Data mining and revenue management methodologies in college admissions
Authors: Rebbapragada Surya , Basu Amit , Semple John 
Published: March 2010

The competition for college admissions is getting fiercer each year with most colleges receiving record number of applications and hence becoming increasingly selective. The acceptance rate in some elite colleges is as low as 10%, and the uncertainty often causes talented students to apply to schools in the next tier. Students try to improve their chances of getting into a college by applying to multiple schools, with each school having its own timelines and deadlines for admissions. Consequently, students are often caught in a dilemma when they run out of time to accept an offer from a university that is lower on their priority list, before they know the decision from a university that they value more. The college admissions process is thus extremely stressful and unpredictable to both students and their parents.
Universities, on the other hand, usually receive far more applications than their capacity. They consider various factors in making their decision, with each university using its own process and timelines. A university typically relies on a weighted set of performance indicators to aid the decision making process. These performance indicators and the associated weights for them are often based on a best guess approach relying mostly on past experience. However, since not all admission offers are accepted, universities send out more offer letters than their capacity and hope that the best students accept their offer.
Figure 1 shows a step-by-step sequence of events in a typical university admission process. The sequence describes a scenario that results in an unfavorable outcome for both the university and the student. The student applies to two different universities and prefers one over the other (Step A) with priority 1 university on the far right of the figure. Each university evaluates the application, and priority 2 university makes an early offer along with a certain deadline to accept the offer (Step B). The student, uncertain about priority 1 university, accepts the offer from priority 2 university (Step C), possibly committing some funds. At a later date, priority 1 university decides to accept the student (Step D) who may no longer be available. This process typically spans a number of months and is fraught with uncertainty, and results in a lose-lose situation for the priority 1 university and the student.

There are two challenges in the admissions process exemplified above:

i. The process of identifying the best applicants involves multiple credentials. Given the complex interactions between these credentials, it is not easy to identify a single model that is effective for this selection process. Furthermore, given the competitive nature of university admissions, there are no normative models in the literature.
ii. Once the most desirable candidates are identified, the decision to make an offer, and the composition of that offer, are both difficult. Better candidates are likely to be sought by multiple schools, so the university has to trade off the risks of chasing (and still losing) these students versus the better chances of getting the next tier of students. Furthermore, in many universities, some admission decisions and offer may have to be made before all applications are received.
We believe that data mining and revenue management techniques can be used effectively to address both these challenges, and thus convert the lose-lose situation into a win-win situation. By applying these techniques, universities can methodically score an applicant and be able to respond almost immediately with an offer, mitigating prolonged uncertainty while increasing transparency. We demonstrate the approach using a simplistic admissions process. Although individual universities may have additional, and possibly subjective features in their admissions processes, we believe that our approach could be adapted to the specific processes of many universities and colleges.