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The annual NSAA Economic Analysis of United States Ski Areas abounds with information about everything from season length to ticket yield ratios to current account balances, all of which is “designed to document economic and financial trends and patterns of interest in the ski resort industry.” The folks at RRC Associates, who produce the report, slice the data in several ways, showing it at the national level, regionally, by size, and by region-size combinations. Chances are, though, you run an individual ski resort, and you’re reading this article to find out what you can glean from the Analysis to help you run that resort.

I hope to offer you a few insights along those lines, but first, let’s cover the highest-level points from the report to put everything into context. Nationally, despite a shorter season and slightly lower visitation numbers, revenues and profits increased. Total snowsport visits to resorts in the 2015-16 season were 52.8 million, a 1.5 percent decrease from the 2014-15 season. The season was also shorter by an average of six days (117 in 2015-16 vs. 123 in 2014-15), a 4.9 percent decrease. However, average resort revenues climbed by 8.6 percent, and expenses nudged up just 0.7 percent from last year, leading to higher operating profits and wider operating profit margins.

The Analysis covers just those resorts that responded to the NSAA’s survey in both the relevant years—in this case, that’s 2014-15 and 2015-16—to ensure an apples-to-apples comparison. As a result, the Analysis does an excellent job at what it sets out to do: “document economic and financial trends and patterns of interest.” But drawing conclusions from it about how you can improve your resort’s operations requires more than reading. It requires, I think, both curiosity and restraint.

One way to use the report is to generate questions for your management team about what you can learn from your peers. However, it’s essential to be careful how you define your peers, a fact the Analysis implicitly states by noting the limitations of national average numbers and by highlighting the regional and size variations that often exist in key metrics.

Everyone Is (Potentially) Awesome

To put it explicitly: no individual is ever average. A recently published book, The End of Average, by Todd Rose, thoroughly explores this idea in a highly engaging way. It covers many implications of the idea, including how you can leverage it to improve employee performance. I highly recommend you read it.

Rose does not suggest that everyone is above average. Instead, he encourages us to use averages appropriately. If you are comparing two groups, the average is useful. However, “the moment you need to make a decision about any individual, the average is useless. Worse than useless, in fact, because it creates the illusion of knowledge, when in fact the average disguises what is most important about an individual.” For example, two people could get the same average score on an IQ test, indicating they’re “equally intelligent,” but one scored highly on the vocabulary section and the other on the arithmetic section. Quick, whom do you want to hire? Well, it depends on what the person is going to do.

So, if you read the Analysis, be careful what you compare, and remember that none of the resorts that contributed to the averages are actually average. For starters, the report authors point out that in the Analysis, the super-sized Rocky Mountain resorts heavily influence the national averages. More to the point: no single resort saw snowsport visits decline 1.5 percent, season length shorten by 4.9 percent, revenues rise by 8.6 percent, and expenses tick up 0.7 percent.

Each resort, yours included, has specific highs, lows, and constraints that others, even those in the same region-size category, may not. Ultimately, I think peer comparisons are best used as guidelines, not goals, and much can be learned from thinking about how your individual resort is progressing over time.

Reviewing Regional Data

With that in mind, let’s compare some regional data, which differs dramatically from the national data—thanks in large part to 2015-16’s weather. In the western half of the country, which includes the Pacific North, the Pacific South, and the Rocky Mountain regions, snowsports visits rose substantially. But in the eastern half, which includes the Midwest, Northeast, and Southeast, visits were down. The specific data is included in the table.

Remember, we’re comparing groups here, so averages are useful, but exercise caution in comparing your resort to any of the regional data points. As you may recall, 2014-15 was a rotten year on the West Coast, for the most part, and a solid year for the rest of the country. In contrast, 2015-16 was a strong season on the West Coast, and a rotten year east of the Rockies, generally speaking. Comparing these years, though, tells us quite a bit about how much the weather impacts visits. (We’ll also look at 2013-14 compared to 2014-15 to help illustrate the impacts on the West Coast.)

Compare these visits numbers to data on season length in the regions.

In the West, visits increased more, in percentage terms, than did season length. In the Southeast and Midwest, visits decreased slightly less than did season length. The Northeast was the exception. There, visits declined 29.5 percent during a season that was 17.7 percent shorter. It’s possible that unfavorable weather (warm and wet, with little snow in back yards), and limited terrain open at the areas, help explain the Northeast’s large drop in visits, which mirrors the Pacific Northwest’s experience the prior season. (More on that next.)

In the 2014-15 Analysis, which had a slightly—but not substantially—different mix of resorts, the two Pacific regions suffered from historically low snow and visitation numbers. The other regions fared better.

Again, compare these visits numbers to data on season length in the regions.

