Despite the staggering statistics, the banking industry has been slow to adopt AI technology. However, at Micronotes, we've got your problem solved.
Relationships deepen faster with a lot of interaction, listening, and learning -- because the relationship becomes more rewarding. Last week, I met with the chief data officer of a very large U.S. financial service provider (9MM unique visitors per month) and he, too -- is always trying to connect customer click streams back to his machine learning systems so direct expressions of customer interest can be used to train the classifiers used to determine audiences for marketing messages. Unfortunately, advertising and email can only provide a small amount of information on positive interest, and almost nothing on "no interest".
Teaching kids to learn is tricky, I know – my wife’s a teacher and I hear about the challenges, daily. Teaching machines to learn is tricky too, particularly when you don’t have many examples of success to learn from. It's kind of like teaching a toddler what dogs look like, with only a poodle. This is a problem in marketing too, when you have a new marketing initiative but, very little success to teach from.
It seems odd that marketing technology could be used to manage attrition and delinquency risk but, here we are – doing just that. Our customers are now machine learning audiences at high risk of delinquency and selectively offering skip a payment options and engaging early with at-risk customers to avoid loan defaults. Early results are impressive, here they are...
Over the past 2 years, one of our clients interviewed an average of 28% of their entire internet banking audience each and every month. For this client, with about 64,000 unique visitors per month, that level of engagement generated 487,832 clicks that trained our machine learning based targeting systems, and produced 36,620 leads. So, what does that level of engagement do?
Spending money to acquire customers that leave the bank is not only frustrating but, it's expensive! Once effective onboarding and cross-selling systems are installed, banking institutions need a way to predict which customers are at risk of leaving so they can take decisive actions to keep attractive customers.
In 1992, MIT Professor Patrick Henry Winston defined Artificial Intelligence as:“Artificial Intelligence is the study of the computations that make it possible to perceive, reason, and act.” This definition has proven to be pretty durable so, let’s step through an AI-based marketing application using Winston's words as our guide.