Real Estate
Sales & Revenue Analytics
Real estate sales analytics often splits across portal leads, broker spreadsheets, call-center logs, and inventory sheets, so nobody sees lead funnel analysis by source next to site visit booking conversion and aging unsold stock in one place. Marketing funds channels while sales chases closures without a shared view of which sources actually book and which inventory segments stall.
FireAI unifies lead stages, visit appointments, unit availability, and booking registers into real estate sales analytics you can query in chat or scan on dashboards. Leaders track lead funnel analysis by source, site visit to booking conversion rate by project and counselor, inventory aging and unsold unit analysis by configuration and tower, and booking cancellation root cause analysis before revenue recognition and cash collection drift.
The domain is built for real estate sales analytics, lead funnel analysis real estate, site visit booking conversion, inventory aging unsold units, and booking cancellation analysis that sales, marketing, and inventory teams can align on before the next launch or channel review. See how it works: get a demo.
Lead funnel analysis by source
Lead funnel analysis by source breaks when web forms, broker uploads, walk-ins, and paid campaigns land in different tools with inconsistent stage names. Leadership sees headline lead volume without conversion from enquiry to qualified visit, so budget shifts lag real performance by a quarter.
FireAI maps each acquisition source to a standard funnel grain, ties leads to projects and inventory bands, and refreshes stage movement as CRM updates. Real estate sales analytics shows volume, qualification rate, visit scheduled rate, and booking contribution by source so marketing and sales negotiate with the same funnel.
How FireAI solves the problem: It harmonizes source tags, deduplicates where rules allow, and attributes downstream visits and bookings to the originating channel with audit-friendly history.
What FireAI tracks:
- New leads, MQLs, and site visits by source and campaign
- Stage velocity and drop-off between enquiry and booking
- Cost per lead and cost per booking where spend data connects
- Source mix shift week over week and by project
What you can ask FireAI:
- "Which lead sources contributed the most bookings last month versus portal average?"
- "Show lead funnel analysis for paid Meta versus broker referrals for Project East"
Pipeline by source
Site visit to booking conversion rate
Site visit booking conversion suffers when visit logs, counselor notes, and inventory holds sit outside the same timeline as the booking form. Sales managers see visit counts but not which counselors, projects, or inventory bands convert, so coaching and incentives miss the real drivers.
FireAI links visit appointments, no-shows, rescans, unit shortlists, and booking events with counselor and project attributes. Real estate sales analytics surfaces site visit to booking conversion rate by team, tower, price band, and week so operations can fix scheduling, demo quality, or inventory presentation.
How FireAI solves the problem: It ties CRM visit objects to outcome flags, normalizes duplicate visits, and highlights segments where conversion diverges from portfolio average without waiting for manual pivot tables.
What FireAI tracks:
- Visits completed versus scheduled by counselor and site
- Conversion within 7, 14, and 30 days of first qualifying visit
- Inventory band attached to each visit and booking
- Follow-up task completion versus conversion lift
What you can ask FireAI:
- "What is site visit to booking conversion for premium versus mid-segment this quarter?"
- "Which counselors improved visit conversion after the last training cohort?"
Ask FireAI about visits
See how your team can ask questions in plain language and get instant analytics answers.
Inventory aging and unsold unit analysis
Inventory aging and unsold unit analysis is painful when availability, holds, and broker blocks live in different tabs and aging starts from inconsistent dates. Sales pushes fresh launches while slow movers hide in aggregate occupancy charts.
FireAI anchors each unit to launch date, last price change, hold history, and booking attempts where data exists. Real estate sales analytics shows days on market bands, configuration-level aging, and concentration of unsold stock by tower or facing so pricing and channel tactics target the right SKUs.
How FireAI solves the problem: It refreshes a single inventory spine with rules for soft holds, cancellations returning to stock, and broker exclusives, so inventory aging unsold units reporting matches what on-ground teams can actually sell.
What FireAI tracks:
- Units unsold beyond 90, 180, and 365 days with list price trend
- Aging by BHK, floor band, and facing where attributes exist
- Share of inventory under active negotiation or hold
- Markdown or scheme attachment rate on aged stock
What you can ask FireAI:
- "Which configurations drive the oldest unsold inventory in Project North?"
- "Show inventory aging after the last price revision wave"
Unsold inventory health
Booking cancellation root cause analysis
Booking cancellation root cause analysis fails when cancellations are logged as a status change without structured reasons, loan declines, or competitor wins buried in notes. Revenue forecasts and inventory returns wobble without knowing whether cancellations cluster on price, approval, or delivery trust.
FireAI classifies cancellation events using reason codes, counselor notes tags, and optional bank outcome fields you connect. Real estate sales analytics shows cancellation rate by project, inventory band, buyer segment, and month with driver themes so sales and CRM can intervene earlier on repeatable failure modes.
How FireAI solves the problem: It enforces a lightweight reason taxonomy at cancel time, links reinstatement or resale outcomes when units return, and surfaces booking cancellation analysis in chat and causal views for leadership reviews.
What FireAI tracks:
- Cancellation count and rate versus gross bookings
- Top reason families (finance, personal, competitor, pricing)
- Time from booking to cancel and deposit forfeiture patterns
- Units returned to stock and resale velocity
What you can ask FireAI:
- "What drove the spike in cancellations for subvention buyers in Q2?"
- "Show booking cancellation root cause mix for sea-facing inventory"