Of late we have been looking at value provided by sports franchises in different leagues. For most of these analyses, we have basically focused on how much fans are asked to pay for each win. We also make adjustments for factors related to market size, median income and capacity. Today’s analysis looks at pricing in the NHL.
Of all the pricing analyses we have done, the NHL is the strangest. The most surprising result is a lack of a positive correlation between winning rates and ticket prices. Our standard procedure is to develop a model that predicts ticket prices as a function of winning percentage, payroll, market size, median income and other factors that we would expect to be related to demand for tickets. We do a lot of testing in these models in terms of evaluating different specifications (interactions, nonlinear effects, etc…). In none of these specifications did we find a significant positive relationship between winning rates and prices. The most powerful predictor was median income.
The other thing that we have been experimenting with in these models is using social media data as an explanatory variable. The logic is that social media metrics (follows and likes) provide an unconstrained measure of fan support. This provides a means to assess the relative aggressiveness of how team’s price.
Something to consider in these pricing analysis is the question of how prices are set. At one extreme, we might suppose that prices are set in order to maximize revenues. This is a reasonable starting assumption but the implication is that teams are extracting every dollar possible. On the other hand, teams may price below fan’s reservation prices if the team is trying to build brand loyalty. The key point is that while consumers might be willing to pay very high prices, if they don’t view the prices as “fair” then loyalty can be adversely affected. Perhaps the best way to look at our list is that the teams at one extreme price the least aggressively (most benevolently?) while the teams at the other extreme are trying to extract every dollar they can from their fans.
At the top of the list we have Ottawa, Dallas, Boston, San Jose and Chicago. After adjusting for market sizes, income levels and social media presences we find that these teams underprice. This is an interesting list as it contains both high brand equity teams like the Blackhawks and the Bruins as well as less prominent teams like Dallas and San Jose. It is also notable that the Blackhawks and Bruins price above the league average while Dallas and Ottawa price near the bottom. Interestingly, over the past 3 years Ottawa has basically sold out its arena. The implication is that Ottawa (and the other teams on the list) could likely impose a price increase without too much loss of demand).
At the other extreme we have Philadelphia, Florida, Winnipeg, Toronto and Edmonton. Again, this list contains both high (Toronto, Philly) and low profile teams (Florida). Toronto is especially notable as they charge by far the highest prices in the league. Winnipeg’s price are also extreme as they price higher (according to Team Marketing Report) than teams in New York, Chicago or Los Angeles.
Mike Lewis & Manish Tripathi, Emory University 2013.
Today we are taking a look at pricing in the NBA: according to the Team Market Report’s fan cost index there is a wide range of prices across the league. Last year, the Knicks had the highest average price at $123.22 while Charlotte’s average was just $29.27. Rather than compare raw prices our objective is to look at the value provided by teams.
For our first look at value, we created a model of average prices as a function of variables such as team winning percentage, team payroll, metro area population and metro area average income. This model is used to predict how team and market quality influence ticket prices. A comparison of actual prices to predicted prices tells us which teams provide the best value. This is along the lines of looking at the ratio of price to wins but with a bit more sophistication as we also control for factors such as market size and star power.
Astute readers will likely realize that this analysis is somewhat related to our fan equity rankings. A key assumption of that analysis was that teams price in order to maximize revenue. Today’s analysis can be interpreted in two ways: teams that price under the market are pricing low either because they are not trying to maximize revenue or because they are mispricing. For now, we will just say that teams that price below market (according to the model) are providing added-value value.
Over the last 3 years, the top 5 teams in terms of value are the Brooklyn Nets, LA Clippers, Atlanta Hawks, Memphis Grizzlies and Washington Bullets. These teams provide the best product in terms of winning relative to their market positions. At the other end of the spectrum are the Knicks, Celtics and Suns seem to be the most overpriced.
