Operators in end-of-line testing of assembly lines often try out multiple solutions until they can solve a product quality issue. This calls for a decision support system based on data analytics that effectively helps operators in fault diagnosis and quality control. However, existing analytical approaches do not consider the specific data characteristics being prevalent in the area of End-of-Line (EoL) testing. We address this issue by proposing an analytical approach that is tailored to EoL testing. We show how to implement this approach in a real-world use case of a large automotive manufacturer, which reveals its potential to reduce unnecessary rework.