What is dynamic pricing? Who came up with it? What were they thinking? Is it a dynamically greedy tactic that reinforces financial inequality and detracts patrons? Or an elegant and dynamic solution to very annoying problems? In this article, let’s ground ourselves and discuss the straight poop on dynamic pricing and its effects for the betterment of theatres everywhere. The most basic definition of dynamic pricing is that it’s a revenue management strategy that adjusts the price of a product or service based on various factors such as demand, season, time of entry or purchase, number of cancellations/returns, and other ticket market factors. A dynamic pricing strategy is used by many industries, including the electric, airline, hotel, rental car, hospitality, and sports and entertainment ticket industries, to name a few. Remember that dynamic pricing is a big step in ticket management and can have many moving parts.
Long ago, when your local store was the only one for many miles, and travel took many moons, haggling was quite the norm. If you came in looking for horseshoes and the store owner was down to his last set, you’d likely pay a small premium due to the dwindling supply. It was up to shop owners to keep a mental inventory of all their wares, their respective demands, and what a fair price should be for them. This primitive form of dynamic pricing goes back many millennia. The issue with this model is that it relies on wise shopkeepers that take time to train, making it tough to expand to multiple stores, and the haggling would prolong the checkout process, risking upsetting customers. In 1874 the Pennsylvania Quakers introduced the price tag or ‘honest price,’ with John Wanamaker being the first to apply fixed prices in his new department store in Philadelphia. Fun fact: Wanamaker was also the pioneer and the first retailer to offer money-back guarantees. Macy’s in New York later followed suit, switching to fixed prices. This was great! Store owners didn’t need clerks tracking the markets of every item in the store, so more people qualified to be clerks, and businesses could expand to multiple locations faster.
Dynamic pricing later re-appeared through the American airline industry in the 1980s when airlines invested millions of dollars in developing computer programs that adjusted flight prices automatically based on the variations in the time of year, destination, flight time, weather, and any other factor they thought was important enough. Fun fact, this huge investment was a response to the deregulation law passed by Congress in 1978, allowing airlines to set their fares and routes. The airline industry’s response was a ‘new’ dynamic pricing model that proved successful and marked the rise of algorithms. Within the same decade, adjacent industries like hotels and car rental agencies began developing their dynamic pricing models. Surely we’ve all experienced the influence of these algorithms with apps like Uber and Lyft (they like to call it surge pricing).
But is it ethical to effectively gouge your patrons according to whatever metrics you choose? Well, let’s look at a quick chart,
Image credit to FourWeekMBA
As we can see from the image above, dynamic pricing can be an effective way of capturing otherwise ignored revenue. Imagine the x-axis being demand and the y-axis being the number of sales. On the very left of the right chart (the one showing a dynamic pricing model), we can see that when demand is low, sales are unaffected, but as demand increases, sales go down. This model dictates we increase the ticket price when demand spikes, meaning there are fewer purchases. However, they still pull the same amount of revenue due to the higher price tag (denoted as the area of any one square). While a dynamic pricing model captures more revenue, it can invariably split your audience. Loyal patrons that don’t want to have to snipe for moderately priced tickets may leave, but at the same time, those that are late to purchase and more affluent will be allowed to buy a ticket. By changing prices according to demand, a theatre effectively insulates itself from selling out. While that may sound weird, a flexible model also insulates a theatre from pricing tickets too high. The result is a price that’s influenced by free market events but corralled within a realistic range by the venue’s limitations and preferences.
Major ticket brokers have dynamic pricing programs, and the most popular have been used for several years. The earliest reports about the intentions behind this move emerged in 2011, with brokers stating the goal was to eliminate predatory ticket reselling. These new dynamic programs reached maturity around 2018, but largely went unnoticed because artists could simply choose not to use them. The COVID-19 pandemic changed the state of live events, making dynamic pricing an attractive way to recoup significant losses from 2020 potentially. Dynamic pricing has even been successful in the NFL, with at least one-quarter of NFL teams using it in the 2015 season, resulting in significant double-digit percentage increases in single-game ticket revenue. Dynamic pricing allows teams to reset individual-game tickets periodically as demand ebbs and flows. Teams consult with their ticket brokers, which can usually provide analytics to determine how to set relevant values. Most teams these days use a dynamic pricing model. Although the model is more prevalent, the dynamic pricing practice can be considered a form of price discrimination.
