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

• URL: http://arxiv.org/abs/2602.12485v1
• Date: Thu, 12 Feb 2026 23:59:32 GMT
• Title: Latent Customer Segmentation and Value-Based Recommendation Leveraging a Two-Stage Model with Missing Labels
• Authors: Keerthi Gopalakrishnan, Tianning Dong, Chia-Yen Ho, Yokila Arora, Topojoy Biswas, Jason Cho, Sushant Kumar, Kannan Achan,
• Abstract summary: Broad marketing campaigns can erode perceived brand value and reduce return on investment.<n>Existing economic algorithms often misidentify highly engaged customers as ideal targets.<n>This work introduces a two-stage multi-model architecture employing Self-Paced Loss to improve customer categorization.
• Score: 5.470593436045526
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Equity by Design: Fairness-Driven Recommendation in Heterogeneous Two-Sided Markets [44.675845572303324]
  We introduce Conditional Value-at-Risk as a consumer-side objective that compresses group-level utility disparities.<n>Our experiments reveal that the "free fairness" regime, where producer constraints impose no consumer cost, disappears in multi item settings.<n>Strikingly, moderate fairness constraints can improve business metrics by diversifying exposure away from saturated producers.
  arXiv  Detail & Related papers  (2026-02-11T10:59:32Z)
• Learning Personalized Ad Impact via Contextual Reinforcement Learning under Delayed Rewards [36.029144318322686]
  We model ad bidding as a Contextual Markov Decision Process (CMDP) with delayed Poisson rewards.<n>For efficient estimation, we propose a two-stage maximum likelihood estimator combined with data-splitting strategies.<n>We design a reinforcement learning algorithm to derive efficient personalized bidding strategies.
  arXiv  Detail & Related papers  (2025-10-22T22:08:36Z)
• ssToken: Self-modulated and Semantic-aware Token Selection for LLM Fine-tuning [51.133569963553576]
  ssToken is a Self-modulated and Semantic-aware Token Selection approach.<n>We show that both self-modulated selection and semantic-aware selection alone outperform full-data fine-tuning.
  arXiv  Detail & Related papers  (2025-10-21T03:21:04Z)
• Learning Fair And Effective Points-Based Rewards Programs [4.465134753953128]
  Points-based rewards programs have come under scrutiny due to accusations of unfair practices in their implementation.<n>We study the problem of fairly designing points-based rewards programs, with a focus on two obstacles that put fairness at odds with their effectiveness.<n>We show that an individually fair rewards program that uses the same redemption threshold for all customers suffers a loss in revenue of at most a factor of $1+ln 2$.<n>We propose a learning algorithm that limits the risk of point devaluation due to experimentation by only changing the redemption threshold $O(log T)$ times.
  arXiv  Detail & Related papers  (2025-06-04T13:05:16Z)
• Learning Recommender Systems with Soft Target: A Decoupled Perspective [49.83787742587449]
  We propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels. We present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors.
  arXiv  Detail & Related papers  (2024-10-09T04:20:15Z)
• Segment Discovery: Enhancing E-commerce Targeting [8.000199536112937]
  This paper proposes a policy framework based on uplift modeling and constrained optimization.<n>We demonstrate improvement over state-of-the-art targeting approaches using two large-scale experimental studies and a production implementation.
  arXiv  Detail & Related papers  (2024-09-20T18:42:04Z)
• Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning [50.060154488277036]
  In federated learning, client selection is a critical problem that significantly impacts both model performance and fairness.<n>We propose two guiding principles that tackle the inherent conflict between the two metrics while reinforcing each other.<n>Our approach adaptively enhances this diversity by selecting clients based on their data distributions, thereby improving both model performance and fairness.
  arXiv  Detail & Related papers  (2024-05-22T12:27:24Z)
• Modeling the Telemarketing Process using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach [0.06906005491572399]
  This research aims at leveraging the power of telemarketing data in modeling the willingness of clients to make a term deposit. Real-world data from a Portuguese bank and national socio-economic metrics are used to model the telemarketing decision-making process.
  arXiv  Detail & Related papers  (2023-10-30T08:46:55Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• Towards Revenue Maximization with Popular and Profitable Products [69.21810902381009]
  A common goal for companies marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies. Finding credible and reliable information on products' profitability is difficult since most products tends to peak at certain times. This paper proposes a general profit-oriented framework to address the problem of revenue based on economic behavior, and conducting the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
  arXiv  Detail & Related papers  (2022-02-26T02:07:25Z)
• Dynamically Tie the Right Offer to the Right Customer in Telecommunications Industry [0.0]
  This work presents a conceptual model by studying the significant campaign-dependent variables of customer targeting. The outcomes of customer segmentation of this study could be more meaningful and relevant for marketers.
  arXiv  Detail & Related papers  (2020-10-18T16:44:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.