December 14, 2019
Customer Data Platform (CDP) systems are the newest answer to an old question: how to assemble a complete view of each customer. This session explores the reality of what CDPs can and cannot do, how CDPs differ from other systems, the types of CDP systems available, and how to find the right CDP for your purpose, especially with regard to data science projects and predictive modeling. You will come away with a clear understanding of where CDP fits into the larger data management landscape, what distinguishes CDP from older approaches to customer data management, and the state of the CDP industry in Europe.
Dr. Dominik Ballreich
Estrelsaal C5 & C6
Machine learning algorithms for time series forecasting have become increasingly powerful in recent decades. Nevertheless, not only the quality of the forecasts is important, but also their acceptance by the staff. Especially with regard to automatic forecasts, distrust may arise among dispatchers. Furthermore, long-standing employees often have a detailed overview of customer behavior, market situation and other important factors. Therefore, it makes sense to include this expert knowledge in the predictions of complex algorithms. This can be achieved through the maximum entropy approach, which is discussed in this presentation. The approach is derived in detail and applied to real data.
Surprisingly, the majority of companies dealing with predictive analytics do not consider the availability of accurate data to be a challenge. However, the lack of relevant and clean data is one of the biggest barriers to successful predictive analytics. Chris Schneider explains how companies can effectively use the vast amount of relevant data. Because only those who understand patterns from the past can predict the future.