Education
Know which students are likely to drop before they do
Enrollment records, attendance data and financial aid status connected to flag retention risk while there is still time to intervene.
Why this decision matters.
Withdrawal is the end of a pattern, not an event. Attendance slips, assignment submission slows, an advising appointment is missed, a balance goes unpaid. Each signal lives in a different system, and each on its own looks like a normal bad week.
The failure mode is a retention program that starts after the term. Outreach happens once the student has already disengaged, when the intervention that would have worked was a conversation four weeks earlier.
Retention economics make early detection unusually valuable: keeping an enrolled student is far less expensive than recruiting a replacement, and the student outcome is better. The goal is a short, ranked list an advising team can actually work through.
Comparing tools for education? See how SIGNLD compares with Julius AI. For the underlying numbers, read tracking enrollment against staffing cost.
How SIGNLD answers it.
Step 01
Connects to your source systems
Read-only access to your student information data store (PostgreSQL), the cohort and attendance sheets staff maintain (Google Sheets), and your admissions and advising CRM (Salesforce).
Step 02
Builds the graph across those systems
Students, courses, attendance, submissions, advising contacts, and account balances are linked so several weak signals about one student are recognized as one risk picture.
Step 03
Returns a ranked brief
The brief ranks students by withdrawal risk with the specific signals behind each, and recommends which need advising outreach, academic support, or a financial conversation.
Reads from.
SIGNLD connects read-only to your existing systems. 800+ integrations available.
What the brief looks like.
The question
Which currently enrolled students are at the highest risk of not completing this term?
What SIGNLD found
14 students show a combined risk profile: attendance below 70%, financial aid status flagged for verification, and no advisor contact in the last 30 days. This profile matches 84% of students who withdrew in prior terms. None of the 14 are currently flagged in any active intervention program.
Evidence
- 14 students: attendance below 70%, financial aid verification pending, no advisor contact in 30 days
- This combined profile matches 84% of prior-term withdrawals
- Average revenue impact of a mid-term withdrawal: $8,400 in tuition plus potential impact on institutional aid metrics
Recommended move
Assign an advisor to each of the 14 students for immediate outreach. Prioritize financial aid verification support - this is the most actionable barrier. Estimated revenue retention if 60% of the 14 stay enrolled: $70K this term alone.
31% reduction in early student departure
Illustrative brief. Figures are sample data, not customer results.
Questions.
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