The optimal mix — why 100% occupancy isn't the goal

Monday morning Adam walks into Daniel’s office, the monthly report in hand: “This is fantastic, Daniel! Last month, 96% average occupancy. We’ve never been this full.”

Daniel smiles for a moment, then nods: “Yes, the hotel is filling up. But our TRevPAR that same month is 9% lower than a comparable month last year — when we ran at 88%. This isn’t a success. This was selling blind.”

Adam sets down his coffee. “But how can we be worse off at 96% than at 88%?”

And this is exactly the question that starts yield thinking. A hotel owner’s first reflex is naturally that the fuller the hotel, the better. That’s a shop reflex. A revenue manager, though, has to learn that a full hotel is not the same as maximum revenue — and the gap between the two is often a double-digit percentage.

What “the optimal mix” is

The optimal mix is a piece of jargon for the question: for a given night, how do our 80 rooms split across the different price levels and segments so that total revenue is maximized?

Let’s look at three possible answers for the same 80-room Hotel Peaqplus City on a given Saturday night. Each cell shows how many rooms sell in that segment:

Two things stand out:

• Version “B” — at 94% occupancy it produces EUR 685 more revenue than “A” at its full 100%. More revenue despite five empty rooms.
• Version “C” — at 87%, with 10 empty rooms, it still earns more than the 100% “A.” Here the higher-TRevPAR segments (direct + wellness package) pull the value up.

This is not a trick. It’s a real business decision that every city hotel’s revenue manager makes daily. The question is never “should we fill the hotel,” but what composition of capacity usage produces the most revenue — even if that means less than 100%.

Why a “full hotel” can collapse

Version “A — Full at any price” earns less because, to reach 100%, it had to take on cheap OTA bookings. 30 rooms at EUR 78 = EUR 2,340. The same 30 rooms in version B would have gone at direct EUR 105 or wellness package EUR 120. The difference is EUR 800–1,200 on those 30 rooms on that single night alone.

So version “A” filled capacity with the cheap OTA segment before the higher-value direct and wellness bookings could arrive. The cheap segment “crowded out” the expensive one — this is the phenomenon of displacement.

This is hard to grasp by a shop analogy, because a shop can put another bottle of shampoo on the shelf if the first sold out on discount. A hotel cannot. The 80th room is the 80th room — and if you sold it at EUR 78 to the OTA because you filled capacity on reflex, then the direct guest who arrives later and would have paid EUR 105 finds no room. That EUR 27 is lost forever.

The displacement concept gets a deeper treatment in lesson 40 (Group displacement analysis). But it’s already important to understand that order matters: a hotel’s capacity is valuable, and whoever fills it first gets the “shelf space” — even if it’s the lower-paying guest.

Opportunity cost — the “what if instead”

The mathematical sibling of displacement is opportunity cost: the value of the other option you have to give up in order to keep the current one.

A concrete situation: in front of Daniel is a 25-room group inquiry for a Saturday night, at EUR 75 / room, six weeks before check-in. Should he accept it?

If occupancy thinking dominates: “25 rooms × EUR 75 = EUR 1,875 of guaranteed revenue — of course!”

If opportunity-cost thinking dominates:

• What would these same 25 rooms be if we didn’t sell them to the group and waited for transient guests?
• Last year on the same Saturday the hotel’s transient ADR was EUR 115, and the hotel filled to 97%.
• 25 rooms × EUR 115 = EUR 2,875. Plus the TRevPAR effect (transient spends more on F&B than a group with its own contracted catering) — about +EUR 25 / room × 25 = EUR 625.

With the group: EUR 1,875 + low F&B spend ≈ EUR 2,250. Without the group, waiting for transient pickup (based on last year’s pattern): ≈ EUR 3,500.

So the opportunity cost is about EUR 1,250. That’s the “price” of choosing the “safe money” over the “probable more money.”

And here’s the key: this doesn’t mean the group should always be rejected. Opportunity cost is a calculation that becomes an input to the decision — not the decision itself:

• Risk tolerance: if your financial position is tight, EUR 1,875 of guaranteed money can be worth more than EUR 3,500 of probable money. A small hotel owner often picks the former.
• Pickup uncertainty: if that Saturday night is not typical (no event in the calendar, the compset signals weakness), the transient pickup isn’t certain. Opportunity cost only holds if the higher-paying guest would actually come.
• At the hotel level: if the hotel’s value rests on consistent high occupancy (an investor narrative), sometimes you have to accept lower-margin deals too.

