How Banks Can Use AI to Maximize the Customer Experience and Business Opportunities
Artificial intelligence provides deep, actionable insights into what customers are experiencing.
Are you baffled by what to do with the mountain of data you’ve collected? Everyone in your organization, from business development to marketing to customer service, is questioning how they can draw actionable insights from that wealth of information. And for good reason: The answers highlight opportunities to meet your customers’ needs and thereby maximize your brand and bottom line.
Sure, businesses have access to more customer data than ever before, but typically the result of that access is information overload. One obstacle is unstructured data, such as call center transcripts and social media posts. It’s challenging enough finding the nuggets of actionable insight in those mountains of data. It’s even more challenging trying to connect the dots between unstructured and structured data, such as between the raw text of social media posts and the firmly defined categories of demographic information.
Enter Artificial Intelligence
Like other industries, the banking sector experiences these challenges, but banks also have strategies for overcoming them. For example, artificial intelligence (AI) platforms such as IBM Watson can pull structured and unstructured data from disparate sources, including external ones, and identify problems and opportunities that a small army of humans might take weeks to uncover — if ever.
What are your customers really going through right now? And how can you help? As business development manager, I’ve helped banks and other businesses identify all of their organization’s data resources and apply AI to answer these questions and strategically support the company’s business goals.
For example, AI could analyze a customer’s checking account inflows and outflows to identify life events. A sudden and ongoing series of debit card transactions at Babies“R”Us and Gymboree, and checks written to an OB-GYN with “insurance co-pay” on the memo line, all strongly suggest that this customer is about to become a parent. That information could mean opportunities to upsell the client on life insurance or a 529 plan for college savings — products that the customer might not be aware their bank even offers.
Another case involves using AI to tag and track visual content such as marketing collateral. Then the bank’s marketing department knows when each customer views that collateral on any of their devices, such as a PC or smartphone. Those viewing impressions can be correlated with data from other platforms to identify, for example, when and where a customer followed up on an advertisement. That’s useful for understanding which campaigns, messages and so on resonate with certain demographics.
Sometimes AI does analysis in real time. One example is a virtual assistant, which many banks have added to their instant voice response systems, website chatbots and mobile apps. Surveys show consumers overwhelmingly prefer not only self-service options, but ones that allow them to converse with a virtual assistant as if it were a live agent.
AI enables virtual assistants to provide an even better customer experience. Machine learning uses each interaction as a teachable moment so the virtual assistant keeps getting better at understanding customers. AI also enables sentiment analysis, so the virtual assistant can tell when a customer is getting frustrated and seamlessly transfer them to a live agent for white-glove service.
Regardless of whether a data set is structured or unstructured, it usually winds up being used by only the department that’s responsible for it. Start getting the most out of your AI investment. Develop a strategy to link as many data sources as possible so that your AI system can start mining away and unearth the riches inherent in business intelligence.