Air fares to Europe are up significantly this year, as I recently discovered when my girlfriend and I started planning a trip to France to see relatives. To try to get a sense of whether there were any deals to be had, I started manually checking prices every day and tracking them in a Google Docs spreadsheet.
I did this for about two weeks. It was laborious, but it was fascinating in that it let me decrypt just a little bit of the arcane logic that goes into the fluctuation of ticket prices. There’s not a tremendous amount of pattern recognition that you can glean from a sample size as small as fourteen days, but the airline industry’s pricing models and schedules are so opaque and inscrutable that even seeing real prices tracked over a short amount of time — watching how they rise and fall — is instructive.
Of course, I realized too late that there are probably Web tools that can automate this kind of search for me. I hunted around a bit and found Yapta, which I’d never heard of before but does more or less what I’m looking for. Yapta layers a tracking service on top of a Kayak-powered booking engine. That’s fortunate because Kayak is my preferred travel booking site and so the search methods were therefore very familiar to me. Yapta returns what are essentially Kayak-flavored results, and you can click on any itinerary to start tracking its fare fluctuations in your Yapta account. The data presentation is rather lackluster in that there’s no graphical charting of pricing trends, and if you’re tracking multiple itineraries, you have to click through to each to see its full pricing history. But Yapta does automatically update and record the prices, saving me a lot of manual labor.
There may be other, more powerful tools out there that do this same thing as well or better than Yapta. (If you know of any, I would welcome tips.) I’m not sure if this is something that a lot of people know that they need, but it would certainly seem to be something a lot of people would use if it were packaged elegantly and if it were better integrated into the booking process.
More importantly, though, what this brought to mind for me was how lopsided the data collection dynamic is in ticket pricing. Over the course of the several weeks when I was actively checking prices, looking for a deal, I was handing over a nontrivial amount of behavioral data to the booking sites and airlines I was patronizing — not just where and when I want to go, but also my preferred carriers, routes, price tolerance and more. Clearly, that would be more than enough data to gouge a customer if a company wanted to, though I’m not accusing these sites of doing that. I’m just saying that the early promise of online travel was that it would allow for pricing transparency, that increasingly sophisticated booking tools would let consumers find the best possible deals. As it turns out, the situation we have today is that those who set the prices know more about how we buy tickets than we know about how they price tickets. That doesn’t seem very empowering to me.
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From the age of five to the age of seventeen, I lived in the D.C. metropolitan area, where I spent a lot of time riding the