How to Change Hotel Prices Based on Occupancy: A Guide to Pickup, Booking Pace, and Forecast

Let's consider a 20-room boutique hotel.

For the Saturday 30 days out:

10 rooms are sold.

Occupancy:

50%.

Room price:

8,000 TL.

Should we raise the price?

At first glance the answer may seem simple:

“50% occupancy is lowish, let's wait a bit.”

But let's add another piece of information.

In the last 7 days:

6 new rooms were sold.

A week ago, only:

4 rooms were booked.

Now the picture has changed.

Because even though current Occupancy is still 50%:

demand is coming in fast.

Let's consider another scenario.

The same hotel.

The same distance from the date.

Again 50% occupancy.

But in the last 14 days:

no new reservations have come in.

The Occupancy of the two dates is the same.

But the economic meaning is completely different.

That is why, in professional Revenue Management, price:

is not changed based on occupancy alone.

For a sound pricing decision, at least these four questions must be answered together:

What is Occupancy?

What is Pickup doing?

What is Booking Pace doing?

What does Forecast say?

And a fifth question must always be added:

How many days are left until the stay date?

This is where the real logic of occupancy-based pricing begins.

1. What Is Occupancy-Based Pricing?

Occupancy Based Pricing:

is the hotel reassessing its room price according to the occupancy level on a given date.

A simple system can be set up like this:

The logic of this system is as follows:

As room inventory shrinks:

the economic value of the remaining rooms may rise.

But there is a very important word here:

may.

We are not saying it necessarily rises.

Because Occupancy is:

only one input to the pricing decision.

2. Why Is “Occupancy Rose, Raise the Price” an Incomplete Approach?

Let's consider two dates.

Both have:

70% Occupancy.

Date A

There are:

60 days until the stay.

Date B

There are:

2 days until the stay.

The same occupancy.

But the sales opportunity is not the same.

To sell the remaining 30% of capacity, Date A has:

60 days.

Date B has:

2 days.

Therefore:

the same Occupancy does not call for the same price decision.

3. The Five Core Data Points in a Pricing Decision

A practical Revenue Management system can read these five data points together:

1. Occupancy

How full are we right now?

2. Pickup

How many new rooms have we picked up recently?

3. Booking Pace

Are our reservations coming in faster or slower than normal?

4. Forecast

Where do we expect to be when the date arrives?

5. Days to Arrival — DTA

How much time do we have left to decide?

When these five are read together:

occupancy stops being a number and becomes a demand signal.

4. What Does Occupancy Tell Us?

Occupancy shows:

how much of the current room capacity has been sold.

In a 20-room hotel:

if 12 rooms are sold:

Occupancy = 60%

But Occupancy doesn't tell us:

• when were these 12 rooms sold?
• has demand accelerated in recent days?
• are reservations coming in early?
• how many more rooms are expected to sell?
• are we selling fast only because the price is low?
• how many days are left until the date?

That is why:

Occupancy is the current snapshot.

Revenue Management wants to see, alongside the snapshot:

the movement

as well.

5. What Is Pickup?

Pickup:

is the net change in reservations for a specific stay date over a specific time interval.

Basic form:

Pickup = Current OTB – Previous OTB

OTB stands for:

On The Books

meaning the reservations currently in the system.

6. How Is Pickup Calculated?

For July 20:

June 1:

6 rooms booked.

June 8:

10 rooms booked.

Pickup:

10 – 6 = +4 rooms

In other words, over the last 7 days:

4 rooms of Pickup

have been generated.

7. Why Is Pickup So Important?

Because Occupancy tells us:

where we are.

Pickup shows:

how fast we are moving.

For example:

Date A

Occupancy: 60%

Last 7 days Pickup: +1

Date B

Occupancy: 60%

Last 7 days Pickup: +7

Current occupancy is the same.

But Date B's demand is much stronger.

So for Date B, the following may come onto the agenda:

• raising BAR,
• closing the low-priced Rate Plan,
• stopping the promotion,
• considering Minimum Stay.

