Virtual Reality (VR) technologies enable strong emotions compared to traditional media, stimulating the brain in ways comparable to real-life interactions. This makes VR systems promising for research and applications in training or rehabilitation, to imitate realistic situations. Nonetheless, the evaluation of the user experience in immersive environments is daunting, the richness of the media presents challenges to synchronise context with behavioural metrics in order to provide fine-grained personalised feedback or performance evaluation. The variety of scenarios and interaction modalities multiplies this difficulty of user understanding in face of lifelike training scenarios, complex interactions, and rich context. We propose a task-based methodology that provides fine-grained descriptions and analyses of the experiential user experience (UX) in VR that (1) aligns low-level tasks (i.e. take an object, go somewhere) with multivariate behaviour metrics: gaze, motion, skin conductance, (2) defines performance components (i.e., attention, decision, and efficiency) with baseline values to evaluate task performance, and (3) characterises task performance with multivariate user behaviour data. To illustrate our approach, we apply the task-based methodology to an existing dataset from a road crossing study in VR. We find that the task-based methodology allows us to better observe the experiential UX by highlighting fine-grained relations between behaviour profiles and task performance, opening pathways to personalised feedback and experiences in future VR applications.