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. 

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. 

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.

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.

Our hard work continues to get noticed as Nucleus Research, a global research and advisory firm, has once again recognized us as an Expert in their most recent CRM Value Matrix.

It’s interesting that artificial intelligence is such a hot topic these days because AI itself is not new. The concept of an ‘artificial brain’ was discussed by scientists in the 1930s. In 1950, Alan Turing created the Turing Test to distinguish machines from ‘thinking’ machines.

It’s been astonishing to see the amount of media attention around Artificial Intelligence (AI) in recent months. From the dystopia of HBO’s Westworld to the hype of Salesforce's Einstein, we’ve been inundated with larger than life claims about AI and its abilities. 

The flexibility and cost effectiveness of Apache Hadoop was quickly recognized by many organizations as an effective delivery vehicle to empower business users with operational self-service query and analytic capabilities.  Many organizations established, or are presently establishing, data lakes for operational intelligence query and analytics capabilities for the field personnel who need them most, best understand the data, and are the most capable of actioning insights gleaned.