Marketing & Lead Generation Analytics

Digital lead analytics education breaks when platforms report form fills while CRM shows duplicates, test leads, and counselor-added rows in the same bucket. Cost per lead looks cheap until you exclude junk and measure cost per admission on the same cohort.

FireAI normalizes source and medium from UTMs, campaign names, and offline import tags, then joins to qualified enquiry rules you define. Education marketing analytics shows volume, qualification rate, and CPL by channel with optional campus and program slices so paid search, Meta, portals, and content syndication compare on quality, not only volume.

How FireAI solves the problem: It applies dedupe and qualification logic once, versions definitions by intake cycle, and carries the same lead keys into application and enrollment so CPL and cost per admission stay on one lineage.

What FireAI tracks:

• Raw versus qualified lead counts by channel, week, and campaign
• CPL and rolling cost per qualified lead after spend allocation rules you set
• Conversion to application and to enrollment by channel for the same intake
• Geo and device splits where tagging supports them without breaking privacy norms

What you can ask FireAI:

• "Which digital channel had the lowest CPL but the worst application rate this month?"
• "How did Meta lead volume move versus Google after we changed the landing page?"

Campaign roi education needs the full path from impression or click proxies through enquiry, application, offer, and enrollment. Spreadsheets that stop at leads overstate winners and hide programs or regions where the same campaign fills the CRM but not seats.

FireAI maps campaign and ad set identifiers to CRM batches and enrollment IDs where connectors allow, and supports manual campaign codes for offline bursts. Education marketing analytics shows funnel conversion by campaign, creative, or intake wave so you retire underperforming flights while scale is still adjustable.

How FireAI solves the problem: It keeps a campaign dimension on every stage transition you track, supports blended attribution windows you configure, and surfaces drop-off after lead creation so creative and landing fixes target the real leak.

What FireAI tracks:

• Lead to application and application to enrollment rates by campaign
• Cost per admission and marginal enrolls per incremental thousand rupees of spend
• Time from first touch to enrollment for campaign-sourced students
• Comparison to organic and referral baselines in the same cycle

What you can ask FireAI:

• "Which active campaigns beat our portfolio cost per admission target?"
• "Where do campaign-sourced leads stall after qualification?"

Brand awareness analytics education fails when annual surveys sit in slide decks and never connect to media flights, open days, or competitor moves. Leadership asks whether unaided recall moved; marketing only has a static PDF.

FireAI ingests survey exports with wave, cohort, and geography fields you define, and can join coarse campaign calendars for context. Brand awareness analytics education surfaces aided and unaided recall, consideration, and preference trends with significance notes where sample size allows, alongside digital lead analytics education for the same windows.

How FireAI solves the problem: It standardizes wave labels, carries filters for student versus parent respondents, and lets you overlay intake performance so brand lifts meet enrollment reality.

What FireAI tracks:

• Wave-on-wave movement in awareness and consideration metrics
• Segment cuts by city tier, program interest, and alumni parent status where collected
• Correlation prompts between survey shifts and lead quality, not causal claims without design
• Open-text theme counts when you enable safe processing policies

What you can ask FireAI:

• "Did unaided recall improve in our priority cities after the outdoor burst?"
• "How does parent consideration for STEM programs compare to last wave?"

Open day and education fair ROI often stops at footfall and brochure counts. Finance and the board ask for enrolled students and margin per rupee of event cost; field teams only have attendance sheets.

FireAI links event registrations, check-ins, counselor follow-up tasks, and downstream applications to a single event or series code. Education marketing analytics shows attendance, qualified lead creation, application rate, and enrollment attributable to each open day or roadshow, with cost per admission for the event cohort.

How FireAI solves the problem: It applies consistent attribution rules (e.g., 30-day window, first event touch) and surfaces no-shows and late cancellations so next year’s capacity and staffing plans use data.

What FireAI tracks:

• Registrations, show rate, and qualified leads per event
• Application and enrollment conversion for event-tagged cohorts
• Fully loaded cost per attendee and per enrolled student
• Comparison to digital-only cohorts in the same intake week

What you can ask FireAI:

• "What was the ROI of the metro open day versus the regional fair series?"
• "Which events produced high attendance but weak application follow-up?"