I am often reminded of a specific parable whenever someone drops the words 'cloud', 'private cloud', 'hybrid cloud' or 'SaaS'. It's the one about the blind men and an elephant, who describe the animal based on which part of its anatomy they feel, then come to blows because they assume the others are being dishonest. 

A lot of people have the misconception that getting banks to change is like pulling teeth. It's a misplaced analogy, first because banks really are more open to embracing emerging technologies than most people think, and second because the last time pulling teeth was painful was probably around 1846, when the first successful surgical procedure was performed with anesthesia.

In my last blog [Invested Users: Best Practices of Maximizing User Adoption, Part 2], I discussed NexJ's second set of three best practices for user adoption, and why planning, partnering, and encouraging leadership are crucial steps in the process of engaging your users with your CRM.

In my last blog, I discussed NexJ's first three best practices of user adoption, and why developing, analyzing, and aligning are crucial steps in the process of engaging your users with your CRM. Today, I'd like to discuss the next three of the 9 best practices for user adoption, which are planning, partnering, and encouraging.

In a previous blog, I discussed how to measure your user adoption rates, and the effectiveness of comparing quantitative and qualitative results. Because users can log into your CRM system, without actually using it, it's possible to have extremely high quantitative results without having high user adoption.

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. 

To deliver optimal value to our customers, NexJ leverages our vast experience in deploying our software at the most recognized financial services firms in the world, our strict focus on addressing the specific business needs of the sub-vertical markets within financial services and our fervent passion for innovation.

Analytics and machine learning are all about data.  The quality and quantity of your data plays a critical role in determining the effectiveness of the models.  But even with reams of the best data, if your process doesn’t use it properly, your results will be dubious.

http://dmradio.dataversity.net/julia-python-r-the-rise-of-jupyter-notebooks/

Click above to listen to Martin Sykora, on DM Radio. Martin joins the discussion at the ten-minute mark, and for the roundtable discussion at the 49-minute mark!

In "The Great Wealth Transfer is Coming, Putting Advisers at Risk," an article in Investment News, they pointed out that 66% of heirs fire their parents' financial adviser after they receive an inheritance. Marketing Wiz's "The State of Independent Financial Marketing" whitepaper discusses how every day, $2 billion in assets move from Baby Boomers to Generation X and Generation Y heirs. Combining these two statistics paints a concerning picture for financial advisors. How can Wealth Management firms address the needs of these inheriting children, to prevent losing 66% of their asset base?

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.