CONTRIBUTION OF DIGITAL MARKETING CHANNELS IN THE ONLINE STUDENT ACQUISITION Cover Image

ПРИНОС НА ДИГИТАЛНИ РЕКЛАМНИ КАНАЛИ В ПРИВЛИЧАНЕТО НА СТУДЕНТИ ОНЛАЙН
CONTRIBUTION OF DIGITAL MARKETING CHANNELS IN THE ONLINE STUDENT ACQUISITION

Author(s): Atanaska Reshetkova, Kostadin Bashev, Krista Parvanova
Subject(s): Economy, Business Economy / Management
Published by: Стопанска академия »Д. А. Ценов«
Keywords: online advertising; higher education; attribution models; multichannel campaign

Summary/Abstract: Bulgarian higher education that has been undergoing significant changes in the recent years has created the need for the universities to apply typical marketing instruments in order to reach potential students. At the same time, digital channels are the preferred sources of information for young people. The frequent use of paid online communication by the higher educational institutions renders it necessary to manage the used channels in an effective way. This paper aims to present an approach to determining each digital channel’s contribution in the acquisition of students online, by applying multi-touch attribution model that considers the special features of educational products. The data-based attribution model takes into account the interest of the potential students to use different digital marketing channels and the extent to which they find the information in the specific channel useful, as well as their past experience with the channels. A review of possible applications of digital marketing channels has been made, as well as an overview of the data based attribution models proposed in the scientific literature so far. Recommendations have been made on the use of specific channels in the field of higher education marketing that, based on the estimated parameters of the tested model. It has been found that the channels that contribute most to the acquisition of students online are: (1) display advertising; (2) social media advertising (Facebook); (3) paid search channel. The tested model has a satisfying predictive validity.

  • Issue Year: 25/2018
  • Issue No: 1
  • Page Range: 66-94
  • Page Count: 29
  • Language: Bulgarian