The original segmentation in 2020 and the 2022 segmentation were completed using an online methodology targeting both current and prospective students of Arden. For the 2022 refreshed study based on student attitudes and needs, a robust sample of 1,225 students was achieved.

We used latent class analysis to form the segments and employed a range of research analysis techniques: correlation and regression (key driver), k-means cluster analysis, significance testing cross-tabs.

Once we had a good understanding of the types of variables we wanted to use, we started creating segmentation models using a combination of these variables. All promising segmentations were then profiled against the survey questions to decide which was the most meaningful segmentation in terms of a clear profile/story behind each segment that would be actionable for Arden. The Bayesian Information Criterion (BIC) was used as a guide for the optimum number of segments within each model.