Student Search Modeling Case Studies
Result: After identifying the bottom half of the inquiry pool (24,415 names), and adjusting for campus visits, the client chose not to send print pieces to 22,750 of those students. This resulted in an estimated savings of $273,000. Ultimately, only five (5) enrolled students came from the bottom half of the inquiry pool.
Strategy: We developed a predictive model to target low-need students in geographies not previously searched. The campaign deployed in mid-summer, with copy/creative customized to appeal to a low-need audience. Inquiries and Engaged students were then included in the fall Senior Search campaign.
Result: We increased inquiries in these states between 29% and 55% (630 students); inquiries from eliminated areas fell by only 1% (22 students). So, for the same budget, we generated 608 more inquiries.
Strategy: We utilized alumni data to identify previously unidentified potential legacy matches on existing Search and inquiry lists, and sent them specifically targeted messages from the alumni office.
Strategy: We developed a predictive model to strategically expand into a market on the opposite coast by purchasing names of students most likely to apply and enroll. We then customized our copy/creative to appeal to students in this market.
Strategy: We developed a predictive model to evaluate in-state markets, and purchased only the names of students identified as likely to apply and enroll, rather than purchasing the names of all students in the entire state. We removed 743 ZIP Codes from the Search geography used by the client’s previous vendor.
Result: We met or exceeded our client’s goals – every single month – for applications, completed applications, admits, and deposits. Year-end goals for completed applications and admits were met in March 2018.
Result #2: The client received 10% more applications, admitted 16% more students, and enrolled 11% more students, resulting in a return on investment of 52 times their first-year Search investment in net revenue (including discount rate).