With driverless cars, chatbots, smart speakers, drones and data-phishing creating sensational headlines on a daily basis, forward thinking brands should be asking the question; "What does the arrival of Artificial Intelligence and Automation REALLY mean for customer experience?" There are massive shifts underway in consumer behaviours, habits and attitudes, making it harder to predict the future. In our Whitepaper on AI and Automation, we sort the fact from the fiction, with revealing expert opinions and the results of a Webhelp commissioned YouGov study on public attitudes towards this booming technology and most importantly – the impact of machine learning on the CX industry. It's clear that young people have become a potent force for change and that age-related preferences could play a more critical role in defining behaviour than financial or situational factors. The infographic below shares some of the report findings in a bite-size format and our Disruptor Series takes an even deeper dive into the challenges faced by the customer experience industry.

In his latest novel, Machines Like Me, Ian McEwan reimagines what 1980s Britain might have looked like if certain iconic events had swung a different way: Argentina winning the Falklands war; Margaret Thatcher battling Tony Benn for power. And then there's the catalyst for the book – what might have happened if Alan Turing had achieved a major breakthrough in the field of artificial intelligence. As with most visions of AI's future – or hypothetical past – the author speculates a dystopia in which sentient, anthropomorphous robots are embedded into our everyday lives. The pros and cons of AI are debated at an ethical and moral level; with less credence given to the practicality or the technological constraints. Whilst undoubtedly a necessity for a science fiction novel, in the real world, this lack of practical consideration for AI and the related constraints has been responsible for a huge amount of hyperbole in the enterprise technology market; especially in relation to CRM.

From Amazon's Alexa to Tesla's smart cars, mobile devices and online banking systems, the prevalence of artificial intelligence (AI) in daily life is clear.

Early adopters are already creating competitive advantages, and the gap with the laggards looks set to grow," is how McKinsey Global Institute puts it in their report Artificial Intelligence, The Next Digital Frontier?. They add: "In our survey of 3,000 AI-aware C-level executives, across 10 countries and 14 sectors, only 20% said they currently use any AI-related technology at scale or in a core part of their businesses. Many firms say they are uncertain of the business case or return on investment." However, investments in AI for the enterprise are ever-increasing and industry case studies are demonstrating the disruptive potential of AI. What is certain is that, whatever the industry, there's no longer any guarantee it will remain unaffected by the next Uber-like app. The IoT is real and ever-expanding.

AI has disrupted the retail industry, and that's a good thing. Consumer behavior is increasingly managed by AI, leading to an unparalleled online shopping experience. AI-powered tools have made it easier for even non-techie CMO's to deliver a highly personalized experience by anticipating consumer needs and making real-time predictions. AI-powered marketing is helping marketers to satisfy increasing customer expectations. It's with the help of artificial intelligence that even small- and mid-scale retailers are able to provide an exceptional one-to-one customer experience like Amazon, Walmart and Nordstrom.

Big data is at the core of algorithms that power artificial intelligence. From machine learning algorithms to predictive modeling, artificial intelligence is trained to scour big data to look for patterns and establish a correlation between variables. The AI algorithms that make the highest level of personalization possible for marketers aren't crystal-ball magic but are driven by big-data analytics. Today, e-commerce marketers are getting overwhelmed by tons of data generated every second. And to extract meaningful insights out of this data maze, marketers need to have a proper analytics framework in place.

Big data is at the core of algorithms that power artificial intelligence. From machine learning algorithms to predictive modeling, artificial intelligence is trained to scour big data to look for patterns and establish a correlation between variables. The AI algorithms that make the highest level of personalization possible for marketers aren't crystal-ball magic but are driven by big-data analytics. Today, e-commerce marketers are getting overwhelmed by tons of data generated every second. And to extract meaningful insights out of this data maze, marketers need to have a proper analytics framework in place.

Having a complete 360 degree view of each customer is imperative for predictive sales success. Where does the data for this comprehensive customer profile come from? And when should you start creating your profiles? Last week, we looked at what AI-based predictive selling, also known as predictive sales, is doing right now. And it's providing a glimpse into a future that is much more data driven. But what makes predictive selling work?

There is more and more data that is available that can help inform businesses about their customers, and those businesses that successfully utilize these new sources and quantities of data will be able to provide a superior customer experience. However, predicting customer behavior remains very challenging. This post is the first in a series where we will go over examples of how Joe Blue, a Data Scientist in MapR Professional Services, assisted MapR customers in identifying new data sources and applying machine learning algorithms in order to better understand their customers. The first example in the series is an advertising customer 360; the next blog post in the series will cover banking and healthcare customer 360 examples. MapR works with companies who have solved business problems but are limited with what they can do with their data, and they are looking for the next step.

This post is the second in a series where we will go over examples of how MapR data scientist Joe Blue assisted MapR customers, in this case a regional bank, to identify new data sources and apply machine learning algorithms in order to better understand their customers. If you have not already read the first part of this customer 360 series, then it would be good to read that first. In this second part, we will cover a bank customer profitability 360 example, presenting the before, during and after. The back story: a regional bank wanted to gain insights about what's important to their customers based on their activity with the bank. They wanted to establish a digital profile via a customer 360 solution in order to enhance the customer experience, to tailor products, and to make sure customers have the right product for their banking style.