With a fixed capacity, a highly disposable product and high fixed costs, hotels are a natural candidate for the application of revenue management. Originally developed by the airlines in the 1970s, these analytics-based techniques help predict consumer behavior at the hotel’s market level so that the hotel can sell each room each night at the optimum price. With modern-day rising acquisition costs and distribution complexities, revenue management techniques have increasingly been adopted by both small and large hotel companies, making a comprehensive understanding of segmentation, forecasting and pricing an essential requirement for today’s hospitality professionals. The purpose of this course is to provide a core understanding of the fundamentals of revenue management, which ties into the larger picture of revenue strategy. The course is structured to provide an insightful look into Revenue Management. Created in conjunction with Duetto, this course is hosted by a group of revenue management leaders in both theory and with hands-on experience at properties around the world. Upon completion of the course students should be empowered with industry best practices, which can be applied across the vast diversification the hotel industry to empower those to optimize profits. Duetto delivers powerful revenue strategy solutions to the world’s leading hotels and casinos, combining unparalleled expertise with world-class cloud-based technology. Acknowledgement: Bela Nagy, Agnes Roquefort , Jonathon Liu, Frederic Toitot, Pierrick La Masne, Markus Keller - ACCOR Simone Truscello - ACE HOTELS. Richard Valtr - MEWS SYSTEM. Jean-Luc Chrétien - FAST BOOKING. Jeannette Ho - FAIRMONT RAFFLES HOTELS & RESORTS. Chinmai Sharma - TAJ HOTEL & RESORTS. Cindy Estis Green - KALIBRI LABS. Jason Thielbar - RED LION. Christopher Cooper - ROCCO FORTE. Rom Hendler - SANDS. Rafi Rejerano - AB Hotels. Riko Van Santen - KEMPINKI HOTELS & RESORTS. Vinod Sukhija - BANYAN TREE HOTELS & RESORTS. Olivier Flement - WALT DISNEY COMPANY. Cidalia Pinto Coehlo - LOUVRE HOTELS. Trevor Stuart-Hill - REVENUE MATTERS. Vincent Cusma, Marco Benvenuti, Eric Stoessel, Gayle Ehrean, Nevin Reed - DUETTO. Claire Bertrand, Vincent Chatain, Jessica Moses, Benjamin Six (ESSEC Business School), Emilie Dupré (IMAGE-IN), Gregory Halidy (TRIPLAY)
Basic terminology
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En provenance du cours de ESSEC Business School
The Fundamentals of Revenue Management: The Cornerstone of Revenue Strategy
360 notes
ESSEC Business School
360 notes
Cours 2 sur 4 dans Specialization Hotel Management: Distribution, Revenue and Demand Management
À partir de la leçon
Introduction to Forecasting & Budgeting
In this third module, you will have an overview of forecasting and its basic terminology, explore forecasting goals, trends and tools, and understand how to link forecasting and budgeting together.
Rencontrer les enseignants
Peter O'ConnorProfessor of information systems, Director of the MBA in Hospitality Management
Information systems
Augustin CacotDirector of Customer Success, EMEA
DUETTO
Nathaniel GreenSenior Global Solutions Engineer
DUETTO
[MUSIC]
To help us better understand the forecasting process,
there are 15 basic forecasting terminologies I'm going to explain prior
to moving forward with the presentation.
You have variance, rolling forecast, compression,
forecast a budget, occupancy, forecast accuracy, segmentation,
booking window, and a few others that I put into the etcetera box.
Variance is a comparison of data between time periods.
The way you see variance is that you'll have variance compared to same time
last year referred to STRY.
You'll have variance compared to week over week, and
then you'll have variance compared to budget.
An example of a variance would be looking at revenue for
June 2014 at a hotel and comparing the revenue with June 2015.
The difference between the two years would be a year over year variance.
If there is a large variance understanding what caused that variance is key to
being able to adapt a pricing forecast.
The property might have missed something.
Like an unexpected event when planning for its forecast.
Making sure that you understand where an error may have occurred
which may have caused a variance will enable you to fix and
clean-up the forecast and develop a new pricing strategy as necessary.
Same time last year or STLY is year over year.
And it looks at how changes on an annual basis occur.
If you're doing a daily variance, using same time last year, you do so
by looking at the same day of the week and the same week of month as the prior year.
Week over week is a variance comparison as to where you were last week for
a given stay date and where you are this week for that same stay date.
To understand week over week let's look at an example.
Imagine it currently is September 17th and
we were looking at the stay date of December 15th.
One week later, September 24th.
What's the variants in metrics such as rooms sold, forecasted revenue for
that same date of December 15th?
