Finding your best customers with machine learning

Seacoast Bank enhances customer value using AI and SAS^® Visual Analytics on SAS^® Viya^®

As banking services move online, it’s critical for banks to get a clear view of their most loyal customers to answer questions like: What’s the lifetime value of a customer? What’s driving that profitability? And what’s the best opportunity to increase that value?

Banks have a lot of customer data, given their role as financial intermediaries. The challenge is tapping into it to understand customer value and, ultimately, find new ways to better serve, retain and acquire customers.

With plenty of customer data at hand, Seacoast Bank turned to SAS to gain insights into customer wants and needs. The first step: wrangling the data from different sources to make it useful in a consistent, trusted manner. With that done, Seacoast uses machine learning to better understand its customers, and SAS Visual Analytics on SAS Viya to make that insight readily available to employees.

Machine learning delivers personalization at scale

Improving the customer experience through advanced analytics is necessary for modern banks to stay competitive. Seacoast excels in this arena due to its proprietary customer analytics platform, which is powered by the SAS Platform and used to unearth customer insight for many purposes.

After aggregating and contextualizing its data for analytics, the bank used SAS^® Enterprise Miner™ to build a customer lifetime value (CLTV) model, which looks at every customer, measures their value and specifies why they’re valuable. Calculating the CLTV is critical for estimating a customer’s potential and how much the bank should invest in reaching and serving that customer to receive maximum ROI.

“Because we’re more aware of which customer groups drive value, we can fine-tune our customer treatment strategies as well as our acquisition efforts to generate very high returns,” says Jeff Lee, Chief Marketing Officer for Seacoast.

With the CLTV model in place, Seacoast added predictive models and applied machine learning to solve specific business problems, such as personalization at scale. With the SAS Platform, the bank has the capability to market to each customer individually based on their preferences and transactional history.

“Without the SAS Platform, we really couldn’t do what we’re doing,” Lee says.

The customer analytics platform has delivered gains in both efficiency and profitability. With customer insight flowing throughout the organization, marketers can automate campaigns, front-line staff can strengthen the bank’s relationship with its most valuable customers, and commercial bankers can look at their personal portfolios of customers and track their performance using interactive dashboards.

Real-time insights powered by SAS Viya

In the past, if Seacoast employees wanted to get more information about a customer, they had to run a service request for each query. “You’d make a request and wait to hear back,” says Robert Stillwell, Analytics Officer at Seacoast. “It wasn’t on demand, and you couldn’t interact with data or explore it to find opportunities.”

Now, instead of waiting for the IT department to create a spreadsheet, Seacoast allows the appropriate staff to access – and visualize – data to create insights. Surfacing data from the customer analytics platform, SAS Visual Analytics on SAS Viya makes information available in a governed, on-demand framework, so business unit leaders can consume those insights any way they please – and have trust in their data. And because speed and computing power are mission critical, Seacoast relies on the in-memory capabilities of SAS Viya for faster computations and discoveries.

“Previously, we could see only one-year trends because we couldn’t feed enough data into the system,” Stillwell says. “With SAS Viya, we can now see four-year trends, which is very important for us.”

Seacoast also benefits from faster processing speeds. “Before, our processes were so heavy that it took almost a full day to calculate the things we needed each month,” Stillwell says. “The parallel processing of SAS Viya allows us to get that information out much more quickly.”

Bank services ripe for AI

Since investing in SAS, risk-adjusted revenue per customer has grown by 30 percent, while ROI for automated marketing campaigns is in the high triple-digits. This success has driven the bank to look at expanding its use of analytics to enhance the customer experience in other ways.

Artificial intelligence is top of mind for Lee, who says the service-enhancing technology is increasingly needed to please consumers who expect things better, faster, cheaper and available 24/7.

“Think about a chat interaction, a phone interaction, a dot-com interaction – all those are ripe for AI,” Lee says. “There are multiple use cases across our business that are perfectly aligned with AI. And now that our data is in order and we’re maturing with regards to machine learning, AI will be an important part of how we operate our business going forward.”