Machine Learning in Customer-Centric Web Design- The Website of a Portuguese Higher Education Institution

https://drive.google.com/open?id=1C1GhmwXOmBzehBO9wDCOpFPJvmXuaCFU&usp=drive_fs

Prospective students interact with the brand of higher education institutions (HEI) via several channels throughout their journey to choose a course to enroll. The institutional website is among these channels and the way it is designed might influence how engaged these visitors are. Web analytics tools allow collecting high amounts of user behavior data, which can generate insights that help to improve higher education institutions website and the students’ incentives to apply for a course. Techniques of Data Mining are presented as a proposition to help generating insights with an applied case study of a Portuguese HEI. The CRISP-DM method was used to generate suggestions to improve user engagement. The tools applied from Google Tag Manager, Analytics, BigQuery and RapidMiner allowed to collect, storage, transform, visualize and model data using the machine learning algorithms Naïve Bayes, Generalized Linear Model, Logistic Regression, Fast Large Margin and Decision Tree. The main results showed that: the course pages do attract volume of users, but their engagement is low; the general undergraduate course page is more successful to bring users who see course content and that; masters and other course pages do attract engaged users who see undergraduate that content.