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 product 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 product 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 product helped in identify top contributing factors for admissions and optimize marketing campaigns.