Len Testa from Touringplans.com
Carl - Let's start off with, tell me just a little about yourself, your background and how you got started with Touringplans.
Len - I'm Len Testa, I am the president of Touringplans.com and also co-authored the unofficial guides for Walt Disney World, the Disney Cruise Line, Washington D.C., and I contribute to Disneyland and Universe of Orlando as well. I got started, and this is actually a great technology story. Back in the 90s, mid 90s, right before I started graduate school I went to Walt Disney
World with my twin sister. We waited in line for 2 hours at the Great Movie Ride. Somewhere in that two hours, Carl, before I passed from heat stroke I thought to myself, "You know, there's got to be a better way to see these rides". While I was in line I came up with the idea of a computer program that would tell you the order in which you should ride the rides to minimize your wait in line. So, essentially a guide that walks you around the park and told you where to go next. So, I went back to my school, I started school in the fall.
I talked to my thesis advisors about this and I said, "You know, I think I want to study this." They had two questions, and they actually sent me to an actual library, you remember libraries, Carl?
Carl - (Laughing) Yeah, with books.
Len - Actually I used microfiche, which, kids today, I pretty would be speaking French. They had two questions. One, is it, was it hard enough, like was this question that a student's going to study was it hard enough to be a thesis topic? Then two, and this is the funny question, does anyone but you care about this? So, it turns out that building a schedule for Walt Disney World is one of the classic problems in computer science; even has a name, it's called a traveling salesman problem. For your readers or listeners who are technology minded it's in this class of problems that's called Np-complete, and it means that there is, there's no really efficient way to solve those kinds of problems with computers for very big instances of it.
So let me give you an example. In the Unofficial Guide to Walt Disney World, I think our standard 1-day touring plan for the Magic Kingdom has like 25 rides in it. If you were to ask a computer for the exact solution, the perfect route for those 25 rides it would probably take somewhere around 500 million years for the computer to figure that out. The problem is this, there are 25 ways to see the first ride, there's 24 ways to see the second ride, there's 23 ways
to see the next ride, 22 ways and so on. So, it's 25 times 24 times 23 times 22 and so on possible touring plans for that one day in the Magic Kingdom and no computer is going to be able to solve that. I think it's like 155 septillion combinations, I mean just a big number.
So, the way that you solve those problems is using these things called heuristics or rules of thumb and some of them have been developed over, you know, the last 40 or 50 years or so and I still hate those. Interestingly, here's the interesting thing about it, the problem of minimizing your wait in line at Disney World is exactly the same problem that companies like Fed-ex and UPS face everyday when they're trying to minimize the cost of delivering packages.
So, imagine you're a driver for UPS or Fed-ex, the next place that you visit isn't necessarily the one that's going to be closest to you, it's the one with the least amount of travel time. What that means is traffic is important right? We all know this; you go on the interstate at 5 o'clock in the morning it's vastly different than at 5 o'clock in the evening right? Yes?
Carl - Yeah, absolutely, oh sure.
Len - Yeah, so travel time is what's important there and that's what UPS and Fed-ex seek to minimize. In fact, my master's thesis expanded on the PHD dissertation of UPS'S lead research scientist, that tells you how close these 2 problems are. So, during graduate school I worked on this and I emailed, actually I wrote Bob Sehlinger an actual letter, like on paper with a stamp. And, I said, "Hey I know you wrote this book and you've got these touring plans, and I'm looking at this problem too, do you have any data that I could use?" It turns out he was looking at the problem a completely different way. He was working with some guys at M.I.T. to solve the problem, and he was approaching it from the point of view of somebody running a theme park. I approached it from the point of view of a customer in a theme park where you have vastly less information like Bob needed to know things like how many boats were running at Splash Mountain and stuff like that and I just looked at the wait times which is a huge simplification.
So it turns out we couldn't share the data but Bob was providing tips along the way of things like how to handle restaurants, how to handle things like Tom Sawyer Island, how to handle things like, do you remember the Diamond Horseshoe Saloon used to serve lunch?
Carl - Yes.
Len - So, if you wanted to eat lunch and see the show the most efficient way to do that was to visit the Diamond Horseshoe Saloon at noon right?
