Student Persistence Recovery Playbook
Goals
Suggested Tasks
(3)Priority outreach to Richard Hartwell (risk 92%) — not contacted yet
Marcus Thompson
Review student support grant budget cap — acceptance rate at 48%, approaching $80K limit
Sarah Chen
Reassign Catherine Zhao's program review — her advisor just resigned
Elena Vasquez
Origin Signals
Insights
Attendance Risk by Student Segment
Risk scores across student groups
Execution plan
Owners
Sarah Chen
VP, Student Wellbeing
Marcus Thompson
Senior Operations Lead
Elena Vasquez
Chief Student Success Officer
ProDIP AI Agent
Autonomous Research & Monitoring
Targets
Schools
Brooklyn Prep
42 staff
River STEM Academy
28 staff
Roles
Individuals
Elena Vasquez
Chief Student Success Officer
Marcus Thompson
Senior Advisor
Risks
Budget
Agent reasoning
I analyzed student data across your Student Success schools and found a significant attendance risk pattern concentrated in upper-middle enrollment bands. Here is why I am recommending immediate action.
Reasoning chain
Data reviewed
Read 14,287 student profiles, 2.3M student records, and 847 advisor interaction logs from the Student Information System and Historical Student Data Warehouse.
Pattern identified
Found that students who reduce activity by more than 40%, skip scheduled support reviews, and decrease digital engagement are 6x more likely to leave within 90 days.
Scale assessed
Identified 50 students matching this pattern in Brooklyn Prep and River STEM Academy, representing 340 students retained potential across the next academic cycle.
Competitor context
Cross-referenced competitor and district feeds. 67% of recently departed students moved to schools with stronger personalized digital support. This is not just a pricing issue; it is an engagement issue.
Action designed
Recommended proactive advisor outreach with personalized talking points per student, student support grants for high-risk cases, and review sessions. Estimated total cost is $71K.
Data the agent read
Based on the data I reviewed, I'm 94% confidentin this recommendation. The three columns below break down what I'm sure about, where I have uncertainty, and what assumptions underpin this proposal.
What I'm sure about
I had full access to student profiles and historical engagement history across both target schools, with no data gaps.
The behavioral pattern (reduced activity -> skipped reviews -> disengagement) is consistent across 18 months of historical departures. This is not a one-off signal.
The 50 at-risk students I identified are a well-defined, actionable list — not a vague segment.
What I'm less sure about
Cost estimate assumes about 50% intervention-plan acceptance. If acceptance is higher, cost could overshoot by 20%. I recommend a hard cap at $80K.
Competitor analysis is based on market and district context feeds, not direct exit interviews. The engagement thesis could be wrong for some students.
I cannot fully assess advisor relationship quality from interaction logs alone, so some student concerns may still be unlogged.
Assumptions I'm making
Proactive outreach within the 90-day window will be more effective than reactive family engagement after a student signals intent to leave.
Support grants and student success reviews are the right levers for this segment, though some students may need different interventions.