Lending

3 Growth Opportunities Most Financial Services CMOs Haven’t Tapped

5 mins read
March 12, 2019
By
Total Expert

We recently wrote about four top priorities that financial services CMOs have on their plates in 2019: owning customer experience, winning customer trust, activating employees as brand ambassadors and growing the customer base. In this piece, we’ll explore three often-overlooked strategies for achieving that last item: winning more customers.

According to the latest data available from the CMO Survey, most companies are less focused on growing their customer base than on improving their core offering: while about a quarter of current spend goes to product development, only 13 percent goes to market development (i.e., finding new customers for existing products).

What’s more, over the last few quarters, spend on product development has inched up while market development spend has crept down. That suggests that CMOs must look to non-traditional strategies for growing the customer base. Here are three to consider.

1. Training and Development

While CMOs acknowledge that having the right talent is the most important factor in their organic growth, fewer than half report including money for team training and development in their marketing budget.

While that may seem like a good way to cut costs in the short term, failing to invest in training could seriously damage profits in the long term. Companies that invest in employee training and development enjoy 218 percent higher income per employee than those that don’t, as well as 24 percent higher profits overall, largely because of productivity gains.

Just as important: training your team can help you attract and retain top talent, especially among millennial workers (who now make up more than 35 percent of the workforce). A whopping 87 percent consider training and development opportunities important in a job. In this era of full employment, providing the resources to ensure that your best employees stick around will be key to achieving sustainable growth.

2. Alignment with Customer-Facing Teams

As we’ve mentioned in the past, the new CMO is responsible for the entire customer experience. But only 17.3 percent of marketing teams today lead customer experience efforts (down from 19.6 percent in 2014).

That means there is likely to be a gap between customer-facing teams and those in the marketing department – a gap that potential customers could be falling through.

The opportunity here is for CMOs to coordinate with heads of customer service on messaging, information capture and goals. In an ideal scenario, CMOs empower every customer facing role to translate customer interactions into data that can fuel new and ongoing marketing and nurturing efforts aimed at developing the kind of brand affinity that inspires repeat transactions.

3. Acquisitions

While training and development can help maximize productivity and loyalty of your existing team, sometimes you can achieve faster, more efficient growth by acquiring another company. This is especially true if you’re looking to add new skills, expertise, or technology quickly.

While not often considered the realm of the CMO, a strategic acquisition can enable a company to enhance its current marketing capabilities and reduce activation time for a new channel, which is often crucial for connecting with new customer segments.

Today, only 2.1 percent of CMOs report that they’ve acquired another company as part of an effort to improve marketing capabilities.

Winning New Customers for Life

Of course, winning new customers is only half the battle for tomorrow’s CMOs. Sustainable growth requires that CMOs establish processes to ensure that employees are focused on building meaningful relationships and delivering hyper-relevant communications for the long term – and by doing this, ensuring that new customers remain customers for life.

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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

Too many financial services leaders have experienced what I call “AI failure to launch (and scale).” They’ve rushed to try unintegrated AI-enable offerings and bolt on AI tools—often generalist chatbots, white-labeled versions of generative tools, and/or hooking up to MCP servers—without a clear sense of how these tools will solve their business problems or add potential risk. The result? The occasional value-add result. However, what we see more is poor user adoption, wasted spend, and limited impact.

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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