So he'd provide tips like that. So, I get to graduate school, the university patents my research,
obviously I get through graduate school, I publish my thesis and we take the software and we continue to build on it for like 3 more years. So I graduate in 2000, in 2003 the software was actually good enough to start replacing the touring plans in the book and we've been developing the technology ever since then. So in 2008 we provided custom touring plans to people online. In 2011 we launched the app and then we allowed people to optimize their touring plans while they were in the park. This year we did, we started doing crazy things like we'll automatically detect when rides are down and we'll automatically re-route you around those and all kinds of other support for things like the morning magic hours now, the after hours events, all kinds of, the evening safari stuff is now a special feature.
That's all the technology going on behind the scenes, and behind the scenes we're using Amazon's cloud.
So this is technology, so everything's running on Amazon's cloud, the entire website, all of the optimization stuff happens there and I'm a huge fan of Amazon's cloud servers, we use it extensively.
Carl - Yeah, they do work almost all the time, they're really, really good.
Len - So that, that's the interesting thing so there's 2 interesting parts about Amazon's cloud. One is you don't know when a server's going to go down so you have to build around that. Are you familiar with Amazon's queuing services or anything like that?
Carl - I am, but go ahead and explain it.
Len - So, message queues, if you're just using a regular web browser and you're communicating with a server one of the problems with that is that if the communication goes down for a second like if you're in the middle of a call to a web server or if the server's in the middle of delivering
you a response and for whatever reason the connection is broke. Like, you're using your cell phone and you just happen to go out of cell phone range or whatever. All that communication's lost, so in order to guarantee the delivery of the touring plans we use these things called message queues and it's essentially a pipeline that never goes away even if either end of the connection is broken. What it does is when you make a request for a touring plan we put a request on this message queue thing and then eventually a server on Amazon reads it and Amazon guarantees that message queues are persistent and reliable. But here's the interesting thing, the way that they guarantee that it's persistent and reliable is by making multiple copies of them, of the message queue and putting them around the country.
So, whereas you and I look at one message queue, and by the way this is the most technical conversation I've had.
Carl - (Laughing) Yeah!
Len - So, you and I look at it as one message queue but there are actually 6, and the interesting thing about that is when you ask a question like, "Is there a message on the queue?" It's only going to send that question to 1 of the 6 queues and the answer you get might be different if you ask the question twice because some message queues might not have all of the messages yet. So you end up with this probabilistic thing where you ask, you ask the message queue, "Do you have any messages?" "No." Then you ask it again, "Do you have any messages?" "Yes." And you ask it again, "Do you have any messages?" It could say no.
So, you end up with this, we're having to write code that handles not a definitive answer but a probable answer.
And that was fascinating, that took us like 2 days to figure out. Like, how do you handle a probabilistic answer of do you have a message? That was super interesting stuff. We figured our way around it and you know, we do load balancing across all the servers so everything is efficient.
Carl - Just so everybody understands, this message queue thing is happening in tiny fractions of a second.
Len - Oh, milliseconds… So the time it takes to get a message, once we get the message from you, to route it through all the infrastructure, to do all the load balancing, to get to optimizer is under a tenth of a second. In fact, from the time that you push the button to optimize your request usually takes about a minute. Of that minute probably half of it is just network latency. Like, to get it through all the different systems, especially if you're on your cell phone the vast
majority of that is network latency. Carl - Yeah. Okay, enough of the geeky stuff (laughing). Len - (Laughing) It's kind of awesome though, yeah?
Carl - Geek talking to geek, well we could do this all day, but okay. So, lets talk about your crowd predictions. How does that happen?
Len - So, we do this thing where we tell you the order in which you should ride the rides to minimize your wait in line, that's our route-planning thing in our lines app. But, in order to do that we have make predictions about how long you're going to wait in line at every ride so we actually have professional statisticians. We have a data scientist, Fred, we have a statistician, Steve whose job it is to build models that tell you how long you're going to wait in line at every ride, every day for the next year. So, to tell you the commitment that we give this, there are lots of websites that have crowd counters, we are the only people that do this scientifically. I think everything else, the vast majority of websites out there that have crowd counters are doing this for like search engine optimization. We spend hundreds of thousands of dollars here on these predictions.
So what we do is this, everyday we collect wait times from every ride in every park that Disney operates, every 5 minutes.
