Increased student pool by 50%, admission enrolment rates by 15%, and reduced drop-out rate by 10% for a leading medical school


A leading global medical school was looking to expand its student base. The goal for the first year was to identify and enroll 700 new incremental students. The educational institution lacked the means to target students likely to convert, which cities and towns to target, and the factors that will help in the conversion. Successful outcomes required not only bias for high enrolment but retention and successful placement in a residency program.


Our ML-based solution helped the client in gathering data and market insights to identify and attract medical student applicants with a higher probability of placement in a residency program. The solution put in a place a propensity model to optimize conversion across different stages of the student application pipeline. The client was able to leverage data and ML technology to identify additional lookalike applicants with high certainty of enrollment to build future applicant pool. In addition, the solution helped in identify top contributing factors for admissions and optimize marketing campaigns.


increase in student pool
increase in enrolment to admission rate
reduction in drop-out rate