Simple Hostel Yield Management Example

Continuing on from my thoughts in Yield Management in Hostels?, in this article I present a simplified example of how a Hostel might use simple Yield Management principles to increase its profitability.

Yield Management or Revenue Management or Revenue Optimization is a set of theories and practices that help companies, typically in the transportation and hospitality industry, gain the most revenue possible by selling a limited product where short-term costs are, for the most part, fixed. Simply put, this is why the prices of plane tickets change every time you check and why you can save on hotel rooms by booking in advance.

Consider a simplified hostel. Another time I will discuss some of these simplifications. This hostel takes only single-person bookings for a maximum of a 1-day stay. This hostel has the following rooms: 6 private single rooms and one 6 person dorm. The beds in the single rooms go for £20 and beds in the dorm go for £10. The hostel has entirely fixed costs, meaning they would rather fill a bed at 1p than have it be empty.

Our simplified hostel realizes demand in two streams. The cheapskate travelers desire the cheap dorm rooms, and the wealtheir backpackers are willing to splurge on a single room. The cheapskates would choose the single rooms if they were the same price, and this is the key to my example.

Our hostel is considering bookings for July 1. Currently 1 of the 6 single rooms are booked and the dorm room is full with 6 of 6 beds taken. Currently revenue for this day is £80. This is low compared to the maximum potential of £180, but we're not concerned yet because there are still several days left to take bookings for the single rooms. However, during this time we may also have to turn away some cheapskates, as our dorm is full. Now we ask the question: What would happen to our revenue if we gave one of our cheapskates a free upgrade to a single room, freeing up a dorm bed for more bookings? Let us consider the scenarios in the following table:

New Single Room Booking Requests	New Dorm Room Booking Requests	Resulting Occupancy With Upgrade	Resulting Revenue With Upgrade	Resulting Occupancy Without Upgrade	Resulting Revenue Without Upgrade
5+	0	6/6 Single, 5/6 Dorm	£160	6/6 Single, 6/6 Dorm	£180
5+	1+	6/6 Single, 6/6 Dorm	£170	6/6 Single, 6/6 Dorm	£180
x<=4	0	(2+x)/6 Single, 5/6 Dorm	£80+£20x	(1+x)/6 single, 6/6 Dorm	£80+£20x
x<=4	1+	(2+x)/6 Single, 6/6 Dorm	£90+£20x	(1+x)/6 Single, 6/6 Dorm	£80+£20x

I've colour coded the scenarios above so we can see when we would benefit from upgrading a guest, when we would suffer, and when we are indifferent. In the first two scenarios we receive enough single room booking requests that we could have filled our single rooms at £20, and thus putting a cheapskate in there for £10 hurts our total revenue. In the third scenario we do not receive enough booking requests to have to turn anyone away, so we are indifferent between the upgrade and not. Finally, in the last scenario, if we offer an upgrade, a cheapskate sleeps in as single room for £10 that would otherwise have gone empty and the dorm remains full.

Evaluating the decisions is then a matter of estimating the likelihood of each scenario and calculating the expected revenue for each choice. We evaluate the decision in the same way you would evaluate the following game: I flip a fair coin. If it lands heads I give you £2 and if it lands tails you give me £1. Naturally you would calculate that 0.5*£2 - 0.5*£1 = £0.50 and thus the game is worth playing. The expected value of the decision to play is £0.50.

In order to carry this example through, suppose the probability of there being 5 or more single booking requests is 20% and 4 or fewer is 80%. Suppose the probability that 1 or more dorm booking requests is 75% and 0 is 25%. All probabilities are independent.

Expected value of offering an upgrade = 20%*25%*£160 + 20%*75%*£170 + 80%*25%*(£80+£20x) + 80%*75%*(£90+£20x) = £103.5 + £20x
Expected value of not offering an upgrade = 20%*25%*£180 + 20%*75%*£180 + 80%*25%*(£80+£20x) + 80%*75%*(£80+£20x) = £100 + £20x

As we can see, in the example that I have just constructed, we can expect to make £3.50 by giving a guest an upgrade in the same manner that we expect to gain £0.50 by playing the coin tossing game. Now £3.50 may not sound like a lot, but scale this up to a multi-hundred bed hostel and we're talking about more money.

What made this a winning decision? The £10 we might gain by replacing our upgradee with another guest in the dorms outweighs the £20 we might lose if we have to turn someone away from the single rooms.

