Admissions & Enrollment Analytics

The admissions funnel is the control tower for intake: without stage-wise counts and conversion rates, you cannot see whether the problem is weak top-of-funnel, slow follow-up, or offer acceptance. Most institutions export fragments from CRM and SIS and rebuild the funnel in Excel every week.

FireAI standardizes stage definitions and timestamps so education admissions analytics shows enquiry, qualified lead, application submitted, test or interview, offer, and enrollment in one view. You see conversion rate at each step, time-in-stage, and where the largest leaks appear by campus, program, or intake cycle.

How FireAI solves the problem: It joins CRM events to application and enrollment records with rules you own, so funnel definitions stay consistent and historical trends compare like for like across cycles.

What FireAI tracks:

• Volume and conversion % by funnel stage, week, and intake
• Median and P90 days from enquiry to enrollment for faster diagnosis of delays
• Stage backlog and owner workload to balance counselor follow-up
• Cohort view by program, quota, and reservation category where data exists

What you can ask FireAI:

• "Where did the largest week-on-week drop in conversion happen this intake?"
• "Show me enquiry-to-offer time for PG vs UG this quarter"

Lead source tracking education programs need ties spend and counselor effort to enrolled students, not just lead volume. A cheap digital lead is expensive if it never converts, and referral channels often win on quality with minimal cost per seat.

FireAI attributes enquiries and downstream enrollments to source tags in your CRM with optional UTM join from campaigns. It computes cost per lead, cost per application, and cost per enrolled student by channel for education admissions analytics that marketing and admissions can align on before budgets are committed for the next cycle.

How FireAI solves the problem: It rolls up to enrolled student as the success event, includes offline walk-in and school referral tags in the same model, and supports blended views when a student has multiple touchpoints before conversion.

What FireAI tracks:

• Enquiries, applications, and enrollments by source and campaign
• ROI and payback on paid digital versus organic and partner referral
• Seasonal mix shifts (e.g., education fair months vs always-on search)
• Source quality score: conversion rate to offer, not just to application

What you can ask FireAI:

• "What was our true cost per enrolled student for Meta versus Google last quarter?"
• "Which referral schools sent the highest offer-to-acceptance rate?"

Application to seat conversion by program is how academic leadership sees whether quotas, cut-offs, and waitlists are working. A program with many applications but low enrollment may signal pricing, timing, or perception issues; one with high yield may justify capacity expansion next year.

FireAI maps application IDs to seat allocation, waitlist status, and final enrollment in your SIS or admissions module. Education admissions analytics shows acceptance rate, yield, and overbooking risk by program, mode, and campus so you can adjust fee deadlines, interview slots, and communication templates before seats stay vacant.

How FireAI solves the problem: It refreshes as applications and offer responses update, and highlights programs where yield diverges from plan so intake committees act while alternate candidates on the waitlist are still available.

What FireAI tracks:

• Application volume, offer count, and enrollment by program and batch
• Yield rate and compare to plan and to prior year same week of cycle
• Waitlist movement velocity and unclaimed seat inventory
• Cut-off and entrance score bands versus enrollment to spot mismatch

What you can ask FireAI:

• "Which programs are below plan yield with two weeks to close?"
• "Show application-to-enrollment for MBA vs BBA this intake"

Scholarships and fee waivers move seats, but they also shift margin and can attract applications that do not complete payment if the rules are unclear. Finance needs fee payment analytics alongside admissions to see realization and aging; academics need to know whether merit waivers are filling capacity in priority programs or cannibalising full-fee acceptances.

FireAI links scholarship bands, early-bird offers, and sibling or alumni discounts to application stage and final fee paid. The view supports education admissions analytics for leadership to compare incremental enrollment against concession cost, and to model scenarios where eligibility criteria tighten or expand.

How FireAI solves the problem: It shows waiver cost per enrolled student in the same cohort view as full-fee students, and tags outcomes when a large waiver still results in no payment so policies can be refined without a separate audit every cycle.

What FireAI tracks:

• Enrolled count and revenue by fee band, waiver type, and program
• Net tuition after waivers vs budget for the intake
• Payment completion rate for students on partial vs full fee paths
• Comparison of pre- and post-waiver offer acceptance rates in A/B time windows you define

What you can ask FireAI:

• "What share of our enrolled students is on a merit waiver this intake?"
• "Did the early-bird fee deadline improve fee realization versus last year?"