Customer churn is a term used to describe customers leaving a company by no longer using their services or products. Companies should develop and target retention strategies towards customers at risk of churning, because customer acquisition is more costly than customer retention. At-risk customers can be identified using predictive machine learning. Previously, predictive churn modelling has typically been made for companies offering contractual products, where payments are made on a regular basis following a subscription or other contract. In these cases, the moment a customer churns is intuitively identified. Defining when a customer churns from a company offering non-contractual products, where the purchase occasions are sporadic, is more difficult, as the exact churn moment is both subjective and hard to identify. No studies of non-contractual customer churn have been made in the winter tourism industry, the industry in which non-contractual churn is defined and predicted in this thesis. The purpose of this thesis is to define and predict non-contractual customer churn in the winter tourism industry. The purpose is fulfilled by creating two different definitions of customer churn; one where the complexity of non-contractual churn is captured through the integration of industry knowledge and the theoretical background, and one that is based solely on the theoretical background. Five frequently used machine learning classifiers are evaluated for the prediction, revealing that our first definition of churn yields the highest AUC performance when predicting customer churn in this case. We conclude that if the definition of churn is sufficiently complex, non-contractual churn in the winter tourism industry can be predicted with a high performance using an XGBoost classifier. When data of previous reservation and purchase patterns is considered, the classifier achieves what is considered to be an excellent AUC performance at nearly 86%.