Carl - Now, how do you do that? Do you do that by looking at Disney's app or do you have somebody in a park?
Len - We do 3 different ways, so one we have staff whose job it is to be in the parks everyday and our guy in Disneyland for example, his name is Guy and he is tasked with recognizing when he looks at a line whether Disney's app has the right wait time for it. So lets say that you walk into Peter Pan and the posted wait time is 5 minutes but the line is all the way through the queue, all the way out the door and down Fantasy Land. Well something's wrong there right? The wait isn't 5 minutes so at that point Guy will get in line and start timing his actual wait in line to tell us what is actually correct. So staff, we have people whose job is to be in the parks everyday that's their job.
The second thing that we do is we have users, who through our lines app can tell us both the posted wait time and then their actual wait in line. So we have hundreds of thousands of users of our lines app and millions of users of our website. At any given time, any given day we have between 3 and 5 hundred families in the park, generally more during the holidays and they're constantly giving us wait times for how long they're waiting in the parks.
The third way that we do it is yeah, we look at Disney's wait time app.
So we get anywhere between 8 and 10 thousand wait times a day. We have a database of around 11 million wait times that goes back a number of years. For each wait time that we collect we append to that wait time anywhere from 2 to 6 hundred other pieces of information. Everything from the extra magic hours schedule for all the parks for that day, to the park hours, to the weather, to things like how many other rides were down in the park. For example if Soarin' is down at Epcot it's going to affect the wait times at Test Track.
So you need to know how much capacity is lost. The other things that we look at are even more esoteric. We look at things like the consumer price index trend over the last 30, 60, 90, and 180 days to see how much the U.S. economy factors into that. We look at unemployment rates. We look at currency conversions for Canada, Mexico, Brazil, and the U.K., and lagging indicators as well, so what was the economy for those countries like a month ago, 2 months ago, 3 months ago and so on. Because, what's happening today in the parks is the result of people planning trips months ago, not yesterday.
So when you're making a decision you're making a decision to go to the park, you know 4 months from now based on the economic reality that today is, right?
So we look at all that. Then, we have a really sophisticated infrastructure that takes all these data and makes predictions from one day to 365 days in advance. We do some, we originally started off doing a statistical regression, this was back like 2001 to 2012. It turns out that regression is not the best way to do that. You tend to run into problems when you have more than like a
dozen or two dozen variables that you need to predict off of. So, we look at, we moved to some machine learning packages in 2012. There's this thing called Treenet, which is a stochastic gradient boosting tree algorithm.
Carl - Oh now that's a big word. You're going to have to explain that one.
Len - That's why it cost money, yeah so everyone knows what a decision tree is right? So, like, imagine you're trying to make a decision about whether you need a jacket and an umbrella today right? So the first question, the first decision you might ask is the probability of rain greater than 50%? If yes, bring an umbrella, if no don't. Then the next question might be do you need a jacket, is the temperature going to be more than 70 degrees, yes or no? So imagine a tree where the first layer is asking a question about whether you need an umbrella and the second layer is asking a question about whether you need a jacket. So there are four possible answers there right? No jacket, no umbrella. Jacket, no umbrella.
Carl - Right.
Len - Umbrella, no jacket. Umbrella ... Okay and at the end of the day you can go back and tell that tree whether you were right, right? So maybe there was only a 30% chance of rain but it actually rained, right? So at the end of the day you can give credit or penalize the decision tree based on how successful it was, so you assign various weights and penalty functions to it. Imagine now instead of one tree you had a thousand trees that looked at all of these different aspects of Walt Disney World like how many schools are in session? Is it a holiday? Is it an extra
magic hour morning, right? So each tree is making one decision or maybe two decisions but you combine them all right, so that the output of each of those trees, which is essentially going to be a wait time. You take the output and you combine it magically, we'll skip over how, into a prediction about what the wait time is going to be at a particular ride in a particular time on a particular day, that's what that does.
So the stochastic gradient boosting part, stochastic means that the prediction from one tree depends on the prediction at a previous point in time, so stochastic is a time series thing. Gradient boosting, gradient looks at the curve of the error in the trees and then boosting is essentially how you adjust the weights. So, yeah, so we've been using that since 2012. Last year I started looking at even more sophisticated, or sorry we started looking at more sophisticated machine learning things. So instead of having one set of decision trees what if you have like fifty sets of decision trees and you use like even more sophisticated statistical analysis and you combine these trees in different ways how does that work?
