Revenue management at it’s core is the practice of changing the price of something in order to maximize revenue. It’s commonplace in air travel and lodging, but is starting to be seen in a variety of other industries such as sports and retail.

Uber and AirBnB are two companies well-known across the country but they are at different stages in their approach to revenue management. Uber raises their rates for high-demand periods, stating on their site “Our goal is to be as reliable as possible in connecting you with a driver whenever you need one. At times of high demand, the number of drivers we can connect you with becomes limited. As a result, prices increase to encourage more drivers to become available.” [1] AirBnB cannot control an individual listing’s price but recently built a tool that gives hosts price tips within their site. “We’ve developed a mathematical model that’s learning how likely a guest is to book a specific listing, on specific dates, at a range of different prices. It uses different types of information, including your listing type, its location, your current price, your availability, and how far off each available date is.”[2]

Looking at the ski industry, there are many parallels between the lodging (AirBnB) and transportation (Uber) industries, in that all three deal with highly variable demand and have limited variable costs. By illuminating those parallels, I hope to provide more clarity to how the ski industry can use revenue management and specifically how Liftopia fits into that picture.

Uber’s Surge Pricing Algorithm

At the most basic level, Uber’s surge pricing program is Economics 101. When demand is higher due to a concert, inclement weather, a holiday, etc. the demand curve shifts to the right.


In a free market, suppliers would raise their prices to reflect the increase in demand, resulting in a higher price and quantity at equilibrium (P1 > P, Q1 > Q). The above graph assumes that there is no change to supply, while Uber’s claim is that by raising their rates during surges in demand, they are incentivizing more drivers to hit the road. This would represent an increase in supply, shown in the graph below.


By being able to react to shifts in demand quickly, Uber is able to provide riders with a better value while ensuring that drivers have an adequate number of fares to make it worth it to drive. But while Uber can actually change the supply (number of cars on the road) ski resorts have little ability to affect their supply (a resort being open vs. closed). This is confirmed by Bill Gurley, a board member at Uber who states: “With hotels, airplanes, and rental cars, supply is relatively fixed. One cannot build more rooms for New Year’s Eve, and then take them down.”[3]

Since ski resorts can’t change their supply to match demand, they are limited to improving conditions to entice customers (grooming, snow making, etc.) and/or changing their prices. One thing that Uber found, and would be expected in any industry, raising prices decreases the quantity demanded. The question is by how much?

Price elasticity is the relationship between quantity demanded and price, meaning if you raise the price by $X, you can expect quantity demanded to decrease by Y units. If a product is price elastic (such as Uber), it means that a small change in price has a dramatic effect on quantity demanded. Ski resorts have both elastic and inelastic demand, with peak dates being less affected by price changes than off-peak. This is where dynamic pricing comes in. Liftopia’s pricing plans are built in such a way that provides the resorts opportunities to generate additional revenue through strategic price increases while providing protection against prices rising too quickly on highly elastic dates.


This graph shows that when prices increase (Y axis) the quantity allocated to each price point (size of circles) increases. For this particular partner, we see a minimum price of $46.99 rising to $51.99 (against a $55 window rate) in $1 increments. In the scenario where a day’s demand is inelastic, allocating fewer tickets in the inexpensive prices allows those prices to sell out quickly, providing revenue upside and opportunities for optimization. If the day has elastic demand, prices rising by $1 between price points will have less of an effect on quantity demanded than if they were $2+.

AirBnB Pricing Tools

AirBnB has grown dramatically since their founding back in 2008. They now operate in over 34,000 cities in 190 countries[4] and in many of those AirBnb have disrupted the hospitality industry considerably. While many hosts operate several listings on their site, some properties are often hosted by a single individual or family, and the ski industry in North America can be thought of in a similar way. Larger portfolios (Peak, Intrawest, Vail) operate alongside not-for-profit hills and individual resorts. These portfolios have larger capital bases to operate with increased complexity while limitations can restrict a smaller resort into operating simply. One example of a restriction is in revenue management.

