Customer Engagement

Bank Marketing Pitfalls Part 1: Failure to Define Customer Personas & Remove Data Silos

5 mins read
April 3, 2019
By
Total Expert

Banks are continually evolving to leverage new technology and meet rising consumer expectations for convenience, value and personalization.

Whether in-person or online, customers demand personalized engagement that makes them more confident in their financial decisions. In exchange, customers will offer their loyalty.

To build trust and increase customer lifetime value, bank marketers must go above and beyond customer expectations while avoiding common marketing pitfalls.

In part one of our three-part series, Total Expert details how banks must define their customer personas and remove enterprise data silos to create personalized marketing and engagement that boosts customer loyalty.

Pitfall #1: Failure to Define Customer Personas

To attract and retain customers, you need to understand what makes your customers tick. Banks have mountains of customer data – from checking accounts to credit scores to demographics and need to leverage this information to define and understand their target customers.

Banks that lack data-rich customer personas are at a disadvantage when it comes to personalizing their marketing. The less you understand your customer, the less you’re able to anticipate their needs with elevated experiences, which leads to higher conversions and higher customer retention, which results in revenue and profitability growth

Blindly sending customers messaging that is neither relevant or helpful is a great way to lose trust and harm customer loyalty.

And with so much data at your fingertips, banks must bring order to their data chaos.

Personas help banks understand their customer on a deeper, more personal level, so they can respond to the needs of consumers in a way that feels natural and spontaneous, not canned or impersonal. The reward? Better business outcomes.

For better customer personas, banks must focus on data that speaks to three key areas: financial literacy, engagement and preferences.
  • Literacy – Banks must consider each consumer’s financial intelligence when marketing across their products and services. Messaging that requires deep knowledge on the part of the customer may fall on deaf ears.

Once you’ve developed personas catered to levels of financial literacy, banks can better target customer messaging that educates and improves financial outcomes, building consumer trust.

  • Engagement – Banks must craft personas that reflect a consumer’s overall engagement with your bank’s brand. Past behaviors – like page views or opening new accounts – typically correspond with life events and future intentions. By monitoring how consumers engage across the customer journey, banks can infer motivations and better personalize their outreach.
  • Preferences – Banks should keep a close eye on the specific marketing elements their customers favor, including preferred channels, products and services, even time of contact. With these preferences in mind, you can fine-tune your personas to drive better results.

For most banks, crafting persona marketing strategies should be a “walk-before-you-run” endeavor, as new data and content will force you to re-evaluate as you go.

Focusing on how customer literacy, engagement and preferences shift will be a positive step towards personalizing the customer experience and avoiding this common pitfall.

Pitfall #2: Failure to Remove Data Silos

Another barrier for bank marketers is silos of customer data. Legacy systems and rogue solutions prevent banks from connecting all their customer information in one place and gaining the ever-elusive 360-degree view of the customer.

Data silos act like vaults for your enterprise systems — and if each “vault” is walled off from the other, it’s nearly impossible to leverage the richness of all your customer information.

Banks that fail to remove data silos are missing out on greater ROI from their technology solutions and falling behind competitors that have integrated their sales and marketing organizations and tech stacks.

Disconnected data across closed APIs or enterprise systems can have other avoidable consequences, too:
  • Missed opportunities – Having an incomplete picture of your customer blinds you from otherwise profitable opportunities. Banks that have a clear view of their customers’ accounts, activity and life events are more capable of anticipating customer needs and meeting their rising expectations in the future.
  • Poor customer experience – Disconnected data creates needless barriers between your customer and your services, such as missed preapprovals or out of place messaging. By connecting enterprise data (across all channels and all departments) banks can elevate their brand with data-driven outreach that is relevant and reduces friction.
  • Lower lifetime value – Banks that lack a 360-degree view of their customers struggle to earn repeat business. That’s because customer value is highest across a lifetime of transactions — but, to build that lifetime loyalty, banks need data insights that anticipate customer needs and serve relevant messaging across life events.

Leveraging your database (or “customer-base”) fully enables banks to become a central financial partner by helping their customers understand make better financial decisions in the future.  

Conclusion

To better serve customers, and to bolster marketing ROI, banks must improve customer personas and remove data silos to reduce friction across the customer journey.

Modern consumers expect their financial institutions to use data to understand them better and deliver one-to-one messaging based on their current life events. Banks must appeal to this consumer preference to avoid these common marketing pitfalls.

Watch for part two of our bank marketing pitfalls series soon on the Total Expert blog.

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AI is no longer a future state—it’s already here, embedded in everything from ride-sharing apps and food service to factories and farms. In the world of financial services, though, this ubiquity comes with pressure to integrate AI fast, appear innovative, and keep up with competitors—all while being mindful of evolving federal and state compliance requirements. Moving fast without a plan or awareness of up and downstream implications often leads to AI-enabled solutions that either underdeliver or don’t deliver at all.

