Insurance markets have always relied on large amounts of data to assess risks and price their products. New data-driven technologies, including wearable health trackers, smartphone sensors, predictive modelling and Big Data analytics, are challenging these established practices. In tracking insurance clients’ behaviour, these innovations promise the reduction of insurance costs and more accurate pricing through the personalisation of premiums and products. Building on insights from the sociology of markets and Science and Technology Studies (STS), this article investigates the role of economic experimentation in the making of data-driven personalisation markets in insurance. We document a case study of a car insurance experiment, launched by a Belgian direct insurance company in 2016 to set up an experiment of tracking driving style behavioural data of over 5000 participants over a one-year period. Based on interviews and document analysis, we outline how this in vivo experiment was set-up, which interventions and manipulations were imposed to make the experiment successful, and how the study was evaluated by the actors. Using JL Austin’s distinction between happy and unhappy statements, we argue how the experiment, despite its failure not to provide the desired evidence (on the link between driving style behaviour and accident losses), could be considered a ‘happy’ event. We conclude by highlighting the role of economic experiments ‘in the wild’ for the making of future markets of data-driven personalisation.