Expedia Revenue Management at Check-out or Rule Compliance

We have all been shopping online for something only to be told after making the purchase decision that it is no longer available or no longer available at that price. This often happens when buying flights, as prices can change minute-to-minute and you can be left with a much higher ticket price which makes you abandon your purchase. Disappointment all around.

However, the opposite happens from time to time as well! The price of a London to Seattle flight, when I found it was £649.07 (including all fees). I clicked to start jumping through all the purchase hoops, but after a couple steps into the check-out process, it flagged up, rather alarmingly, as £616.07. That's a 5% decrease in price. (See, I'm not making it up!)

I was pleasantly surprised, of course. But why would they do that?

I've got 2 suspicions.

1. Revenue Management / Yield Management / Consumer Psychology

In the weeks prior to this screen capture, I've been to the site a few times already looking for the exact same flight. Even though I'm not logged in, I'd venture to guess that the site has looked up my cookies and knew that I've been looking for these flights. Therefore, it should know that I was a likely buyer, rather than a window shopper (pc pun intended). I've been at the check-out stage before, but have abandoned the shopping cart eventually. It would be quite logical for the site to entice me with a lower price as a 'pleasant surprise' to finally get me to spill my moola. Not to mention the positive impression it's left with the shopper (look what I'm doing now - free advertising!).

However, is it worth the 5% price drop? How does Expedia decide 5% was the right balance of customer incentive and revenue loss? I was already a willing customer, ready to bite. Isn't it just giving the 5% away for free? In my case, it's difficult to say whether the move has gained my loyalty to Expedia, because I was already a frequent visitor and buyer there. It may have re-enforced my loyalty though. It would be very interesting to analyse a few year's purchase and cart abandonment data of customers where this has happened to, versus a control group. Would we observe a lower purchase completion rate, which would drive a higher lifetime revenue per customer?

2. Airline price adjustment rule compliance

There could exist such a regulatory rule in the online airline pricing world to protect consumers, such that the vendor must notify the buyer of last minute price changes before the final purchase is completed. Now, I don't know if such a rule exists, but it is possible. However, it sounds extremely difficult for the regulators to enforce and monitor compliance.

I personally think it's more the former than the latter. One way to test the real reason behind the price drop could be to see if it's always a 5% decrease. Time to do some more flights window shopping!

P.S. In a previous article where we observed operational inefficiencies at London's Gatwick Airport, we erroneously stated that the airport operator was BAA (British Airports Authority). In fact, BAA was forced to sell Gatwick to please regulators seeking to break a monopoly on UK's airports. Our apologies to BAA. The current owners are Global Infrastructure Partners, who also owns 75% of the London City Airport.

Update:

Responding to two unconstructive comments, one of which was downright rude and was deleted, we thought we would add to this article.

The commenters suggest that Expedia is not a price setter, but just a re-seller making possibility one above unlikely. That said, the question still stands, "What's going on here?". If the prices that Expedia gives you when you search are cached and not live, that seems to be to be a surprising shortcoming. If they are, why offer a lower price to someone who appears to have already made the decision to purchase?

There are probably a number of factors at play that someone from the online travel community could answer.

If I were reselling through Expedia, I would want my price-updating algorithm to give the higher of the two prices at the point of payment, i.e. more profit. Both Expedia and the vendor are motivated to collect a higher price and therefore a higher commission as a percentage of the selling price.

The commenters may be very correct in saying that Expedia doesn't set the price, but merely re-sells at whatever the price the vendor names. That's why we said there were two possibilities, the second being not revenue management. However, if Expedia is not practicing revenue management in this way, they probably should at least experiment with it. Their commission represents a headroom within which they can optimize and the goal, after all, is not to make the greatest profit on each sale, but instead the greatest profit across all possible sales.

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?