SHA543: Segmentation and Price Optimization School of Hotel Administration, Cornell University

Transcription

1 Welcome. This course is part of a 5-course series on advanced revenue management with a focus on pricing and demand strategies. The first part of this course discusses how to create different prices for the same service or product and then how to effectively capitalize on those prices from a segmentation standpoint. I will refer to this as variable pricing. We later focus on the evolution of these prices as we get closer to the service state. So-called" dynamic pricing." Lastly, I will look at the interplay between booking curves and how they relate to dynamic pricing. As well as discuss some of the implications of implementing a dynamic pricing strategy. I hope you enjoy the course. Welcome, we re going to focus in on price customization and we re going to start that off looking at a relatively famous quote from the former CEO of American Airlines, Bob Crandall. And what Mr. Crandall stated was that if he had 2,000 customers on any given route and was utilizing 400 different prices then he was obviously short 1,600 prices. The idea that we would have an individual price for each individual customer. And so typically we don t go to that extreme but we re going to talk about some way in between one price and 2,000 prices different methods on how to focus in on a dozen prices. Right, well so we ll focus on a level of prices, not a single price. Historically when we talk about multiple prices we can think of this almost as price discrimination. So here we have this downward-sloping demand curve, and if I was to set a single price then I would set that price such that the price defined by that rectangle, so the price quantity pair from that rectangle had the maximum area, and that would be the maximum revenue I could generate from a single price. One of the issues we see here is that there are two areas under this curve that are basically unmet. One of those is unmet demand and one of those is basically what we refer to as consumer surplus. 1

2 Right, for all those customers who were willing to pay more than P-0 in this case, they are quite happy to pay for all those consumers who are willing to pay more than P-0 they re quite happy only having to pay P-0 and as a result they get a consumer surplus. Similarly there s a whole subset of consumers over here on the right-hand side who are willing to pay less than P-0 and as a result of that they re priced out of the market. And the idea is as we go from one price to multiple prices we can capture some of that consumer surplus for people who are willing to pay more and we can also price in to that lowervalued market who is currently priced out of that market. So specifically if we were to also have a price P-1 which is higher than P-0 now we have the subset of consumers, Q-1, who before were paying P-0, now they re paying P-1, and so we generate some incremental profit from these individuals. That s because we re extracting this delta between P-1 and P-0. Similarly if we also price at P-2 which is less than P-0 now we re selling this difference between Q-2 and Q-0 into this lower-valued segment that before was unmet demand and we could continue this process having more prices below P-2, you know, reaching a lower valued market, at the same time more prices above P-1, extracting some of the consumer surplus that individuals have at present. One of the issues we have with this is as I already talked about this was really not revenue management but this was more price discrimination, and what we want to think about is not price discrimination but price segmentation or customer segmentation. And so instead of having one downward-sloping demand curve we have a series of downwardsloping demand curves, each demand curve for each different segment, and then in each of these segments we set an optimal price. So instead of thinking this is one curve where you had multiple prices we have a series of different segments and each of those segments we have an optimal price, or even multiple prices if we can truly segment within that or truly partition demand within that segment. And so this is what we refer to as price customization or differential pricing. Our goal here is to tap into these different segments with different willingnesses to pay at different prices. You know one of the issues is that we only have this one product, right, and so we only have one seat in our airline, we only have one rental car or one room. Obviously we can have some derivations of that product but for the most part a room is a room is a room, and so we have to think about how we set different prices for this same product, and 2

