To increase the efficiency of higher education institutions (HEI), the student centered education plan is introduced. The management of higher education institutions is inextricably linked to the rise and fall of the school’s overall growth. How the leadership can rely on the functioning of the evaluation mechanism to enable the school to construct strong programmes and activities to adapt to the competitive circumstances of the education market, these must be thoroughly thought-out strategies for problem-solving. The increase of e-learning resources, instrumental educational software, the use of the Internet in education, and the construction of student information databases has resulted in massive reservoirs of educational data. A well-done churn prediction model can help the higher education institutions track student’s academic progress, enrolment and drop-out in the most effective way in which the best result can be achieved. This purpose of this paper is to analyse the commonly used method of decision tree to predict and reduce the likelihood of students dropping out from higher education institutions (HEI). Businesses spend countless amounts in Informational Technologies (IT) deployment and update in the world of technology. The research methodology used will be based on different journals, articles, and reports to investigate the effectiveness of customer churn analysis using a qualitative approach.

Keywords: customer data analysis, customer relationship management, churn prediction, educational data mining, higher education institution, technologies, information technologies.