東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Essays on Consumer Heterogeneity and...

Zhang, Chengjun.

Linked to FindBook

Google Book

Amazon

博客來

Essays on Consumer Heterogeneity and Personalized Discounts in an Online Market.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Essays on Consumer Heterogeneity and Personalized Discounts in an Online Market./
Author:	Zhang, Chengjun.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	111 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Home economics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31149217
ISBN:	9798382741529

Essays on Consumer Heterogeneity and Personalized Discounts in an Online Market.
Zhang, Chengjun.

Essays on Consumer Heterogeneity and Personalized Discounts in an Online Market. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 111 p.

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--Georgetown University, 2024.

This 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.

ISBN: 9798382741529Subjects--Topical Terms:

551902
Home economics.
Subjects--Index Terms:

Consumer clickstream

Essays on Consumer Heterogeneity and Personalized Discounts in an Online Market.
LDR:02547nmm a2200421 4500 001 2399783
005 20240916070009.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798382741529
035 $a (MiAaPQ)AAI31149217
035 $a AAI31149217
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Chengjun. $3 3769758
245 1 0 $a Essays on Consumer Heterogeneity and Personalized Discounts in an Online Market.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 111 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisor: Rust, John.
502 $a Thesis (Ph.D.)--Georgetown University, 2024.
520 $a This 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 $a School code: 0076.
650 4 $a Home economics. $3 551902
650 4 $a Finance. $3 542899
653 $a Consumer clickstream
653 $a Consumer heterogeneity
653 $a E-commerce
653 $a Machine learning
653 $a Multi-layer perceptron
653 $a Recurrent neural network
653 $a Personalized discount
690 $a 0501
690 $a 0338
690 $a 0508
690 $a 0386
710 2 $a Georgetown University. $b Economics. $3 1065101
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 0076
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31149217