Hospitality
Hospitality Guest Experience & CRM Analytics
Hospitality guest analytics breaks when guest satisfaction analytics live in a survey vendor, repeat guest rate hotel math sits in a spreadsheet, review sentiment hotel comments stay inside each OTA login, and guest complaint analytics end in a shared inbox with no TAT. Leaders see a strong TR score on one OTA and a poor Google trend without knowing if the same stay types drive both.
FireAI normalizes post-stay scores, PMS stay history, loyalty status, and scraped or API-fed reviews so guest satisfaction analytics compares outlets, room types, and journey stages on one calendar. Repeat guest rate and LTV links nights, revenue, and channel to segments you can retarget. OTA review sentiment analysis classifies text by theme (noise, check-in, housekeeping) and tone before ratings average away the story. Complaint category and resolution TAT tracks intake to closure with owner and root cause for service recovery and training.
The domain covers guest satisfaction score by outlet and room type, repeat guest rate and LTV, OTA review sentiment analysis, and complaint category and resolution TAT, through chat, dashboards, and causal chains guest and revenue leaders can use in weekly stand-ups. See how it works: get a demo.
Guest satisfaction score by outlet and room type
Guest satisfaction analytics often report one property NPS while restaurants, spa, and room product diverge. Room type level scores hide when club floors delight but standard wings drag the average, or when a recent soft renovation shifts sentiment only on certain floors.
FireAI maps survey responses to stay records and outlet spend so guest satisfaction score by outlet and room type shows where scores and comment themes concentrate. You compare business versus leisure, direct versus OTA, and length of stay in the same view hospitality guest analytics can defend in owner meetings.
How FireAI solves the problem: It links surveys to operational segments your PMS already uses so a satisfaction question in chat returns the same cuts as the dashboard card.
What FireAI tracks:
- NPS or CSAT by outlet, room type cluster, and tower or wing where master data allows
- Verbatim theme tagging tied to score deciles with trend versus last month and last year
- Check-in and check-out day satisfaction split for front office follow-up
- Correlation of score to complaint volume and to repeat booking intent when captured
Operations uses guest satisfaction analytics inside hospitality guest analytics to prioritize capex, staffing, and SOP changes with evidence.
Satisfaction by segment
Repeat guest rate and LTV
Repeat guest rate hotel reports count return stays without tying revenue, channel cost, and upsell. Loyalty ID coverage gaps mean a repeat OTA booker looks like a new name while true repeat guests who book direct are understated in the CRM.
FireAI stitches PMS, CRS, and loyalty or CRM keys with fuzzy match rules you approve so repeat guest rate and LTV tracks cohorts and segments consistently. LTV can blend room, F&B, and spa for guests who meet your definition of repeat.
How FireAI solves the problem: It answers who comes back, how often, and what they contribute in one definition marketing and revenue management both trust.
What FireAI tracks:
- Repeat stay rate and nights per year by acquisition channel and segment
- LTV bands with contribution after discounts and estimated acquisition cost for that guest
- Time between stays and early churn signals (no return in expected window)
- Direct share among repeats versus one-time stayers for loyalty program tuning
Marketing uses repeat guest rate hotel and LTV inside hospitality guest analytics to fund retention, member pricing, and OTA to direct conversion tests.
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OTA review sentiment analysis
Review sentiment hotel work becomes a read of star averages while text holds noise, staff praise, and recurring defects. Comparing across Booking.com, Google, and TripAdvisor manually misses velocity when a one-week problem spikes mentions.
FireAI ingests review text and ratings with property and outlet tags so OTA review sentiment analysis surfaces themes, sentiment score, and volume trends together. You see whether complaints concentrate on value, sleep quality, or Wi‑Fi with month-over-month change.
How FireAI solves the problem: It turns text into metrics leaders can put next to ADR and occupancy instead of a quarterly consultant readout.
What FireAI tracks:
- Sentiment index and mention rate by theme (service, room, F&B, location)
- Review volume and average rating by OTA and language
- Correlation of sentiment drop to OTA rank position or search impression proxies where you connect data
- Guest journey stage when review was written (on stay vs post stay) when timestamp allows
Revenue and e-commerce use review sentiment hotel views inside hospitality guest analytics to fix offer pages, retrain teams, and brief recovery scripts.
Review sentiment and themes
Complaint category and resolution TAT
Guest complaint analytics stuck in a log sheet rarely tie to PMS, payment, or guest history. Resolution TAT is averaged across trivial and severe cases, so the GM does not know if housekeeping follow-up missed the 4-hour SOP on VIP stays.
FireAI classifies cases by category, room, outlet, and owner with timestamps from first touch to closure. Complaint category and resolution TAT shows mean and percentile TAT, reopen rate, and compensation cost so service recovery is measurable.
How FireAI solves the problem: It gives leadership one place to see complaint load, speed, and outcome instead of a weekly email thread.
What FireAI tracks:
- Category mix (reservation, noise, billing, maintenance, F&B) with trend
- TAT in hours to first response and to guest-confirmed resolution
- Reopen and escalation count by department and shift
- Optional link to post-resolution survey score for closed loop
GMs and quality leads use guest complaint analytics inside hospitality guest analytics to retrain, adjust staffing, and update preventive maintenance triggers.