The National Baseball Hall of Fame recently inducted Ricky Henderson, one of baseball’s most prolific base stealers with a record 1,406 bases stolen in his career – yet, Major League Baseball has failed to deal with scalpers who steal millions in profits from their franchises every year. Scalpers have seized the lost opportunity where Baseball franchises lock in their ticket prices months before the season starts and choose not to adjust prices throughout the season. A more efficient secondary market thrives due to the scalpers’ ability to factor in several game variables (e.g. strength of opponent, seat type, starting lineup, weather conditions, etc.), as well as buyer-specific factors (e.g. age, attitude, clothing, jewelry, etc.) to determine the maximum (and therefore optimal from the seller’s perspective) price that each person is willing to pay. Another advantage for scalpers is their ability to immediately negotiate if the buyer doesn’t accept the first price, carefully moving the price down until both the buyer and seller agree upon a satisfactory price. To help reclaim these lost profits, the San Francisco Giants are now testing dynamic pricing software to help adjust ticket prices based on the expected consumer demand for each game. So what exactly is dynamic pricing, and is it powerful enough to replace the individualized pricing, negotiation, and sales effectiveness of ticket scalpers?
To answer this question, let’s take a closer look at the solution itself. Dynamic pricing is a form of yield management (also called targeted pricing, flexible pricing, tailored pricing or discriminatory pricing), which formally emerged in the mid-1980s as a means for airlines to capture some value from plane seats that would otherwise go empty by offering, for example, lower than published fares for customers willing to forego other benefits (such as the ability to change a flight date or cancel the ticket). This breakthrough science allows organizations to understand, anticipate and influence consumer behavior in order to maximize revenue or profits from a fixed, perishable resource (e.g. airline seats, hotel room reservations, etc.). In the case of the San Francisco Giants, dynamic pricing is being implemented to allow them to dynamically adjust prices by weighing ticket sales data, weather forecasts, upcoming pitching matchups and other variables to help decide whether the team should raise or lower prices right up until the day of the game.
The problem with dynamic pricing is that it doesn’t enable organizations to truly understand the needs, preferences and spending propensities of each and every customer they serve. For example, the problem I see with dynamic pricing for baseball franchises is that it relies on a basic set of variables (e.g. weather, starting lineup, etc.) to determine how to price to the masses, instead of focusing on – and pricing to – each customer’s specific needs. Let’s say I want to go to a baseball game on my birthday. Will the dynamic pricing system offer me a discounted ticket (or should it predict that I am more spendthrift on my birthday)? If my favorite pitcher is starting will the system recognize my willingness to pay more and increase my ticket price? If I regularly attend games throughout the season will the system consider my loyalty and offer me discounts to other games? The respective answers are no, no and no. The advantage here clearly goes to scalpers, as they can still adjust and negotiate prices with each customer they interact with directly. However, where I see the limitations of dynamic pricing end, the benefit of revenue optimization begins.
Revenue optimization technology can provide baseball franchises an accurate picture of demand down to the single customer-level, where the software can codify each customer’s preferences and adjust prices according to their needs, total amount spent and even longevity as a fan (i.e. brand loyalty). Baseball teams already capture tons of customer data through the MLB web portal, where fans can upload and track their favorite teams/players and purchase tickets and merchandise. All of this data can be mined to figure out each customer’s specific price point for every seat of every game! The technology enables baseball franchises to increase ticket sales volume for less popular games, reduce the number of tickets resold in the secondary market, and increase profits for every game. In addition, baseball teams can begin to cross-sell other items like concessions and merchandise to these loyal fans, or even optimize the sale of bundled tickets and/or merchandise. With this increased ability to effectively market to each fan, baseball franchises will become more adept at selling tickets than the scalpers and can soon “steal” their profits back – forcing scalpers to buy tickets if they want to see Major League Baseball’s most prolific stealers.