Know which students will drop out
6 weeks before they do.
Upload enrollment data, attendance records, or fee collection reports. Get dropout risk scores, fee recovery analysis, and engagement intelligence in under 30 seconds.
6 weeksearly
Dropout Prediction
Before official withdrawal
School-level
Fee Collection Intelligence
Not system average guessing
Module 3avg
Module Cliff Detection
Where 61% of learners quit
+₹1.4Cr/year
Retention Revenue Impact
Per 100-seat program
Real Pain → AI Solves It
Your team faces these every week.
OpsOracle names them and fixes them.
Actual AI output from real education operations data. Upload your report and get this analysis in under 30 seconds.
The Pain
Our MBA program has a 31% dropout rate by semester 2. Academic council wants a root cause but course surveys show everyone is 'satisfied'.
Raw data signal
Cohort 2023: 124 enrolled | 38 dropped by S2 | Avg attendance S1: 84% | Avg attendance S2: 61% | GPA dropouts: 2.4 avg | GPA completers: 3.1 avg | Part-time work: 68% of dropouts | Financial aid: 22% of dropouts
OpsOracle AI Output
Survey satisfaction is a lagging indicator — it captures sentiment after dropout decisions are made. Leading signals: attendance declining 23 points from S1→S2 among dropouts (engagement collapse), 68% of dropouts working part-time (financial pressure overcoming commitment), GPA gap of 0.7 points (achievable with targeted support). This is a financial + workload crisis, not academic dissatisfaction.
[THIS WEEK] Action
Implement attendance alert system: trigger counsellor outreach when any student misses 3+ consecutive classes. Expand emergency bursary fund targeting part-time working students. Create a 'working professional' track with evening-only electives for 40% of the curriculum.
Expected impact: Reduce dropout rate from 31% to 15% = retain 20 students/year = ₹1.4Cr in annual fee revenue. Improve NIRF ranking score on continuation metric.
The Pain
Our ed-tech platform has 92% course enrollment but only 11% completion. Investors are asking why engagement is collapsing.
Raw data signal
Enrolled: 48,200 | Started module 1: 41,800 (87%) | Completed module 3: 12,400 (26%) | Completed course: 5,302 (11%) | Avg session: 8.2 min | Forum posts: 0.4/user | Certificate claimed: 4,100 (8.5%)
OpsOracle AI Output
The 92% enrollment number is a vanity metric. The real funnel shows a cliff at Module 3: 87% start, 26% reach Module 3 completion — a 61-point drop-off. Module 3 is the design problem, not learner motivation. Average session of 8.2 minutes indicates mobile consumption in fragmented time slots — learning design is built for desktop 60-min sessions.
[THIS WEEK] Action
Redesign Module 3 into micro-units of 5–7 minutes each. Add social accountability: peer assignments due at Module 3 exit gate. Send re-engagement email with 'Your cohort is ahead' message to users who stall at M3. Add progress certificate at Module 3 to reward momentum.
Expected impact: Lift Module 3 completion from 26% to 45% = 9,200 additional finishers. At ₹2,400 avg certificate revenue, that is ₹2.2Cr in incremental revenue without acquiring a single new learner.
The Pain
We're a K-12 chain with 14 schools. Fee collection rate is 74%. Principal of School 7 says it's because parents are unhappy with quality, others say it's economic.
Raw data signal
School 7: Fee collected 61% | School 9: 91% | School 3: 88% | System avg: 74% | School 7 NPS: 32 | School 9 NPS: 71 | Parent complaints School 7: 28/month | School 7 teacher attrition: 6 in 8 months
OpsOracle AI Output
This is not an economic issue — School 9 has 91% collection in the same catchment. School 7's 61% rate directly correlates with NPS of 32 (unhappy), 28 complaints/month, and 6 teacher attritions in 8 months. Teacher instability → teaching quality drop → parent NPS decline → fee withholding. It is one failing school masking average system health.
[THIS WEEK] Action
Emergency intervention at School 7: Principal review + teacher retention audit in 48 hours. Hire substitute qualified teachers for unstaffed positions this week. Set up parent advisory committee with monthly fee + quality review. School 7 to move to monthly fee collection (quarterly creates resentment during quality concerns).
Expected impact: Lift School 7 collection from 61% to 80% = ₹38L additional revenue this academic year. Prevent 200+ student departures at ₹24,000 average annual fee = ₹48L at-risk retention.
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