Revenue management in hospitality had its’ origins with American Airlines following the Airline Deregulation Act of 1978. By offering tickets for lower price in exchange for making them non-refundable, capacity restrained, and requiring booking in advance, airlines quickly discovered that they could increase their revenue. Lodging soon followed suit as their ‘good’ was also perishable (date-specific), supply was limited (number of rooms in hotel), and demand would vary across time periods.

Revenue management over time became almost ubiquitous for hotels, and the amount of data that they were gathering allowed them to refine their techniques and optimize their strategies. As technology improved in the 1990’s and 2000’s, hotels were able to forecast demand based on previous years’ demand trends and more effectively price their rooms. These are like the larger ski portfolio companies.

On the flipside, AirBnB hosts do not have revenue management teams on staff, years of data to inform decisions, or expensive workstations to forecast demand. Because of this a number of outside companies developed products to help individuals manage the pricing of their listings. Beyond Pricing is an outside company that provides a tool that gives AirBnB pricing consultancy. They’ll conduct analysis on other vacation rental sites, weather, airline arrivals, seasonality, day of the week, and special events. The output is a calendar with recommended rates.


PriceLabs is a similar service that uses machine-learning algorithms to predict the optimal price that a listing should be at. Renting Your Place is more of a full-service solution that not only provides pricing advice but also what amenities to provide and the language to use in your listing. Each of these takes a small commission off every AirBnB booking, lessening the overall revenue that a host gets with the promise of driving more overall revenue.

In the ski industry, many resorts use a 3rd party site (GetSkiTickets, Liftopia) to reach a broader audience, much like the audience AirBnB provides for hosts. As mentioned above, hosts (and resorts for that matter) have varying levels of information at their disposal, so utilizing the pricing recommendations of one of the companies above is similar to a resort seeking pricing recommendations from an outside company for sale on GST or Liftopia. However in 2015 AirBnB released what they called Price Tips[5], which is a consultancy on how to price listings for hosts. This is more like Liftopia or GetSkiTickets recommending a pricing strategy to sell on their own sites.

As AirBnB hosts get savvier, what does this mean for more traditional properties in the hospitality space? At bare minimum, AirBnB increases room supply. The Wall Street Journal estimates that “Airbnb increased the city’s lodging supply by about 17%” when the Pope visited earlier this year.[6] For periods of extremely high demand, AirBnB hosts help to drive prices down for lodging customers. When demand is lower, AirBnB hosts can take their listings down, providing a much more modest boost to lodging supply. In a vacuum, these would cause RevPAR (revenue per available room) for hotels to drop, but this has only recently been the case, as shown in this chart for New York City[7]:


A Boston University research paper puts a number on it: “Using this DD specification we find that, in Texas, each additional 10% increase in the size of the Airbnb market resulted in a 0.37% decrease in hotel room revenue.”[8]


The ski industry has long considered itself as ‘different’ from other travel and transportation industries because of the weather concerns. However, due to the unpredictability of weather combined with high infrastructure costs in ski, resorts absolutely must be willing to embrace revenue management to thrive.

Unlike Uber, ski resorts have very little control over the supply side of their business. On powder days, resorts can’t grow the resort, build more lifts, or change their location. When conditions are poor, they can’t decrease the driving distance to the resort to entice customers to make the trip. Because of this, price (often combined with marketing), is their sole lever to maximize revenue in peaks and troughs.

Some ski resorts are in a better spot when it comes to the data they possess about their revenue and visitation patterns. Being able to look back on 5-10 years of visitation and predict which dates are the highest demand is half of the equation, with the other half knowing how much to charge on those high demand days, and when. Liftopia and resorts use previous season’s data and data from similar resorts to build a pricing forecast, but resorts, Liftopia, and Liftopia’s competitors must get more systematic, data-driven, and scalable to thrive.