3 at the same time prevent the P-1 people from buying down to P-0, so what we would refer to as prevent diversion. Right, so yes, I had this P-1 above P-0 but I only capture that gain if the P-1 people don t pay P- 0. So in the RM space we typically think about restrictions or fences to help us achieve this segmentation. Some of these are more effective than others. We think about nonrefundable versus refundable products, we think about advanced purchase restrictions, you must purchase at least 21 days before arrival, are you staying a weekend or is it just a weekday stay? Right, so we have lots of different variants we try to structure around these different price points. For most hotels we can also segment by room types. Right, so if we have two doubles, a king suite, also potentially by floor obviously an airline is going to separate business from economy but they d also like to do some segmentation even within economy. Right, and so we typically do this with some form of restrictions or what we all refer to as "fences," and we ll elaborate on this in subsequent sessions. The Grand Casino to France has recently added a 20,000-seat auditorium in which their guests and others can enjoy internationally acclaimed performances. Currently, they are charging 200 per seat, but rarely do they ever sell out a performance. The casino is looking for some help in setting future prices. The auditorium is divided into three zones, zones A, B, and C. Zone A is the most desirable with a capacity of 3,000 seats. Zone B, the second most desirable, has 12,000 seats. And Zone C, the least desirable, has 5,000 seats. At their current price of 200, Zone A has a demand of 10,000, Zone B has a demand of 6,000, and Zone C has a demand of 2,000. As you can see, other than Zone A, there is surplus capacity in Zones B and C. They ve engaged a marketing research firm to help them understand consumers' responses to price changes. Specifically, they ve looked at three prices, that is the current 200, as well as decreasing down to 100, and then increasing up to 300. For Zone A, demand at 200 is 10,000 seats. If they were to decrease price to 100, demand would increase 100 percent or double to 20,000. Similarly, if they were to increase price from 200 to 300, then demand would decrease by 30 percent, decreasing from 10,000 to 7,000. 3

4 Similarly, for Zone B, the base demand of 6,000 at 200 will double to 12,000 at 100, and then shrink to 50 percent to 3,000 at 300. Lastly, for Zone C, demand is very price responsive for Zone C, and we ll see here that if they drop price from 200 to 100, demand goes from 2,000 to 6,000 seats. Similarly, if they increase price from 200 to 300, demand is exhausted and goes to zero. So the question that we face is which of these prices should they be using, 100, 200, or 300? Should they have the same price across the entire auditorium? Should they have one price that s potentially different for each of the different zones, or at the extreme, should they have multiple prices for each of the zones? Instead of just selling at one price per zone, perhaps sell at both 100 and 200 in each of the zones. We re going to look at how we might tackle these questions knowing how responsive demand is to price. If we think about sales and revenue at a single price, let s focus on 100. We know that base demand was 10,000 for Zone A, doubling to 20,000 for Zone A at 100. Given that capacity is only 3,000, that means that even though demand is 20,000, we can only sell 3,000 seats. So 3,000 at 100 translates to 300,000 in revenue. For Zone B, if we were to price it at 100, the base demand of 6,000 would also double to 12,000. Capacity is actually 12,000, so we can meet all demand and generate 1.2 million in revenues. Lastly, for Zone C, if we were to decrease price from 200 to 100, demand triples from 2,000 to 6,000, and given that capacity is 5,000, we can only sell those 5,000 seats at 100 for a total of 500,000 resulting in a total revenue across all three zones of 2 million. Now, let s go one step further and have fixed prices per zone, but potentially different prices across the zones, so one level of variable pricing. For argument s sake, let s set a price of 300 in Zone A. We know that at 300, demand decreased 30 percent from 10,000 down to 7,000, but we still only have 3,000 seats, so we re going to have sales of 3,000 generating 900,000 in revenue for Zone A. For Zone B, if we were to price at 200, our base demand of 6,000 stays the same, which generates 1.2 million in revenue. If we were to set a price of 100 in Zone C, remember our demand now went from a base demand of 2,000 to 6,000, but capacity is only 5,000 for Zone C, so we can only sell 5,000 seats even though demand is 6,000. Those 5,000 seats at 100 are generating 500,000 in revenue for a total of 2.6 million in revenue across the entire facility. Again, it s 2.6 million, higher than our 2 million at a constant price of 100 across the facility. Now, we can go one step further. Basically, we can have variable prices within a zone as well as across the zones. What this means is that Zone A may have a different price than Zone B, but Zone B may have multiple prices. If we remember back to our step one where we had our base demand and we looked at how that demand changed as a function of price, our base demand at 200 was 10,000, 6,000, and 2,000 across the three zones. That demand went to 20,000, 12,000, and 6,000 across those three zones at 100, and then decreased to 7,000, 3,000, and 4

