AI and Machine Learning will become an integral part of your marketing analytics life so before Matt Gershoff explains how it works, Jim walks you through what it is and how it is being used. From natural language processing and computer vision to chatbots and robots, you'll see how AI is applied to customer interaction. Then, Jim dives into machine learning so you can determine which software services are worth your time, communicate better with the data scientists in your company, decide to become one yourself, and figure out how and where to bring AI and ML into your marketing tool suite.

With BigQuery business and organisations have a unique chance of taking there analytics data and start the transformation towards a data lake. By combining customer, analytics, marketing and CRM data here we not only get a repository where can have room to add or work with data as we see fit, we also open up for the opportunity to use machine learning to actually sift through our data to help determine the causality and relationship between the individual data points. This way we use the full power of data to define our segments and profiles based on their actual behavior and not our prejudice.

The increasing complexity of digital landscape on one side and huge business expectations on the other side are the driving force of change in e-commerce. Fueled by tons of data machine learning and artificial intelligence are slowly becoming the norm. But algorithms themselves won't be able to change the companies and deliver success. Entire companies need to change as well. How to embrace this change? Where to start and what to expect? How to organize yourselves? We'll deep-dive into data-driven digital marketing framework, followed by insights and case studies from clients and finish up with a stack of tools and takeaways you can use to produce some quick wins.

Nowadays businesses have too much customer data for a human to handle. Sifting through heaps of it manually trying to find insights is not only gruelling, sometimes it's no longer possible. Solution: let the self-learning algorithms find patterns in data that yield business value. We'll show how a major online retailer, a DIY store and an airline booking service cooked a conversion prediction system out of their various sources of data – CRM, web, and app. This won't hurt, almost anyone can do it – and we'll demostrate how.

Machine learning can be thought of as sitting at the intersection of computer science and statistics. "Computer Science has focused primarily on how to manually program computers, Machine Learning focuses on the question of how to get computers to program themselves ..." – Tom Mitchel

The data available to marketers -- literally at their fingertips by way of a few mouse clicks -- has exploded over the last decade. Yet, while there is more data -- and it is more accessible -- than it has ever been, the way we think about and use data has hardly evolved at all. With the recent advances in cloud computing and processing power, the industry is abuzz with talk of machine learning and artificial intelligence. How, then, will we get from the world of Microsoft Excel (or Tableau) to a world where "the machines" are automatically and dynamically optimizing all aspects of our marketing?