The field of Marketing Analytics has long included methods for setting prices based on demand. “Demand” based pricing is rapidly becoming relevant to sports fans. Specifically, we have witnessed an increased usage of techniques referred to as “variable” and “dynamic” pricing. Michigan announced a few weeks ago that they would be implementing dynamic pricing for football. Notably, the adoption of dynamic pricing is expected to increase football revenues by $5 million. To a fan, an increase in revenues probably sounds a lot like an increase in ticket prices.
While most of the material on our site involves more macro or league level issues such as comparisons of fan bases or topical issues such as the O’Bannon lawsuit, our interests also include team level marketing questions. Dynamic pricing is currently a hot topic for teams and our suspicion is that many teams are struggling with the implementation of these techniques. We would also guess that many fans are concerned that dynamic pricing is really just another label for increasing prices. The adoption of dynamic pricing is in the short-term a technical challenge, and in the long-term a potential game changer for the team’s relationship with its customers. Over the next few months, we will be publishing a series of short articles that highlight many of the key issues associated with dynamic pricing.
To begin, we wanted to touch on some of the basic terminology of demand based pricing and how these techniques impact fans. First, it appears that the industry has adopted the term variable pricing for pricing schemes that vary prices based on opponents, and dynamic pricing for practices that vary prices for specific games based on realized and expected demand. The basic idea in these approaches to pricing is that each game on the schedule possesses different characteristics that will cause consumer demand to also vary. If this is true, each game is really a unique product and the team should set prices based on game specific demand.
Basing prices on expected demand levels is, of course, nothing new. What is new in the current move towards dynamic pricing is that teams are now using data to quantify demand and optimize prices. This use of data for demand forecasting and price setting has been commonplace in the airline and other travel industries since the 1980s. As most travelers know, the price of a ticket on a given flight frequently varies over time and the prices paid by customers sitting next to each other can vary by hundreds of dollars. In the travel industries (airlines, hotels, rental cars, cruise ships, etc…), these pricing techniques have been known as yield and/or revenue management. We will talk about the mechanics of dynamic pricing and revenue management in more detail over time, but the key point is that at the core of these systems are demand-forecasting models.
Mike Lewis & Manish Tripathi, Emory University 2013.