One of the simplest examples of price discrimination (differential pricing, personalized pricing, equity pricing, preferential pricing, tiered pricing, or group pricing) is a student discount; the price of the same ticket/product is adjusted for a particular market segment, namely students. Generally, it’s only feasible for a company to effectively apply price discrimination if they hold the lion’s share of the market or otherwise have a ticket with unique attributes. If a company meets this criteria and exercises price discrimination, they also have to consider whether they want to fight scalpers that will try to buy up all of the discounted tickets and pocket the difference in price to a regular ticket as a form of arbitrage. This quickly becomes a slippery slope where it can appear the market is working against the consumer, as the monopoly seller benefits from restricting access to pricing information and discouraging reselling. While economists will say this is a free market way to determine the elasticity of demand (a measure of how sensitive the quantity demanded is to price) and recoup deadweight loss (the difference in production and consumption, i.e., those two triangles on the top and right sides of the square in the left chart above) to capture consumer surplus, we have to remember that monopolies and oligarchies are bad for competition, bad for fair pricing, and are routinely broken up through government intervention to prevent further unfairness to consumers.
It’s not all doom and gloom, though, as industries can argue they benefit the greater good by influencing consumer behavior through price. One example would be power utilities, where electricity will cost more during peak demand hours than during low demand periods. Since the utility company is insulating itself from selling more power than it can produce, it helps to manage its grid more efficiently, which benefits everyone. This also encourages consumers to be more conscientious electricity users. In the hospitality industry, it’s standard practice for hotels only to charge operating costs during low demand seasons, but then capture the majority of their annual profits from high-demand seasons and special events (also known as long-run marginal cost pricing). As mentioned previously, apps like Lyft and Uber will change rates depending on how many people are hailing a ride in the same area, how many drivers are available, and many other factors that will ultimately benefit the rider with a shorter ride time or more compatible driver. Some cities have been using congestion pricing or road pricing long before these apps, where the tolls will have higher prices when traffic is heavy. The San Francisco Bay Bridge charges more during weekend peak hours, and London has had one of the largest Congestion Charge Zones (CCZs) operating in the heart of the city since 2003, where a driver is charged more during waking hours to help fight horrendous traffic jams.
London also introduced Ultra Low Emission Zones (ULEZs) to help combat toxic pollution in urban areas. While the city made 2.6B pounds in additional gross revenue during the first ten years of the effort, almost all went back into road, bridge, and public transit improvements. London was inspired by Singapore's Electronic Road Pricing (ERP) system. The ERP was implemented in 1998, which was a modern update to the Singapore Area Licensing Scheme (ALS), a congestion pricing effort that started in 1975, showing that these aren’t new ideas. Still, with the advent of new detection tech, it’s become easier to set up and enforce. Even seemingly efficient transport options like trains charge more during peak hours; some examples are the Washington Metro and the Long Island Rail Road. One of the obvious downsides to dynamic pricing is how violent price fluctuations can be; they can significantly influence your patron’s price fairness perception (not to mention ruin or make their day). Studies show that when a person sees that someone else bought the same ticket at the same time for a lower price, it induces perceptions of price unfairness. Other studies show that the reverse is also true. Patrons that see they got a better deal than someone else are more inclined to be loyal to the venue in the future.
It should be obvious that dynamic pricing is a powerful technique that can increase revenue, and there’s much more detail we can get into (maybe in a part II). Still, these strategies require careful implementation and monitoring to avoid negative customer experiences. Some backlash to dynamic pricing claims the practice is price gouging and favors only the most affluent buyers. There have also been privacy concerns because while all of the data captured on patrons can be used to make more accurate price predictions, it also creates a centralized target repository of sensitive data that is vulnerable to leaks and theft. Dynamic pricing can cause enough trouble that patrons claim exploitation and work to tarnish a theatre’s reputation. The most common legislation cited for these alleged transgressions is the Robinson-Patman Act of 1936. Some notable cases are the surge pricing that New York saw during a storm in 2013 when Uber fares went up eight times their typical price! After this, Uber capped how high surge prices can go (although they still seem extreme sometimes). Another notable example is Amazon, which had listings during the COVID-19 pandemic that showed shockingly high prices for sanitizers and masks, triggering a backlash. Amazon denied any wrongdoing and claimed this resulted from a few greedy sellers, despite some of the listings being under Amazon’s own ‘essential product’ branding. Bruce Springsteen also garnered some attention for a 2023 US concert he partnered with Ticketmaster on for dynamic pricing to capture high demand. Some of the seat prices soared to over $5000, to which Ticketmaster responded that most tickets were sold for around $100 and that the mechanism was largely meant to take advantage of the high initial demand while helping eliminate reselling in secondary markets. Despite Ticketmaster’s best efforts, a thriving community of scalpers and resellers on secondary markets exists.
So as you prepare for your journey into dynamic pricing, start slow by dipping a toe or two, as mistakes can be costly. Theatres must strike a balance between revenue optimization and maintaining customer satisfaction to ensure long-term success with such an approach. This is one of the reasons data is so valuable to a theatre since a set of historical charts for various sales metrics (like those available through the Fourth Wall Tickets platform) can provide powerful insights into what is selling and why. Heavily researching and carefully choosing the variables in your dynamic pricing model algorithms provides more flexibility in experimentation to find your sweet spots. Like most things in life, the more effort you put in, the more value you’ll get out.