8. Pickup Isn't Only Positive

Pickup:

can also be negative.

For example:

Last week:

15 rooms OTB.

Today:

13 rooms OTB.

Net Pickup:

-2 rooms

is possible.

The reason could be:

• Cancellation,
• Reservation Modification,
• Date Change.

That is why it is not enough to say only:

“We took 5 new reservations this week.”

If, in the same period:

7 reservations were cancelled:

Net Pickup:

-2

is possible.

What matters for Revenue Management is:

net reservation movement.

9. Over Which Periods Should Pickup Be Monitored?

In practice, you can monitor:

• 1-day Pickup
• 3-day Pickup
• 7-day Pickup
• 14-day Pickup
• 30-day Pickup

For near-term dates:

1–3 day movement

may matter more.

For more distant dates:

7–14–30 day movement

may be more meaningful.

But the aim is always the same:

Is demand accelerating or slowing down?

10. What Is Booking Pace?

Booking Pace:

shows, as the stay date approaches:

at what speed and with what timing reservations are formed.

For example, for a certain type of Saturday date, the hotel's normal booking curve might be:

This table forms the hotel's:

Booking Curve

or reservation curve.

11. What Does Booking Pace Tell Us?

Booking Pace answers the question:

“Is this date moving fast or slow relative to our normal booking behavior?”

For example:

on similar Saturday nights last year:

30 days out:

45% occupancy

was normal.

This year, if at 30 days out:

70% occupancy

demand may be well ahead of normal.

In this case:

the current price level needs to be reassessed.

12. The Difference Between Pickup and Booking Pace

These two concepts are often confused.

Pickup

The change in reservations over a specific time interval.

+5 rooms in the last 7 days.

Booking Pace

Shows the speed and pattern with which reservations arrive over time.

30 days out, we were 40% full last year; this year we are 65% full.

In short:

Pickup = What happened recently?

Booking Pace = How is our booking curve progressing compared to normal?

13. What Is Forecast?

Forecast:

is the estimate of the final performance expected when the stay date arrives.

Forecast is not made only for Occupancy.

For example, you can build:

• Forecast Occupancy
• Forecast ADR
• Forecast RevPAR
• Forecast Room Revenue

But for pricing, the first critical question should be:

“How many rooms do we expect to have sold by the end of this date?”

14. The Difference Between OTB and Forecast

For example:

A 20-room hotel.

A date 30 days out.

Today:

12 rooms are booked.

OTB Occupancy:

60%

But if historical Booking Pace and current Pickup:

indicate that 19 rooms will be sold by the end of this date:

Forecast Occupancy:

95%

is possible.

If the manager looks only at OTB, they may say:

“We're only 60% full.”

If they look at the Forecast, they may say:

“This date is already heading toward 95%.”

The second perspective can completely change the price decision.

15. How Is Forecast Built?

The following data can be used to build a Forecast:

• Current OTB
• Historical Booking Pace
• Current Pickup
• Cancellation
• No-Show
• Lead Time
• Day of Week
• Season
• Events
• Remaining Inventory
• Room Type Demand
• Market Signals

A simple boutique hotel doesn't need a complex algorithm.

But at the very least, the logic of:

OTB + Expected Additional Net Pickup = Forecast

can be established.

This is a simplified management approach.

16. What Is Days to Arrival — DTA?

DTA stands for:

Days to Arrival

meaning:

the number of days left until the stay date.

For example:

Stay Date:

July 20.

Today:

July 10.

DTA:

10 days.

This data point is very important.

Because:

50% Occupancy + DTA 90

and:

50% Occupancy + DTA 1

do not describe the same economic situation.

17. How Does DTA Change the Pricing Decision?

For example, a 20-room hotel:

50% full.

DTA 60

10 rooms sold.

60 days remain.

There is time for demand.

DTA 7

10 rooms sold.

7 days remain.

Time is running short for the remaining 10 rooms.

DTA 1

10 rooms sold.

Tomorrow night.

