How to track historical trend data in admissions: The first of seven critical categories

This blog is excerpted from the first section of the 2013 Noel-Levitz white paper, Seven Categories of Admissions Data to Guide Decision Making.

Tracking historical admissions trend data is foundational to building an effective strategy for marketing and recruitment. You want timely, meaningful data for comparisons, so you can stay on top of campus trends. The data should be date- and year-specific, for example: “On this day last year, we had 221 applications from Los Angeles County. This year, we had 277 applications from Los Angeles County, a 25 percent increase.”

For effective benchmark comparisons, we advise campuses to store and analyze three-to-five years of comparative data. Student data should be organized into carefully-defined categories, such as the following eight stages:

1. Prospects (students who have not yet expressed interest in the institution, such as students whose names and addresses were purchased from a list vendor)
2. Inquiries (students who have expressed interest in the institution)
3. Applicants
4. “Stealth applicants” (students who made their first contact by submitting an application without inquiring beforehand; often categorized dually as applicants and inquiries)
5. Applicants who fully completed the application process
6. Accepts/admits
7. Deposits/confirms
8. Matriculants

You will also want to track students in defined target groups, such as those listed below, depending on your unique enrollment goals, trends, and circumstances:

• First-year vs. transfer
• Adult vs. traditional-age
• Geographic market area/counselor territory
• Academic profile
• Academic and co-curricular interest (noting if certain majors are rising or falling within your pool)
• Racial/ethnic category
• Resident/commuter
• Financial need level

Further, you will want to group and track your applicants and admitted students by FAFSA institutional position, ACT institutional position, predictive modeling scores, and qualifying codes used by the institution to rate enrollment likelihood.

With the above data set in hand, one can project fall enrollment and yield by monitoring and comparing each target group’s rates of movement toward enrollment in each week of the recruitment cycle. This also highlights the need for targeted interventions. For example, groups of students who are moving slowly from admit to matriculant (the yield rate) require different interventions than those who are converting slowly from inquiry to application.

To keep reading about the critical data you need in admissions, download the complete white paper, Seven Categories of Admissions Data to Guide Decision Making. You can also e-mail me any questions you have about collecting, analyzing, and acting on your admissions data.