5 nothing at 300. Keep in mind our capacities are 3,000, 12,000, and 5,000 across each of those three zones. If I look at demand for Zone A at 300, that demand of 7,000 is greater than capacity, so life is easy for Zone A. We would simply just price at 300, our highest price and it clears all our inventory. Zone B is a little trickier. For Zone B, we had demand of 3,000 seats at 300. If we also priced at 200, remembering total demand at 200 was 6,000, but if we were to price it at 300, we ve shaved off that top 3,000 initially. That remains the 6,000 minus the 3,000 and results in demand for another 3,000 seats at 200. Similarly, total demand at 100 was 12,000 for Zone B, but we ve already sold 3,000 at 300, another 3,000 at 200, which leaves 6,000 seats of remaining demand at Zone B. If we look at Zone C, we had demand of 2,000 at 200 and demand of 6,000 at 100. If we were to price at 200, we would sell those 2,000. If we were also to price at 100, then we would have the remaining 4,000, which we could sell at Zone C. Keep in mind for Zone C, total capacity is only 5,000, so even though demand was 4,000 for 100 at Zone C, we re only going to be able to sell 3,000 of those seats owing to that capacity constraint. So the question that remains is how should we price. We ve looked at three levels of complexity. For each of those levels of complexity, we have a set of optimal prices. If we were to set one price for the entire auditorium, what would that price be? Would it be 100, 200, or 300? We can go through that exercise and calculate total sales and resulting total revenues at 200, compare that to our 100, and do that again if we price at 300 and compare that to 200 and 100, and then decide which our optimal single price is across the facility. Similarly, if we focus on one price per zone, with those prices in each zone potentially being different, then what should the Zone A price be? What should the Zone B price be, and what should the Zone C price be? Earlier, we looked at a Zone A of 300, a Zone B of 200, and a Zone C at 100, but potentially, those aren t the revenue maximizing prices. Lastly, if we had multiple prices in each of the zones, what should those prices be? We went through the total demand and total sales of each of those prices. What s the resulting revenue as a function of those prices, and how does that compare to our first two cases? So what we re going to do first is focus on three of the key aspects of prospect theory that relate to pricing. 5

6 First: Letting consumers know the trade-offs of not booking. Basically, trade-offs of potentially paying higher prices if current prices become unavailable. Second: Realizing that gains are looked upon differently than losses. And then lastly: Once consumers have agreed to spend a certain amount, it s relatively straightforward to get them to pay a little more in subsequent transactions. So let s look at a potential airline display. This airline display is a very common display where I ve searched for a set of departure and arrival dates, and I ve also indicated that I m flexible to travel in and around those dates. So what the airline has provided me is my base price in the middle of the matrix, as well as prices around my travel dates, allowing me to visualize those tradeoffs that if I so choose to travel on these days, this is my price, but if I m flexible I can move to lower prices. Or if I wait, I may have to pay higher prices for later departure or arrival times. We can look at a different airline display. Similar to our last airline display, this airline display has, across the top, different prices for different travel dates, but then below that, they have different prices for different flights within that travel date so each one of these rows is a different departure time on that same day. But then within each one of those departure times, there s a series of different prices across the different products. So they have unrestricted products, and then they have business products, and a full spectrum in between, showing how prices increase across that spectrum. So allowing consumers to make those tradeoffs across potential itineraries and potential travel dates. Now interestingly, this is a third airline display. While initially, this display may look a lot like our last display, there s something that s fundamentally different here. As we go down the rows, we see that we have different departure times, like we have in our last display. But as we go across the columns, what we see here versus our last display is that these prices are decreasing from left to right, whereas before, our prices were increasing from left to right. So as I read across the screen so as you re looking at this online, as you re reading it across your screen then prices are decreasing. So this is a very different display than our last one. In addition to that, they re also providing some information of what product classes are unavailable so creating this sense of urgency of booking now as part of the prices become red or zoned out, and unavailable. Prospect theory here would indicate that now as the consumer has sort of put his reference price on the first column price the higher price and then as he moves across the columns, those different prices are perceived as gains as prices decrease, whereas in our last display, the price on the most left-hand side was the lowest price. And as I moved across the different price points, each one of those incremental price points was perceived as a loss as the prices went up. Given that gains are perceived differently than losses, the consumer is more apt to book at a higher price, with our high-to-low display, than they are with a low-to-high 6

