trained and run on.
Bottom line: Super aggregation isn’t a feature of an efficient and effective marketing approach. It’s a prerequisite for one. Institutions that recognize this will gain a durable advantage over those still settling for a fraction of the intelligence available to them.
