The success of businesses depends on their ability to convert consumers into loyal customers. A customer's value proposition is a primary determinant in this process, requiring a balance between affordability and long-term brand equity. Broad marketing campaigns can erode perceived brand value and reduce return on investment, while existing economic algorithms often misidentify highly engaged customers as ideal targets, leading to inefficient engagement and conversion outcomes. This work introduces a two-stage multi-model architecture employing Self-Paced Loss to improve customer categorization. The first stage uses a multi-class neural network to distinguish customers influenced by campaigns, organically engaged customers, and low-engagement customers. The second stage applies a binary label correction model to identify true campaign-driven intent using a missing-label framework, refining customer segmentation during training. By separating prompted engagement from organic behavior, the system enables more precise campaign targeting, reduces exposure costs, and improves conversion efficiency. A/B testing demonstrates over 100 basis points improvement in key success metrics, highlighting the effectiveness of intent-aware segmentation for value-driven marketing strategies.

翻译：企业的成功取决于其将消费者转化为忠诚客户的能力。客户价值主张是这一过程中的主要决定因素，需要在可负担性与长期品牌资产之间取得平衡。宽泛的营销活动可能削弱品牌感知价值并降低投资回报率，而现有经济算法常将高互动客户误判为理想目标，导致互动与转化效率低下。本研究提出一种采用自步损失的两阶段多模型架构以改进客户分类。第一阶段使用多类别神经网络区分受营销活动影响的客户、有机互动客户及低互动客户。第二阶段应用二元标签校正模型，通过缺失标签框架识别真实的营销驱动意图，在训练过程中优化客户细分。通过分离外部激励互动与有机行为，该系统能够实现更精准的营销活动定向，降低曝光成本并提升转化效率。A/B测试显示关键成功指标提升超过100个基点，凸显了意图感知细分对价值驱动营销策略的有效性。