7 display. So this is some of the key aspects of something as simple as how we show these prices are we going to get people to buy up versus always buy that lowest available product. So this sort of keeps in mind that when we re sort of thinking about quoting rates, we need to be going from high to low. This is something that we ve cognizantly done at the call center or over the phone for years, but have really sort of resisted to implement online. We re basically communicating that we need to do those same strategic behaviors that we ve seen on the phone, online when we sort of communicate those prices. This would be a standard hotel-related display. What we see here is a display that s linked to logic logic by room type or package type and not really fully capitalizing on prospect theory, in that prices are not monotonically increasing or decreasing. They re structured by product categories, and not fully capturing sort of the implications of consumer behavior upon how we set those prices, really leaving a lot of opportunities out there for the more innovative user of how they post those prices. Now, we re going to focus on what I refer to as online upgrades and a further use or implementation of prospect theory. Prospect theory indicates that it s relatively easy to get consumers to agree to spend a little bit more given that they ve already spent something. We would refer to this as the upgrade or the up-sell. You might think of it as getting nickeled and dimed to death. This is relatively easy to do at the front desk, but how do we do this online? You can think of a guest who is checking in late at night. The last thing they want to do is go through an up-sell at the front desk, so how do I move some of that upgrading process into the electronic world? What we can refer to here is the idea of the postconfirmation, nonguaranteed upgrade. Postconfirmation basically means the guest has already booked their reservation online, has received their confirmation code. Given that we re going to do this upgrading process 30, 60, or 90 days before check-in, we don t know what demand is going to happen in the next 30, 60, or 90 days. We want to offer this on a nonguaranteed basis. Basically, the supplier has the option, but not the right, to grant this upgrade. The consumer sees this offer, either accepts or rejects the offer, and then the supplier later, once demand has been realized, can also decide whether or not they want to accept or reject this offer. If we look at how this would be facilitated online, I ve made my reservation, I m at the hotel s Web site, and I've made my reservation. I receive my confirmation code. There might be a link to a discounted offer. It s important to communicate that this discounted offer 7

8 is an offer but not a guaranteed offer. If the user clicked on this nonguaranteed offer, they would see a subset of potential upgrades. Maybe for $20 or $40, they could upgrade to one class of room. For $80, they could upgrade to two classes of room, and then for a higher price, they could upgrade to some sort of suite. It s important that you communicate to the user that what this upgrade is is a request for an upgrade. It's not a guaranteed upgrade. We may also send this upgrade offer via as well as right after that purchase decision. To allow for a separation between purchase event and upgrade event, we may later go through this process again, but via , so sending that confirmed guest an . Within that , we have this same offer. One of the things to remember is we can also send these upgrade offers to members of our loyalty programs. Presently, most of your loyalty program guests are probably expecting a free upgrade. What we would do is simply send them a set of offers, maybe three potential upgrade offers, and the first one would be free. Here we re communicating, this is your base-level upgrade. It costs you nothing, but here are two other higher levels of service, which are incrementally more expensive. Here s a great way to communicate that we value your service, and here s what we re going to upgrade you to for free, but just in case, here are a couple better classes of service and an opportunity for you to even extract further revenues from these high-valued guests. So as we move from variable pricing to dynamic pricing, remember that dynamic pricing is the evolution of those variable prices over time. What that requires is for us to have some idea of how demand is going to materialize over time. And booking curves are our most common form of understanding how demand is evolving over time and hence what are the opportunities to capture with a dynamic pricing strategy. Booking curves are relatively straightforward to construct; they are basically the average reservations on hand, for different days before arrivals, so some sort of snapshot of how demand typically materializes over time. So typically on the right-hand side we have 90 or 120 days before arrival and then over time demand picks up as we get closer to departure for a plane or check-in for our guest. So this basically shows the evolution of reservations as we get closer to the service date. 8

9 So we would typically have different booking curves for different segments, or more specifically, each segment should have a different booking curve, so we would have different booking curves for our product classes or rate classes, different booking curves for length of stay, and different booking curves for day of the week. One could argue that if the booking curves are not distinct then those are really not distinct segments because they are booking in the same fashion. And so ideally as we compare these booking curves from one product class to another, we would expect them to have different shapes and different levels of materialization and that would sort of tell us that these are potentially different segments. One extreme might be comparing your discounted booking curve versus your full-fared booking curve. And as this particular example shows, here we have our discount demand coming in relatively early, and we have our full-fare demand coming in relatively late. So what this tells us is that my discount rate can be available probably all the way up until three, four, or five days before check-in and I have very little potential for dilution. Very unlikely that a full fare customer is going to arrive and purchase a discounted product because those full-fare people are not in the market yet, their booking curve says they really don t start to pick up any traction until somewhere around four days before arrival. And so this tells that if we re going to have prices evolve over time that we could have our low price out there right up until about five days before arrival and then if we kept our low price in the market after five days, we run the risk of dilution of our high-priced demand buying down to that low-priced product. So we re going to further continue our discussion on booking curves. Here we re going to focus on how to use these booking curves to understand segments, and at some level control inventory. So here, what I m gonna show is a series of booking curves for demand into the Hawaiian Islands. And this is specifically demand that s generated at an online travel agent. Each of these graphs is going to show three different series: a solid line, which is a package product. A package product would be someone purchasing a hotel room and an airline seat together as one product. We have the dotted series, where the consumer is purchasing their hotel room opaquely so that basically means they know it s a four-star hotel, they don t know which fourstar hotel. And then lastly, the dashed line is a consumer who s purchasing just the hotel room so he s not purchasing air along with that hotel room just the hotel room. 9

