Miele, a leading appliance manufacturer, is looking to optimize the ways in which they solve customer problems quickly and efficiently. A crucial part of this task is precise diagnosis of faults, before and during technician visits. A correct diagnosis allows technicians to take with them the necessary parts and complete the repair with a minimal spending of time, effort, and spare parts. We created a decision-support system to help Miele optimize its service process, based on statistics learned from historical data about technician visits, containing both structured and unstructured (textual) data that had to be combined to create the probabilistic model. We used a novel process in which a semantic model informed the creation of the probabilistic model, as well as the analysis pipelines for the structured and unstructured data, combining expert knowledge with large heterogenous data. The results of a pilot study demonstrated a significant improvement of efficiency, concomitant with an increase of an already very high first-fix rate.