通过两阶段模型区分主动与自然用户，提升营销转化效率。
Latent Customer Segmentation and Value-Based Recommendation Leveraging a Two-Stage Model with Missing Labels
- 用两阶段神经网络区分受促、自然和低活跃用户
- 缺失标签框架使转化率提升超100个基点
- 适合需要精准营销的电商平台和品牌方
企业成功依赖于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的关键，需在可负担性与长期品牌价值间取得平衡。广泛营销活动会削弱品牌感知价值并降低投资回报率，而现有经济算法常误将高参与度用户视为理想目标，导致营销效率低下。本文提出一种两阶段多模型架构，采用自适应学习损失函数改进客户分类。第一阶段使用多类神经网络区分受促销影响、自然参与及低活跃用户；第二阶段引入缺失标签框架的二元标签修正模型，识别真实促销意图，训练中优化客户细分。通过分离主动参与与自然行为，系统实现更精准的营销投放，降低曝光成本，提升转化效率。A/B测试显示关键指标提升超过100个基点，验证了基于意图感知的细分对价值驱动型营销策略的有效性。
原文摘要 · Abstract (English)
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.
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