10 So what we see here is the opaque product and the hotel-only product seem to exhibit the same booking characteristics the shape of the curves is relatively the same. What the solid line shows is our package purchases are those consumers who are purchasing both air and hotel together. Its distinct shape from the hotel-only product indicates that these consumers are in the market much earlier than those looking for just hotel room only. Specifically for the package, roughly 90 percent of those purchases are made prior to 14 days before arrival. So here s a nice way to segment. You know that people who are in the market early the pricesensitive consumers are more likely package purchasers. So you could have a discounted hotel rate as part of a package, and have your higher-price hotel rate as part of a nonpackage product. But if we look at those same curves in a different way so here, see we have the same three curves package, hotel-only, and opaque but now we look at a short length of stay. So what we see here now is that these booking curves are not all that distinctive. So this is a guest who s staying one, two, or three nights. If we compare this graph to our next graph, which shows those same three curves but for a longer length of stay, we see here that a large part of the segmentation is length of stay, and not necessarily the purchasing mechanism. If someone is purchasing a week, that they tend to purchase that further in advance than someone who s purchasing two or three nights. So one could sort of think about the segments here, the individual flying into the Hawaiian Islands for two or three nights is probably a business traveler, whereas the consumer flying into the Hawaiian Islands for seven days or longer is probably a leisure traveler, is in the market earlier for more discount prices. So in addition to segmenting by package versus hotel-only, we can augment that with short and long length of stay. If we compare and contrast this across product quality so here we re going to go through a series of booking curves each of these booking curves is for a quality of service. Initially here we have one- to two-and-a-half-star hotels. Our next graph is three-or four-star hotels. What we see here is that the behavior is consistent across the different channels, across the stars. So stars star quality is not as prolific a segmenter as, say, the channel or the length of stay. So in summer here we saw that length of stay was a good segmenter, also that package versus hotel-only was a good segmenter. These are very logical, and we re just confirming that logic by looking at our booking curves. So quite often booking curves are simply testing some of these logical hypotheses that you have put forth from a marketing standpoint. All right, our focus today is going to be on strategic consumers, and ultimately their impact upon segmentation, and then how that leads to pricing. 10

11 So historically, if we think about how prices evolve for a particular airline flight so that flight might start going on sale six months prior to departure. Then if we look at how those prices evolve as we get closer to the plane actually departing, those prices have historically looked like a very squished U. Where early on, prices are relatively high, as we get closer to departure they decrease, and then ultimately they increase as we get to the last two or three weeks prior to departure. But more recently what s happened is a series of information providers which potentially has some implications upon that price curve. The first we ll focus on is Bing so, Microsoft s search engine. If I was to perform a search on Bing for a flight so here s a flight from Newark to San Francisco so not only do they provide me with information on that flight, they also potentially suggest some alternative routes. So instead of flying to San Francisco, maybe I want to fly into San Diego or Sacramento. Instead of flying from Newark, maybe I want to fly from LaGuardia. So they re already leading me to other routes that may be more price-competitive. In addition to that, when I actually focus on my route, they show a set of prices, but then they also provide to me a barometer of where they think prices are going to go Should I wait, or should I buy now? Basically, they come up with a probabilistic estimate of whether or not they think prices are decreasing. If they think prices are decreasing, then we should wait; if prices are increasing, then you should buy. So really sort of providing some insight into that movement of prices, i.e., where are you in that sort of flattened out U shape. If we think of the other big search engine in this case Google Google has a similar product called Flights. Little bit different than Microsoft, but they basically provide an array of flights for different origins and destinations. But they also provide some metrics on if your travel is flexible, if you can adjust your departure dates, they ll provide this sort of graph of how those prices are evolving at these different departure dates. So if you re flexible in your travel plans, then maybe you should choose an alternative time to fly, basically trying to pick a spot in that U for different departure dates. They also allow to sort of simplify that set of flights that you look at into what we refer to as an efficient set so basically flights that are short and cheap. So we can sort of look at a two-dimensional view of prices. So I only want flights that are less than ten hours and that are less than $ So I don t consider flights outside of that set. So what s the implications of this? Well, if we think of the information provided by these two search engines, and then if we think about that U-shaped set of historic prices, well really, we re not going to achieve some of that desired segmentation. If we were pricing relatively higher early on in the sort of selling window, so back at six months maybe to three months before departure, and the consumer is now privy to this information from these information providers that prices are decreasing and that they should wait then that really means that our ability to sell at those higher prices early on is lost. And so we ve lost some of that desired segmentation as customers will delay that purchase decision. So as more consumers are privy 11