The remaining 10 rooms carry a serious Unsold Inventory risk.

Therefore:

time is a critical variable in a pricing decision.

18. An Occupancy + DTA Matrix Can Be Built

A simple starting matrix:

This table:

is not an automatic pricing algorithm.

But it disciplines decision logic.

19. The Biggest Mistake: Turning an Occupancy Threshold into an Automatic Price Rule

For example, a system might work like:

50% → +5%

70% → +10%

85% → +20%

This can help.

But on its own it is dangerous.

Because:

85% Occupancy + 90 DTA

can be a very strong demand indicator.

But:

85% Occupancy + 0 DTA

now means only a few hours of selling opportunity left.

Moreover, Pickup:

may have been zero for the last 7 days.

That is why:

an Occupancy Threshold can trigger a price change, but it should not make the decision on its own.

20. Price Decisions in Four Scenarios

Let's consider a 20-room hotel.

Base BAR:

8,000 TL.

Scenario A — Low Occupancy, Strong Pickup

Occupancy:

40%

DTA:

30

Last 7 days Pickup:

+6

Booking Pace:

Above LY

Forecast:

95%

In this case:

“We're only 40% full, keep the price low.”

can be a wrong thing to say.

Demand is coming in fast.

BAR:

8,000 → 8,500 / 9,000 TL

can be considered.

Scenario B — High Occupancy, Weak Pickup

Occupancy:

80%

DTA:

3

Last 7 days Pickup:

0

Booking Pace:

Below LY

Forecast:

82%

Here:

“We hit 80%, raise the price.”

may not be the right mechanical approach.

Demand for the remaining 4 rooms may have stalled.

BAR can be held, or sales conditions can be reassessed.

Scenario C — High Occupancy, Strong Pickup

Occupancy:

85%

DTA:

14

Pickup:

+5

Booking Pace:

Above LY

Forecast:

100%

This is:

a very strong Revenue signal.

Raising BAR,

closing discounts,

Minimum Stay,

limiting low-value channels

can be considered.

Scenario D — Low Occupancy, Weak Pickup

Occupancy:

35%

DTA:

5

Pickup:

0

Booking Pace:

Below LY

Forecast:

45%

Here the problem should be investigated.

• is the price too high?
• is a channel closed?
• is Minimum Stay a barrier?
• is it a visibility problem?
• is demand really weak?
• are competitors more aggressive?

If necessary, the price or value proposition can be reworked.

21. A Simple Revenue Matrix with Four Data Points

Even this table is:

a far more advanced system than a small boutique hotel changing prices by looking at occupancy alone.

22. What Do You Do When Forecast Is High?

If Forecast is heading toward:

95–100%:

• BAR can be raised,
• low-priced Rate Plans can be closed,
• campaigns can be removed,
• Minimum Stay can be considered,
• CTA / CTD can be considered,
• Yield can be applied by room type.

But the aim is:

not to punish the guest, but to manage limited inventory more wisely.

23. What Do You Do When Forecast Is Low?

When Forecast is low, the first reflex should not be:

“Lower the price.”

Diagnose first.

Check:

Demand

Is there demand in the destination?

Distribution

Are we open on the OTAs?

Inventory

Are the rooms open for sale?

Restrictions

Are Minimum Stay, CTA, or CTD blocking demand?

Pricing

Is BAR high relative to the market?

Product

Are photos, reviews, product, and perceived value sufficient?

Channel

Are we visible in the right market?

If the problem isn't price:

a price cut may not solve it.

24. Last Minute ≠ Discount

DTA:

1 day.

This does not automatically mean:

“Lower the price.”

For example:

Occupancy:

95%

Pickup:

strong

Remaining Inventory:

1 room

then:

there may be no strong reason to lower the price.

The opposite:

Occupancy:

45%

Pickup:

0

Remaining:

11 rooms

then:

price or value proposition may be reassessed.

Therefore:

last-minute pricing is determined not by time but by the relationship between remaining inventory and demand.

