Abstract:

This work examines the early, promising results of bench-marking a University's brand asset value to forecasting subsequent enrollment. It was accomplished by sampling the brand perceptions of 230 newly accepted students at a Southeastern US University using an adaptation of the seminal Brand Asset Valuator® framework (Young and Rubicam's BAV, 2009.) The brand's Differentiation, Relevance, Esteem and Knowledge along with student Psychographic Profiles were used to: (1) Describe the University brand's strategic position; (2) Develop a descriptive/predictive bench-marking linear regression function; and (3) Based on the same data, develop a “user friendly” enrollment forecasting model to discriminate probable matriculants from non-matriculants.

Using IBM/SPSS® Modeler’s Auto Classification NODE, the eight most accurate model forms were estimated and ranked. Binary logistic regression was suggested as the optimal choice. Multiple variations were produced with the most parsimonious delivering 91.3% internal classification accuracy using only 6 variables. These results paralleled and strongly supported the additional use of Modeler's 4th choice, the related discriminant analysis procedure. Provocative results indicated: (1) The University's brand was lead by Esteem and Knowledge, the 3rd and 4th most important BAV constructs, while the more important Differentiation and Relevance constructs followed well behind; (2) The regression function explained 50% of brand latent construct variation – a good initial empirical benchmark for reassessing brand positioning strategy; and (3) The logistic regression classification model showed great promise, especially if its primary limitation, the imbalance of sample matriculants vs. non-matriculats, could be corrected by further data accumulation.

This line of research holds out hope and help for those institutions unable to participate in Y&R's 50,000+ brand syndication program with Harvard, Cambridge, NYU and Stanford. Moreover, these results are particularly provocative for Enrollment Officers. After running their personalized intake models, and discovering the probability of each applicant's matriculation, they would know the expected value of each applicant to the admissions process. This, in turn, would suggest the maximum amount of marketing resources to be expended on each student.

NB Dr Green welcomes new cross-cultural model comparison and collaboration, especially with Spanish, French and Italian Universities.

Keywords:

University branding, resource allocation, enrollment forecasting, Young & Rubicam, Admissions expected value, Binay Logistic Regression, Discriminant Analysis.