Revenue management is usually presented in one of two ways: as software that decides on its own, or as a sales instinct that nobody can explain. WisDom RM was born from a third view, where two fields of study meet: mathematics and hotel management.
Mathematics asks: “What does this number really say, and what does it not say?” Hotel management asks: “What is behind it?” A guest, a team, a cost. The ideas below sit where the two questions meet. On our homepage they fit in a few lines; here we unfold them.
An independent hotel is not a small chain
The classic definition is the right room, for the right guest, at the right rate, at the right time. It was written for airlines and large chains, where statistical methods work because they rest on thousands of bookings.
In a hotel with a few dozen rooms, every night is a small sample. Three cancellations move occupancy by several points, which in a chain would be noise. If you give it the tools of a chain, it gets the weaknesses of volume without the volume. But it has something a chain does not have: a manager and a front desk who know which guest comes back, and why. It has less data and more knowledge.
And it has a measure that every hotel management student learns early: revenue is not profit. Every room sold has its own cost, and high occupancy does not make that cost disappear.
A full hotel at the wrong rate is tired, not successful.
A number says only as much as it can bear
Mathematicians do not trust numbers more than other people do. They trust them exactly as far as the numbers can bear.
This has simple consequences:
- The unknown is not zero. A cost that was never recorded is not a cost of zero. A month with a “better margin” may simply be the month in which less was recorded.
- One extreme night pulls the average. The median is not affected.
- Seven nights are not a rule. Two more nights can change the conclusion.
For the future we never give a single number, always a range. The final occupancy forecast in WisDom RM is built from the booking pace of your own similar nights, not from a generic curve. It shows their middle 50%, with the median at the centre. When the history is not enough, it gives no forecast, because a forecast built on invented numbers would be false precision. And it checks itself: every night that has already closed is forecast again as if the result were unknown, and you see how often the actual result fell inside the range.
In the first year, with no last year to compare against, the honest answer is often “I don’t know yet”. And every number states where it came from, inside the same sentence. You will repeat it to the owner, and a footnote is never read aloud.
The two mistakes do not cost the same
In decision theory it is not enough to ask how likely a mistake is. You also ask what it costs. In room pricing the two mistakes are unequal. A wrong rate cut loses revenue for good, because a room sold cheaply cannot be sold again. A wrong decision to wait only costs time. When mistakes do not cost the same, caution is not cowardice. It is arithmetic.
That is why a tool that always has an answer is not smart; it is talkative. When bookings are slow but you do not know what the hotels around you charge, the right answer is “I cannot tell whether the rate is to blame”. In that case the WisDom Suggestion does not propose a cut without a basis.
Here hotel management adds a cost that no model measures. Even when the overbooking statistics “work”, you will never read “sell 49 rooms out of 47”. In an independent hotel, the guest you walk knows your name. It is a blow to your reputation, not a statistic.
What does not fit in an equation
A model sees that Friday filled up. It does not know that it filled because of a wedding that will not happen again. It does not know that the holiday falls on a Tuesday this year, or that the competitor across the street is closing for renovation. The front desk knows these things: they hear them, they see them, and they usually write them on a piece of paper that gets lost at the shift change.
For a mathematician this is not romanticism. It is missing data: if the information is not recorded, no calculation can find it later. That is why our order is human → technology → human. The team records what exists in no file, the tool carries the calculations, and the manager decides. Recording is not bureaucracy. It is the quality of the data, and every analysis is only as honest as that quality.
The calculation suggests, the person signs
“Automatic” tools change the rate and tell you afterwards. We believe the opposite: the rate is a decision with a signature, and the signature belongs to the person who will pay for the mistake.
A calculation that does not show its steps cannot be checked. You can only obey it or ignore it. That is why the WisDom Suggestion writes its “why” next to the number: first the cause, then the basis, and what is missing. The decision goes into the Rate Sheet only by your hand, and from there you pass it on to your channel manager.
“I change nothing” is also a decision. It is worth writing down why you kept the rate, just as you would write down why you changed it. In three months you will not remember. And a decision without its reason cannot be checked, so it cannot teach you anything either.
Facts, not judgements about people
A tool that records shifts can easily become a tool for watching people. That is not what we wanted to build.
Hotel management knows that a team holds together through trust, not through scores. Mathematics adds one more thing. A score for a person, based on a few shifts and without knowing what happened during them, is noise. It is not an assessment; it is prejudice with decimals. That is why we do not judge people, because we cannot, we should not and we do not know. We give facts, with their sample stated. The night auditor is the hotel’s best source of data, not the suspect.
Simple. Deep. Human.
I studied mathematics and then hotel management, and I have worked the night shift for many years. Every night, when I closed the day, I had the numbers of a whole day in front of me: occupancy, rates, cancellations, whatever the team had seen. And every night the numbers just stayed there.
The mathematician in me found this hard to accept. This was data that existed, that someone had worked to collect, and that never reached the rate decision, so it never reached the profit either. The hotel person in me knew one more thing: that behind every number there is a guest and a team, and that no formula sees them.
I built WisDom RM where the two views meet: the numbers work for the decision, as far as they can bear and no further, and the decision stays with the person. We did not want to replace the manager. We wanted to give the manager what the big players have, the analysis, and to keep what they lost: human judgement. The person keeps the last word; the tool gives them the time to use it.
Mathematician with studies in hospitality management and years of night shifts in hotels. WisDom RM — and this blog — were born on the night shift.
LinkedIn ↗