So what? Just how likely is this scenario? Consider Smart Russel Square, a large hostel in central London, UK. As of 9:00 pm local time on Sunday, the current bookings* for Tuesday are as follows:

• Large Dorms (10 person and above) 159/160 booked
• Small Dorms (9 person and below) 135/276 booked.

*data gleaned from Hostelworld.com, reliability uncertain.

Based on your gut feeling, what are the odds that they could realize an expected benefit from upgrading some of their large dorm guests to small dorms? 10 guests? 20 guests? If the large dorm beds were filled this could represent £100-£300 in additional revenue. Minus the marginal costs of the guest including their free breakfast of course. Food for thought.

Later I would like to generalize this simple scenario, discuss the simplifications, assumptions, limitations and extensions. That's all for now, though.

Edit:
The way I've set this up might seem strange. Why go to the trouble of upgrading someone from the dorm when you could simply sell a single room as a dorm room? This is because I'm already looking forward to implementation. I don't anticipate hostel management IT systems to have the ability to do this. Instead I envision hostel management IT systems linking bed inventory directly to what is offered online, and thus for us to offer beds at the dorm rate, there must be beds available in the dorms on our system. Additionally, rather than being handled directly by the IT systems, I envision a clerk/manager manually intervening in the system and upgrading a booking. This person might follow a simple set of decision rules compiled from analysis of past data in order to make their decisions. If this strategy proved to be profitable, then it's integration into IT systems might occur.

Yield Management in Hostels?

In my recent travels in Europe I have again had significant exposure to the Hosteling Industry. As readers of this blog will know, we can't help but seeing Operations Research or opportunities in our daily lives. Sure enough we find ourselves analyzing our surroundings and considering the pricing structures of our hostels. In this article I hope to begin an exploration of pricing strategies in the hostel industry that I will continue after I have collected some of your thoughts and more of my own.

The Hostel industry has been rapidly developing throughout the world. According to Wikipedia, youth hostels had their humble origins in German Jugendherberge (1912), non-profit hostels for youths by youths. Fast forward to today and you can witness the evolution to profit-maximizing corporate hostels sometimes exceeding 500 beds.

That said, sophistication in the industry seems to be developing more slowly. In particular, possibly due to it's origins, there is significant resistance to profit-maximizing activity like yield management. I also believe that there is a growing suite of hostel management IT systems with some direct interfacing with booking websites. I can't claim to be an inside expert in the industry, though we did have a nice informal chat with the manager of a small-to-medium-sized non-profit hostel over beers in Munich.

Youth hostels face a problem that is similar in some ways, but different in others to that faced by traditional hotels. Apart from the obvious similarity of product, the primary similarity is that both face an expiring good that is booked ahead of time and cannot be stored.

Hostels, however, do not have business customers. Traditional revenue optimization approaches for hotels centre around price discrimination. With leisure customers and business customers that can be separated by booking time, hotels can sell rooms early at a discount to money-saving leisure customers and sell the remainder later to late-booking, price-insensitive business customers. Hotels can sell some rooms to leisure customers who would otherwise have gone to the competition had they been charged full price, and hotels can then later sell the remaining rooms at a higher price to business customers who would otherwise have only paid the flat rate that leisure customers pay. Hostels on the other hand face an exclusive stream of budget-sensitive travellers. The differentiation achieved by time of booking is thus only a question of how far the customer plans ahead and may say little about their willingness to pay.

Hostels have a wider range of product. I'm not an expert in the hospitality industry, so maybe I can ask our readers to confirm this, but I believe your typical hotel offers simply twin, triple, double, queen, and king rooms. The Meininger City Hostel and Hotel in Munich, Germany for example offers 9 distinct products on hostelworld.com: Single Private Ensuite, Twin Private Ensuite, 3 Bed Private Ensuite, 4 Bed Private Ensuite, 5 Bed Private Ensuite, 6 Bed Private Ensuite, 6 Bed Mixed Dorm Ensuite, 6 Bed Female Dorm Ensuite, 14 Bed Mixed Dorm Ensuite. Something that bears noting is that for the most part these products can be ranked such that any customer will unconditionally prefer one over those below it. For the most part, no customer would prefer to sleep in a 14 Bed Mixed Dorm when they could be in a 6 Bed.

Other factors relevant to the question of YM in hostels: I estimate that the majority of hostel stays are booked through internet booking websites, with the majority of those coming from hostelworld.com. The majority of these bookings are thus made after some moderate price comparison making the market fairly competitive. Many of these bookings will also be made factoring in reviews of the hostel. Sometimes hundreds of website users will have given the hostel a rating for things like security and cleanliness.

