Latent Customer Segmentation and Value-Based Recommendation Leveraging a Two-Stage Model with Missing Labels

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.

Campaign-2-PT-RAG: LLM-Guided Semantic Product Type Attribution for Scalable Campaign Ranking

E-commerce campaign ranking models require large-scale training labels indicating which users purchased due to campaign influence. However, generating these labels is challenging because campaigns use creative, thematic language that does not directly map to product purchases. Without clear product-level attribution, supervised learning for campaign optimization remains limited. We present \textbf{Campaign-2-PT-RAG}, a scalable label generation framework that constructs user--campaign purchase labels by inferring which product types (PTs) each campaign promotes. The framework first interprets campaign content using large language models (LLMs) to capture implicit intent, then retrieves candidate PTs through semantic search over the platform taxonomy. A structured LLM-based classifier evaluates each PT's relevance, producing a campaign-specific product coverage set. User purchases matching these PTs generate positive training labels for downstream ranking models. This approach reframes the ambiguous attribution problem into a tractable semantic alignment task, enabling scalable and consistent supervision for downstream tasks such as campaign ranking optimization in production e-commerce environments. Experiments on internal and synthetic datasets, validated against expert-annotated campaign--PT mappings, show that our LLM-assisted approach generates high-quality labels with 78--90% precision while maintaining over 99% recall.