In this case, season length did not change nearly as much from one year to the next for most regions: only in the Pacific regions was variation more than 10 percent. However, for those Pacific regions—particularly the Pacific North—the season was a doozy.

Preparing for Extremes

A key point emerges here, and it suggests questions management should explore. Visitation usually correlates with season length, but not always. At the extremes, demand (as measured in visits) appears to vary more than length of season. Both Pacific regions, for example, saw 2015-16 visitation gains that were much larger than the season length gains. In 2014-15, the Pacific North’s visitation numbers fell by much more than the season length did. The first question to ask yourself, then, is “How well prepared is my resort for extreme years—both good and bad?”

A related question is, “Can I find a way to take advantage of an extremely bad year?” I am serious: Why not look for those silver linings? For example, maybe a low-snow/low-visitation season is the perfect time to find ways to improve processes at your food and beverage operations, evaluate new retail vendors, or develop marketing campaigns. For the eastern resorts, it would make sense to ask, “How well did I take advantage of 2015-16?”

Another way to prepare for bad years is to make the most of the good years. Ask yourself: how well has our resort done that?

In 2015-16, average total gross revenues were down in the three eastern regions and up in the three western regions, which should come as no surprise. However, on a per-visit basis, as shown in the table, revenues behaved differently. In fact, revenues per visit were down in the Pacific regions. This trend is probably due mostly to guests spreading their snowsports budgets (and season’s pass cost) over more visits. Are there other explanations? If so, what can you learn from them?

It can also be instructive to look at where your revenue comes from. The Pacific regions are the most dependent on ticket revenues; that is, ticket revenues per visit make up a large share of total revenues per visit. Can other revenue sources mitigate lost ticket revenue in a down year—especially in regions where ticket revenues are a lower percentage of total revenues?

In the eastern regions, ticket revenue per visit is a smaller share of overall per-visit revenues. For example, in the Southeast, food and beverage revenue per visit was $17.11 in 2014-15 and $20.95 in 2015-16, the highest in absolute terms of any region. The Southeast also had the second-highest total revenues per visit of any region in 2015-16. It’s probable that some resorts in the region are doing a great job of maximizing revenues per visit.

Let’s return to the 2014-15 report’s results. Remember, that report compared a slightly different group of resorts than the 2015-16 report did, so we’re not reproducing them linearly as 2013-14, 2014-2015, and 2015-16.

In this case, revenues per visit were up across the board, as were ticket revenues per visit. Great news, and a great point of comparison for further investigation, as in, “What did we do in 2014-15 that we can apply to future, similar years?” For the eastern resorts specifically, it could be valuable to ask, “What did we do to make sure 2014-15 contributed, at least in part, to helping us survive a future extreme season?” and “What could we have done better in that respect?”

Certainly, it’s impossible to precisely predict when an extreme year will occur, but it’s foolish to believe they won’t occur, and it’s careless to not prepare for them. Now is the perfect time for that preparation.

Profiting from Profitability Data

Finally, let’s take a look at my favorite part of every year’s report: the profitability data, which the Analysis puts in an appendix, and which shows just how important what I’ve not yet discussed—expenses—are. The Analysis categorizes reports into “top-half profit,” “bottom-half profit,” and “loss.” Here, we just look at data from the 2015-16 report, which compares 2014-15 to 2015-16.


One of the best pieces of news from this table is that the number of areas losing money fell from 44 in 2014-15 to 34 in 2015-16, and the size of the loss for those still in the loss category decreased in terms of profit before tax. Even the resorts in the loss category managed operating profits, which accounts for a wide range of expenses, from property taxes to marketing to ticket sales and lessons. It was the other expenses, specifically depreciation, amortization, operating leases, and interest expense, that created the loss for those resorts. At the same time, reductions in all these expense categories helped reduce the size of the loss.

Now, it’s a bit tricky to make comparisons over time with this data. Although it’s the same overall set of resorts in both years, the resorts in the three categories may have switched places, and the resorts outside the top-half profit category have definitely changed. This data leads to two more questions, particularly for those resorts in the loss category. First, “What did we do well when it came to reducing depreciation, amortization, operating leases, and interest expense that we can keep doing?” Second, “How will rising interest rates affect us, and what can we do about it?”

(For another perspective on the profitability data provided in a previous Analysis, please see the May 2015 issue of SAM, which includes a discussion of the 2013-14 season in these terms. That article explores another aspect of the profitability data, resort size in particular.)

As you read the Analysis, you’ll undoubtedly think of other questions to explore. As you do, remember no individual is average, and neither is your resort. So, compare carefully, and stay focused on what your resort can and should be doing, rather than what a mythical average resort is managing.