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

Researchers have introduced a novel two-stage AI model that significantly improves customer segmentation for marketing. The system first uses a neural network to categorize customers as campaign-influenced, organically engaged, or low-engagement. A second stage then applies a binary label correction model within a missing-label framework to identify true campaign-driven intent. This separation of prompted and organic behavior allows for more precise targeting. A/B testing demonstrated an improvement of over 100 basis points in key success metrics.