Robust portfolio optimization model for electronic coupon allocation

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six type of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean–variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.

当前，诸多电商平台将线上/电子优惠券（online/electronic coupons）作为推广各类商品与服务销售的有效手段。本研究聚焦于电商平台场景下受预算约束的客户优惠券最优分配问题。我们将基于客户细分（customer segmentation）的稳健投资组合优化模型（robust portfolio optimization model）应用于优惠券分配问题。此外，我们利用随机发放优惠券的真实数据开展数值实验，以此验证所提方法的有效性。本研究的主要贡献分为两点：其一，本研究涵盖六种优惠券类型，这使得精准估算不同优惠券的效果差异极具挑战性；其二，通过详实的数值实验结果，我们证明稳健优化模型的销售增量优于常用的多选背包模型（multiple-choice knapsack model）与传统均值-方差优化模型（mean–variance optimization model）。本研究结果表明，稳健投资组合优化作为实用的优惠券分配工具，具备广阔的应用潜力。