Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation

TL;DR

This paper introduces a mixed policy approach for coupon allocation that balances immediate revenue maximization with future policy improvement through off-policy evaluation, using a multi-objective optimization framework.

Contribution

It proposes a novel mixed policy combining deterministic and randomized strategies, and formulates the optimal mixture ratio as a multi-objective optimization problem.

Findings

Effective in balancing revenue and exploration in synthetic data experiments.

Flexible adjustment of data collection and revenue trade-off demonstrated.

Framework enables quantitative evaluation of the trade-off.

Abstract

Coupon allocation drives customer purchases and boosts revenue. However, it presents a fundamental trade-off between exploiting the current optimal policy to maximize immediate revenue and exploring alternative policies to collect data for future policy improvement via off-policy evaluation (OPE). To balance this trade-off, we propose a novel approach that combines a model-based revenue maximization policy and a randomized exploration policy for data collection. Our framework enables flexible adjustment of the mixture ratio between these two policies to optimize the balance between short-term revenue and future policy improvement. We formulate the problem of determining the optimal mixture ratio as multi-objective optimization, enabling quantitative evaluation of this trade-off. We empirically verified the effectiveness of the proposed mixed policy using synthetic data. Our main contributions are: (1) Demonstrating a mixed policy combining deterministic and probabilistic policies, flexibly adjusting the data collection vs. revenue trade-off. (2) Formulating the optimal mixture ratio problem as multi-objective optimization, enabling quantitative evaluation of this trade-off.