Alumni & Placement Analytics

Placement rate analytics stall when offers are logged in personal folders while sanctioned strength lives in the ERP, so program heads cannot compare batch year to batch year with the same definition. Boards ask for placement rate analytics by program before expanding seats, but the answer is rarely ready in one pass.

FireAI aligns enrolled strength, eligible students, placed count, and higher-study or startup exclusions to rules you publish once. Education placement analytics shows placement rate by program and batch year with filters for campus, degree level, and intake cycle so TPOs spot structural dips early.

How FireAI solves the problem: It refreshes as placement sheets and SIS updates land, keeps eligibility rules versioned by batch, and surfaces outliers where a single employer drove the rate versus broad distribution.

What FireAI tracks:

• Placed % of eligible students by program, batch year, and campus
• Year-on-year placement rate analytics with confidence flags on small cohorts
• Higher study, entrepreneurship, and deferred placement tagging
• Time-to-first-offer distribution within the placement season

What you can ask FireAI:

• "Which UG programs fell more than 5 points in placement rate versus last batch?"
• "Show placement rate analytics for CS vs IT by batch year for the last four years"

Salary package analytics lose trust when stipends, internships, and full-time offers sit in one column, or when CTC mixes fixed and variable without a standard definition. Parents and rankings care about median salary trend, but teams only publish a single average that hides compression at the bottom.

FireAI normalizes offer components, flags internship-to-PPO paths, and computes median and average salary package analytics by program and batch. You see salary package analytics trends across years, employer tiers, and roles so curriculum and branding align with real compensation.

How FireAI solves the problem: It applies compensation rules you approve (fixed-only views, variable caps, currency normalization) and keeps audit trails when employers restate offers.

What FireAI tracks:

• Median and mean CTC by program, batch, and role family
• P10, P50, P90 bands for salary package analytics and equity
• Domestic versus international offer splits where data exists
• Inflation-adjusted trend lines for multi-year comparison

What you can ask FireAI:

• "How did median salary for MBA finance change over the last three batches?"
• "Show salary package analytics for product roles versus services roles this year"

Campus recruiter analytics rarely answer which employers drive quality hires versus one-off spikes, or which accounts stopped returning after a bad season. Placement cells track visits in calendars while CRM history lives in inboxes.

FireAI links recruiter accounts to drives, offers accepted, and repeat participation flags. Campus recruiter analytics shows top recruiter contribution by offers, compensation bands, and return rate year on year so corporate relations prioritizes partnerships that scale.

How FireAI solves the problem: It deduplicates parent company and subsidiary names, attributes offers to the visiting account, and scores return rate and average time between drives.

What FireAI tracks:

• Offers and selects per recruiter with trend
• Return rate and gap seasons for campus recruiter analytics
• Concentration risk when a few employers exceed a share of placed students
• Role diversity score per recruiter (engineering, analyst, sales)

What you can ask FireAI:

• "Which recruiters increased offers by more than 20% versus last year?"
• "Who stopped visiting after 2022 but used to be a top five contributor?"

Alumni employment tracking breaks when graduation cohorts never update LinkedIn or when surveys go out without structured follow-up. Institutions need sector mix and mobility to prove long-term outcomes, not only first job.

FireAI blends periodic alumni surveys, employer verification where available, and self-service updates into alumni employment tracking dashboards. You see sector distribution, geography, role progression bands, and year-since-graduation slices for education placement analytics that extend beyond day-zero placement.

How FireAI solves the problem: It respects consent and privacy rules, deduplicates alumni records, and links back to program and batch for consistent alumni employment tracking.

What FireAI tracks:

• Employment share by sector and function for each graduation cohort
• Alumni employment tracking for geography and remote versus on-site
• Response rate and freshness score for survey cycles
• Comparison of first job sector versus current sector where data exists

What you can ask FireAI:

• "What share of our 2018 MBA cohort is now in consulting versus when they graduated?"
• "Which programs show the strongest alumni employment tracking response this quarter?"