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Are you also afraid of overselling? You’re not alone | Ira Vouk
As we know, overselling (or overbooking) is a technique used in Revenue Management to offset anticipated
cancellations and no-shows (wiki has an article on this subject). In other words, if you expect 2 cancellations and
1 no-show – you oversell by 3. That’s the optimal behavior that maximizes revenue. Pretty simple, right?
But nonetheless, not very many hoteliers embrace this practice. It’s still very common for most managers
(especially in the middle tier segment) to close out availability on all channels before they reach 100% occupancy
mark for a certain day. In most cases, this decision is dictated by the fear of dealing with this:
However, correctly implemented overbooking practices will minimize the chance of a walk while leading to a
noticeable increase in Revenues (as well as profits). One doesn’t need to have a lot of Revenue Management
experience or knowledge to be able to achieve this goal.
In this article, I will try to describe some overbooking techniques and best practices. My goal is to show that
overbooking is definitely underestimated. It is also not as scary as it may seem and leads to great revenue
increases if done right.
Two types of overbooking techniques
As mentioned above, the scary image of a walked guest is generally occupying the minds of those who strongly
disagree with the overbooking policy. Most popular mistake that leads to walks is picking the wrong time to
overbook. As mentioned above, overbooking is designed to offset cancellations and no-shows. Let’s ask
ourselves: what is the main difference between those two occurrences? It is timing.
ï?· cancellations can happen at any point in time, starting from 56 weeks before arrival (standard allowed
lead time for transient bookings) until the end of the cancelation deadline
ï?· no-shows always happen on the last day
Thus, we need to separate 2 different overbooking techniques: those that address cancelations and those
that address no-shows. The latter is easy: calculate the anticipated number of no-shows and overbook on the
last day by that amount of rooms. There are a number of good articles written on this subject (for example,
“Overbooking ratio step-by-step” by eCornell).
But! What do we do with cancellations? There are articles explaining how to calculate average anticipated
number of cancelations but no one tells you WHEN to do this, at what point in time (a little but a very important
Timing is the key
Let’s say you’re planning your overbooking strategy for the 4th of July this year. You looked at your last year’s
performance report for the same day and discovered that you had total of 15 cancelations. That’s the final
number but it doesn’t describe at what point in time those cancellations happened. If you’re doing this exercise 3
days before arrival and you’ve just allowed the hotel to sell 15 rooms over capacity, chances are, you will be in
Ã¢â?¬Â¦simply because you didn’t take into account the timing of your cancelations.
So here’s the key: it is not enough to just calculate the total number of rooms to oversell. You need to build
a curve describing the forecasted number of potential cancelations at any point in time.
Or, if you don’t have any automated Revenue Management tool that would help you with this, you can use a
simple excel spreadsheet. Something like this:
Note: if you don’t have an RMS at your hotel, the forecasted number of potential cancelations can be manually calculated as an average of
actual cancelations from similar days in the past.
Thus, in this example, you shouldn’t worry if you find yourself overbooked by 5 rooms 10 days out. However, if
you’re looking at tomorrow’s occupancy (1 day out), you should allow no more than 1 (unless you also anticipate
no-shows, but that is a different exercise, very well described in the eCornell article referenced above).
Here’s another trick: when anticipating cancelations, in order to maximize your revenue to its highest potential,
overselling needs to happen at the peak of demand. This will ensure that:
ï?· you sell those rooms at the highest possible price
ï?· and (in some cases) you will still have plenty of time to wait for those precious cancellations
What do I mean by “peak of demand”? Guess what, demand is a curved line, it’s not even for any day that you’re
selling in the future. Here’s a good exercise: pick one day from last year (4th of July for example) and track the
number of reservations booked per day, starting from 365 days before arrival.
You may get something like this:
Obviously, we’re talking about high demand days only (when you expect to sell out) so there’s only that many
days that you would need to review for your property. You can use an excel spreadsheet to build this graph, if
you don’t have any automated Revenue Management tool that would help you do this.
The graph above is a courtesy of iRates LLC (www.i-rates.com) and describes the actual demand flow for the
busiest convention in San Diego Ã¢â?¬â?? Comic Con that brings about 130,000 visitors every year. The curve is similar
for the majority of the city-wide conventions in San Diego.
