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Three Essays on Data-Driven Personalization and Targeting for Marketing Interventions
Three Essays on Data-Driven Personalization and Targeting for Marketing Interventions
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103128
- ISBN
- 9798280718586
- DDC
- 310
- 저자명
- Huang, Ta-Wei.
- 서명/저자
- Three Essays on Data-Driven Personalization and Targeting for Marketing Interventions
- 발행사항
- [Sl] : Harvard University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 267 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Ascarza, Eva.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2025.
- 초록/해제
- 요약Companies today face significant challenges in personalizing marketing interventions while balancing long-term objectives, adhering to privacy regulations, and managing complex decision spaces. This dissertation addresses these core issues through three interconnected essays and provides practical methodologies for enhancing personalized marketing interventions in today's data-driven environment.The first essay (Chapter 1) demonstrates and tackles the challenges of optimizing long-term business performance through targeted interventions. It highlights how cumulative unexplained variations in repeated customer behavior can weaken the direct optimization of long-term out- comes. To address this, the essay introduces a surrogate index methodology using short-term signals, coupled with a novel separate imputation strategy to handle the churn and purchase processes driving customer value. Through simulations and a real-world marketing application, the essay demonstrates that the proposed approach significantly outperforms traditional methods in enhancing long-term targeting effectiveness.The second essay (Chapter 2) examines how Local Differential Privacy (LDP) - a strong privacy technique that introduces noise into individual-level data - affects the accuracy of personalized marketing interventions based on Conditional Average Treatment Effect (CATE) predictions. We show that LDP induces heterogeneous and model-dependent errors that hinder accurate personalization. To address these limitations, we propose an honest post-processing method using an unbiased but noisy proxy combined with iterative boosting and a subgroup cross-learning strategy to ensure honesty and mitigate overfitting. Empirical tests demonstrate that our method significantly improves prediction accuracy and treatment prioritization, enabling organizations to achieve effective personalization despite privacy constraints.The final essay (Chapter 3) introduces Incrementality Representation Learning (IRL), a novel multitask framework for predicting heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets personalized interventions without extensive testing. It extracts generalizable low-dimensional representations of intervention features and customer covariates. Empirical validation using data from 274 promotional campaigns demon- strates that IRL significantly enhances targeting accuracy and effectively generalizes predictions to both known and untested interventions and customer segments, addressing challenges in high-dimensional decision spaces and cold-start scenarios. Additionally, the essay develops a decision framework and interpretation tool to assist firms in identifying critical design features and tailoring promotions for maximum profitability.
- 일반주제명
- Statistics
- 키워드
- Causal inference
- 키워드
- Data privacy
- 키워드
- Machine learning
- 키워드
- Personalization
- 키워드
- Surrogate index
- 기타저자
- Harvard University Business Administration
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798280718586
■035 ▼a(MiAaPQ)AAI31939149
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a310
■1001 ▼aHuang, Ta-Wei.▼0(orcid)0000-0002-6735-2954
■24510▼aThree Essays on Data-Driven Personalization and Targeting for Marketing Interventions
■260 ▼a[Sl]▼bHarvard University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a267 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Ascarza, Eva.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2025.
■520 ▼aCompanies today face significant challenges in personalizing marketing interventions while balancing long-term objectives, adhering to privacy regulations, and managing complex decision spaces. This dissertation addresses these core issues through three interconnected essays and provides practical methodologies for enhancing personalized marketing interventions in today's data-driven environment.The first essay (Chapter 1) demonstrates and tackles the challenges of optimizing long-term business performance through targeted interventions. It highlights how cumulative unexplained variations in repeated customer behavior can weaken the direct optimization of long-term out- comes. To address this, the essay introduces a surrogate index methodology using short-term signals, coupled with a novel separate imputation strategy to handle the churn and purchase processes driving customer value. Through simulations and a real-world marketing application, the essay demonstrates that the proposed approach significantly outperforms traditional methods in enhancing long-term targeting effectiveness.The second essay (Chapter 2) examines how Local Differential Privacy (LDP) - a strong privacy technique that introduces noise into individual-level data - affects the accuracy of personalized marketing interventions based on Conditional Average Treatment Effect (CATE) predictions. We show that LDP induces heterogeneous and model-dependent errors that hinder accurate personalization. To address these limitations, we propose an honest post-processing method using an unbiased but noisy proxy combined with iterative boosting and a subgroup cross-learning strategy to ensure honesty and mitigate overfitting. Empirical tests demonstrate that our method significantly improves prediction accuracy and treatment prioritization, enabling organizations to achieve effective personalization despite privacy constraints.The final essay (Chapter 3) introduces Incrementality Representation Learning (IRL), a novel multitask framework for predicting heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets personalized interventions without extensive testing. It extracts generalizable low-dimensional representations of intervention features and customer covariates. Empirical validation using data from 274 promotional campaigns demon- strates that IRL significantly enhances targeting accuracy and effectively generalizes predictions to both known and untested interventions and customer segments, addressing challenges in high-dimensional decision spaces and cold-start scenarios. Additionally, the essay develops a decision framework and interpretation tool to assist firms in identifying critical design features and tailoring promotions for maximum profitability.
■590 ▼aSchool code: 0084.
■650 4▼aStatistics
■653 ▼aCausal inference
■653 ▼aData privacy
■653 ▼aHeterogeneous treatment effects
■653 ▼aMachine learning
■653 ▼aPersonalization
■653 ▼aSurrogate index
■690 ▼a0338
■690 ▼a0463
■690 ▼a0800
■71020▼aHarvard University▼bBusiness Administration.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357083▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
