The increasingly competitive higher educational environment compels the management of universities and colleges to assign high priority to an overall maximisation of client services. Consequently, while academic leaders must become familiar with the aspects of on-line communication much favoured by today’s younger generation, the intensification and improvement of the quality of available on-line services cannot be imagined without reliable information on the Internet use habits and behaviour of clients.
The managers and administrators of Hungarian college and university websites are mostly unfamiliar with the web-related conduct or habits of their customers as in case of long-running web-pages based on an unchanging structure only basic visitor statistics are available at best. Yet marketing communication decisions should be based on information reflecting real website-consumer traits acquired via a more professional analysis. Data mining is one such decision-making support mechanism.
Data mining models are capable of revealing and predicting information hidden beneath the respective critical mass. Therefore inspired by the methodology of marketing science this type of research concentrates on the segmentation of on-line consumers via the elaboration of visitor clusters.
The present article provides a scientific overview and analysis of the main difficulties related to cluster construction, especially the development of the relevant algorithmic forms. The successful application of the model provides much-needed reliable and vital support to the institutional decision making process. Thus pertinent data yielded by cluster research can facilitate more effective on-line service customized to the needs of the users.