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Where enterprise AI goes wrong

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This is the same trap we saw with “digital transformation” a decade ago, or the original horizontal SaaS applications that evolved or were replaced by vertical-specific solutions. AI-enabled solutions offer tremendous, generational promise but they risk becoming vanity-first, value-later tools. We are focused on the former.

AI that thinks and adapts: Welcome to agentic AI

Let’s make one thing clear: not all AI is created equal.  

Chatbots have been commonplace in financial services for a decade now, but remain rigid, rule-based tools that handle repetitive tasks.  I’ve worked with “AI” services for more than 15 years and each had their own place and potential when used properly. Herein lies the opportunity. Modern lenders that are focused on retaining and growing their customers in an ultra-competitive market need something more dynamic. Enter AI agents that can understand context, adapt on the fly, and speak in a human-like way. These agents are coachable, brand-aware, and learn from every interaction. They don’t follow scripts—they think in real time. And when built correctly, they become a seamless part of your customer experience.

This is the evolution from AI as a support function to AI as a trusted team member.

Total Expert recently launched an AI Sales Assistant that puts this principle into action. It functions as a scalable, intelligent teammate—able to engage leads, deliver personalized conversations, and identify high-potential opportunities—all while staying aligned with your brand voice and compliance requirements. It’s not a chatbot bolted onto a CRM—it’s a fully integrated AI-enabled solution, utilizing data, embedding within workflow orchestration, and playing nice with application logic because it has the necessary context to work within your lending ecosystem.

The real “why” behind AI adoption

Before choosing any AI solution, or any technology solution, financial services firms must ask themselves: What business problem are we solving?

For example, when mortgage rates dropped for a few weeks in September 2024, our customer intelligence capabilities identified nearly $2 billion in immediate refinance opportunities. But no team of loan officers could scale quickly enough to reach every qualified lead. That’s where AI tools prove invaluable—automating first-touch outreach at scale, surfacing the best opportunities, and empowering human teams to scale up execution to drive retention and growth.

Why embedded beats bolted-on

The types of AI-enabled solutions we are talking about can’t function effectively in isolation. Without access to timely and accurate customer data, and invoked within a specific workflow process, it can’t personalize interactions, anticipate needs, or drive conversions at the right time.

Picture an AI assistant offering a refinance to a customer, only to stall when asked for more details. If it doesn’t know the customer’s current rate or financial profile, the experience feels hollow. That’s not just ineffective—it damages trust.

By contrast, when AI-enabled solutions are embedded within a unified customer experience platform like Total Expert, it draws on a 360-degree view of the customer. It knows the data, understands the history, and delivers contextually rich conversations that convert.

This is why we’re designing our AI capabilities with a focus on the unique needs of financial services organizations. The same purpose-built approach has earned the Total Expert platform its unmatched reputation for usability and time to value.

Generalist AI offerings can be a gamble that increase costs—and time to value

Implementing AI that’s not purpose-built for financial services introduces two major risks:

1. Usability failure: Your team must spend months customizing and configuring a generalist AI tool to make it work for your specific needs—if it will ever work at all. For example, imagine you’re a loan officer and one of your referral partners introduces you to a borrower. Now, you have to choose the best way to approach the first conversation with this borrower. There are countless permutations of questions and answers which all require deep personalization, compliance awareness, and consistent representation of the sales processes and brand tone of the lender. Generalist AIs will quickly reach their limitations in these complex use cases.

An industry-focused AI offering will be trained on this specific use case and provided with the context needed to hold a dynamic conversation with the borrower. This type of AI learns and adapts with each interaction, performing the most time-consuming tasks so you don’t have to.    

2. Compliance risk: Without built-in industry guardrails, you’re gambling with regulatory violations and brand safety.  As we know, the compliance landscape for financial services is broad and evolving at the federal and state level.  Look for AI offerings that are regulatory aware and enable you to configure them based on your organization’s risk tolerance and interpretations.

Lenders don’t need more tools—they need the right tools—ones that work out of the box, understand industry nuances, and deliver immediate, compliant value.

Ask these questions before you commit to an AI offering  

To maximize the probability of success, here’s a quick checklist for vetting solutions:

  • Can it solve a real, high-value business problem, and how? Review specific examples and ask to speak with other organizations that have implemented the tool.
  • Does it function as a true AI agent, not a static bot?
  • Can it be deeply integrated into your core system(s), workflow orchestration, and data?
  • Does it include financial industry compliance and brand guardrails?
  • Can it scale without sacrificing quality or regulatory integrity?

Building the future with purpose-built AI

Total Expert has always designed technology with financial services in mind, and our approach to utilizing AI is no different. We’re not chasing hype. We’re solving problems.

Our focus on AI isn’t simply building standalone features—it’s about embedded, intelligent, and deeply integrated AI solutions. It’s helping lenders scale smarter, engage more meaningfully, and turn data into action. Our AI Sales Assistant is just the beginning—an example of how purpose-built, AI-enabled solutions can solve real problems and deliver tangible value. We are already testing and exploring other AI-enabled solutions and I could not be more excited about the current and potential value our clients and our market will achieve.

Because when AI works, it’s not just impressive—it’s indispensable.

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