Pricing has always been a central tenet at Liftopia, and it is something we focus on every single day. We have always been proponents of data-driven, dynamic strategies and recently we had the opportunity to observe how varying strategies can directly affect revenue. Over the past three seasons, three resorts within a short driving distance with the same weather tested three different pricing approaches. One focused exclusively on raising yield, one balanced yield and revenue, and the third focused on driving revenue.

The Data

We looked only at sales to ensure an apples-to-apples comparison over the past three seasons. We focused on adult single day sell rate and on sales through February 13th of each season to ensure consistency.

The yield focused resort’s strategy involved raising starting prices and reducing price variability. The result was a jump in the average sell rate of the 1-day ticket a whopping $17.69, or 32%, from the 14-15 season to the 16-17 season. While this bump was nice on paper, the revenue nose dive that came with it was much less desirable. Revenue declined 19% over the same time period. So, while the price per skier was higher, the total number of visits through the channel declined by 40%. A smaller number of skiers means less on mountain spending, as the lift ticket is only part of the overall total revenue from the visit. It is clear that average sell rate per skier was increased at much too quickly a rate here which directly caused a significant decrease in revenue.

While the advance sell rate increased by 32%, the resort’s window rate also increased from 14-15 to 16-17 by $12, or around 14%. Raising window rate is generally a good idea to increase the potential price ceiling on peak days, but raising advance purchase rates faster than you raise a window rate is not something that works well as evidenced by this example.

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The second resort focused on increasing yield but did not want to do so at the expense of revenue. To do so, starting prices were increased slightly, but a dynamic pricing plan stayed in place. As a result, the average price per skier was increased by $9.46, or 16%, from the 14-15 season to the 16-17 season. This is about half the rate of increase as the yield focused resort, so not nearly as aggressive. During the same time period, revenue increased 76%, a huge difference from the yield focused resort whose revenue declined significantly during the same period. While it can be hard to pinpoint, it appears that the yield focused resort was punished exponentially in revenue by their average price per skier bump. With a more modest increase, they may not have sacrificed nearly as much revenue.

Yield and Revenue 02

Finally, we arrive at the third resort. This resort’s goal, which was clearly communicated to the Liftopia team, was to drive revenue. After discussing and agreeing upon this goal, we got to work in designing a revenue focused plan. Liftopia ensured that yield did not dip too significantly, but focused on capitalizing on the peaks and valleys of demand that we saw in our pricing model. The result was a modest 5% decrease in average sell rate, but a whopping 229% increase in revenue! The massive increase in revenue far outweighed the decrease in yield, and the resort walked away with far more revenue at the end of the season.

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Achieving a solid price per skier while maximizing revenue is something we strive to achieve every single day here at Liftopia. From this multi-year case study, it is clear that focusing too much on yield can have a disastrous effect on revenue, which is what matters most at the end of the day. It also appears that yield and revenue do not have a linear relationship. A small increase in yield can have a much more adverse effect on revenue, and a small decrease in yield can have an extremely beneficial bump on revenue. Finding the right price by looking at the data is paramount.

At first glance, it also may make appear to make sense that as you raise your window rate, you should raise your advance purchase rate. But, upon closer inspection of real life examples, this logic doesn’t pan out. Window rates went up by 5.6% nationwide from 14-15 to 16-17, but pre-purchase rates need to remain closer to flat or a modest increase to maximize revenue. If you attempt to raise pre-purchase rates at the same rate or even faster than a significant window rate bump, it is clear that poor revenue performance will be a result.