Customer data transforms marketing strategies at traditional financial institutions

The hype around customer data right now isn't overblown — it supports smarter strategic decisions and lets marketers deliver more relevant experiences throughout their customers' journeys.

Customer data transforms marketing strategies at traditional financial institutions

The hype around customer data right now isn't overblown — it supports smarter strategic decisions and lets marketers deliver more relevant experiences throughout their customers' journeys. As competition rises, retail banks and credit unions need to adapt in order to survive. This means starting to leverage data to positively affect revenue, engagement, and customer loyalty in the long run.

Evaluating your tech stack

Financial institutions are often slow on the uptake of new technologies, especially when it comes to marketing and data analytics. According to The Financial Brand’s Digital Marketing Report Research, when banks and credit unions worldwide were asked about the most important marketing trends for the next 12–18 months, less than half believed advanced analytics was a high priority for the future. It’s no wonder that they report that their biggest challenges are around leveraging their data.

But, for most financial marketers, leveraging advanced analytics generally requires implementing new technology that can centralize data for better accessibility, doesn’t require intensive training, and integrates with the marketing systems they use on a daily basis. These are some big hurdles, but overcoming them is essential, as almost 80% of bank operations leaders say their organization’s existence could be threatened if they don’t update technology to be more flexible and capable of supporting rapid innovation.

These threats predominantly come from big banks that have already fully operationalized advanced analytics and VC-backed fintechs. Much of the appeal of these younger, more aggressive companies comes from their ability to harness customer data and quickly generate insights and predictions to deliver more relevant experiences — which consumers now expect.

One of the most prominent and increasingly important kinds of technology being adopted today is customer-focused AI. Using historical data to make predictions about future customer behavior, find new leads, and engage current customers can help financial organizations optimize their time and budgets across multiple departments. In fact, research has found that brands that have adopted AI for marketing strategies have seen a 37% reduction in costs along with a 39% increase in revenue. Taking the time to dig deep into your data and find opportunities to use AI can pay off.

Understanding customer journeys

According to The Financial Brand’s State of the Financial Marketer report, fewer than half of financial organizations use customer journeys to organize their marketing efforts. And yet, understanding customer journeys is instrumental in offering relevant products that encourage customers to stick around.

Highly effective customer journey mapping starts with having access to meaningful data. The challenge for many marketers at financial institutions is that this data is scattered and siloed across many different internal systems, making it difficult to use, much less access. To that point, the State of the Financial Marketer reports that 19% of financial organizations don’t use any data-driven insights, and only 9% of organizations report having centralized data.

But acknowledging the lack of access and use is not enough — financial organizations need to put in the work to centralize their data, get to know their customers, and understand levels of engagement. This often looks like implementing software that brings all customer data together, has the ability to analyze it, and makes the results and value of those analyses clear so they can easily be applied to marketing initiatives.

Mapping customer journeys isn’t only for marketers, though. It also helps sales and customer service teams understand where a customer is coming from and anticipate their needs. This helps financial organizations move past the purely transactional aspect of banking and build sound relationships, which in turn build loyalty and trust. Using thoughtful, data-driven methods to retain customer base helps organizations remain competitive and efficiently grow revenue.

Learn how your brand can use behavioral predictions to optimize your customer lifecycle

Developing a framework for personalization

Personalization is necessary to build loyalty, and in turn, revenue. BCG estimates that for every $100 billion in assets that a bank has, it can achieve as much as $300 million in revenue growth by personalizing its customer interactions.

Customers’ desire for their banks to understand them and cater to their needs is ever increasing, and meeting those expectations shows that you are putting your customers first. However, putting the tools and practices that will enable data-driven personalization in place has proven difficult. Despite 49% of financial organizations believing personalization is a high priority, 75% consider themselves inept at the basic applications of data and AI that fuel personalization efforts.

Customer loyalty and trust also benefit heavily from financial organizations personalization of product offerings, nurture campaigns, and customer service engagement. According to EY, 40% of customers say they would be more likely to stay with their financial services provider if it offered more personalized service. This is especially important to note as competition from fintechs continues to grow and those services become more convenient to use over a bank or credit union.

Good personalization strategies depend on marketing automation platforms that can facilitate and automate segmentation and content delivery across the customer journey. Working with even just a few key identifiers (e.g. customer journey, geographic location) can help you target your customers and prospects with relevant, engaging content that goes far beyond a generic, masses-oriented approach.

That said, the more you know about your customers’ lives outside of their transactions with you, the more likely you are to generate content and timely offerings that will appeal to them. To add more context to their customer data, some institutions will license third-party data from a data vendor, and others will leverage AI software that has those data points built in. Choosing a strategy depends on an organization’s budget, internal data infrastructure, and its ability to make sense out of data analyses and insights.

Even if organizations only work with first-party data, the opportunity to improve customer relationships with personalization still exists, particularly if customer journeys are being tracked. Just because there isn't third-party data involved doesn't mean that marketing decisions won't be effective or resonate with customers — just remember to let the data lead the way.

Learn how Faraday helps brands surface powerful insights and scale personalization

Learn how Faraday helps financial institutions operationalize AI to let their teams work smarter and deliver better experiences across their customers' journeys.