Robust portfolio optimization model for electronic coupon allocation

被引:1
作者
Uehara, Yuki [1 ]
Nishimura, Naoki [2 ,3 ]
Li, Yilin [4 ]
Yang, Jie [4 ]
Jobson, Deddy [4 ]
Ohashi, Koya [4 ]
Matsumoto, Takeshi [4 ]
Sukegawa, Noriyoshi [5 ]
Takano, Yuichi [2 ]
机构
[1] Univ Tsukuba, Masters Program Policy & Planning Sci, Tsukuba, Japan
[2] Univ Tsukuba, Inst Syst & Informat Engn, Tsukuba, Japan
[3] Recruit Co Ltd, Prod Dev Management Off, Data Management & Planning Off, Chiyoda City, Tokyo, Japan
[4] Mercari Inc, Data Sci, Minato City, Tokyo, Japan
[5] Hosei Univ, Dept Adv Sci, Chiyoda, Tokyo, Japan
关键词
Coupon allocation; portfolio optimization; robust optimization; treatment effect; prescriptive analytics; E-commerce;
D O I
10.1080/03155986.2024.2386494
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:646 / 660
页数:15
相关论文
共 28 条
[1]   LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm [J].
Ai, Meng ;
Li, Biao ;
Gong, Heyang ;
Yu, Qingwei ;
Xue, Shengjie ;
Zhang, Yuan ;
Zhang, Yunzhou ;
Hang, Peng .
PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22), 2022, :2310-2319
[2]   Optuna: A Next-generation Hyperparameter Optimization Framework [J].
Akiba, Takuya ;
Sano, Shotaro ;
Yanase, Toshihiko ;
Ohta, Takeru ;
Koyama, Masanori .
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2019, :2623-2631
[3]   E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling [J].
Albert, Javier ;
Goldenberg, Dmitri .
PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022, 2022, :2863-2872
[4]  
Athey S., 2015, ARXIV150401132V2
[5]  
BenTal A, 2009, PRINC SER APPL MATH, P1
[6]   Robust discrete optimization and network flows [J].
Bertsimas, D ;
Sim, M .
MATHEMATICAL PROGRAMMING, 2003, 98 (1-3) :49-71
[7]   From Predictive to Prescriptive Analytics [J].
Bertsimas, Dimitris ;
Kallus, Nathan .
MANAGEMENT SCIENCE, 2020, 66 (03) :1025-1044
[8]   Theory and Applications of Robust Optimization [J].
Bertsimas, Dimitris ;
Brown, David B. ;
Caramanis, Constantine .
SIAM REVIEW, 2011, 53 (03) :464-501
[9]  
Bishop Christopher M., 2006, Pattern recognition and machine learning
[10]  
Broadie M., 1993, Annals of Operations Research, V45, P21, DOI 10.1007/BF02282040