talk-data.com

The talk will answer the core questions of how we can balance a better customer centric understanding of individuals behaviour over time with respect for privacy. As technology changes rapidly the technologies will make it possible to build complete new types of "device" and ID graphs while the respect for privacy tightens this needs to be done to be benefit of consumers. In this talk we'll look at the "why, what and how" and cover:​

Growth marketing has been all the rage, but what is it? Is it new age optimizers looking for a fancy title? Marketers spinning their services in a new way? A buzzword for a game of bingo? User lifecycle marketing? In this session, Krista walks you through what Growth Marketing is, why it's important, how it's different, how we can all do it, and most importantly, how measurement works for growth.

With the abundance of data available in analytics we are no longer competing on the volume of data we have access to, but the quality of the questions we ask. The challenge is that we have never been taught to ask or taught other people to ask the right questions from analytics. So we spend our lives answering questions like "what is our bounce rate?" or "how much did the last campaign convert?" when we could be asking important and business critical question. This means that we in analytics have to consider putting the KPQ (Key Performance Question) before the KPI (Key Performance Indication) to start asking better questions.

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.

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.

Marketing attribution is a buzzword that has everyone the paid advertising community talking. But what does it mean for you? Is it really a magic bullet, or is attribution just a bunch of bullshit? This talk goes through the need for attribution, shares practical applications and technologies, and ends with a framework for you to get started.

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

There is no shortage of guides and tutorials about how to track everything from videos to the weather (Thanks Simo!). There are very few examples of anyone actually using Google Analytics to take actions and drive change. In this session, I will share my favorite examples from landing page optimization, content ideas, to marrying qualitative with quantitative data. In addition, everyone talks about attribution, but rarely is anyone able to show why it matters for their business. I'll share quick examples to show how to start a meaningful conversation around attribution and show why it matters.

The European digital marketing industry is inevitably shifting towards programmatic advertising, which poses difficult challenges for all involved parties – publishers, marketeers and analysts alike. As the technical infrastructure is getting more complex, it is increasingly difficult to properly collect, integrate and analyse the appropriate customer behavioral data sets, while those players who succeed first get a strong competitive advantage on the market. Luckily, it is not required anymore to employ a swarm of data engineers and data scientists to get started, as many of the critical business questions can be answered with relatively little effort using off-the-shelf tools.

Join renowned expert Julien Coquet around the fireplace as he takes the role an a digital analytics Gordon Ramsey. Like most digital marketing projects, digital analytics projects suffer from a lack of vision and planning. This leads to poorly executed projects that yield poor results. Sometimes, analytics become a concern after a new manager arrives, only to discover how bad things turned out! Julien will share stories about the worse analytics situations he ever encountered - along with simple yet effective solutions to these problems.

In this presentation, Daniel will talk about data visualization techniques to effectively uncover insights and communicate data. He will talk about charts best practices, types of visualization for different purposes, and some design principles.

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.

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?

Moe shares the most valuable analytical technique she learned from her time in government – ACH. Often, confirmation bias (the tendency to interpret new evidence as confirmation of one’s existing beliefs) railroads an analyst’s findings. ACH draws on scientific methodology, cognitive psychology and decision analysis to avoid this analytical trap. The strength of ACH is that it is difficult for an analyst’s natural biases to influence the outcome of their analysis. Moe teaches how to build your own hypotheses matrix and set up to disprove hypotheses using a case study. And yes, she’s applying ACH to her work at THE ICONIC, Australia & New Zealand’s largest online retailer.