Nowadays, traditional forms of psychotherapy are increasingly complemented by online interactions between client and counselor. In (some) web-based psychotherapeutic interventions, meetings are exclusively online through asynchronous messages. As the active ingredients of therapy are included in the exchange of several emails, this verbal exchange contains a wealth of information about the psychotherapeutic change process. Unfortunately, drop-out-related issues are exacerbated online. We employed several machine learning models to find (early) signs of drop-out in the email data from the “Alcohol de Baas” intervention by Tactus. Our analyses indicate that the email texts contain information about drop-out, but as drop-out is a multidimensional construct, it remains a complex task to accurately predict who will drop out. Nevertheless, by taking this approach, we present insight into the possibilities of working with email data and present some preliminary findings (which stress the importance of a good working alliance between client and counselor, distinguish between formal and informal language, and highlight the importance of Tactus' internet forum).