fb-pixel Skip to main content

Data mining has come to ski industry

Trace is a water-resistant sensing device that attaches to skis and snowboards.photo courtesy alpinereplay

As a tradition-bound industry, skiing is known more for reliance on what has worked in the past rather than being receptive to change. But two California companies whose founders have Massachusetts roots are transforming the way resorts make business decisions: Liftopia and AlpineReplay are evolving into leaders in snow sports data mining — even though neither set out to do so.

Liftopia launched in 2006 with the goal of selling discounted lift tickets online. Cofounded by Topsfield native Evan Reece, the startup was at first supported by only seven resorts. But skiers embraced the concept, and as the number of participating ski areas climbed into the hundreds, searches for ticket deals soared into the millions.


Just as the company was becoming recognized for online ticket sales through its own site, Liftopia shifted gears. In 2011, it rolled out Cloud Store, an e-commerce platform that gives resorts direct control over an online storefront. Partner mountains get access to a wealth of data, allowing them to tailor deals in real time through dynamic pricing. Reece said this led to a “breakout” 2012-13 season in which Liftopia generated more revenue for ski areas than it had the previous six years combined. In September, Liftopia secured $5 million in venture capital funding, based in part on the success of Cloud.

“Because resorts are being more efficient in their pricing strategies, they’re actually able to make more money while also giving people more value,” Reece said. “And that’s been really awesome to see behind the scenes. [Skiers] are spending less but resorts are actually generating more revenue.”

AlpineReplay, the flagship product of the company ActiveReplay, is a different type of technology — at least on the surface.

Cofounded by David Lokshin, a Harvard University graduate with a degree in applied mathematics, AlpineReplay launched in 2011 as a free racking application for GPS-enabled smart phones to measure speed, distance, vertical drop, air time, and the location of trails you ski. You can compete against friends or compare results on worldwide leaderboards, tracking data over a day or a season.


Although Lokshin will not reveal the number of AlpineReplay users, he said that two years after its debut, AlpineReplay boasts data from 460,000 skier days at 1,400 resorts in 43 countries, including 5 billion vertical feet skied and 2.5 million jumps taken.

And AlpineReplay, too, is undergoing a business shift: In 2014, the firm will begin selling Trace, a durable, water-resistant sensing device that attaches to skis and snowboards (and other action sports equipment) to give more robust readings without risking damage to a smart phone. Lokshin said his company raised $161,000 in 45 days through a social funding drive on Kickstarter to launch Trace.

But separately, Lokshin said he is enthused about harnessing the aggregate power of AlpineReplay’s data, which he did not at first envision brokering to resorts.

“We’re learning things that we never set out to learn, like which trails are most skied, so resorts know where to deploy grooming and snowmaking. Where is skier traffic the heaviest?” said Lokshin. “There’s all sorts of things the data can be used for. And the more data that we collect, the more useful it gets.”

But even though the industry is coming around on the value of data mining, individual resorts are not quite prepared for the daunting task of extracting the most useful nuggets.


“That’s just the nature of the industry. Even with all these tools, it’s hard because a lot of resorts don’t have the time or the resources yet to sift through it all,” said Gregg Blanchard, who writes about data mining in his snow sports marketing blog, SlopeFillers.

“When I look at the skiing industry, it reminds me a lot of ‘Moneyball,’ ” Blanchard said, referring to the breakthrough book about baseball analytics. “You have people who trust in the time they’ve put in the industry, and that experience is what they lean on to make all their decisions. So, there’s all this instinct out there, but without numbers behind it, you really can’t do much more than guess.”

Reece said that one of the hurdles to acceptance is that both skiers and the industry are wary about products that appear to give an advantage to the other side.

“There have been, without a doubt, in the history of skiing a huge number of products and fly-by-night companies that benefit only the resort or the consumer,” said Reece. “And all of them tend to plateau and peter out, because the other side figures out over time it is not to their benefit [to use the product]. For us, we’re not building toward some short-term gain. We believe we have an opportunity to fundamentally impact an industry that we love.”

Lokshin agreed on the need to find a proper, non-intrusive balance with data mining.


“For example, I just helped Outside magazine with an article that’s going to be coming out in January, and the gist of the piece was basically, ‘Which resorts are the best to go to over Presidents Day weekend?’ ” Lokshin said. “Everyone knows that Presidents Day weekend is super busy. But where do the lift lines slow down the least? At which resorts do people get the most amount of skiing in?”

Reece said he can foresee a time in the not-too-distant future when Lokshin’s data that tracks trail and lift congestion meshes with Liftopia’s dynamic pricing system.

“AlpineReplay has done a tremendous job of capturing and communicating consumption data in real time,” Reece said. “That certainly can inform pricing strategy, and vice versa. Without a doubt, you have to grab data from different sources.”

Blanchard said it’s up to resort operators to take this concept of blended data one step further.

“Whenever I think about a convergence of run-tracking data and lift-ticket data, the really cool third ingredient in that equation is weather,” Blanchard said. “Is there any dissatisfaction because conditions aren’t as good as a customer had hoped?”

Blanchard gave the following example of how the two technologies could be merged: It’s a lousy day weather-wise. The resort knows what you paid for a ticket (Liftopia) and can see whether that ticket is still in use (AlpineReplay). So, if Skier A bought a deeply discounted ticket far in advance but left early, the resort might court favor by following up with a coupon for future use. But if Skier B paid a higher “day before” rate for the same ticket and felt compelled to stick it out and endure the poor conditions, he too could be rewarded, maybe with a midday text message offering a price break for that day’s lunch on the slopes.


“I’m not sure anyone knows how to make the best use of this data yet,” Lokshin said. “That’s one of the caveats of new technology — it’s a trial and error process.”