We present a novel method, Contextual goal-Oriented Data Augmentation (CODA), which uses commonly available unlabeled trajectories and context-goal pairs to solve Contextual Goal-Oriented (CGO) problems. By carefully constructing an action-augmented MDP that is equivalent to the original MDP, CODA creates a fully labeled transition dataset under training contexts without additional approximation error. We conduct a novel theoretical analysis to demonstrate CODA's capability to solve CGO problems with our offline data setup. Empirical results also showcase the effectiveness of CODA, which outperform other baseline methods across various context-goal relationships. This approach offers a promising direction to solving CGO problems using offline datasets.