First, a decision tree is a visual representation of a decision situation (and hence aids communication).
Second, the branches of a tree explicitly show all those factors within the analysis that are considered relevant to the decision (and implicitly those that are not).
Third, and more subtly, a decision tree generally captures the idea that if different decisions were to be taken then the structural nature of a situation (and hence of the model) may have changed dramatically. This is in contrast to an Excel model with sensitivity analysis (or a Monte Carlo simulation model) in which a change of parameters in the model does not represent any structural change to the situation. Capturing the logic and conditionality that is present in a tree would be complex to do in such modelling environments.
Fourth, and arguably the most powerful, a decision tree allows for forward and backward calculation paths to happen (taken care of automatically when using PrecisionTree) and hence the choice of the correct decision to take (optimality of decision making, or optimal exercise if embedded real options) is made automatically.
We explore this further in Part II of the series.