--- title: "Why Banks Struggle to Turn Disparate Data Into Consistent Customer Experiences" id: "96667" type: "post" slug: "why-banks-struggle-to-turn-disparate-data-into-consistent-customer-experiences" published_at: "2026-09-08T14:35:00+00:00" modified_at: "2026-09-04T14:36:15+00:00" url: "https://tealium.com/blog/customer-experience/why-banks-struggle-to-turn-disparate-data-into-consistent-customer-experiences/" markdown_url: "https://tealium.com/blog/customer-experience/why-banks-struggle-to-turn-disparate-data-into-consistent-customer-experiences.md" excerpt: "A customer opens their banking app and starts researching a new product. Later, they visit the website. The next day, they call the contact center with a question. Maybe they eventually walk into a branch. To the customer, this is..." taxonomy_category: - "Customer Experience" --- Customer Experience # Why Banks Struggle to Turn Disparate Data Into Consistent Customer Experiences Gary AlbertsonSeptember 8, 2026 A customer opens their banking app and starts researching a new product. Later, they visit the website. The next day, they call the contact center with a question. Maybe they eventually walk into a branch. To the customer, this is part of the relationship with the bank. Inside the bank, it can look like four completely different interactions. Every day, customer signals pour in from digital banking platforms, websites, mobile apps, contact centers, branch systems, CRM platforms, marketing tools, data warehouses, and other internal systems. Banks are expected to turn all of that activity into seamless, personalized experiences while also meeting some of the strictest privacy, consent, and regulatory requirements of any industry. On a strategy slide, that can sound like a straightforward data problem. In practice, it is one of the hardest operating challenges in modern banking, and one I have seen institutions struggle with throughout my career. The reason is fairly simple: most banking technology stacks were never designed to create one shared, real-time understanding of the customer. They were built over time. Channel by channel. Vendor by vendor. Team by team. The website may capture customer behavior differently from the mobile app. Server-side systems behave differently from both. Branch and offline data may arrive hours later through batch processes. Even when all of that information eventually makes its way into a warehouse, the data does not necessarily use the same language or arrive at the same speed. And that fragmentation compounds. Different vendors use different naming conventions, schemas, and event structures. One system may define an application start one way, while another calls it something different or splits the same journey across several fields. Teams then spend valuable time reconciling schemas and normalizing terminology after the fact. The data itself may also live in different places with very different update cycles. Behavioral signals might be available immediately. Customer profile information may sit in the CRM. Transaction history may live in a warehouse or lake. Branch and contact-center activity may not arrive until an overnight batch runs. Some signals are immediate. Some are delayed. Others are effectively stranded. Then a new source gets added. In a recent conversation with a major banking client, the challenge was described plainly: bringing in another data source meant rebuilding ingestion across multiple teams, without a common standard, while inefficient reporting slowed the decisions that depended on that data often resulting in actions that are no longer relevant because the customer has moved on.. ## **The Customer Feels the Fragmentation** Eventually, most of this data may make its way somewhere useful. But “eventually” is not how customers experience a bank. Marketing may be working from one version of the customer while service sees another. Analytics teams may work from yet another. AI models can be trained on information that frontline systems cannot act on in the moment. That creates a gap between what the bank technically knows and what it can actually do. A customer can browse a product on mobile, encounter a problem, call the contact center, and then visit a branch while every employee they interact with sees only part of the story. The opportunity to respond in the moment disappears. An agent asks the customer to explain something the bank already knows. A relevant offer arrives after the customer has moved on. Analytics can tell the business what happened yesterday while the channel serving the customer right now still lacks the context to respond. In financial services, there is another layer of complexity. Privacy preferences, consent, and governance also have to travel with the customer. When data is fragmented, those controls can become fragmented as well. This is why the problem cannot be solved simply by adding more storage, another customer-facing system, or another analytics tool. Banks need a common operating layer for customer data. ## **Creating a Common Language for Customer Data** That operating layer has to do several things well. It needs to collect signals across the major customer touchpoints and standardize them into a common framework. It needs to resolve identity across devices, sessions, and channels. It needs to enrich events with context and apply business rules as those events happen. And it needs to do all of this while consistently enforcing consent and governance. This is the role Tealium