12 to that information, our ability to have that desired set of prices, and ultimately achieve that segmentation, is decreased as a result of improved information in the eyes of the consumer. All right, we re going to continue our discussion on strategic consumer behavior. This time we re going to focus on the acquisition of hotel rooms. So similar to air, we think about hotel search, Bing is providing lots of information to the consumer. So I ve searched for a hotel room, say, in New York City. In addition to providing me with a list of prices for a set of hotels in New York City, what the search engine is doing here is providing some indicator of what they think about those prices specifically, are those prices deals, not deals, or typical rates? So they have this sort of barometer of rates. So I can either see that in the list, or I can see that as a map, and so I get a sense of what part of the city are prices high or low. So they re not providing me any indicator of whether or not I should wait or buy, but they re at least providing me a check of whether or not these are realistic prices for that market. Bing provides me some information on how it estimates this sort of deal or not deal, and that s basically by comparing current rates to historic rates for similar travel periods. So I m traveling on the fourth Monday in March, and so Bing might compare current prices for this fourth Monday in March to the fourth Monday in March last year, or maybe the fourth Monday in March this year to the second or third Monday in March. So I get a sense of how prices stack up to similar prices the kind of thing that I would do as though I was a regular traveler into that market. If I m an irregular traveler and I have no sense for those prices, this is some sort of barometer of where those prices are. Similarly, Google has a product called "Hotel Finder," which provides, again, that sort of barometer of prices. Unlike Bing, which has categorized it into three, basically what Google does is provide this continuous scale of the percent of current prices to typical to give you a sense of whether or not it s a good time to be in that market for a hotel room. More recently, online travel agents have sort of started offering new products, which are similar, or facilitating, this shopping behavior. The first of those was Orbitz, which offered a product called "Price Assurance," which basically indicated that if you booked a hotel through Orbitz, and somebody else after you booked that same hotel room the basic same hotel, same room type for the same check-in and check-out dates and got a better deal than you did, then Orbitz would refund you the difference between what you paid and that later-arriving customer paid. So some incentive to book with those to get access to this deal. 12

13 More recently, Tingo has entered this space, and basically what Tingo is doing is taking Orbitz s model one step further, and instead of monitoring who has booked after you, they simply monitor prices. So if you ve booked a hotel room and prices drop, then what Tingo does is automatically cancel your reservation and rebook you into that same hotel, same room type, but at this decreased room rate. So unlike Orbitz, Tingo is shopping and automatically doing this sort of cancel-rebook, whereas Orbitz is only doing it if there s a transaction. So what are some of the implications of this strategic consumer behavior? Well, anecdotally, we observe that approximately 20 percent of hotel prices are decreasing from the time of booking to check in. And these typical price decreases are around 10 percent, so the product of the 10 and the 20 is 2 percent. So if all consumers were to cancel and rebook if prices decreased, then prices would drop by about 2 percent. So this gives the idea that we need to focus more on segmentation perhaps create some more structured products that have cancellation fees, or are all prepaid products. So trying to make the hotel booking similar to the air booking, where it s very hard to cancel and rebook so as to deter that strategic behavior on the act of the consumer. So if you think the consumer thinks they re going to travel, then they should buy now versus waiting. Ultimately, our goal is, consumer looks at our prices, finds them attractive, then we want them to transact now. The last thing we want to do is have a consumer who looks at our prices, sees them, and decides not to transact, but to wait in the future and potentially transact later. So in the best-case scenario, when they decide to transact later, they may still transact with us, but if they decide to postpone their purchase decision and our prices go up, and one of our competitors' prices goes down, then now we ve potentially lost that consumer. So our goal is to transact when the consumer was happy with our price, and just sort of deter that strategic behavior. 13