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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Best Practices for Executive Teams Deploying AI in Financial Services

The AI revolution feels like humanity just discovered fire—and everyone is racing to see what they can ignite.

That means a rush of AI pilots and proofs-of-concept across all industries, many of which launched without evaluating each use case against actual business value.

As I meet with CEOs and executive teams from leading mortgage lenders and financial institutions, the conversation has shifted from “What can AI do?” to “How do we deploy AI responsibly, at speed, and with measurable impact?”

The market leaders I work with are outpacing competitors by following a remarkably consistent playbook. They’re not just testing AI, they’re embedding it across their organizations with purpose, speed, and discipline.

Below, I’ve distilled the best practices I’ve observed from the institutions getting the most from AI today.

Anchor AI strategy to business outcomes

Tie every AI initiative to a clear business priority—whether it’s loan growth, customer retention, or operational efficiency.

Define KPIs, ROI targets, and adoption metrics before a project begins. No project should exist without a measurable path to value.

Start with high-impact, low-friction wins

Focus first on areas where a proof of concept or pilot is feasible within 30-60 days. Conversational and Voice AI solutions provide many options for pilot use cases. Other common use cases involve document classification, predictive churn modeling, or intelligent lead scoring. These early wins build momentum, prove ROI, and prepare teams for more complex deployments.

Invest in data quality and governance early

AI is only as good as the data feeding it.

Start by creating a single source of truth for customer and loan data. Then, anticipate obstacles to deploying AI with your data, such as consumer consent and preference management, and start addressing these things ASAP. Investing in tools like Customer Intelligence will help enrich your data and increase its value.  

Embed compliance and risk management from day one

Regulations such the Gramm-Leach-Bliley Act (GLBA), TCPA (Telephone Consumer Protection Act), and UDAP (Unfair, Deceptive, or Abusive Acts or Practices) will be a few key areas where regulators dig in and look for companies cutting corners.

Create a cross-functional AI task force

Bring together leaders from product, compliance, data science, operations, and customer experience. Avoid siloed pilots—alignment ensures every initiative supports the broader business strategy. Include change management expertise to drive adoption, not just deployment.

Prioritize customer experience and trust

Every organization has gaps in their customer journey and can benefit from leveraging AI to provide human-like touch points throughout the experience. Use AI to remove friction, improve transparency, and deliver personalization at scale. Keep humans informed about high-stakes decisions and be transparent with customers about how AI is used and how their data is protected.

Build for integration, not isolation

Select AI solutions that integrate seamlessly with your CRM, LOS, core banking systems, and data lakes. Use APIs and modular architectures to avoid “AI silos” that slow scale and ROI.

Focus on talent and change management

Embracing AI with a growth mindset should be table stakes. Incentivize adoption so teams see AI as an enabler—not a threat to their roles. Upskill executives and frontline teams in AI literacy. When needed, recruit or partner for deep ML and data science expertise.

Measure, monitor, and iterate

AI is not a one-and-done project—it’s a living product. Track performance, user adoption, and ROI continuously, and refine models quarterly to maintain accuracy and relevance.

Choose the right tech partners: favor vertical specialists

Partner with vendors who understand financial services—especially your unique customer journeys or workflows. Deep domain understanding on core systems, database schemas, compliance, and other nuances will be a key factor in the results you achieve.

Benefits of vertical-focused partners:

  • Deep understand of unique data sets and customer profiles
  • Faster implementation with industry-specific models
  • Built-in regulatory and risk controls
  • Product roadmaps aligned to lending and banking trends

Horizontal AI tools have their place, but without deep domain expertise, they often require heavy internal customization and a slower time to value.

The future is here

AI today is not the same as the project in 2018 that failed to deliver those operational efficiencies in the back office everyone was promised. Its potential to transform nearly every part of our businesses is becoming increasingly clear. Every day you delay, competitors are building up their capabilities and you will struggle to catch up. As one of my investors put it bluntly, “Every day you fail to execute a comprehensive AI strategy, the value of your business goes down.”  

To learn more about how Total Expert is working with our customers on high-impact AI initiatives, please reach out to our team.  

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