Calls-to-action (CTAs) are central to digital campaigning, yet computational research has largely focused on binary detection only. We address CTA type classification in German Instagram campaign texts (posts and ephemeral Stories), distinguishing Support, Inform, Interact, and No CTA. With limited annotated data, we benchmark a fine-tuned GBERT model against GPT models using zero-shot, few-shot, and retrieval-augmented few-shot prompting in a multi-label setup. Both approaches reach similar performance in five-fold cross-validation (macro-F1 ≈ 0.79), with persistent difficulty on the rare Interact category. As a proof of concept, we apply the selected setup to the 2021 federal election corpus and show that parties varied not only in overall CTA use but also in how they balanced appeals across posts versus Stories. The results demonstrate the feasibility of CTA type classification with modest data and position retrieval-augmented prompting as a practical alternative to supervised fine-tuning.