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

Keerthi Gopalakrishnan, Tianning Dong, Chia-Yen Ho, Yokila Arora, Topojoy Biswas, Jason Cho, Sushant Kumar, Kannan Achan

TL;DR

This article introduces a two-stage multi-model architecture that employs Self-Paced Loss to enhance customer categorization and allows businesses to enhance their marketing campaign strategies and target prompted engaged segment, reducing exposure rates while boosting conversion rates.

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.

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

TL;DR

This article introduces a two-stage multi-model architecture that employs Self-Paced Loss to enhance customer categorization and allows businesses to enhance their marketing campaign strategies and target prompted engaged segment, reducing exposure rates while boosting conversion rates.

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.
Paper Structure (12 sections, 5 equations, 4 figures, 3 tables)

This paper contains 12 sections, 5 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Latent Customer Segmentation
  5. Overall Architecture
  6. Experiment
  7. Dataset
  8. Baseline and Metrics
  9. Offline Evaluation
  10. Online evaluation
  11. Online Deployment
  12. Conclusion

Figures (4)

  • Figure 1: The proposed model architecture for predicting customer behaviors and intent, including label correction and incremental gain identification.
  • Figure 2: Loss gradient by probability, illustrating the relationship between predicted probability and loss gradient for SPLC.
  • Figure 3: Cumulative gain curve. The curve illustrates how many customers to target and what returns to expect from a marketing campaign.
  • Figure 4: Overall framework of system deployment.