Market segmentation usually includes a typing tool that predicts who falls in which segment: vital for a successful implementation. Using commercial real-life datasets, we compare support vector machines and neural nets against a common approach in commercial practice: linear discriminant analysis. We show that ML approaches yield superior performance in terms of segment-level prediction, interpretability and expected profitability.