A new report available from Celent authored by Daniel Latimore, Karlyn Carnahan and William Trout
That timeless principle — “Know Your Customer” — has never been more relevant than today. Customer expectations are escalating rapidly. They want transparency in products and pricing; personalization of options and choices; and control throughout their interactions.
For a financial institution, the path to success is to offer those products, choices, and interactions that are relevant to an individual at the time that they are needed. These offerings extend well beyond simple product needs and pricing options. Customers expect that easy, relevant experiences and interactions will be offered across multiple channels. After all, they get tailored recommendations from Amazon and Netflix — why not from their bank, financial advisor, or insurance company?
Financial institutions have all the data necessary to know the customer deeply. It’s there in their financial transactions: the credit card purchases and checking account transactions showing they’ve purchased baby furniture or sent a tuition check to a university. It’s there in the public data showing the purchase of a new house or a marriage. It’s there on Facebook and LinkedIn as customers clearly talk about their life changes and new jobs.
One of the newest trends is dynamic segmentation. Institutions are pulling in massive amounts of data from multiple sources, creating finely grained segments and then using focused models to dynamically segment customers based on changing behaviors.
This goes well beyond conventional predictive analytics. The new dimension to this is the dynamic nature of the segmentation. A traditional model uses demographics to segment a customer into a broad tier and leaves them there. But with cognitive computing and machine learning, an institution can create finely grained segments and rapidly change that segmentation as customer behaviors change.
This report describes uses of advanced analytics and cognitive computing in financial services to transform the customer experience. Specific examples across banking, wealth management, and insurance are provided as well as advice for how to get started.
“Using advanced analytics and cognitive computing allows an institution to humanize a digital interaction and, in a live channel, to augment the human so they can scale, says Dan Latimore, senior vice president of Celent’s Banking practice and one of the authors of the report. “This allows the human to focus on what they do best: build relationships with customers and exercise judgment around the relationship.”
“Whether a bank, wealth manager, or insurer, sophisticated financial institutions are using advanced analytics and machine learning, as a powerful tool to find unexpected opportunities to improve sales, marketing and redefine the customer experience,” adds Will Trout, senior analyst with Celent’s Wealth Management practice and coauthor of the report. “These powerful tools are allowing institutions to go well beyond simple number crunching and reporting and improve their ability to listen and anticipate the needs of customers.”
To pull off this level of intervention at scale, an institution needs technology that works simply and easily, pulling in data from a wide variety of sources — both structured and unstructured,” advises Karlyn Carnahan, research director with Celent’s Insurance practice and coauthor of the report. “The technology needs to be able to handle the scale of real-time analysis of that data and run the data through predictive and dynamic models. Models need to continuously learn and more accurately predict behaviors using cognitive computing.”