Digital online platforms have extended experiments to large national and international samples, thus increasing the potential heterogeneity present in responses to the examined treatments. Therefore, identifying and studying such heterogeneity is crucial in online behavioural experiments. New analytical techniques have emerged in computational social science to achieve this goal. We will illustrate an example from a study conducted about the COVID-19 pandemic, which applies model-based recursive partitioning to data from an online experiment to increase vaccine willingness in eight European countries. Another valuable information generated by this approach is identifying particular segments of the sample under investigation that might merit further investigation. Identifying ‘local’ models of the population is not just a matter of chance. When applied to independent variables involving socioeconomic and behavioural measures, this possibility/technique allows us to detect/determine subgroups characterised by a particular socioeconomic or cognitive pattern shared by that group. Such a group could very well be transversal to traditional sociodemographic categories.