Awakening dormant users, who remain engaged but exhibit low conversion, is a key driver for incremental GMV growth in e-commerce platforms. Existing methods often underperform since they typically rely on single-step estimation of an item’s intrinsic value (e.g., immediate click probability). This overlooks the instrumental effect of items, where specific interactions act as triggers to shape latent intent and drive subsequent decisions along a conversion trajectory. To bridge this gap, we propose RoleGen, a framework that synergizes a Conversion Trajectory Reasoner with a Generative Collaborative Executor. Specifically, the LLM-based Reasoner explicitly models the context-dependent Functional Role of items to reconstruct intent evolution. It further uses counterfactual role exploration to mitigate interest collapse and generate diverse conversion-oriented candidates. These reasoned candidate items are then injected into the Executor, which is optimized via a feedback-driven co-training mechanism to ensure grounded execution. Extensive offline experiments and online A/B testing on a large-scale e-commerce platform show a 5.82% gain in Recall@1 and a 6.71% increase in online order volume, confirming RoleGen’s effectiveness in activating dormant users.