So that's what we're working on now, literally like this morning I spent doing that. It's, you know I tell friends like, we've gone beyond basic statistics, we've gone beyond basic machine learning at this point. We're like, the places that we're getting ideas from are essentially people's PHD dissertations, which just to put in perspective, I've seen Disney's internal predictions for some of these rides. We're more accurate than they are.
Carl - Yes.
Len - I'm not saying it's perfect, you know there are things that go wrong. Weather for example is one thing but we are very good. So the interesting thing is we've been looking at this, you know for the last year. One of the fundamental questions I had for these stats guys is how good or how accurate can the predictions be, right? Knowing that I have to make a prediction sixty days in advance and I don't know what the weather's going to be like and I don't know things like Disney's staffing schedule. How good can we be, and I think the answer that we're at is we can be somewhere between eight and fifteen minutes of accuracy. So if you tell me you want to predict the wait time for noon at Space Mountain on a given day generally speaking we'll be between plus or minus eight to fifteen minutes.
But here's the interesting thing. The biggest problem that we face isn't that we don't know like, what crowds are like at noon because we know what crowds are like at noon. The biggest, one of the biggest problems that we face besides weather is that Disney intentionally manipulates those wait times and they don't seem to have a pattern in doing it. So, you've seen this, you've been to Disney World. Carl - Yup.
Len - Like the Magic Kingdom in like, December.
So a couple years ago, this is interesting, this is a technology thing. In Disney's My Disney Experience app a couple years ago, when they posted the wait time the internal data feed inside of My Disney Experience also had the actual wait time for the ride. Now they didn't show it to you but it was in the data feed, so your phone was getting that information it just wasn't showing it to you. We had access to that information and one of things that we noticed is that when it was very crowded at certain rides like Space Mountain or Kilimanjaro Safaris, or Expedition Everest, Disney would intentionally raise the posted wait time as a signal for you to go somewhere else. So, for example over Christmas in 2014 Disney raised the posted wait time for Space Mountain to 240 minutes when it knew the actual wait time was 45.
Carl - (laughs) Oh wow!
Len - Right? So, a 200, think about that a three hour and change gap and it's not because they thought that the wait time was going to be 240 minutes. They were trying to send a signal to people to go somewhere else because they knew Tomorrowland was going to be crowded later on. So they're using, Disney uses wait times as a crowd control measure and the problem is we can't predict when or to what extent they're going to do that. That seems to be largest source of error for us right now.
Carl - Now, I know you said you saw that back in 2014, are you still seeing that today?
Len - Toy Story Mania every night, man!
So if you go to Toy Story Mania, in fact a user just, just emailed me this so last week at one point the posted wait time for Toy Story Mania was like seventy minutes or something and we had predicted it would be ten, an actual ten minute wait and the user didn't believe us, got in line, and waited seven minutes and sent me an email on it like nobody in my family believed me so I took one of my kids because the rest of the family didn't want to wait an hour in line. It was like 8:30, the park was supposed to close at 9:00 something like that, got in line you know, and was done in ten minutes. The reason they do Toy Story Mania is they want to close the park on time so they don't, you have to pay people.
Carl - Right.
Len - So it's artificially inflated then as well so that's another example of it.
Carl - Yeah, I didn't know.
Len - Every theme parkdoes it right?
Carl - Right.
Len - Disney's not the only, every theme park does it.
Carl - And I did have that same experience because I tested lines, it was shortly after it came out, a year or so after it came out and I was going to say okay how accurate is it, lets test it. I looked at their wait, the posted wait, I even tracked it.
Len - Oh that's awesome, did you really?
Carl - Yeah, I even tracked it, and I said, "Yup, lines is more accurate."
Len - So the interesting thing that we do there, so obviously we're getting real wait times from our users in the park. But, one of the interesting things that we do is we have our crowd counter that predicts stuff, you know 1 to 365 days in advance, but once you're actually in the park we use a completely different prediction technology to update the predictions while you're in the park. So today once the park opens at, opened at 8 o'clock or 9 o'clock, once we got like ten minutes worth of wait times we started making day of predictions and those are vastly more accurate. We use a straight regression method for that, but essentially what it tells us is what's actually happening in the park that day. So, if you're using a touring plan and you're using the app and you're there in the parks, we will tell you, we'll redo your touring plan based on what's actually happening.