25. Early Booking and Last Minute Are Two Ends of the Same Curve

In Revenue Management, the timeline can be thought of like this:

90+ days: Early Booking → 60 days: BAR / Early Booking → 30 days: BAR → 14 days: Demand-Based BAR → 7 days: Forecast Review → 3 days: Last Minute Review → 1 day: Final Inventory Review → Arrival Day: Same-Day Inventory

Price over this process:

doesn't necessarily go down.

26. Price Can Also Go Up Over Time

For example:

At 90 days out:

7,000 TL

At 60 days out:

7,500 TL

At 30 days out:

8,000 TL

At 14 days out:

9,000 TL

is possible.

Because demand is stronger than expected.

The opposite can also happen:

8,000 → 7,500 → 7,000

Therefore:

time doesn't set the price; time signals when to reassess the price.

27. Why Is Remaining Inventory Critical?

In a 20-room hotel:

Occupancy 90%.

Remaining:

2 rooms.

In a 100-room hotel:

Occupancy 90%.

Remaining:

10 rooms.

The percentage is the same.

But the remaining inventory is different.

That is why, in a price decision, along with:

Occupancy Percentage

you should also see:

Remaining Rooms

Especially in a boutique hotel, a few rooms:

can represent a large part of total capacity.

28. Track Pickup by Room Type

Hotel-wide:

Occupancy 70%.

But:

is possible.

Standard rooms are selling out fast.

Suites aren't selling.

Raising the price of the entire hotel at once may not be right.

Standard:

BAR can be raised.

Deluxe:

can be held.

Suite:

can be supported with an upsell or value proposition.

That is why:

Revenue Management should be done not only at hotel level but also at Room Type level.

29. Read Pickup by Channel Too

Last 7 days:

+10 rooms of Pickup

occurred.

Nice.

But where did it come from?

This table shows:

that most of the demand came from OTAs.

In this case, you should also examine:

• Net ADR,
• commission,
• Direct price,
• web visibility.

Because:

high Pickup does not always mean high-quality revenue.

30. Cancellation Can Distort Pickup

For example:

Last 7 days:

8 new reservations.

But:

5 cancellations.

Net Pickup:

+3.

If management says only:

“We took 8 reservations.”

they may read demand as stronger than it is.

The right analysis should be done as:

Gross Booking + Cancellation + Net Pickup

31. How Do We Compare Booking Pace with Last Year?

For example, for August 15:

At 30 days out:

This year 60%.

Last year 45%.

So:

we are 15 points ahead.

This may be a strong demand signal.

32. But Comparison with Last Year Isn't Enough on Its Own

Last year:

• the price may have been very low,
• there may have been a different channel strategy,
• there may have been an event,
• holiday dates may have shifted,
• the hotel's Review Score may have been different,
• the number of rooms may have changed.

That is why Booking Pace:

is a reference, not an automatic decision.

33. Forecast Accuracy Must Be Measured

Making a Forecast is not enough.

How accurate the Forecast is must also be measured.

For example:

Forecast at 14 days out:

90%

Actual:

72%

If this kind of error keeps recurring:

the Forecast model may be too optimistic.

On another date:

Forecast:

70%

Actual:

95%

then:

demand may be consistently underestimated.

That is why:

a Forecast → Actual → Variance → Learning

cycle must be established.

34. Why Does Forecast Error Matter?

If the Forecast is consistently wrong:

• prices are set wrongly,
• staff planning is disrupted,
• purchasing is done wrongly,
• channel strategy is disrupted,
• campaigns are opened at the wrong time,
• Revenue opportunities are missed.

That is why a professional system:

doesn't just produce a Forecast; it also measures Forecast quality.

35. What Might a Simple Forecast File Look Like?

When a manager looks at this table:

they can quickly see which date requires action.

36. Set Up a Simple Revenue Action System

For each date:

HOLD

Keep the price.

UP

Consider raising BAR.

DOWN / VALUE

Reassess the price or value proposition.

CLOSE DISCOUNT

Close the low-priced Rate Plan.

