Unlocking True Personalization: Why Banks Are Stuck at Basic Segmentation

For years, financial institutions have aimed to tailor services to their customers, yet most remain tethered to rudimentary segmentation strategies. While a select few have successfully deployed advanced AI-driven hyper-personalization, the vast majority continue to categorize customers using broad strokes: demographics, product ownership, or life stages. This approach persists despite compelling evidence that 74% of consumers desire more personalized banking experiences, and 66% are comfortable with their bank leveraging data to achieve this.

Significant investments have been poured into data and analytics platforms. However, challenges such as fragmented systems, conflicting priorities across business units, and mounting compliance concerns often steer banks towards safer, broader segments instead of embracing genuine one-to-one personalization.

The Core Challenge: Banks now possess both the necessary data and the explicit permission from consumers to personalize. The critical missing piece is a robust architectural framework capable of transforming insights into actionable strategies across products, channels, pricing, and regulatory compliance.

Key Obstacles to Personalization in Banking

• Segmentation is a Symptom, Not a Strategy: The reliance on broad segments stems from data, decision-making, and operational models that are not designed for real-time, individual customer-level interactions.
• Architecture as the Bottleneck: Achieving true 1:1 personalization demands secure, shared data, coupled with a real-time activation stack and an engagement layer, all with integrated compliance from the outset.
• Relationships as the Ultimate Metric: The effectiveness of personalization should be measured by its impact on long-term customer retention, increased wallet share, engagement frequency, and product consolidation – moving beyond mere campaign click-through rates.

Why Banks Default to Broad Segmentation

Despite possessing rich customer data, many banks struggle to utilize it effectively. A study sponsored by SAS and IDC revealed that 54% of North American banks find their data foundation isn’t centralized or optimized enough for AI, with 30% still operating with siloed data infrastructures.

Nikhil Lele, banking and capital markets consulting leader at EY, explains, “Most banks remain anchored in broad segmentation because insight is not yet embedded into the way decisions are made at scale. Most institutions still operate around legacy products, channels, and organizational silos, which naturally leads to broad, static segmentation.”

When customer data is spread across separate systems for lending, deposits, and cards, each business line often develops its own isolated view of the customer. This fragmentation makes it incredibly difficult to coordinate a single, tailored offer across multiple products and channels.

Adam Neiberg, global banking manager at SAS, highlights this, stating, “Siloed data and competing priorities among lines of business can leave a bank stuck at broad segmentation rather than taking a more personalized and data-driven approach to marketing.” He adds that banks might inadvertently promote a profitable product like a credit card when a customer’s immediate need might be a savings account.

Preetha Pulusani, CEO of DeepTarget, identifies three primary barriers hindering smaller institutions:

• Success Plateau: Initial wins with basic segmentation can create a comfort zone, stifling further innovation towards advanced personalization.
• Awareness Gap: Many banking executives are simply unaware that sophisticated 1:1 personalization platforms exist and are accessible without prohibitive complexity or cost.
• Structural Friction: Legacy core banking systems, persistent data silos between product lines, and limited staff bandwidth impede real-time execution, prioritizing day-to-day operations over personalized engagement.

Essential Infrastructure for Real-Time Personalization

While banks have invested in data and analytics platforms, these tools, while valuable for understanding past events, cannot independently power real-time personalization. The crucial missing component is an architecture that securely connects structured data to immediate action.

Mehdi Heidari, global head of product management at Giesecke+Devrient, emphasizes, “The foundation is secure and structured data sharing.” Banks require data to move seamlessly and securely between systems, adhering to stringent privacy and regulatory standards, with clear controls over its residency.

Heidari clarifies, “It’s less about one single breakthrough technology and more about orchestration in terms of how encryption, tokenization, secure data exchange, and residency controls work together to create a trusted infrastructure. When that infrastructure is sound, personalization can be layered on top with confidence.”

Once secure data movement is established, banks need the operational systems to act upon it. Pulusani argues, “To deliver 1:1 personalization at scale, banks must move beyond the data lake and build a real-time Activation Stack.” She stresses that it’s not just about data possession, but the infrastructure enabling banks to respond in milliseconds – for example, between a customer opening an app and seeing a personalized screen.

Pulusani outlines four vital components for this infrastructure:

• An automated data activation layer that ingests granular, transaction-level behavioral data.
• A real-time decisioning engine capable of translating this data into individualized offers.
• Dynamic content orchestration to deliver these tailored offers consistently across all channels, including mobile, email, and ATMs.
• Closed-loop analytics to link every customer interaction directly to revenue, demonstrating the tangible ROI of 1:1 personalization efforts.

Personalized pricing is also integral to this orchestration. Banks aiming to tailor deposit rates, loan offers, or credit cards at the individual level need sophisticated pricing engines that integrate with the decisioning layer, apply risk and compliance rules, and react instantly. Currently, most institutions still price by product and tier, not by individual customer.

The overarching goal is to enable core systems to perform their primary functions while an independent engagement layer handles the complexities of real-time personalization.

Integrating Compliance from the Ground Up

Compliance often acts as a major deterrent, pushing banks towards broader, safer customer segments. A manual compliance review process, manageable for a few customer groups, becomes unfeasible when personalization generates thousands of unique offer combinations. Leading banks are overcoming this by embedding compliance directly into the decision logic from the outset.

Key Insight: Heidari strongly advises, “Compliance cannot be an afterthought. It must be embedded from the moment data is received until the moment it is consumed and ultimately purged.” His approach focuses on integrating compliance across the entire data lifecycle, from secure data ingestion channels to encryption, storage, and eventual deletion, all within certified, privacy-compliant environments.

Pulusani clarifies that real-time personalization does not eliminate human oversight. Instead, technology can amplify human judgment. Banks can define rules upfront, allowing the system to assemble offers from pre-approved building blocks – such as compliant headers, legal disclosures, and brand-aligned imagery. A robust QA playbook can then check samples against compliance standards, with centralized policy management ensuring consistency in offers and tone across all channels.

Moving Beyond Pilots: Scaling Personalization

While personalization pilots are common, successfully scaling them across the enterprise remains a significant hurdle. The IDC Data and AI Impact Report highlights that only 4% of North American banks have achieved an optimized data environment, and isolated initiatives rarely translate into a cohesive, unified customer experience.

Lele observes, “As banks try to move personalization from isolated pilots to enterprise-wide execution, they often run into structural limits rather than a lack of consumer readiness.” He points out the growing disparity: 60% of consumers already consent to data sharing, and over a third actively use AI in daily life, underscoring the gap between customer readiness and banks’ ability to scale personalized experiences.

Crucial Shift in Measurement: Lele emphasizes that success metrics must evolve. The true measure of personalization lies in its ability to foster customer trust and establish the bank as the primary financial partner. Therefore, banks should prioritize tracking metrics like retention, wallet share, engagement frequency, and product consolidation over time, rather than solely focusing on short-term campaign clicks.

Neiberg adds, “AI efforts tied to enhancing customer experience generate higher ROI than AI initiatives developed for cost-cutting.” This underscores the importance of evaluating personalization based on its impact on customer experience (e.g., uplift, engagement, loyalty), not just efficiency. If 1:1 personalization fails to deliver clear, sustained gains in both revenue and key performance indicators, a strategic adjustment is warranted.

Source: thefinancialbrand.com