And last week, you mentioned you were there, I was there too. We actually tested this in the Magic Kingdom so two steps into my touring plan the lines recognized that Peter Pan's wait times were not growing as fast as we expected and it re-routed me over to Peter Pan because, to take advantage of the lower waits.
And it ended up saving me 35 minutes that day because Laurel was following, was with me in the park following the exact same touring plan but a printed version and she had 35 more minutes of wait. The only step that lines changed for me using the app was Peter Pan, and it saved me 35 minutes.
Carl - That's cool, and actually going out and testing it against -
Len - Side by side.
Carl - Side by side testing that's just, that's priceless right there.
Len - Yeah, I mean, we do it all the time, it's how we know that, you know on average you'll save four hours in line.
Carl - Right.
Len - Because we've recruited families to do this again and again so...
Carl - Yeah, it's the way you should do it. Thank you for doing it. Did you all catch the big crowds that happened in January and February of this year?
Len - We did actually, I think we did fairly well on those yeah.
Carl - Yeah, just curious because ...
Len - Last, so the reason why I went back and started looking at the machine learning stuff is because we didn't catch last September and October. Those crowds, remember those were crazy.
Carl - Right, right.
Len - So we did better in January and February and again you know it's this combination of Disney's trying to you know boost their ... They can do sales, they can do pin codes, and we can't always see those things.
Carl - Right.
Len - But, you know we're getting better at it so ...
Carl - Especially when they do them outside the U.S., which is what I think happened early this year.
Len - Yeah, because before Brexit the pound was actually fairly strong against the dollar, it's pretty much collapsed since then but I think it's down like what a third or something like that?
Carl - Something like that.
Len - Yeah but that stuff's only going to effect tourism in 2017 so ...
Carl - Okay, so lets move on. Lets talk about the hotel part of Touringplans that you came up with. I think that's just cool.
Len - Thanks, so we have this thing called the hotel room finder and what it is is it's photos of the view you get from every hotel room in Walt Disney World.
Carl - I will say there are some that need to be updated.
Len - Yeah, there's a few, we're working on it.
There's 32 or 33 thousand photos and we're working our way through it.
Carl - I was, I'm a lover of The Polynesian so I clicked on The Polynesian today, wait a minute... that construction's all done. (Laughs)
Len - Yeah, yeah we're working on that. We had this, our main photographer was a retiree who lived in Orlando and he actually moved here to North Carolina where I'm at now so we've got to go back and redo those. But the idea was this, back in like 2011 when, before the site got really really huge I was answering all of my own email, and I still answer most of my email but some standard questions get handled by customer service. That year I answered 16 thousand emails by myself, which when you think about it is like almost like 55-60 a day or something like that.
At least 50 a day, and one of the questions we got most frequently was based on something we said in the book. In the book we say, you know, if you want the best hotel rooms in this particular
resort, like the best rooms at Caribbean Beach are these and we explained why. But people would write in and say you know, I understand that you recommend this room but I'm looking for something closer to a bus stop, or I'm looking for something that's very quiet, or I don't care about quiet, and I don't care about walking, I want the prettiest view, what do you recommend? We were getting thousands of these emails a year, I mean, as you can imagine.
So, me and Bob are sitting around one day, we're at Pop Century kind of walking through and you know we're sitting down for a second and I'm like, "How long do you think it would take to get a photo of the view from every hotel room here?" So, we figured out there's like you know, 30 buildings, there's 192 rooms I think in each building, what if it takes a minute then you're looking at like, a couple days. So we get our photographer out and we're like okay take one building, 192 rooms, take a photo of the view from every window and tell me how long it takes for you to do those photos. It took him less than an hour, right? So we're like okay, well you could conceivably do photos for every room in Pop Century in a day? He's like, "Yeah I think I can do it in a day." I was like okay well then we could do every resort in a month!
