Date of Award

Fall 11-1-2016

Document Type


Degree Name

Doctor of Science in Information Systems


Business and Information Systems

First Advisor

Deb Tech

Second Advisor

Shuyuan Deng

Third Advisor

Ronghua Shan

Fourth Advisor

Mark Hawkes


Analytical models are already in wide use in the e-commerce marketing sector, however, their use in higher education marketing is limited. Many higher education institutions are struggling to establish a reliable and sustaining engagement strategy with their prospects’ due to the lack of awareness about the kind of information a prospective student might be interested in receiving during the decision-making period. Before purchasing a higher education program, prospects invest a significant amount of time researching different programs by visiting the institutional websites. The use of information generated in such process by the institution is generally limited to reporting and website optimization purposes.

To efficiently engage the prospective students, there is a need for analytical models that would extract the prospect navigational behavior on the website and help the institutions analyze the prospects’ needs. Although institutions are adopting newer engagement strategies, delivering the right information that addresses individual prospect’s needs remains a difficult problem.

The objective of this research is to design and test an analytics framework that would significantly aid higher education institutions in identifying prospective students’ interests and optimize their engagement strategies. This research intends to evaluate the framework by extracting the traces of prospective student information hidden in institutional server logs, profile the prospects based on the pre-inquiry, inquiry and post inquiry phases and further discuss the impact of such prospective student profiles on the institutional efforts in engaging the prospects and overall institutional advancement. The framework will help the institutions to make use of web content mining to extract the contextual profiles from the institutional web pages and apply the web usage mining to extract the prospect navigational profiles from web server logs and compare them to suggest the mass-customized communication sequences.

Finally, the framework is tested in a public university and contributed to the institutional marketing efforts to better engage the prospects in the higher-education sales cycle.