Final answer:
To predict customer churn, a decision tree model is typically used because of its ability to represent choices and their possible consequences in a tree-like structure, helping to delineate paths that might lead to customer departure.
Step-by-step explanation:
To predict if a customer is likely to churn you would use a model of type decision tree. A decision tree is a model that works by splitting a dataset into branches at certain decision points, where each branch represents a possible outcome based on certain criteria. This type of model is useful for churn prediction because it can help in understanding the path a customer might take that leads to their decision to leave, based on historical data.
Options like switch and champion challenger don't pertain to predictive modeling in this context. A switch is a general term that might relate to networking or decision-making but not specifically to churn prediction. A champion challenger is a term used in A/B testing where a new version (the challenger) is tested against the current version (the champion). Finally, a decision table is a tabular representation of logic that helps in decision making, but it's not a predictive model by itself.