Carl - Yeah, that's impressive because that's 2,884 rooms-
Len - Yeah that's a lot of rooms-
Carl - In Pop Century-
Len - Yeah, that a lot. So, sorry Pop I think had, it's Art of Animation that has the 192 rooms. But yeah, so we're like okay, so he did, and it turns out that getting the photos is the easy part (laughs).
So what we did was this, we decided to build maps, and we built a search engine on top of it, and it works like this. We have maps showing for every building and every floor where those rooms are located and so we have a little rectangle for example that shows you where every room in every building of Caribbean Beach is or every room in every building of Port Orleans French Quarter or Riverside. And, if you tell us the kind of room that you booked, like a standard view two queen bed room, we'll show you on the map where all of those rooms are located floor by floor, and if you click on one of the rooms we'll show you the view you get from that room. In addition to that we'll tell you like, you know, the kinds of beds that you have, we'll tell you whether the room is ADA accessible, and what amenities it has. We'll give you a rating for how quiet we think it is, how far it is from the bus stop, how far it is from the lobby and so on, and all this is searchable so you can find the perfect room you want. By the way, I've got to say this is another example of where Disney won't officially acknowledge that they cooperate with us, but they totally cooperate with us.
Carl - Right, right.
Len - There's no way we would get that information without them, and they were fantastic about it. Yeah so, and things change and what not, but we're trying to keep up with it the best we can. The interesting thing was this, it took us the most time to number the photos and build the maps. So, you know when you take a photo on your camera it's like Img_1234.jpg or something like that, right Carl?
Carl - Right, absolutely.
Len - We had to rename those in a system so that we could look it up automatically. So, you know, we had to realize that IMG_1234 was really Caribbean Beach, building 34, room 23. That and building the maps took us a year! (Laughs)
Carl - Oh yeah, I can imagine.
Len - The organizational system for all of it was just incredible, but it's done now. The other thing that we added to it was this, we built a fax API, or we hooked into a fax API so that if you tell us you like a particular room, and you tell us when you're going to check in, and give us your reservation number, we will automatically fax your request for that room to Disney five days before you check in. We've talked to room assigners across property about how to do that most efficiently to make their lives easier and to make your lives easier. So there's a success rate for
that of about 70% right now. So if you ask for a specific room or a set of rooms, you'll get one of those rooms 70% of the time.
Carl - That's pretty incredible, that's really really ... Len - It's kind of amazing, the most common reason for not getting your room is that someone's already in it. There's actually rooms at The Contemporary where, you know our users, our Touringplans community, they essentially occupy the room 365 days out of the year. I won't specify the actual room number because there's already enough competition for it. I'm going through the images one day and I'm like why do we have 53 images from this one room, and I'm like ... So I'm talking to Guy, our Disneyland guy who handles the process and he's like yeah liners stay there all the time. So it's one of those rooms where, you can see the Magic Kingdom but you don't get charged the Magic Kingdom view for it.
And so it's like one of this little piece of knowledge that's passed along from our community, so I'm like you know Epcot, there are places within The Contemporary where we actually don't have a photo from a particular room and like one room next door I've got like 53 photos, because it's the best room.
So that works out really, really well. We talk to room assigners all the time about it that the hotel fax thing is kind of awesome. I'm particularly proud of that. The interesting thing too we actually patented that and we did it two ways, obviously the ability to look the room is something to patent, but we also patented, we were trying to figure out how Disney would use this, so we patented a revenue optimization process on it where you can charge more for your hotel if you allow people to choose their specific room and view.
I know Hilton is starting to do it now but I don't think they're charging more on it. For Hilton Honors members you can pick your room-
Carl - Right.
Len - View at certain properties. I don't think they're yet doing any revenue optimization on it but ...
Carl - Now is that, I know you have subscription services, is that part of the subscription service or is it outside the subscription service?
Len - So, we talked about touring plans those are free, you don't need a subscription to do that.
Although you do need an account just so we can track, you know whose touring plans belong to whom. The crowd counter is subscription based because of the amount of money we spend on
that every year. The room finder is free, the fax service costs money because we pay every month for the fax service and that's a ton of money.
Carl - Right.
Len - Anything that really costs us money externally is the things that we pay for but we try and keep as much stuff free as possible.
Carl - You do a good job of it, I mean, logs and everything you do is good stuff. So you have some other properties that you deal with with Touringplans which other properties do you work with?
