For those in Wealth Management, what has emerged is the acknowledgment that customer engagement is more critical than ever before.
Advisors who understand their clients better, and then use this understanding to meet specific needs more effectively, are going to emerge from this unprecedented crisis in better shape than the rest.
Innovation and a global approach
You may have the best possible CRM solution on the market, a deep vertical award-winning software geared to meet your every need. What you will always struggle with is poor user
Take Machine Learning and Deep Learning, both of which make an appearance whenever a discussion of AI begins. What defines Machine Learning? How does it work? Isn't Deep Learning just another form of Machine Learning? We thought it made sense to try and simplify answers to those knotty questions. So, here goes.
Today, firms have access to information at an unprecedented level and must contend with a highly regulated industry as well as the commodification of products and services. For a CRM solution like NexJ, this represents a challenge as well as a great deal of opportunity, because more information about a customer is a powerful tool when used effectively.
It doesn't take a genius to figure out that Artificial Intelligence (AI) has changed all kinds of industries and workplaces in a number of significant ways. Attitudes towards the use of AI have also shifted, along with the ways in which it has been approached. One of the biggest shifts has been the emphasis on top-down reasoning rather than bottom-up big data.
Picture this: You, an advisor at a financial services firm, are interacting with customers. You have at your disposal an enormous amount of information related to their likes and dislikes, along with a comprehensive overview of their finances.
Some of you may be familiar with Next Best Action in the context of Sales & Marketing, where the consideration is which offer is most appropriate for which customer at a point in time. Extending Next Best Action to customer service seems a natural progression, considering the service representative is already engaged with the customer, and presuming the interaction went well, means extending the dialog with an appropriate offer.
Enterprise computing is undergoing a revolution, but it’s not the first. It has undergone a number of phases, or waves, throughout its history, each looking to introduce efficiencies in how we work. The first two waves of computing were centered on the back-office.
The customer experience, now more than ever, is the bar we use to predict the health and growth potential of a business. Most major financial institutions are taking this to heart by adapting their services to deliver the “delightful” customer experience we’ve come to expect as consumers (think Amazon, Netflix, and Uber.) I was reminded of the sea change that is moving our industry towards intelligent customer management while at the Chief Data Analytics Officers (CDAO) event in Boston last month. I contributed to a panel discussion about the emergence of machine learning in financial services, where I was joined by industry peers with first-hand experience transforming their business with data-driven insights. The efforts of fellow panelists and thought leaders, like José Murillo of Banorte, were on full display. Our lively exchange made clear that the disruptive forces of Artificial Intelligence (AI) and Machine Learning are here to stay.
Last week's CDAO presentation on Single-Family Data Governance & Management by Freddie Mac illustrated how traditional back office activities are aligning and impacting front office processes. We continue our recap of lessons learned at CDAO with this week's focus on risk management. This April, we were delighted to attend as well as participate in the Financial Services-focused Chief Data & Analytics Officer conference in Boston. This annual gathering brought together senior-level data practitioners in financial services to share their latest innovations, best practices, challenges and use cases. The concept of monetizing or commercializing data assets is revolutionizing the Financial Services industry by using governed data strategies partnered with business initiatives to realize data-driven transformation benefits.