This classic data analysis can furnish fresh insights for high school visit strategy

Expert Insight

This classic data analysis can furnish fresh insights for high school visit strategy

Admission counselor time is an increasingly precious resource that needs to be distributed in the most effective way possible—but visit strategy, for most schools, is a quirky business that often relies on legacy relationships with certain high schools. How do counselors know whether the time they’re spending at traditional feeder schools really pays off? And how do they identify the schools that might turn into tomorrow’s feeders?

RFM: An oldie but a goodie

Decades ago, mail-order catalog distributers faced a similar issue: it was too expensive to send full-length, glossy catalogs to every potential customer on their mailing lists. Instead, they needed a way to understand which customers were likely to purchase again so that they could focus their resources on cultivating those more-likely sales. The exact origin is hazy, but the direct marketing industry eventually recognized that they could accurately predict return customers if they ranked them through a simple equation, called the “RFM model”:

Multiplying a customer’s recency, frequency, and monetary value provides a single composite measure that captures the multiple dimensions of a customer’s value—necessary for making critical prioritization decisions.

Teaching an old metric new tricks

With some tweaks, the RFM model can be applied to similarly prioritize an admission counselor’s potential repeat “customers”: high schools. Wichita State University innovated on the old classic in order to make tough choices during the Great Recession. Skyrocketing gas prices and slashed state budgets meant they could no longer afford to send counselors to as many schools as they had in the past. Instead, they turned to data to decide where to focus their attention.

The metrics in the RFM model map easily onto data that most universities already collect about the high schools where they recruit:

  • Recency: What is the trend in applications received from the high school over a set period of time (e.g., three years)?
  • Frequency: How many students have yielded from this high school over that period?
  • Monetary Value: How much net tuition revenue has come from students from that school over the same period?

Multiplying these three metrics together provides a simple way for admission offices to predict how fruitful counselors’ efforts will be in the future, using just a spreadsheet.

RFM aids tough decisions…and provides insights

Integrating data into decision-making not only helps admission offices make crucial decisions about priorities, but also can help them notice new things about their recruitment field. Using an RFM model, Wichita State identified their 50 most valuable feeder schools—about 65% of their incoming classes come from these 50 schools.

However, they also use RFM to understand how some high schools are changing in value. In a recent year, their RFM analysis ranked two private, religiously affiliated high schools in their top five, over a large local public school that has traditionally been one of their best feeders. This simple data analysis re-focused their counselors’ attention on students they would otherwise have thought were uninterested—while also keeping an eye on the relationship with their traditional feeder school.