Numbers first. Names on request.
Every figure on this page is a real client outcome, measured against a baseline agreed in writing before the deployment began, and anonymized because chains don't publish labor costs under their flag. Illustrative numbers, where we use them elsewhere, are always labeled "worked example". Here: only measured results.
250-room city hotel, Bangkok · Cloudbeds
Breakfast covers swung 190–420 a day with tour groups; the roster was flat at 11.
Read how it was measured →Resort, Bali · Mews
Three outlets ran full rosters into a 48%-occupancy monsoon January.
Read how it was measured →Business hotel, Tokyo · OPERA Cloud (OHIP)
Golden Week departure waves doubled room-turn load; the 30-day manual forecast missed them.
Read how it was measured →3-property group, Singapore · Mews
81% occupancy, hiring capped, queues from 17:00 to 20:00 while mornings sat idle.
Read how it was measured →All figures: measured results, properties anonymized at clients' request; metrics verified against an agreed baseline.
Baseline → optimize → same report, both sides
1. Baseline in writing
Trailing labor CPOR, overtime, schedule-build time and service coverage, agreed before anything changes.
2. 90-day pilot
Two departments live on demand-matched rosters; managers approve every schedule. The method →
3. Monthly measurement
The same numbers, against the same baseline, delivered to your side and ours. If the saving is not there, you see that too.
Questions prospects ask about these numbers
Why are the properties anonymized?
Hotel chains rarely allow labor-cost numbers to be published under their flag, cost structure is competitively sensitive. We publish the numbers and the method instead, and clients will speak to serious prospects in reference calls.
How is the baseline set?
Before the pilot starts we agree, in writing, the trailing period and metrics: labor cost per occupied room, overtime hours, schedule-build time and service coverage. Results are the delta against that baseline, seasonally adjusted where relevant, the same report goes to both sides.
What if the saving is not there?
Then the monthly report says so, and you keep it. The pilot is fixed-scope precisely so that a negative answer costs a known amount, that is the deal that makes the positive numbers credible.
Are these results typical?
They sit inside the published ranges for demand-matched scheduling: 6-8% of labor cost (Unifocus, conservative) to 10-20% (Gartner). Your number depends on how far your current roster is from the demand curve, the calculator gives a first estimate in two minutes.
Or estimate first: the labor savings calculator takes two minutes.