is designed to play. Tealium collects, orchestrates, and activates customer data across real-time, cloud-first, and hybrid architectures, with identity, consent, and AI governance built into the platform. Instead of allowing every new channel or system to create another version of the customer, banks can create a governed data layer that captures signals across channels and enterprise sources, resolves identity, adds context, and routes that information to the model, application, warehouse, agent, or experience that needs it. Most importantly, this does not require banks to rip out the systems they have spent years building. The goal is to make the existing environment work together more effectively. That matters because banking technology stacks are not getting simpler. Institutions will continue adding partners, compliance controls, AI models, digital experiences, and cloud platforms. They will continue modernizing core systems while balancing digital scale with the need for human service in branches and contact centers. Without a common data language and orchestration layer, every new investment risks creating another silo. With one, those investments can become more useful together. ## **What That Looks Like in Practice** Legal & General (L&G), a UK financial services provider, faced a familiar contact-center challenge. Customers were abandoning complex insurance applications. When those customers later called for help, agents could not easily see what had happened during the digital journey. A customer might research coverage, move several steps through an application, get stuck, and then call. The agent would begin by asking the customer to reconstruct everything that had just happened. The company already had much of that information. The person answering the phone simply could not see it. L&G connected Tealium's real-time data orchestration with Snowflake's AI Data Cloud. As customers moved through web and application experiences, those signals could be unified into a profile and used to identify where friction occurred. When the customer called, the agent could see the relevant digital behavior and better understand the likely reason for the call before the conversation even began. The interaction could move from: *“Tell me what happened.”* to *“I can see where the application became difficult. Let’s work through that step.”* That is a very different customer experience. It also changes the economics of the contact center. Agents spend less time reconstructing journeys, searching across systems, or repeating discovery questions. They are better equipped to address the likely issue during the first interaction. First call resolution goes up and average handle time goes down. Tealium’s contact-center materials cite a 54% increase in successful first-call resolution for L&G, while the published case study reports a 54% increase in call-to-lead conversion when customers were routed to agents who already understood their specific pain points. This is what real-time customer context looks like in practice. ## **From More Data to Better Decisions** The same principle can extend beyond servicing. One financial services organization was trying to recover customers who had started pre-approved loan applications but abandoned them before completing the process. The problem was not a lack of data. Web behavior existed. Mobile-app activity existed. CRM records existed. Internal credit scores existed. But they existed in separate systems. As a result, contact-center outreach was manual and difficult to prioritize. By the time an agent reached a prospect, the customer's intent may already have cooled. The organization implemented Tealium AIStream to bring those signals into persistent, person-level profiles. It created dynamic audiences based on application status, loan eligibility, propensity, and recency, then prioritized contact-center outreach based on the customers most likely to convert. Real-time call lists were sent directly to the contact-center stack, with email and push notifications providing additional touchpoints when calls did not connect. The organization reported a 2x increase in contactability, an 11% lift in conversion for the targeted recovery journey, and 12% growth in annual digital sales attributed to recovered loan applications. Just as importantly, the organization could update its rules and audiences in less than a day rather than waiting through a lengthy engineering or data cycle. That changes more than campaign performance. It changes how quickly the business can learn. ## **The Goal Is Not More Data** Banks already have enormous amounts of customer data. The challenge is making that data useful at the moment it matters. That requires a trusted, shared customer context that can move across web, mobile, branch, contact center, analytics, and AI without losing its meaning, timing, consent, or governance along the way. The institutions that solve that problem will not simply have a cleaner data architecture. They will be able to respond faster, reduce operational friction, give employees better context, and create customer experiences in their channel of choice that feel like one relationship with one bank. Which, from the customer's perspective, is exactly what it was supposed to be all along. ## Related Content [Powered by Tealium](https://tealium.com/blog/teal-on-teal/how-we-used-our-own-stack-to-personalize-tealium-com/) ### Loading Learn more ### Loading Learn more ### Loading Learn more ### Submitted! Thank you!