By Ally Akins
Marketing and data have always been closely linked. While nearly half of bank marketers now report having a documented data strategy, most still struggle with fragmented systems, limited analytics resources and inconsistent access to timely customer insight. As banks face mounting pressure to personalize engagement and prove marketing ROI, those that succeed aren’t simply collecting more data — they’re building the governance, skills and cross‑functional infrastructure needed to turn information into action.
In the recent ABA survey of bank marketers (fielded November 2025; n = 130), 48.5% of respondents reported that their bank has a written or documented data strategy. While still far from universal, this represents meaningful progress. In the comparable 2024 survey, only 29.4% of marketers reported the same.
As Jonathan Cheris, Data Analytics Consultant and Advisor at CPG, notes: “In a dynamic environment such as a bank, documentation alone isn’t enough. Analytics relies on trust through execution and shared wins.”
The banks making the most progress are not those with the longest strategy documents — but those translating data into decisions, outcomes and momentum.
Governance and ownership: Who owns analytics — and does it matter?
When asked whether marketing oversees the data analytics function at their bank, only 25.6% of respondents answered “yes” in 2025, down from 35.9% in 2024. This shift likely reflects the continued formalization of analytics as a discipline, with teams increasingly housed within IT, business intelligence, finance or enterprise data functions.
Marketing does not need to own analytics to be successful — but it does need timely access to customer data. Access to accurate, timely and well-governed customer data directly impacts marketing’s ability to make quality decisions, personalize engagement, and measure outcomes.
Best-practice banks treat analytics governance as a shared responsibility, ensuring that marketing requirements are incorporated into data models, prioritization decisions and technology roadmaps.
Resourcing and skills gaps
Survey responses reveal a consistent pattern: Most banks operate with very lean analytics resources.
Across respondents, the most common structure is 1-2 employees involved in data or analytics, with most banks falling in the 1-5 persons range. Dedicated, full-time analytics teams remain rare — particularly among community banks. Instead, analytics responsibilities are frequently distributed across marketing, IT, business intelligence and management roles.
Common themes include:
- One analyst wearing multiple hats
- Managers performing ad hoc analysis
- Analytics treated as a secondary responsibility rather than a core role
Despite these constraints, there is positive momentum, as 55% percent of respondents indicated that leadership plans to expand the data function within the next three years.
Still, resourcing remains a challenge. Among marketers who oversee or participate in analytics governance, only 46% feel they have sufficient training and resources to do so effectively. This gap between expectations and capacity continues to limit how far banks can move up the analytics maturity curve.
Biggest challenges bank marketers face
Respondents most frequently cited:
- Difficulty integrating data across systems
- Difficulty linking marketing activity to measurable business outcomes
These challenges point to a persistent gap between marketing execution and enterprise performance measurement.
They are further compounded by limited staff expertise and constrained resources. Even when data is technically available, access to timely, usable sales and customer data remains inconsistent — limiting optimization, personalization and ROI measurement.
Advanced analytics require integrated, clean and governed data environments. Many banks are still building this foundation, making more advanced use cases — such as attribution modeling, predictive targeting and lifecycle marketing — difficult to scale.
Analytical tools that drive value
Most bank marketing organizations rely on a mix of four core analytics techniques, progressing in complexity and impact:
- Descriptive analytics. The foundation. Dashboards and reports that summarize campaign performance, customer behavior and channel effectiveness.
- Diagnostic analytics. Moves beyond what happened to why it happened, such as understanding why one segment outperformed another.
- Predictive analytics. Uses historical data to anticipate future behavior — such as propensity-to-buy models, churn prediction, or response likelihood.
- Prescriptive analytics. Applies predictive insight to recommend or automate next actions at the individual level. Examples include next-best-offer programs and event-triggered communications.
Each of these techniques depends on accessible, integrated data — from customer and transaction data to campaign performance and digital behavior. The ability to synthesize data across sources is what enables insight to translate into action. While many banks understand these analytics techniques conceptually, operationalizing them at scale remains difficult.
Data and technology availability: the foundation of decisioning
Data-driven decision-making depends as much on access and usability as it does on analytic sophistication. Leading banks treat customer data and marketing technology as shared enterprise assets, with marketing actively involved in their design and use.
Most banks find they must provide governed access to the data warehouse for marketing users. Data recency and quality directly impact marketing’s ability to add value through analytics-informed programs.
Best practices include:
- A centralized, integrated customer data environment
- Clear ownership of data quality
- A shared customer data model with common definitions across Marketing, Finance, and business lines
On the technology side, tighter integration between analytics outputs and marketing automation platforms enables insights to be activated at scale. Embedded segments, rules and triggers allow for automated or semi-automated execution — particularly important in digital channels where customers expect near–real-time responsiveness.
Conclusion
Banks are making progress in formalizing their data strategies, but marketers continue to face structural barriers in accessing and activating data.
To move forward, leading institutions are focusing on:
- Establishing governed access and shared customer data models
- Tightening integration between data warehouses and martech platforms
- Modernizing analytics stacks — progressing from descriptive to prescriptive
- Building cross-functional ownership and data quality governances
- Expanding CRM adoption to unify sales and marketing pipelines
- Developing skills in diagnostic, predictive and prescriptive analytics
The opportunity is clear: Banks that close the gap between data availability and decision-making are best positioned to drive measurable growth through marketing.
Ally Akins is the sales and marketing practice co-lead at Capital Performance Group, a strategic consulting firm that assists banks in designing and optimizing