You would notice 2 peaks: one about 140 days out, and the other one a few days before arrival.
It is also important to know that price elasticity (and thus, price expectation) is not constant either, it’s also
curved. For this event, the peak of the price expectation falls around the first peak of demand and then goes
So regardless of the fact that the second peak of demand is higher, the most profitable bookings for this event
can be captured 3-4 months out. This is when the majority of the reservations need to happen in order to
I need to note here that if you’ve planned your pricing strategy correctly, you won’t often find yourself in a
situation when you’re close to 100% occupancy that many days out. However, these situations do happen when
demand greatly exceeds supply and you have reached the ceiling of Rack rate allowed at your hotel, so you can’t
increase your rates any further (for various reasons, one of them being price gauging). In this case, you
implement other non-pricing Revenue Management techniques (which is a whole separate discussion) but even
then you may end up selling out in advance. The situation shown in the graph above is not very common and is
just being used as an example. In your case, the peak of demand may fall on 7 or 5 days out for the majority of
high demand events, or maybe always on the last day. Each hotel has its own unique demand structure and you
would need to analyze it.
In any case, the general rule is to oversell at the peak of demand in order to maximize your profit. Do this and
enjoy looking at those opaque 40% off bookings (those that you never meant to allow but somehow they always
slip through) turn into high-profit Rack rate reservations from direct customers.
Here’s another example for better understanding.
Below is an excerpt from an excel spreadsheet that describes the booking pace for an event.
ï?· Manager 1 doesn’t like to oversell and employs Scenario 1
ï?· Manager 2 understands overselling techniques and uses Scenario 2
Let’s review the numbers.
As you see from the spreadsheet, the peak of the demand and price expectation falls on the 11-9 days before
arrival. Under scenario 1, the manager closes availability on all channels during the 3 days, unwilling to oversell.
This leads to total of 10 denials that could have been booked at the premium rate. After that, when demand
decreases (this may happen due to various reasons, i.e. convention hotels releasing their group blocks thus
increasing the supply in the market, or competitive hotels lower their rates pursuing full sell out), booking pace
slows down and cancelations start coming in. Thus the manager is forced to respond to the slowing demand
conditions with the lower rate, in order to not end up with too many empty rooms. Manager aims towards full
occupancy and is able to achieve it at the end, if not for the 3 no-shows. As a result, 3 rooms remain
unoccupied. One doesn’t need to have a pretty Revenue Management certificate to understand that this strategy
is far from optimal and leads to significant revenue losses.
Let’s review scenario 2. In this case, the hotel is not closed out at the peak of demand and total of 10 rooms are
booked over capacity at the premium $239-$259 rate (i.e. denials turn into actual reservations), anticipating
future cancelations and no-shows. The strategy allows achieving 100% occupancy as a result. What we also see is
that under this scenario, the manager is not forced to lower the rate to reach full sell out. Moreover, he even
increases it further as the demand stays strong, and accepts the denials from the first scenario. The rate remains
at $259 until the day is closed.
Estimated amount of lost revenue under Scenario 1 vs Scenario 2 is about $1750 (considering cost of empty
rooms and lower ADR of the latest reservations). And this is just for 1 day. For a 4-day event, multiply this
number by 4 Ã¢â?¬â?? and you get $7000 of pure loss (that goes straight to the bottom line) from incorrectly
managing your overbookings! Multiply this by the number of events during the year. This could add up to a 6figure number. Another note: example above is taken from a real 100-room middle-tier property. For a larger
hotel, the gains/losses would be proportionally greater.
To summarize, I would like to emphasize one more time that the effect of incorrect overbooking policies (or the
lack of thereof) is being widely underestimated in the industry, especially in the limited service/middle tier
segment. Proper overselling at the peak of demand helps hoteliers sell their rooms at the premium rate and
not leave money on the table from empty rooms due to anticipated cancelations and no-shows. This is
optimal strategy that leads to revenue (and profit) maximization. The additional revenues gained from the
overselling techniques go straight to the bottom line.
Ira Vouk, CRME
Co-founder, iRates LLC.
Bridging the GAP between the Revenue Manager and CFO | By
Richard B Evans
“The Trials and Tribulations of the RM in Reaching Sell Outs””
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