  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). While
online A/B testing can validate new policies, it risks compromising short-term
revenue. Conversely, relying solely on an exploitative policy hinders the
ability to reliably estimate and enhance future policies. 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 flexibly adjusting 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
between a model-based revenue maximization policy and a randomized exploration
policy for data collection. We empirically verified the effectiveness of the
proposed mixed policy using both synthetic and real-world 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. By optimizing
the mixture ratio, businesses can maximize revenue while ensuring reliable
future OPE and policy improvement. This framework is applicable in any context
where the exploration-exploitation trade-off is relevant.
