![]() The bottom line: the accuracy of current fare-prediction technology is pretty modest, but you may want to use it anyway, even if you might not save any money. I decided to compare two real ticket-purchasing strategies: buying the ticket two weeks ahead of my scheduled departure (my old method) versus buying only when the price predictor - in this case Kayak - recommended that I buy (the algorithm). My own travel bookings are limited and biased, so to determine whether to trust airfare predictors I adopted a more scientific approach. And if this technology was really as powerful as claimed, Microsoft wouldn’t have recycle-binned it. In their 2013 book “Big Data,” Viktor Mayer-Schönberger and Kenneth Cukier wrote that using Farecast’s technology, Bing Travel “was saving consumers a bundle.” My guess is that my loyalty to Bing Travel saved me a pinch, not a bundle. ![]() A buy recommendation came with an explanation, similar to what Kayak says today: “There is a 79% chance that the price would increase by $20 or more in the next 7 days.” Likewise, a wait recommendation came with a statement of confidence about the future movement of prices. For each search, Bing Travel advised users whether to buy now or to wait. Oren Etzioni, a computer science professor at the University of Washington who founded Farecast, told me the early adopters of airfare predictors were quantitative types like myself. Should I take the current price, or wait? I wondered if airlines really did set their prices to the lowest level two weeks out. But I always experienced a nagging doubt. Before Farecast, I purchased tickets two weeks before my departure date, on the conventional wisdom that I could get good prices around that time. (I now use the travel Web site, Kayak, which has a similar feature.) Like most travelers, I long ago abandoned travel agents, preferring to book my flights online. I was a loyal user of Bing Travel’s price predictor, so its removal came as a shock. With Microsoft’s exit from the price-prediction game, it’s time we re-assessed the technology to see if what doomed Farecast was instead a failure of Big Data. ![]() Initial reports suggested executives had made a business decision to focus resources on travelers’ other needs. The acquisition helped make price prediction the key differentiator of Bing Travel, a core asset of Microsoft’s new “decision engine.”īut a few weeks ago, right as I started gathering data for this article, the heralded price predictor vanished from Bing Travel. A poster child of the Big Data revolution, Farecast analyzed hundreds of billions of airline ticket prices using a machine-learning algorithm and told consumers when to buy. In 2008, Microsoft snapped up Farecast, a company in the business of predicting airfares, for a handy $115 million. ![]()
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