4 minute read

How to Apply Predictive Marketing & Artificial Intelligence to Consumer Banking

As a data scientist, I couldn’t be more excited about the massive opportunity financial services industries have ahead of them with such immense amounts of customer data.  Within a marketer’s grasp, there is so much opportunity to optimize all of that data to vastly enhance the customer experience.  It’s possible to really put that data to work by leveraging the potent combination of real-time behavioral automation, predictive marketing, and artificial intelligence to fully understand what your customers want and what their current relationship is to your brand. This equips brands  to build trust and reliability with customers by sending messages at the right time with the right content based on their collective behaviors.

Real-time Behavioral Automation 

Marketers, like many business applications, can be hesitant to make the leap from applying automation to drive consumer touch points as opposed to the more traditional “human” or manual send approach. Often there is a false sense of security associated due to the familiarity of “this is the way we’ve always done it.”
Automation minimizes the risks associated with more ad hoc driven process by taking advantage of a process flow, edited appropriately and audited heavily to ensure that the right information is delivered at the right time with the right frequency. Our process control flows mitigate the risk associated with over messaging or compliance risk associated with consumer data.

Automated process control flows also mitigate the risk of flooding customers with too many emails by applying something called frequency capping. This prevents the unfortunate consequence of customers hitting unsubscribe from your branded content. When brands allow real-time automation through behavioral marketing to drive the process instead, marketers can strike when the iron is hot with targeted sends to customers as they engage with a brand. When we add in predictive analytics, not only do marketers take advantage of these basic business intelligence rules, but they can further optimize who to target, how to target, and when to target using historical actions customers take, driving the most favorable time to message customers based on  subsequent engagement with previous campaigns.

Predictive Marketing

AI-driven predictive marketing methods leverage information inherent in consumer actions to give our data science-driven models so marketers can understand unique actions customers take. The magic of models like these is that in addition to the data we collect, the information we can derive from these interactions give us the ability to deploy deep experience-building models tailored to your brand’s unique market and potential customer base.

One of the most important characteristics of a behaviorally-driven predictive marketing approach is the ability to hyper-personalize customer interactions. By communicating and personalizing messages to current and future customers, we can also combine specific ways they interact with your products with the predictive modeling methods to target the highest probable, statistically-significant methods to drive conversions.

For example, marketers can use predictive marketing to reach a customer when they begin to disengage, saving them from making a switch. You can also use predictive marketing to message a customer who is considering new products and services, who is signing up for a credit card. Predictive marketing enables marketers to help customers make the leap.

Artificial Intelligence

The major concern about artificial intelligence is some fear of “big brother” collecting your information. But, millennials — who make up more than 95 million shoppers worldwide — have said loud and clear that this doesn’t bother them at all. In fact, 74% of them prefer to share data if it means they’ll get less marketing spam and more customized messaging from brands. Data also proves this builds brand loyalty, which increased from 6.5% to 28% when emails and online messages were targeted and focused to consumers directly. Financial services is built on trust, so this is data that simply cannot be ignored!

Artificial intelligence also allows marketers to automate decisioning around messages sent to customers based on behaviors from current and new customers whenever or wherever they interact with your financial institution. Maybe they are using a mobile banking app to check balances, or maybe they’ve visited your branch location to inquire about a personal or business loan. The Federal Reserve found that 91% of American consumers use both branch and online locations to connect with financial brands.

This powerful combination of real-time behavioral automation, predictive marketing, and artificial intelligence shows your customers that you know them, and most importantly that you understand their specific needs at a given time. Rather than “spraying and praying,” financial services brands can use the information customers provide willingly to effectively mirror back in your communications and say “We hear you.”

Your customers, new and current, want to keep a relationship with you and will gladly hand over information about what they need based on interactions they take with your website or branches. Don’t miss out on using this behavioral data to build customer loyalty and trust. Like any relationship, it isn’t good enough to understand wants and needs, but also using that data to nurture and foster loyalty and trust.

Looking for specific campaigns to get you started? Use behavioral marketing to target customers who abandon forms for new services or download our Financial Services Behavioral Marketing Playbook.

Casey Barwell is a Senior Data Scientist with SmarterHQ. Connect with him on LinkedIn and Twitter!