Mobile Gamer Lifetime Value Prediction via Objective Decomposition and Reconstruction

2025-10-09

AI Summary

Accurate user lifetime value (LTV) prediction in real-time bidding (RTB) remains challenging due to the heavy-tailed distribution of LTV and high sensitivity to outliers, leading to poor predictive stability and accuracy. Method: This paper proposes a target decomposition and reconstruction framework that decouples LTV into two orthogonal subtasks—purchase frequency and average transaction amount—thereby avoiding direct modeling of the skewed LTV distribution. Leveraging purchase behavior patterns specific to mobile gaming users, we design a decomposition-based model, train it on an industrial-scale dataset, and deploy it within TapTap’s RTB system for online A/B testing. Contribution/Results: Experimental results demonstrate that our approach significantly outperforms state-of-the-art baselines—including the Zero-Inflated Log-Normal (ZILN) model—in both prediction accuracy and robustness under real-world advertising conditions. The improved LTV estimation enables more effective dynamic ad inventory allocation and enhances platform-level return on investment (ROI).

Technology Category

• Machine Learning: Online Learning & Bandits
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

For Internet platforms operating real-time bidding (RTB) advertising service, a comprehensive understanding of user lifetime value (LTV) plays a pivotal role in optimizing advertisement allocation efficiency and maximizing the return on investment (ROI) for advertisement sponsors, thereby facilitating growth of commercialization revenue for the platform. However, the inherent complexity of user LTV distributions induces significant challenges in accurate LTV prediction. Existing state-of-the-art works, which primarily focus on directly learning the LTV distributions through well-designed loss functions, achieve limited success due to their vulnerability to outliers. In this paper, we proposed a novel LTV prediction method to address distribution challenges through an objective decomposition and reconstruction framework. Briefly speaking, based on the in-app purchase characteristics of mobile gamers, our model was designed to first predict the number of transactions at specific prices and then calculate the total payment amount from these intermediate predictions. Our proposed model was evaluated through experiments on real-world industrial dataset, and deployed on the TapTap RTB advertising system for online A/B testing along with the state-of-the-art ZILN model.

Problem

• Predicting mobile gamer lifetime value for advertising optimization
• Addressing distribution complexity in LTV prediction accuracy
• Decomposing purchase behavior to reconstruct total payment amount

Innovation

• Decomposes LTV prediction into transaction count forecasting
• Reconstructs total payment from price-specific transaction predictions
• Addresses distribution complexity via intermediate prediction steps