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Essays on Consumer Heterogeneity and Personalized Discounts in an Online Market
Essays on Consumer Heterogeneity and Personalized Discounts in an Online Market
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151140
- ISBN
- 9798382741529
- DDC
- 640
- 저자명
- Zhang, Chengjun.
- 서명/저자
- Essays on Consumer Heterogeneity and Personalized Discounts in an Online Market
- 발행사항
- [Sl] : Georgetown University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 111 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Rust, John.
- 학위논문주기
- Thesis (Ph.D.)--Georgetown University, 2024.
- 초록/해제
- 요약This thesis delves into consumer heterogeneity in an online marketplace from an empirical lens on business practices. Furthermore, it evaluates the welfare consequences of employing personalized discounts as a strategic marketing approach. The first chapter utilizes comprehensive consumer clickstream data to construct and refine demand models for smartphones on an e-commerce platform. The narrative unfolds through the exploration of increasing levels of consumer heterogeneity, built upon the conditional logit framework. The last model directly leverages consumer historical clickstreams with a recurrent neural network (RNN), offering detailed individual-level preferences and realistic product substitution patterns. This model excels by outperforming other models in both in-sample and out-of-sample fit.The second chapter, building upon the demand model established in the first, conducts a counterfactual analysis that enables the issuance of personalized discounts tailored to individual consumer preference parameters. Using a numerically stable algorithm, this chapter presents empirical evidence that highlights the welfare implications. The findings illuminate a mutually beneficial scenario for firm profitability and consumer welfare, in conditional expected terms.
- 일반주제명
- Home economics
- 일반주제명
- Finance
- 키워드
- E-commerce
- 키워드
- Machine learning
- 기타저자
- Georgetown University Economics
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382741529
■035 ▼a(MiAaPQ)AAI31149217
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a640
■1001 ▼aZhang, Chengjun.
■24510▼aEssays on Consumer Heterogeneity and Personalized Discounts in an Online Market
■260 ▼a[Sl]▼bGeorgetown University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a111 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Rust, John.
■5021 ▼aThesis (Ph.D.)--Georgetown University, 2024.
■520 ▼aThis thesis delves into consumer heterogeneity in an online marketplace from an empirical lens on business practices. Furthermore, it evaluates the welfare consequences of employing personalized discounts as a strategic marketing approach. The first chapter utilizes comprehensive consumer clickstream data to construct and refine demand models for smartphones on an e-commerce platform. The narrative unfolds through the exploration of increasing levels of consumer heterogeneity, built upon the conditional logit framework. The last model directly leverages consumer historical clickstreams with a recurrent neural network (RNN), offering detailed individual-level preferences and realistic product substitution patterns. This model excels by outperforming other models in both in-sample and out-of-sample fit.The second chapter, building upon the demand model established in the first, conducts a counterfactual analysis that enables the issuance of personalized discounts tailored to individual consumer preference parameters. Using a numerically stable algorithm, this chapter presents empirical evidence that highlights the welfare implications. The findings illuminate a mutually beneficial scenario for firm profitability and consumer welfare, in conditional expected terms.
■590 ▼aSchool code: 0076.
■650 4▼aHome economics
■650 4▼aFinance
■653 ▼aConsumer clickstream
■653 ▼aConsumer heterogeneity
■653 ▼aE-commerce
■653 ▼aMachine learning
■653 ▼aMulti-layer perceptron
■653 ▼aRecurrent neural network
■653 ▼aPersonalized discount
■690 ▼a0501
■690 ▼a0338
■690 ▼a0508
■690 ▼a0386
■71020▼aGeorgetown University▼bEconomics.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0076
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160944▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
