Every hotel with a PMS (Property Management System) is generating thousands of data points every single day. Check-in times, room preferences, cancellation patterns, upsell acceptance rates, F&B spend, loyalty tier distribution — it's all there, sitting in a database that most operations teams never open.
After working across multiple Swiss luxury properties, I've seen the same pattern repeatedly: the data exists, the questions exist, but the bridge between them doesn't. Here's what's typically available — and what you can actually do with it.
What your PMS already tracks
Most hotels use Opera, Protel, or Mews. All of them capture far more than just reservations. Here's a snapshot of what's typically available:
| Data source | What it contains | Typical use |
|---|---|---|
| Reservation records | Lead time, channel, rate code, room type, length of stay | Revenue management |
| Guest profiles | Nationality, repeat stay history, preferences, complaints | Mostly ignored |
| Housekeeping logs | Clean times, late checkouts, no-shows | Mostly ignored |
| F&B point of sale | Per-cover spend, peak hours, menu item performance | Occasionally reviewed |
| Cancellation logs | Cancellation timing, reason codes, rebooking rate | Rarely analysed |
The three questions most hotels can't answer — but should
1. What is our true cancellation cost?
Most revenue reports show net revenue after cancellations. But the real cost includes the rebooking rate, the revenue gap between the original reservation and the replacement, and the operational cost of uncertainty. A proper cancellation analysis — segmented by lead time and channel — often reveals that OTA bookings cancel at 3× the rate of direct bookings, with a much smaller revenue replacement.
2. Which guest segments are actually profitable?
A guest paying CHF 400/night who books direct, stays 4 nights, and spends CHF 80/day in F&B is more valuable than a guest paying CHF 500/night via OTA who orders nothing and cancels 20% of the time. Calculating true guest profitability requires joining PMS data with F&B POS and distribution cost data — something almost no hotel does today.
3. What drives repeat visits?
Guest profile data in PMS systems contains repeat stay flags and preference histories. A simple cohort analysis on returning guests vs. one-time visitors usually reveals clear patterns: room type, booking channel, and length of stay are the three strongest predictors of return. Yet most hotels market to everyone equally.
Why this data goes unused
It's rarely a technology problem. Most PMS systems have export functions or APIs. The real barriers are:
- No one owns the data — revenue managers focus on pricing, front office on operations, GM on costs. Nobody is responsible for analysis.
- Excel fatigue — manual exports into spreadsheets don't scale and break when someone leaves.
- No visualisation layer — raw PMS reports are dense and unreadable for operational decisions.
Where to start
If you're a GM or revenue manager reading this, you don't need a data warehouse to start. Three practical steps:
- Export 24 months of reservation history from your PMS into a flat file
- Build a single Power BI report with occupancy, RevPAR, cancellation rate, and lead time by channel
- Review it weekly with your revenue and front office teams — the questions it raises will tell you exactly where to dig next
The data is already there. It just needs someone to ask the right questions.
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