Len - So we cover Universal Orlando and we do touring plans and crowd counters for that. We haven't yet done hotel photos for that but I'm totally interested in doing that. Then we do Disneyland, and for that we do touring plans and crowd counter, no hotel photos yet, and we're just getting ready to launch Washington D.c. early in 2017, and that will have touring plans, so the ... Have you been to Washington D.C.?
Carl - Yes I have, many times.
Len - I really like it for a couple reasons, one is all the good museums are free, that's where your tax dollars go.
Carl - Right.
Len - But also it's one of the places where you don't necessarily need a car to get around so if you stay anywhere in the area that's serviced by the Metro, The DC Metro.
You can use that to get around, you know, pretty much anywhere, and it's relatively inexpensive and it's fairly efficient. So, if you want to go on vacation and you don't want to worry about driving, D.C.'S a great place, and again most of the museums are free. So we spent most of 2015 and a good chunk of 2016 literally going through each of the museums and writing up, you know, reviews of them. We investigated the stories behind every piece of art and if that was at like, the National Gallery of Art then we tried to explain, like, how a particular painting fits into the overall art scheme. So we talk about for example, the French impressionists and we go artist by artist and year by year and we explain how all these pictures fit in to the trend or story.
Over at Air and Space we actually give you the story behind all the major aircraft and all the major space craft that are there, and the reason is some of the times the galleries themselves, sometimes they do great jobs at describing why this particular thing is important but they've got thousands of things and they can't do the same level of detail on each. So I'll give you an example. Over at Air and Space on the second floor ... Have you been to Air and Space?
Carl - Absolutely, I have a funny story about that one.
Len - Air and Space generates the most funny stories, I don't know what it is. So over at Air and Space, second floor, there's this triangle shaped aircraft. It's the American L2M2 Lifting Body and the Air and Space museum says that NASA used this as a test vehicle for designing the original space shuttles. So the space shuttles, they're essentially lifting bodies. They don't generate lift through their wings, they get lift from the shape of the body. The shape of the body's the thing that holds the aircraft up in the air. That's what Air and Space says but I was doing some research on this particular one that they display and it turns out that the backstory is the most important thing. Do you remember the TV series "The Six Million Dollar Man", Carl?
Carl - Yes (laughing) yes.
Len - At the beginning of The Six Million Dollar Man, Steve Austin crashes-
Carl - Yes he does...
Len - An aircraft, okay. The aircraft that he crashes is the exact vehicle shown in the Smithsonian Institute.
Len - They rebuilt it, they flew it once, they donated it to the Smithsonian. I'm like dude you don't say that it's the lifting body for the space shuttle, you say this is Steve Austin's plane, that's who you say it's for. So I call up the media guy for the Smithsonian and I'm like, "Dude, I need you to verify this" right, because they only built like three of them. Actually they built two and when the second one crashed they rebuilt it and gave it serial number 3, so there are really only two of them. I'm like, "Dude, as far as I can tell this is Steve Austin's plane." So, I call him up, and he's like, he confirms it right, we think that if this story is true then this is, as far as we can tell this is true, and while he's on the phone he's like, "I've got a William Shatner story do you want to hear it?"
Which I will not repeat… but Air and Space generates the funniest, funniest thing. So anyway, that's one of the things we did so when we launched touring plans for Washington D.C. not only will it give you the most efficient tour of Washington D.C., but will tell you the stories behind the pieces of art.
Carl - You also do Disney Cruise don't you?
Len - We do the the Disney Cruise Line as well. So that was extremely difficult to research. We had to go on all the different
Carl - Tough job ...
Len - Last year we did ... So my favorite ship, you know, prior to the first ship I ever went on was the Fantasy. I always said it was my favorite ship but last year, and again this tells you how difficult my job is, we did a back to back Alaska and Hawaii cruise on the water.
And then spent five days at Aulani and that was one of the best trips I've ever taken yet, I was gone for three weeks. You know, before that my big concern was that the Wonder is the oldest ship, it was in need of refurbishment, and it doesn't have a Remy, has a Palo, and I was like I don't know if I can spend, I think it was 17 nights total. It was 7 nights Alaska, 10 nights to Hawaii. I'm like, 17 nights on the Wonder is going to be a stretch, but, before that I had done 13 straight, or 11 straight cruises in the Caribbean so I was done with the Caribbean, right?
