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

TL;DR

This paper presents a novel two-stage neural network architecture with missing-label correction to improve customer segmentation and targeting, leading to more efficient marketing and higher conversion rates.

Contribution

It introduces a multi-model framework with Self-Paced Loss for better customer categorization and intent detection, addressing label ambiguity in marketing data.

Findings

Over 100 basis points improvement in key metrics

Enhanced segmentation accuracy through intent-aware modeling

Reduced marketing costs by targeting true engaged customers

Abstract

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.