Many companies have problems to detect anomalies (outliers) in their customer base in an automated way. The reasons for this are manifold, such as data availability or rule-based approaches that cannot cover the full data potential. Unsupervised machine learning models based on a fundamental holistic customer view can help to identify relevant outliers and highlight corresponding outlier reasons.