The point is opportunity cost as a thinking framework: the “safe money” is never free — there’s always an alternative you give up for it.

The “marginal room” idea

A deeper building block is the marginal room concept. It asks: how much is the next one room worth to us — the 51st, the 71st, the 79th?

Hotel Peaqplus City, a Saturday evening in October, 6 p.m. Occupancy is 60 / 80 = 75%, 20 rooms still empty. A walk-in arrives, offering EUR 80. Do you accept?

• For the 61st room (the next one): probably yes — but it’s worth weighing. The pickup trend for past Saturdays says, on average, another 4–6 walk-ins and late bookings will come, and there’s still plenty of capacity for higher-priced bookings. The EUR 80 is “certain” now.
• For the 79th room (the second-to-last): almost certainly yes. Even if the marginal guest pays only EUR 35, that’s still +EUR 35 (minus a little extra cost). The alternative: EUR 0.

The value of the marginal room falls as we approach the capacity limit (because there are fewer alternative guests we could serve). But it stays positive all the way to the last room — provided the marginal cost is low (we covered this in lesson 2).

This means: the last 1–2 rooms are almost always worth selling, even “cheaply.” But in the middle (say, the 41st room of 80, when 39 are still free) it’s worth being selective — waiting for higher-paying guests, because there’s still time and capacity. The revenue manager doesn’t decide this by feel: it’s precisely the pickup trends and booking curves (lessons 10 and 37) that give the answer.

Back to Adam’s question

Adam’s question was: “But how can we be worse off at 96% than at 88%?”

You can now translate it into a revenue manager’s answer:

• “Worse at 96%, because to reach 96% we took on cheaper segments — OTA discount, last-minute promo, dump rate.”
• “The 88% back then, last year, was a mixed mix: 30% direct, 15% wellness package, 20% corporate, 23% OTA standard. The larger share of higher-value segments lifted the average rate by EUR 18.”
• “On top of that, the direct and wellness guests also spent more on F&B — the TRevPAR difference didn’t show up in room ADR alone.”
• “In climbing to 96% we crowded out the higher-value bookings, because we filled up with the cheap segment first. That’s displacement.”
• “Our ‘optimal mix’ is probably in the 88–92% range — the remaining 8–12% of rooms should be left empty if the only alternative is a cheap dump.”

This kind of answer is business-mature RM thinking, far from an owner’s reflex. And it’s exactly what a modern hotel pays a senior revenue manager for.

How to find the optimal mix

There’s no single formula. But there’s a practical thought-protocol every RM follows:

1. Segment-level TRevPAR ranking: know which of your segments brings the most TRevPAR per room (lesson 4 had a sample table for this).
2. Pickup trend by date: know what guest arrivals to expect for a given Saturday night across the different segments (lessons 16 and 37).
3. Order thinking: leave the low-value segments for later, let the high-value segments in earlier. This doesn’t mean rejecting the cheap OTA — only not letting it fill capacity early.
4. Restrictions: lessons 24 and 42 cover CTA, CTD, MLOS and min-stay rules — with these you can finely limit which segment fits in when.
5. Measured learning: at the end of every month, look back — how far did the actual mix deviate from the optimal, where did we crowd out a high-value booking for a low one? This is retrospective yield analysis.

The optimal mix is not a number, but a continuous question you re-ask for every single night.

Key takeaways

• 100% occupancy is not the goal — maximum revenue is. The two often diverge dramatically, because reaching 100% requires taking on low-value segments.
• The optimal mix tells you: for a given night, how capacity splits across segments so that total revenue is maximized — often at 85–92% occupancy.
• Displacement occurs when a cheaper booking “crowds out” a higher-paying one. Order matters: who fills capacity first.
• Opportunity cost is the core of the “safe money vs. more probable money” decision. Group acceptance and corporate contracts can all be evaluated with this frame.
• The marginal room’s value falls as you approach capacity but stays positive — the last rooms are almost always worth selling, while the middle should be selected.