RESTRICTION

Consider Minimum Stay / CTA / CTD.

DISTRIBUTION CHECK

Check channels and Availability.

This system turns the Revenue meeting:

from a report-reading meeting into a decision meeting.

37. Example Revenue Calendar

This table can be extremely powerful even for a small boutique hotel.

38. How Much Should BAR Be Raised?

There is no universal answer.

For example:

7,000 → 7,200 TL

or:

7,000 → 7,500 TL

or:

7,000 → 8,000 TL

are all possible.

The size of the increase should be determined by:

• Price Sensitivity,
• Pickup,
• Remaining Inventory,
• DTA,
• Booking Pace,
• Forecast,
• product positioning,
• past sales behavior.

But what matters is:

measuring the result of the price change.

39. What Should Be Monitored After Raising the Price?

For example:

BAR:

rose from 8,000 to 9,000 TL.

Over the next 3–7 days, you should monitor:

• Did Pickup continue?
• Did Booking Pace slow down?
• Did Cancellation increase?
• Did the position relative to competitors change?
• Did the room type sales balance change?
• Is Forecast still strong?

If the effect of a price change isn't measured:

Revenue Management can't learn.

40. What Should Be Monitored After Lowering the Price?

BAR:

was lowered from 8,000 to 7,000 TL.

Then:

Pickup:

went from 0 → +5.

Successful at first glance.

But these should also be asked:

• would these reservations have come anyway?
• what happened to Net ADR?
• did RevPAR rise?
• was the discount deeper than necessary?
• which channel sold?

In other words:

reservations coming in does not by itself prove the price cut was right.

41. Is It Possible to Change the Value Proposition Instead of the Price?

Yes.

When demand is weak, BAR doesn't always have to be lowered.

For example, you can offer:

• breakfast included,
• free transfer,
• late Check-Out,
• room upgrade,
• long-stay benefit,
• a special package.

This way:

the Headline Rate is preserved while the Value Proposition is strengthened.

This is especially valuable for boutique hotels that want to protect their brand's price perception.

42. Don't Forget Restrictions

Sometimes a hotel has low occupancy.

Management thinks:

“The price is high.”

But the problem may be:

Minimum Stay 3 Nights.

Or:

CTA

may be wrongly open.

Or the room may be closed on the OTA.

That is why, when Pickup is low, the order of checks can be thought of as:

Demand → Availability → Restriction → Distribution → Pricing

Price:

is not always the first culprit.

43. When Can Minimum Stay Be Used?

For example, Saturday:

Forecast 100%.

Friday:

Forecast 95%.

Sunday:

Forecast 40%.

Taking bookings for only the Saturday night:

can make it harder to sell Sunday inventory.

In this case, a rule such as:

Minimum 2 Nights

can be considered.

But the rule:

should not be applied automatically.

Because an overly aggressive Minimum Stay:

can block demand entirely.

44. When Can CTA and CTD Be Used?

CTA — Closed to Arrival

New Check-In is closed on a specific date.

CTD — Closed to Departure

Check-Out is restricted on a specific date.

The aim:

is to optimize the booking pattern and room inventory during high-demand periods.

These are advanced Revenue tools.

If used wrongly, they can cause lost sales.

45. A Simple Pricing Decision Algorithm for a Small Boutique Hotel

For each date, ask the following questions in order:

1.

What is the DTA?

2.

What is the Occupancy?

3.

How many rooms are left?

4.

What is the last 7 days Pickup?

5.

Is Booking Pace ahead of last year?

6.

What is the Forecast?

7.

How is Cancellation?

8.

What does the Room Type distribution look like?

9.

What does the Channel Mix look like?

10.

Are there any Restrictions?

11.

What is the current BAR?

12.

Does this data say the price should be:

UP / HOLD / REVIEW?

Even this system is far more professional than the approach of:

“Occupancy hit 80%, raise the price.”

