Date of Award

4-1-2023

Degree Name

Doctor of Philosophy

Department

Industrial and Entrepreneurial Engineering and Engineering Management

First Advisor

Timothy J. Green, Ph.D.

Second Advisor

Steven E. Butt, Ph.D.

Third Advisor

James Burns, Ph.D.

Fourth Advisor

Ola M. Smith, Ph.D.

Abstract

As societies become increasingly global the competition for customers continues to increase. In competitive markets, it is important to identify a process to acquire and retain the most profitable customers. To discriminate customers, it is important to understand the value different customers bring to an organization. Much research has been done modeling customer value using customer lifetime value, however, there is limited research on the effect current customer diversity has on future customer lifetime value of future customer portfolios.

In this dissertation, a Lifetime Value Model considering the interaction between customer diversity, revenue management, and customer lifetime value in an academic, higher education setting was used to provide insight for recruitment and retention, using tuition discounting strategies. Using higher education as the analyzed industry, students assume the role of customers. That is, customer diversity was modeled by matriculated student profiles of higher education students; revenue management was described as tuition discounting; and customer lifetime value was modeled using value created by students via net-tuition revenue, and the components of market position. The model analyzed how customer (student) diversity impacts the portfolio of customer (student) lifetime value and whether changes in revenue management strategies impact the portfolio of customer (student) lifetime value.

This research presents several models to measure and account for the volatility in customer lifetime value. These models take into account risk associated with the contemporaneous and lag diversification factors of the customer base. The analysis of these relationships utilized multivariate statistical analysis, structural equation modeling, hierarchical linear modeling, and data envelopment analysis. Together these models present a unique picture of customer lifetime value accounting for factors at the individual and cohort levels. This research shows that Hierarchical Linear Modeling (HLM), Structural Equation Modeling (SEM) and Data Envelopment Analysis (DEA) can be applied to compute measures of risk and value for institutions of higher education. However, irrespective of the methodology the empirical findings regarding the impact of customer base diversification remain similar.

Access Setting

Dissertation-Open Access

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