Das komplette Programm folgt in Kürze.
Marketing professionals and digital analysts of all stripes need a map, a destination and navigation to help you acclimatize to how artificial intelligence is going to impact your world. Jim Sterne has an impressive track record looking over the horizon and telling the rest of us what to expect. He wrote his first book, "World Web Marketing:" in 1995. He wrote "Web Metrics" in 2002, He wrote "Artificial Intelligence for Marketing" in 2017. This time, he explains what to expect - and what to do about it. Lern about today's branding necessities, new tools & new rules, the augmented marketer, staying relevant and tomorrow's ecommerce conundrum.
With the help of upper funnel pre-heat analysis (e.g., Social conversations, Google Searches, Internal searches) you are able to give an indication and build hypotheses on how later product launches will perform. This methodology is not only valuable to for future sell-out predictions and allocation planning, but also helps to support an active leak management in the marketing landscape. In this session Dr. Franziska Engelhard will give you insights how pre-heat analysis is used at adidas.
How do you provide data effectively to your organization? How can it help you drive digital transformation in your department? How can the analytics team transform themselves from reporting squirrels to analytics ninjas?
In this session we will show how Siemens AG uses data as a key ingredient in their communications activities. Get insights on the necessary framework and lessons learned from implementing a truly data driven approach in their comms department. Learn how to drive democratization of data and the sharing of knowledge with some real life examples that can easily be implemented.
Data quality is crucial for successful data-driven decision-making processes. However, companies struggle with improving their data quality and keeping it on a high level. This difficulty increases with the size of an organization, the complexity of tracking requirements and the up-to-dateness of an IT-infrastructure. This talk takes a journey through the data analytics lifecycle from tracking design, over integration to aggregation, analysis, and interpretation. On this way, it emphasizes on common data quality issues and how to avoid or improve them in existing productive systems. Overall, the talk aims at providing best practices for investigating and improving data quality by showing common real-world scenarios and hands-on recommendations.