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.
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.