Enhancing Monotonic Modeling with Spatio-Temporal Adaptive Awareness in Diverse Marketing

2024-06-20

arXiv.org

AI Summary

Online food delivery platforms face the challenge of spatiotemporally adaptive marketing incentive allocation under budget constraints. Method: We propose Constrained Monotonic Adaptive Networks (CoMAN), the first framework to jointly embed spatiotemporal awareness and convex/concave sensitivity priors into a differentiable monotonic neural network, enabling end-to-end optimization of sensitivity prediction and incentive allocation. CoMAN overcomes the limitation of conventional monotonic models—lack of spatiotemporal robustness—by explicitly modeling dynamic user responses across time-varying geographic locations. Results: Offline experiments show a 3.2% AUC improvement; online A/B testing yields an 11.7% ROI gain. CoMAN achieves significantly higher budget efficiency than state-of-the-art monotonic methods, establishing a new paradigm for multi-scenario marketing resource optimization that balances theoretical rigor with engineering practicality.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Machine Learning: Online Learning & Bandits

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

In the mobile internet era, the Online Food Ordering Service (OFOS) emerges as an integral component of inclusive finance owing to the convenience it brings to people. OFOS platforms offer dynamic allocation incentives to users and merchants through diverse marketing campaigns to encourage payments while maintaining the platforms' budget efficiency. Despite significant progress, the marketing domain continues to face two primary challenges: (i) how to allocate a limited budget with greater efficiency, demanding precision in predicting users' monotonic response (i.e. sensitivity) to incentives, and (ii) ensuring spatio-temporal adaptability and robustness in diverse marketing campaigns across different times and locations. To address these issues, we propose a Constrained Monotonic Adaptive Network (CoMAN) method for spatio-temporal perception within marketing pricing. Specifically, we capture spatio-temporal preferences within attribute features through two foundational spatio-temporal perception modules. To further enhance catching the user sensitivity differentials to incentives across varied times and locations, we design modules for learning spatio-temporal convexity and concavity as well as for expressing sensitivity functions. CoMAN can achieve a more efficient allocation of incentive investments during pricing, thus increasing the conversion rate and orders while maintaining budget efficiency. Extensive offline and online experimental results within our diverse marketing campaigns demonstrate the effectiveness of the proposed approach while outperforming the monotonic state-of-the-art method.

Problem

• Efficiently allocate limited budget in marketing campaigns
• Predict user monotonic response to incentives accurately
• Ensure spatio-temporal adaptability in diverse marketing

Innovation

• Spatio-temporal adaptive awareness in marketing
• Constrained Monotonic Adaptive Network (CoMAN)
• Learning spatio-temporal convexity and concavity