This talk concerns stochastic optimisation models where some quantity is to be maximised or minimised subject to some constraints which are only partially known in advance. One way to model this is to look for the optima value of the expectation of the random quantity. However, it is typically not possible to find closed-form expressions for the expected value, leading to the need of numerical methods. The sample average approximation (SAA) approach uses samples (called scenarios) from the underlying probability measure and uses the empirical mean over these samples to turn the stochastic into a deterministic problem of much larger size. However, in many applications, the number of scenarios required to realistically model the uncertainty typically makes the optimization problem numerically intractable. In this talk I will give an overview on problem-based clustering to extract information about the stochastic program from the single-scenario solutions. In particular, I will introduce distance matrices on the scenario set that encode the notion of opportunity cost of predicting the wrong scenario. Clustering can then be applied to the scenario set equipped with these distances which leads to approximative feasible solutions as well as performance guarantees in the form of upper and lower bounds. We also obtain interesting new structures on the scenario set. This talk is based on joint work with Samah Abdalrhaman, Shubhajit Dey, Yuchen Ge, Olivia Harrison, Michael Hewitt, Julian Keutchayan and Walter Rei.