How AI-Enhanced CRM is Transforming Community Bank Customer Relationships

With the increased adoption of generative AI, the technology landscape for community banks has shifted dramatically, and customer relationship management (CRM) platforms are no exception. AI is enhancing the capabilities of many CRM platforms to interpret the data community banks use to acquire new customers and cross-sell products to existing ones.

According to Lisa Pent, CEO of AI governance solution provider PentEdge in Albany, New York, one of the biggest enhancements is a shift from a CRM that records customer relationships to one that predicts and recommends the next best customer interaction. 

“This is enabling community banks to target their customers and prospects more narrowly,” Pent says. “It’s also helping banks focus their marketing efforts in the areas where they really want to grow.”

Supercharged community bank CRM: Predictive insights across business lines

Carly Reeves, senior vice president of marketing for Marquis, an SaaS platform specializing in data-driven marketing, CRM and compliance software for banks in Plano, Texas, calls this capability “predictive propensity modeling.” 

“AI can look at customer data and [help] make buying assumptions based on transaction patterns, life stage signals and customer behavior,” she says. “Based on this, the CRM can provide banks with actionable marketing advice.”

Generative personalization is similar. Reeves says AI can look at customer or prospect data to determine what stage they’re at in life and what products they might need right now, and then create content that’s targeted to those specific life stages.

Pent describes a conversation she had with a community bank CEO about how they were using an AI‑enhanced CRM to cross-sell more efficiently. According to Pent, the bank has a wealth subsidiary and an insurance subsidiary, but it didn’t have a mechanism to triangulate customer activity across different business lines. 

“Now, all of this is happening with their CRM platform,” she says.

AI-enhanced CRMs can combine customer behavior and online activity with demographic information and existing product usage to make a wide range of estimates, such as the likelihood a customer or prospect will complete an application or respond to a particular offer, or their expected customer lifetime value. 

With this data, community banks can prioritize their sales and marketing efforts around customers who are more likely to convert. 

“AI can take the information banks have about their current customers and use it to find potential new customers with similar demographics in similar life stages,” says Reeves.

Personalized banking offers through customer journey mapping

AI can also identify customer segments based on behavioral patterns and create personalized customer journeys. For example, a CRM could help identify customers who are on a home-buying journey by analyzing transaction patterns and demographic data. Based on this, a bank can tailor its marketing messages, channel and timing to specific individuals who could be good prospects for a mortgage, instead of to a mass audience.

Using AI, CRM platforms combine a wide range of data to recommend a next best action or next best offer for specific customers, including:

• Customer demographics, like age, income, occupation and business ownership
• Banking behavior, such as transactions, deposits, mobile app usage and branch interactions
• Events, like new business formation, tuition payments and changes in direct deposit amounts
• Customer engagement, such as responses to previous offers and conversations with bankers

“This kind of information immediately makes community banks more competitive with larger banks and fintechs that have teams dedicated to gathering this data,” says Pent. “Meanwhile, community banks retain the personal relationships that set them apart.”

Reeves agrees. “This level of targeted marketing used to require a large marketing staff, which most community banks don’t have,” she says. “AI is enabling smaller banks to compete on a more level playing field with large banks.”

From products to life events: AI-powered cross-selling strategies for banks

AI-enhanced CRMs are enabling banks to transform their cross-selling strategies from product-centric to event-centric. With traditional CRMs, banks send marketing messages to customers based on the products the banks want to sell, but with AI, campaigns start by analyzing customer behavior to gauge their inferred needs. From there, the most appropriate product is marketed, via the most effective marketing channel, to customers with the highest propensity to buy at just the right moment.

For example, large cash balances in a checking account could indicate excess liquidity, which could trigger an offer for a CD or other investment product. A substantially higher payroll direct deposit might indicate increased borrowing capacity, which could trigger an offer for a personal loan or investment products. Higher business deposits might indicate a growing business in need of credit or treasury management services.

According to Reeves, community banks are just starting to dip their toes into the waters of AI-enhanced CRMs. 

“There’s a little bit of hesitancy, because banks have so much regulatory oversight,” she says. “But there’s definitely curiosity among community banks about how they can take advantage of AI in their CRM.”

Reeves also says that community banks shouldn’t view AI as a replacement for the personal touch of human bankers. 

“Rather, AI can help community banks fine-tune their messaging and improve their customer relationships,” she says. “This is how community banks can win with AI.”

---