How Community Banks Can Use AI to Improve Sales and Marketing

Artificial intelligence and machine learning have become vital to many aspects of financial services, from powering chatbots to improving fraud detection. But one area where AI has not gained as much traction, particularly with community financial institutions, is in sales and marketing.

Its use has been steadily ramping up, though: SouthState Bank and Eglin Federal Credit Union are among those using AI to parse data in ways that have resulted in much greater impact for their marketing efforts. Rather than targeting people based on simple demographics like age or gender, they combine internal data with information available beyond their own databases to surface a much more telling — read: predictive — combination of details.

The key benefit has been the ability to more accurately identify when customers are ready to buy a particular financial product and deliver the appropriate messaging to them.

This is a competitive advantage with tangible results, as a recent Eglin auto loan campaign illustrates.

Eglin served a digital ad in its online and mobile channels only to current members with the highest propensity to need an auto loan, says Rocky Magee, senior vice president of information systems for the $2.9 billion-asset credit union in Niceville, Fla.

Data modeling by an AI tool called Predictive Campaigns from the digital marketing firm DeepTarget helped Eglin suss out which of its 123,000 credit union members to target, based on such factors as which ones had recently visited an auto sales site.

Within the first 26 days of the campaign’s Sept. 1 launch, the ads had accumulated more than 26,000 unique impressions and 279 unique clicks, results that the Eglin staff deemed “amazing.”

The AI-driven campaign is credited with leading to 79 new auto loan account openings in that initial period of about three and a half weeks.

“Members are likely to follow up on products and services that are meaningful to them,” Magee says, adding that AI gives Eglin the ability to make sure its offers are more meaningful by virtue of being relevant and timely.

He is particularly pleased to think about getting “a better return on our investment and time” with future marketing efforts. “We will no longer need to blanket market,” he says.

Dig Deeper: How AI is Transforming the Banking Industry

The advent of, and recent advances in, artificial intelligence have proven valuable to smaller financial institutions, especially by allowing marketers to create detailed customer “personas,” says Chris Nichols, director of capital markets for SouthState Bank in Winter Haven, Fla.

Personas consist of a set of characteristics that represent the type of customers a financial institution would like to target with a particular message or an ad. For example, maybe middle-aged outdoor enthusiasts who have rented RVs and vacationed at a national park multiple times in the past two years would be a segment to target with a loan for a recreational vehicle.

“AI helps determine a person’s or businesses’ attributes that are important to any desired outcome,” Nichols says. “For example, most likely to be profitable, most likely to respond to a promotion, most likely to convert to a given product, or most likely to commit fraud. The list goes on — there is an infinite list of questions that AI can improve upon.”

SouthState uses AI to predict the best channel and the best time to deliver a marketing message.

“Instead of mass emailing or texting, machine learning customizes the path and time for each recipient,” Nichols says. “AI can also be used with the tracking data on a website. We tag our content, so machine learning looks at your tag path and then tries to predict your intent and likeliness to need a banking product.”

For smaller financial institutions, adopting AI can be daunting, but one way to make it more manageable is to identify key use cases and start from there, according to a research report from the consulting firm Wipfli.

One use case suggested in the report — an idea that also happens to align with SouthState’s approach — is to use predictive analytics to learn how each customer wants to interact with the financial institution and then pinpoint opportunities to engage the customer in a personalized way using those preferred touchpoints.

“These are the types of opportunities that have a direct impact on yearly revenue,” says the report from Wipfli, a company that advises on strategy and digital transformation.

Learn More: AI Maturity in Banking Lags All Other Industries

Financial education also can be a starting point for a marketing campaign that benefits from artificial intelligence, according to a blog from EverFi, a company that takes a digital approach to engage people in improving their money skills.

Community First Credit Union in Jacksonville, Fla., worked with Everfi years ago to create its “moveUP” financial wellness program, and EverFi says a subsequent marketing campaign helped the credit union add $2 million worth of debt consolidation loans. The moveUP program consists of 22 digital training modules on a variety of topics, including auto loans, mortgages and checking accounts.

As customers show interest in a particular topic — say, auto loans — this is a signal that they might be on the brink of making a big financial decision — such as buying a car, EverFi says in the blog post.

By using a “next best action” AI model to identify such opportunities, financial institutions generally see a 30 percent to 40 percent increase in sales, EverFi contends.

Despite its benefits, many banks and credit unions are not using artificial intelligence to a great degree, whether because of decision fatigue (where to start?), poor data management (the oh-so-persistent silo woes), lack of knowledge among potential users of the technology throughout the financial institution (whatchu talkin’ ’bout, Willis? the movie “AI”?), or lack of an AI strategy.

The bounty of choices for what to tackle with AI can be “overwhelming,” Nichols concedes. “Almost everywhere we look double digit ROIs can be found and so the challenge is determining what projects give you the greatest returns.”

Often financial institutions also have their customer data fragmented in various applications, rather than in a clean, centralized and protected space, where AI models can be easily applied.

But Nichols believes “the missing piece” for most community banks and credit unions is better communication and training, as employees outside the tech department frequently don’t get how to use the technology. “Someone in IT may understand how to use an AI application, but the front line needs a working knowledge so it knows where to apply it,” he says.

McKinsey contends a clear strategy around what to do with AI is the most effective way to overcome such hurdles. The management consulting firm recommends taking a holistic approach by tightly aligning bank technology initiatives with the overall business strategy. Then outline what elements can be handled internally — based on the skill sets of employees — and what would be better in the hands of a partner or vendor (in other words, decide the question of build or buy).

Read More: How Data, AI & Machine Learning Supercharge Personalization for Banks

Within just a few years, AI will handle even more of the workload for sales and marketing operations, Nichols predicts.

“Right now, banks are targeting a very defined and specific audience,” he says. “We pick the audience and the content and then turn it over to the AI to optimize.”

But he expects AI will soon be able to optimize campaigns for each individual. “AI is on the verge of being able to create the copy, graphics, layout and headlines for every individual person and then deliver,” Nichols says. “We are a couple years away from just telling the AI to market our treasury management product and it will optimize the rest.”

Preetha Pulusani, chief executive officer of the digital marketing firm DeepTarget in Huntsville, Ala., says that AI is quickly going from a nice-to-have to a must-have.

That’s because the amount of data available about individuals is exploding to the point where manual processing and human analysis are impractical. “AI can automatically analyze huge volumes of data to identify trends and patterns that would otherwise be difficult or even impossible to discern,” says Pulusani, whose company specializes in digital engagement strategy and works with financial institutions such as Eglin.

DeepTarget partnered with the AI firm Cognitive Scale on the platform Eglin uses, which is designed to help smaller banks and credit unions identify sales opportunities. The platform performs data analytics to glean insights from market, social, government, institutional and proprietary data lakes to predict, for example, consumer propensity to buy within a given upcoming 30-day period.

The partnership between the two companies is reflective of an accelerating trend, as AI spending in financial services is expected to continue growing at a rapid pace. According to data from Emergen Research, the industry invested $11.86 billion in AI in 2020, a number that is projected to hit $85.77 billion by 2028, representing a compound annual growth rate of 42.9% during this period.

Such growth would be no surprise to Pulusani. Personalization holds more promise — but also becomes more of a challenge — in an age of data deluge. “Pretty soon, AI will be the only way for delivering meaningful and highly personalized messages,” she says.