Forecast to budget variance gives you an indication for
your forecast to performance with current production on the books
versus how you've budgeted and how far ahead or behind your pacing.
However, you need to be careful with this metric.
Forecasting to budget is all about trying to hit budget numbers, or
forecasting your pricing strategy to hit theoretical budget numbers.
A lot of people do this if they're behind budget and are forecasting rates or
occupancy to try to hit that budget rather than looking at the market.
It can be seen as scrambling to begin pricing to budget, so it's generally good
to forecast without looking at budget, to always keep a realistic perspective.
A rolling forecast is a live updated forecast completed daily or
weekly from changes in between the current and previous forecast.
Edgar Fiedler,
the economist, famously said, if you're going to forecast, forecast often.
It generally updates 30, 60, 90 day forecasts.
An example of a rolling forecast, would be if, let's say Monday,
May 25th, you might have done a 30, 60, 90 day forecast.
Then this upcoming Monday, you might do another 30, 60, 90 day
forecast based on the pace and pickup changes that occured over a given week.
Compression is when demand exceeds supply, and the result is a compressed market.
Compression means that prices will likely go up in the market as a whole
to curb the increased demand trends.
The goal with compression analysis for a hotel is to be able to make appropriate
pricing increases leading up to the stay date and to sell out day of.
Occupancy is the amount of guests staying at a property.
Most times occupancy is communicated as a percentage,
which is 100% if the property is full.
Within forecast accuracy you hear that you are over or under forecast.
Over forecast, means that you forecast too much occupancy or
revenue than actually has come in.
You're over expected and under delivered.
This result means that you can be over staffed or other expenses can run high.
While income will generally be lower than anticipated.
To under forecast means the forecast is too low, for occupancy or revenue.
You perform stronger than you originally anticipated.
Segmentation, following up from module two, from a forecasting perspective,
hotels normally begin from macro-level breakdown of forecasting their
three main segments.
Leisure, group and corporate.
More complicated hotels navigate a more complex distribution landscape.
So, for them, it usually is worth breaking apart leisure into retail, OTA, or
so on and so forth to have a bit more granular segmentation.
Booking window is the time period during which first booking comes in
on a stay date until the night audit of the stay date.
Different segments have different booking windows.
If you have a longer booking window, you'll need to forecast longer out.
Las Vegas for example is a short term market.
So most forecasts are 90 days out or less.
Meaning there's a heavier emphasis on 30, 60 and 90 day forecasts.
In Las Vegas, 90% of bookings come between zero and
90 days before arrival booking window.
For resort properties, you'd wanna forecast six months out at weekly
intervals because at least 50% of bookings are coming in 90 plus days before arrival.
It's really important to be analyzing these early bookings and
create early forecasts and regularly update your long term forecasts.
Something to keep in mind from booking windows is the terminology DBA,
which stands for days before arrival.
It's a term that is commonly used in the industry and
something that is discussed regularly when we're discussing booking windows.
The last thing is the etc section.
TBB or To Be Booked or
left to sell is the number of rooms forecasted left to be sold.
OTB or On The Books means the current number of rooms sold.
OOO, or Out Of Order, are the number of rooms
which are currently out of service at a specific property.
For renovation purposes, or whatever the property may take them out of service for.
Timeshares, for instance, if it's a resort property, is a common example.
This removes it from being counted in any metrics based on per available room.
Which I'll get into shortly.
And then you have your commit, or
group block, which are rooms committed or on the books for your group block.
A group block is when a group calls in to put a hold on a certain number of rooms
for a certain date, which the hotel will then section off the inventory.
The two main ways hotel register these rooms are options or committed inventory.
A group option means the groups are non-deductible from the hotel's inventory
in their systems, as a customer in the group block hasn't purchased the room.
A commit means the rooms have been purchased by a customer in the block and
are removed or deducted, from the hotel's inventory.
Restrictions, or closing out a rate, is when a hotel closes out availability,
for a certain rate to a customer.
An example of a restriction would be a hotel closing off on Expedia for
this upcoming Wednesday.
Rev par,
is a metric commonly used in the industry which means revenue per available room.
It shows the efficiency of the revenue,
versus the number of customers who stay at the property.
ADR, or Average Daily Rate Is the mean rate charged by
the hotel on any given day for any given segment.
That's the basic hospitality terminology matrix for forecasting.
As you can see there are a lot of highly specific jargon that's used.
Taking some time to understand each of them and differences
between them will help you master revenue management much more quickly.
Now that you have a foundation for the basic hotel vocabulary, we can get
into what hotel forecasting is exactly, which I will explain in the next video.
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