Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation
This work addresses a practical problem in marketing and e-commerce for businesses seeking to optimize coupon strategies, though it is incremental as it builds on existing off-policy evaluation and exploration-exploitation methods.
The paper tackles the trade-off between maximizing immediate revenue and collecting data for future policy improvement in coupon allocation, proposing a mixed policy approach that flexibly adjusts this balance and formulates it as a multi-objective optimization problem, with effectiveness verified empirically on synthetic data.
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.