46. Simple UP / HOLD / REVIEW Logic

UP

Can be considered with these combinations:

• Pickup strong
• Pace ahead
• Forecast high
• Remaining Inventory low
• DTA sufficient

HOLD

Can be considered when:

• Pickup normal
• Pace normal
• Forecast on target
• BAR at the right level

REVIEW

Is needed with these combinations:

• Pickup weak
• Pace behind
• Forecast low
• Remaining Inventory high
• DTA narrowing

But REVIEW:

does not mean lower the price automatically.

The problem must be found first.

47. In Revenue Management, “Do Nothing” Is Also a Decision

Managers sometimes think they need to change the price every day.

No.

If the data shows:

• Pickup normal,
• Pace normal,
• Forecast on target,
• BAR right,

then:

not changing the price may be the right decision.

Revenue Management means:

not constant motion,

but making the right move when needed.

48. Changing Prices Too Often Is Also Risky

Excessive price changes can:

• distort the guest's perception of price,
• confuse staff,
• create channel inconsistency,
• make analysis harder.

That is why:

price movement should be based on a change in data.

Changing the price just because:

“No reservations came in today.”

is not right.

49. Keep a Revenue Action Log

For example:

After a few seasons, this file becomes:

the hotel's Revenue memory.

50. How Often Should Forecast Review Be Done?

A practical structure:

0–7 Days

Daily.

8–30 Days

Daily or every few days.

31–60 Days

Weekly.

61–90 Days

Weekly.

90+ Days

Season / Event / Early Demand check.

If demand is changing quickly, the review frequency can be increased.

51. Which Data Matters More for Near-Term Dates?

As DTA decreases, the following become more important:

• Remaining Inventory,
• last 24/72-hour Pickup,
• Cancellation,
• Same-Day Demand,
• Walk-In,
• channel Availability.

For distant dates, more important are:

• Booking Pace,
• Season,
• Events,
• Forecast,
• Early Booking behavior.

In other words:

the weights of a Revenue decision change with DTA.

52. How Can a 90-Day Revenue Calendar Be Managed?

0–7 Days

Inventory Protection / Final Sales

8–14 Days

Demand-Based Pricing

15–30 Days

Pickup Management

31–60 Days

Booking Pace Management

61–90 Days

Forecast & Demand Development

This classification is not absolute.

But it clarifies:

which data the manager should look at more in which period.

53. Which Fields Should a Revenue Dashboard Have?

For each Stay Date, you can see:

• Date
• Day of Week
• DTA
• Available Rooms
• OTB Rooms
• Occupancy
• Remaining Rooms
• 1D Pickup
• 7D Pickup
• 14D Pickup
• LY Pace
• Forecast Occupancy
• BAR
• ADR
• Forecast ADR
• RevPAR
• Cancellation
• Channel Mix
• Restriction
• Action

This way the manager can read:

the demand + price + inventory

relationship from a single screen.

54. Color Codes Can Be Used

For example:

Green

Demand strong / above target.

Yellow

Monitor.

Red

Action required.

But color should not be assigned based on Occupancy alone.

For example:

40% Occupancy:

at 90 days out can be green.

The same 40%:

at 1 day out can be red.

Even this example shows:

why DTA is as important as Occupancy.

55. What Is the Revenue Manager's Real Job?

A Revenue Manager's job is:

not to raise prices.

The real job is:

reading demand → forecasting the future → managing inventory → optimizing price → measuring the result → learning.

56. 15 Big Mistakes in Occupancy-Based Pricing

1. Looking only at Occupancy

2. Ignoring DTA

3. Not tracking Pickup

4. Confusing Gross Booking with Net Pickup

5. Not monitoring Booking Pace

6. Not forecasting

7. Not measuring Forecast Accuracy

8. Not seeing demand by room type

9. Judging Remaining Inventory only by percentage

10. Thinking last minute = discount

11. Automatically cutting price at low occupancy

12. Automatically raising price at high occupancy

13. Mistaking Restriction and Distribution problems for pricing problems

14. Not measuring the result of a price change

15. Changing prices too often and without reason

What these mistakes have in common:

reducing the price decision to a single variable.

