1) **High-Value Behavior Analysis:** Repeated engagement on a single day signals concentrated, active interest, moving beyond passive browsing. This pattern is a strong early indicator of a specific, high-intent research phase, often preceding a formal request for information (RFI). **高价值行为分析：** 单日内重复互动表明客户兴趣集中且主动，超越了被动浏览。这种模式是进入特定、高意向调研阶段的强烈早期信号，通常发生在正式询盘（RFI）之前。 2) **AI Score & Pool Logic:** Despite the high-value behavior, the predicted score remains at 0, well below the 60-point threshold for a C→B upgrade. The AI likely weights cumulative, sustained engagement over time more heavily than a single-day spike to avoid false positives from one-off research. **AI评分与分级逻辑：** 尽管行为价值高，但预测评分仍为0，远低于C级升至B级的60分阈值。AI系统可能更看重长期、持续的互动累积，而非单日峰值，以避免因一次性调研产生误判。 3) **Triggered Nurturing Actions:** The system should trigger a **personalized, educational follow-up** (e.g., a case study email related to the viewed content) and create a **sales task to qualify intent** in the CRM. A proposal is premature; the goal is to convert interest into a documented conversation. **触发的培育行动：** 系统应触发**个性化的教育性跟进**（例如，发送与浏览内容相关的案例研究邮件），并在CRM中创建**销售任务以确认意向**。此时发送提案为时过早，目标是将兴趣转化为有记录的对话。 4) **Path to 100-Unit Inquiry:** This targeted nurturing moves the lead from anonymous interest to a known, engaged contact. By providing relevant value and prompting a sales conversation, it systematically builds the trust and specific need alignment required for a large-scale, 100-unit distribution inquiry. **转化为100套询盘的路径：** 这种定向培育将潜在客户从匿名兴趣转化为已知的、活跃的联系人。通过提供相关价值并推动销售对话，系统化地建立起达成大规模（100套）分销询盘所需的信任和具体需求匹配。