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Structural Neural Networks Meet Piec...

Cai, Chuan.

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Structural Neural Networks Meet Piecewise Exponential Models on Customer Acquisition and Retention.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Structural Neural Networks Meet Piecewise Exponential Models on Customer Acquisition and Retention./
作者:	Cai, Chuan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	150 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-08, Section: B.
Contained By:	Dissertations Abstracts International85-08B.
標題:	Higher education administration. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30819427
ISBN:	9798381721775

Structural Neural Networks Meet Piecewise Exponential Models on Customer Acquisition and Retention.
Cai, Chuan.

Structural Neural Networks Meet Piecewise Exponential Models on Customer Acquisition and Retention. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 150 p.

Source: Dissertations Abstracts International, Volume: 85-08, Section: B.

Thesis (Ph.D.)--University of Delaware, 2024.

This dissertation examines the dynamics of customer acquisition and retention within the context of higher education with students being recognized as customers. Given the substantial reliance of higher education institutions on tuition fees for operating revenue, student recruitment and retention emerge as pivotal aspects of enrollment management. Furthermore, retention and timely graduation are used to measure institutional reputation and accountability. This study pursues three main objectives: understanding applicant deposit decisions during the admission process, predicting matriculated students' dropout risks, and exploring the impact of student loan debt on timely graduation. The first segment analyzes the determinants of deposit decisions of out-of-state students admitted to the University of Delaware across three academic years. Utilizing three Bayesian hierarchical piecewise exponential models, we deduce that factors like gender and recruitment events exhibit time-varying effects, while others like financial aid remain stable within an academic year but vary across years. The baseline desire to deposit intensifies as deadlines near, though this trajectory shifts annually. Insights derived inform the Admissions Office's marketing and recruitment tactics. The second segment introduces a hybrid model, merging a structural neural network with a piecewise exponential model, to predict college attrition. Benchmarking against two alternative models, the hybrid model demonstrates superior or comparable predictive prowess for the University of Delaware across three springs. Categorizing predictors into academic, economic, and socio-demographic facets reveals academic indicators as key discriminants between students who drop out and those retained, especially from freshman to junior years. Emphasis on academic assessments in intervention strategies is thus recommended.The third segment evaluates the impact of student loan debt on six-year graduation rates by department, over a span of five years. Leveraging five Bayesian hierarchical models, the findings illustrate a pronounced department-wise loan debt effect on first-year students, which attenuates as they advance academically. Tailored financial aid policies, considering academic departments, are posited to amplify the efficient utilization of institutional financial resources. For universities mulling over department-specific financial aid policies, initiation with randomized trials for first-year students is advised.In summary, this dissertation introduces innovative strategies for strategic enrollment management, encompassing admission, retention, and graduation considerations. Particular attention is given to the dynamic nature of applicant deposit decisions, the development of predictive models for student attrition, and the department-specific effects of student loan debt on graduation rates.

ISBN: 9798381721775Subjects--Topical Terms:

2122863
Higher education administration.
Subjects--Index Terms:

Customer acquisition

Structural Neural Networks Meet Piecewise Exponential Models on Customer Acquisition and Retention.
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