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

TL;DR

This paper introduces CoMAN, a novel spatio-temporal adaptive network that improves incentive allocation in marketing campaigns by accurately modeling user response sensitivities across time and space, leading to better budget efficiency and higher conversions.

Contribution

The paper proposes a new CoMAN method that captures spatio-temporal preferences and user sensitivities, enhancing incentive allocation in marketing pricing beyond existing monotonic models.

Findings

Outperforms state-of-the-art monotonic methods in offline tests.

Increases conversion rates and orders in online campaigns.

Achieves more efficient budget utilization.

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.