57. 12 Decision Gates for Pricing

Decision Gate 1

How many days are left until the Stay Date?

Decision Gate 2

What is Current Occupancy?

Decision Gate 3

How many rooms of Remaining Inventory are there?

Decision Gate 4

What are 1D / 7D / 14D Pickup doing?

Decision Gate 5

Is Cancellation affecting Pickup?

Decision Gate 6

How is Booking Pace relative to the past?

Decision Gate 7

What is Forecast Occupancy?

Decision Gate 8

Which Room Type is selling out fast?

Decision Gate 9

Which channel is Pickup coming from?

Decision Gate 10

Is a Restriction blocking sales?

Decision Gate 11

Is the current BAR appropriate for demand?

Decision Gate 12

What was the result of our last price decision?

If these questions can be answered:

pricing moves beyond guesswork and becomes a Revenue Management system.

58. A Minimum Revenue File for a Small Boutique Hotel

A single Excel file or Revenue Dashboard can be enough to start.

For each date, you can keep:

• Stay Date
• DTA
• Available Rooms
• OTB
• Occupancy
• Remaining
• 7D Pickup
• 14D Pickup
• LY Pace
• Forecast
• BAR
• ADR
• Restriction
• Action
• Result

What matters is not the complexity of the software:

but the discipline of the decision system.

59. Let's See the Whole System in a Single Flow

The Revenue Management pricing cycle:

Season Calendar → Base BAR → Day-of-Week → Current OTB → Occupancy → Remaining Inventory → Pickup → Booking Pace → Forecast → DTA → Rate Decision → BAR / Rate Plan / Restriction → Channel → New Reservations → New Pickup → Updated Forecast → ADR / RevPAR / Net ADR → Revenue Review → Learning → New Decision

This is not a one-time process.

It is a continuously running management cycle.

60. The Simplest Revenue Management Rule

If we reduce this whole article to a single principle:

Don't change the price according to occupancy; change it according to what the occupancy means.

50% occupancy:

can be good.

Can be bad.

Can be an opportunity to raise the price.

Can be a signal to hold the price.

Can be weak demand that requires action.

What determines this is:

not Occupancy itself;

the combination of Occupancy + Pickup + Booking Pace + Forecast + DTA + Remaining Inventory.

Conclusion: Occupancy Is a Snapshot, Pickup Shows Movement, Forecast Shows the Future

One of the most dangerous approaches in hotel Revenue Management is making a single-variable decision such as:

“Occupancy hit 80%, raise the price.”

Because 80% occupancy:

if formed at 90 days out:

can be a very strong demand signal.

If formed at 1 day out:

it can be read completely differently.

Likewise:

50% occupancy may look low.

But if:

last 7 days Pickup is +8,

Booking Pace is far above last year,

Forecast is 100%,

then:

it may actually be a strong opportunity to raise the price.

That is why the Revenue Manager's screen should not show only:

Occupancy.

These should be seen together:

Occupancy + Remaining Inventory + Pickup + Booking Pace + Forecast + DTA + BAR + ADR / RevPAR

And at the end of all this data, one question should be asked:

“What should we do today?”

Raise the price?

Hold the price?

Close the discount?

Apply a Restriction?

Check distribution?

Strengthen the value proposition?

Or:

do nothing?

That is the difference in good Revenue Management.

Not reacting to every change, but responding to the right signal at the right time.

Hotel Investment Guide — Volume V

This article is based on the Reservations and Revenue Management system I cover in Hotel Investment Guide — Volume V: Operations.

In Volume V, occupancy-based pricing is treated within a holistic Revenue Management system together with:

• BAR,
• Occupancy,
• OTB,
• Remaining Inventory,
• Pickup,
• Booking Pace,
• Demand Forecasting,
• Days to Arrival,
• Last Minute,
• Minimum Stay,
• CTA / CTD,
• ADR,
• RevPAR,
• Net ADR,
• Revenue Dashboard.