There's only so many times you can go to Nassau. But it was completely different. When they do the Alaska cruises, it goes out of Vancouver and they change the background music on the ship to be the background music of the Wilderness Lodge, so American West. You know, the Magnificent Seven, and Silverado for you know, American western themes. That really made a huge difference for me on the cruise because I love that music and they change the menus to be Pacific Northwest menus, and Alaska is just ... Have you been to Alaska?
Carl - Yeah, absolutely, did a cruise up there actually. Not Disney, this was before the Disney cruise line. Wonderful, wonderful trip.
Len - Yeah, you're in these fjords and you know, obviously it's deep because you're in the middle of mountains but you can see the mountains. You could swim there if you needed to, it's not like, you know, when you're in the Atlantic and the Caribbean you could be hundreds of miles from the nearest piece of land, but when you're in Alaska you're hundreds of feet. (Laughing) From the nearest land, right?
Carl - And even when you're not in the fjords you're in the passage where you've got land on both sides.
Len - Yeah, it's just, it's bizarre, you're in this cruise ship and you can see mountains above you, which is just again, coming from nothing but Caribbean cruises, was just fascinating. I could've ... I think Alaska is the best cruise I've ... The best destination I've ever been on. Then we did Hawaii. We stayed at Aulani, have you been to Aulani yet?
Carl - Not yet, no.
Len - It's Disney's best resort. Every good idea they've ever had they put in Aulani. I really like that quite a bit. Then we came home. So yeah, we cover Disney Cruise line, that addition to the book just came out this week. So the 2017 edition of the book, that's super super popular. We cover all the destinations. We also did, our co-author Erin Foster did Disney's new river cruises in Europe, and that looks to be super interesting because river cruising is a slower, more intimate pace than ocean cruising. You don't go as far everyday, you just spend more time in each port, and the food is supposed to be better. So, Disney's going really big into river cruising in 2017. I think they're packaging some ABD trips with it.
Carl - Adventures by Disney.
Len - Adventures by Disney, right. So we're doing that. From the technology side one of the interesting things we do with the cruise line, and this is free to anyone, is everyday we check the price of every cabin on every itinerary for every ship that Disney offers. So we're getting like 50 some thousand prices per day, so what we can tell you over time whether the price of your cruise is going up which means you should book now, or it's going down which means you should wait. We'll actually do that historically, so lets say you want to go on a cruise in summer of 2017, and you've got a specific itinerary in mind, you know like four nights on the Dreamer, seven night Eastern on the Fantasy.
You can look at the price trend for that cruise this year, 2016 to see how much they're likely to go in 2017, and whether it's likely to go up or down the closer you get to your cruise. And that saves people, it can save people, you know sometimes it's a few dollars sometimes it's thousands of dollars because depending on the cabin you want there's different booking strategies that we've identified based on this data. So like if you want the least expensive cabin in any stateroom category just book now, that's the strategy. The other interesting corollary to that is if you want the most expensive room in a category, like a category four deluxe family ocean view with veranda you should wait til the last minute. You should wait til within like, 60 or 90 days.
Once the cancellation window finalizes for Disney which I think is 60 days, prices drop.
Carl - Actually it's 90 days. I just booked one.
Len - So yeah, once the window closes and everyone's made their final payments prices drop. So, if you can, we think the sweet spot's somewhere around 60 days out. Where you can still get
airfare without being penalized for being too close on airfare but your cruise price is going down. So if you're looking for the upscale version of each cabin again deluxe family ocean view stateroom with veranda and you can wait until the last minute go do that. If you're just looking for the least expensive room in every category, category 11 inside stateroom just book as early as possible. So for each destination we tell you the best pricing strategies in the book based on this millions of data points that we've got from that.
Carl - So cool. So, what projects have you got coming up?
Len - We've got D.C., we got this machine learning thing that we're doing for the crowd counter.
Carl - Cool, anything else you want to mention?
Len - I think that's it!
Carl - I will, man, it's been great. I really appreciate the interview, it’s been great to talk to you.
Carl Trent interviews Len Testa of Touringplans.com and The Unofficial Guide in this exclusive interview for WDW Magazine.
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