政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/153156
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 113324/144300 (79%)
造访人次 : 51144893      在线人数 : 871
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/153156


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/153156


    题名: 數位客群鎖定：轉換與價值提升模型
    Conversion and Value Uplift Modeling for Digital Customer Targeting
    作者: 王聖淳
    Wang, Sheng-Chun
    贡献者: 莊皓鈞
    周彥君
    Chuang, Hao-Chun
    Chou Yen-Chun
    王聖淳
    Wang, Sheng-Chun
    关键词: 數位行銷
    增益模型
    因果推論
    神經網路
    Digital Marketing
    Uplift Modeling
    Causal Inference
    Neural Networks
    日期: 2024
    上传时间: 2024-09-04 14:04:55 (UTC+8)
    摘要: 在這項研究中，我們與台灣其中一個優秀的銀行Alpha合作，旨在通過因果提升建模技術提高數位行銷的效果。我們透過運用監督式機器學習技術和神經網路模型來解決優化客戶目標策略的挑戰。我們引入了兩階段收益提升模型的概念，並提出了使用神經網路模型進一步改進條件平均處理效果（CATE）估計的方法。我們展示了增益模型在預測客戶反應和優化行銷活動方面的有效性，通過分析客戶數據和進行實際實驗，從而為Alpha帶來了銷售和收益的增加。
    In this study, we collaborate with Alpha, a leading bank in Taiwan, aiming to enhance the efficacy of digital marketing through causal uplift modeling techniques. We address the challenge of optimizing customer targeting strategies by employing supervised machine learning techniques and neural network models. We introduce the concept of the two-stage revenue uplift model and propose further advancements using neural network models to improve CATE estimation. By analyzing customer data and conducting field experiments, we demonstrate the effectiveness of uplift modeling in predicting customer responses and optimizing marketing campaigns, leading to increased sales and revenues for Alpha.
    參考文獻: Ascarza, E. (2018). Retention futility: Targeting high-risk customers might be ineffective. Journal of marketing Research, 55(1), 80-98.
    Baumann, A., Haupt, J., Gebert, F., & Lessmann, S. (2019). The price of privacy: An evaluation of the economic value of collecting clickstream data. Business & Information Systems Engineering, 61, 413-431.
    De Caigny, A., Coussement, K., & De Bock, K. W. (2018). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research, 269(2), 760-772.
    Devriendt, F., Berrevoets, J., & Verbeke, W. (2021). Why you should stop predicting customer churn and start using uplift models. Information Sciences, 548, 497-515.
    Gubela, R. M., & Lessmann, S. (2021). Uplift modeling with value-driven evaluation metrics. Decision Support Systems, 150, 113648.
    Gubela, R. M., Lessmann, S., & Jaroszewicz, S. (2020). Response transformation and profit decomposition for revenue uplift modeling. European Journal of Operational Research, 283(2), 647-661.
    Holland, P. W. (1986). Statistics and causal inference. Journal of the American statistical Association, 81(396), 945-960.
    Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the national academy of sciences, 116(10), 4156-4165.
    Kane, K., Lo, V. S., & Zheng, J. (2014). Mining for the truly responsive customers and prospects using true-lift modeling: Comparison of new and existing methods. Journal of Marketing Analytics, 2, 218-238.
    Olaya, D., Vásquez, J., Maldonado, S., Miranda, J., & Verbeke, W. (2020). Uplift Modeling for preventing student dropout in higher education. Decision Support Systems, 134, 113320.
    Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of educational Psychology, 66(5), 688.
    Rzepakowski, P., & Jaroszewicz, S. (2012). Decision trees for uplift modeling with single and multiple treatments. Knowledge and Information Systems, 32, 303-327.
    Statista. (2023). Digital advertising spending worldwide from 2018 to 2028, by format (in billion U.S. dollars). In Statista; Statista Digital Market Insights. Statista.
    Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research, 218(1), 211-229.
    Zhan, B., Liu, C., Li, Y., & Wu, C. (2024). Weighted doubly robust learning: An uplift modeling technique for estimating mixed treatments' effect. Decision Support Systems, 176, 114060.
    描述: 碩士
    國立政治大學
    資訊管理學系
    111356031
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0111356031
    数据类型: thesis
    显示于类别:[資訊管理學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    603101.pdf1010KbAdobe PDF0检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