The lack of business customers does not mean that hostel customers cannot be segmented. I propose that hostels face two main types of customers. One group comprises the shoestring customers, willing to do anything to save a dollar (or a euro or a pound, etc.). The other group is more differentiating, willing to pay slightly more for a smaller dorm. I'm still working out the significance of this for myself.

I believe there is an opportunity there. Some initial research based on my own experience and some creative use of hostelworld shows that hostels often fill from the bottom up. That is that the largest dorms with the cheapest beds are the first to fill up, and the smaller rooms frequently go empty during the week. This may be a sign that the supply of hostel beds does not match demand. This may show that there are more small dorms in the market than desired and fewer large dorms.

I welcome any comments on the topic. Is there a business opportunity here, or is it just academic? Is the current state of IT and sophistication in hosteling sufficient to work on elementary yield management? Most hostels have a Friday-Saturday price, and everyone in Munich has a low season, high season, and Oktoberfest price, but could we go further?

Starting up in Operational Research: Should I be a generalist or a specialist?

This is the part 2 of 3 of the mini-series on "Starting up in Operational Research".

Question 1: What programming languages should I learn?

Question 2: Should I be a generalist or a specialist as an Operational Research professional?
"As an Operational Research professional, are you usually viewed as a "jack of all trades" or do you usually have to specialize in one area like marketing, government, military, logistics, etc.?"

The short answer is:
First of all, there are two different types of "specializations" in Operational Research: industry specialization, and OR technique specialization. When you are a student at the master's level, you cannot afford to specialize in either industry or technique, because there is so much to learn, and it is all somewhat important. However, once you start working as an OR professional, because of the nature of your work / organization, you will almost be forced to specialize in an industry, such as marketing, healthcare, defense, logistics, mining, energy, etc. However, personally, I would not corner myself into specializing in an OR technique, such as optimization, forecasting, simulation, etc., unless I were an academia. This is because of 'what-if' scenarios for your career. As an OR professional, if you specialize in a technique, you may pigeon-hole yourself into one type of job, which will be difficult to change from if you ever want to. For example, what if you wanted a change from doing simulation models? Personally, specializing in one OR technique could quickly get boring, but that may not be the case for everybody.

Now, let me elaborate a bit more on the above:
As a student of Operational Research (a.k.a. "Operations Research" in North America), there simply isn't time enough to specialize in one field of OR during the studies. At least that was the case for me. My program, Master of Management in Operations Research, run by the Centre for Operations Excellence in the Sauder School of Business, University of British Columbia, is 15 months long. It included 8 months of intensive, mandatory, foundational courses to build up the skills and tools necessary for an Operational Research professional, including but not limited to: optimization, simulation, forecasting, statistical methodology, stochastic processes, decision analysis, operations management and logistics, consulting practices, as well as operations research and management sciences best practices. These are our tools to be a "jack of all trades", and must not be neglected. Then the program included a crucial 4-month (typically) hands-on project, where the student acts as the main consultant on behalf of the school to work with a private or public organization on a relatively high importance OR project, charged with real deliverables to the client. This makes it a "professional degree", instead of a M.Sc. (Master of Science) where the student is expected to do research and produce an academic thesis paper. After the project, the entire program wraps up with another 4 months of courses, but to be chosen by the student. This is the opportunity to specialize if you wish. However, I don't believe 4 months of studies can make anyone a "specialist" in anything. It is the future work that you do that will shape you into whatever specialist you may choose to be.

As a professional working in OR, one will be forced into specializing in an industry or a field of business, such as healthcare, unless you go with a large consulting firm that deals with more than just one type of industry. With the big consulting firms, you may get the chance to be exposed to different industries, but you may have to insist. That experience could be invaluable. From my current job hunting experience in the UK, many industries are rather incestuous, such as energy, finance, insurance, and healthcare. Many jobs will require you to have experience in an industry before they would consider you a worthy candidate. I do not agree with it entirely. Even though there is much to be said about prior industry experience, a good management consultant can transcend industries, because his/her expertise is in the problem solving aspect. Industry knowledge can be picked up quickly by a good consultant, not to be an expert, but enough to solve the problem efficiently. Not mentioning, if an industry keeps hiring from within, not to be cliche, but it just doesn't have the new blood or the out-of-the-box fresh thinking to approach problems from a different angle. I understand if the hiring manager prefers a candidate with prior industry experience over one that does not, but to list it as an essential criteria is over the top and short-sighted.

To learn more about the fields that Operational Research plays a major role in, check this out.