Education · Case study
EdTech admissions and tutor hiring: modelled business case
A modelled projection of what Felina could change for a large EdTech admissions team handling about 1,20,000 leads a month. These are projections from a business case, not results from a deployment.
- Evidence type
- Projection
- Organisation
- A large Indian EdTech platform (not named)
- Relationship
- Prepared for a prospective customer — not a deployment
- Published · last updated
- 18 October 2026 · 18 October 2026
Projected by a business-case model. These are not measured results, and no deployment has taken place.
The situation
Context, baseline and approach
- Business context
- Admissions calls are driven by parents, peak sharply around exams and results, and span many languages. When calls go unanswered, parents rarely call back.
- Baseline operating conditions
- As stated in the source document (not independently verified). Admissions: about 1,20,000 leads a month; about 64% of calls answered and 36% missed or delayed; 3–4 minute waits on peak days; 4.5-minute average handling time; about 38% first-call resolution; 8–9% of leads becoming paid enrolments. Tutor recruitment: about 25,000 applicants a month; about 70% contacted and 42% successfully connected; 8–10 minute screening; recruiters handling 25–30 calls a day; 50–55% shortlisting accuracy; about 30% of applicants lost without follow-up.
- Deployment scope and workflow
- Proposed: Felina as the first voice layer for admissions enquiries and tutor-recruitment screening, integrated with the organisation's CRM and telephony, with counsellors and recruiters handling the conversations Felina hands over.
- Deployment status
- Not deployed. This is a business case prepared for a prospective customer.
- Measurement methodology
- Projected 'after' values are outputs of Felina's business-case model for this organisation. The model applies assumed improvements in answer rate, wait time and follow-up to the stated baseline and the same monthly lead volume. Relative changes on this page are calculated from the stated figures; where a range is given, the midpoint is used, and where a figure is a bound (e.g. 'below 2%'), the change is shown as 'at least'.
Projection — modelled business case
Before and after — as projected
Changes are calculated from the stated figures. Percentage metrics show the change in percentage points (pp) and as a relative change.
| Metric | Before | After | Change |
|---|---|---|---|
| Calls answered | Before 64% | After 98% | 34 pp higher · 53% higher |
| Missed or delayed calls | Before 36% | After below 2% | at least 34 pp lower · at least 94% lower |
| Average wait on peak daysBefore value uses the 3.5-minute midpoint. | Before 3–4 min | After under 5 sec | at least 205 sec lower · at least 98% lower |
| Average handling time | Before 4.5 min | After 3.2 min | 1.3 min lower · 29% lower |
| First-call resolution | Before ~38% | After 67% | about 29 pp higher · about 76% higher |
| Lead-to-enrolment conversionMidpoints of the ranges. | Before 8–9% | After 14–15% | about 6 pp higher · about 71% higher |
| Paid enrolments per monthSame lead volume (~1,20,000 a month). | Before ~10,200 | After ~17,400 | about 7,200 higher · about 71% higher |
| Cost per enrolmentStated by the model. The source document doesn't include the cost inputs, so this figure can't be recalculated. | Before — | After — | 55% lowerAs stated by the source; not recalculated |
| Time to hire a tutorTutor recruitment. The source's summary table states −60%; 15 to 5 days is a 67% reduction. | Before 15 days | After 5 days | 10 days lower · 67% lower |
| Tutor applicants contactedTutor recruitment, about 25,000 applicants a month. | Before 70% | After 100% | 30 pp higher · 43% higher |
| Successful connects with applicantsTutor recruitment. | Before 42% | After 74% | 32 pp higher · 76% higher |
| Average screening timeTutor recruitment. Midpoints of the ranges. | Before 8–10 min | After 4–5 min | about 4.5 min lower · about 50% lower |
| Shortlisting accuracyTutor recruitment. Midpoints of the ranges. | Before 50–55% | After 78–80% | about 26.5 pp higher · about 50% higher |
| Applicants lost without follow-upTutor recruitment. | Before 30% | After below 7% | at least 23 pp lower · at least 77% lower |
| Recruiter efficiencyStated by the model. The source doesn't show how this was calculated. | Before — | After — | 2.5 times higherAs stated by the source; not recalculated |
Projection for a typical month at the stated lead volume. No measurement period applies — nothing has been measured.
Read before relying on these figures
Limitations, assumptions and source
- Limitations and assumptions
- Projections depend on the model's assumptions, on CRM and telephony integration, and on the stated baseline, which has not been independently verified. The source document lists more languages than Felina currently offers; this case only assumes what Felina supports today (English and Coimbatore Tamil). Actual results would need to be measured in a deployment.
- Source and evidence
- Business-case document prepared by Billianceai for the prospect (5 pages). Baseline figures are the prospect's operating figures as stated in that document.
- Disclaimer
- Projected by a business-case model. These are not measured results, and no deployment has taken place. Outcomes for your business depend on your call volumes, workflows, integrations and data.
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