Obviously, we have an issue in that the value provided is negatively correlated with brand equity since the Knicks and Celtics are two of the league’s most prominent brands while the Clippers and Hawks are not. As a further look into pricing we performed an additional analysis, which was similar to the first pricing model but we added social media data (Twitter Follows and Facebook Likes) to the model. These measures are useful because they are largely independent of owner’s objective functions and observable fan interest is not constrained by prices or capacity. We also included an interaction between social media success and market size. We like this model a bit better because it accounts for fan interest and excitement in addition to team and market quality.
When we use this model to compare actual vs. predicted prices we see a few changes. Now Memphis is the best value followed by Brooklyn, Indianapolis, Charlotte and New Orleans. Including social media into the model makes the biggest difference in the results for the Bulls and the Lakers. These teams appear to be underexploiting their brand equity when it comes to pricing. According to ESPN, both teams have had attendance levels of over 99% of capacity for the past three seasons so it seems that price increases are doable. Ticket pricing is tough in sports because observable demand is constrained, but it appears that these teams have more pricing power than they realize. It is also difficult to reach conclusions based on average ticket prices. As we all know there is considerable heterogeneity in prices based on seat quality.
As always, no analysis is perfect and there are factors that we don’t capture in the market. For example, perhaps in the case of the Knicks the team has additional pricing power because fans are willing to buy during down cycles in order to insure tickets during winning years.
Mike Lewis & Manish Tripathi, Emory University 2013.
The Clippers’ video description of their dynamic and variable pricing policies seems to be creating a bit of buzz . We agree with other folks that this video is a pretty good description of these pricing techniques. As an educational tool the video is very effective.
We do have a couple of general observations. First, taking the straight-forward approach of discussing how market factors lead to increased or decreased demand for certain games is a smart technique. One of the potential problems of these new pricing systems is simply that they represent a change. Consumers tend to compare any current offering to some personal or historical reference. When the current offering is complicated, consumers are very likely to have a negative reaction.
The other thing that the Clippers do well is that they frame the policies in terms of the discounts provided to season ticket holders. In other words, rather than emphasize the high cost of coveted single games tickets, the focus is placed on the available discounts. In contrast, think back to the summer when Michigan’s pricing plan quickly became a story of $500 tickets for the ND game. This is doubly smart since the discounts are linked to season ticket holder status. In this way, the Clippers are able to provide a “benefit” to their most valuable customers.
This article in the Kansas City Star discusses the Kansas City Royals’ dynamic pricing plans for the post-season. The key excerpt from the article is…
“Diamond Box seats located behind the dugout on the lower level normally sell for $39 in the regular season. That price jumps to $155 for a wild-card game or the divisional series, $220 for the championship series and $275 for the World Series.
That represents increases of 297.4 percent, 464.1 percent and 605.1 percent. Seem high? Several professionals in the field say they are among the sharpest increases they’ve ever seen for any event.”
The obvious question is “are these prices really too high?” The knee jerk response from dynamic pricing advocates is usually that the prices are fair since the prices are set by the market. The concern I have with the idea of market prices being “fair,” is that fairness is subjective. In other words, it is the consumer that gets to make the judgment as to whether a given practice or price is fair.
There is an academic theory that speaks to this issue of fairness. The theory of “dual entitlement” basically says that consumers evaluate prices with the belief that while the firm is entitled to a profit, the consumer is also entitled to a fair price. In the case of increasing prices of post-season games, the dual entitlement principle suggests that while the team is entitled to some price increase, the consumers should not be exploited with exorbitant prices.
What is the downside to violating this principle? The Royals should be concerned with whether these prices damage their stock of fan loyalty. As a small market team, the Royals are likely to have more losing seasons in their future. If they want fans to stand by the team during the tough times, it seems like extracting every last dollar during a rare playoff series might be a bad idea.
So when is dynamic pricing price gouging? Whenever the fans think it is.
The latest version of dynamic pricing is Northwestern’s purple pricing. We pointed out a while back that this program seemed primarily designed as a means for extracting revenue from visiting fans. This video explains in more detail how the system works and how it contains advantages for fans. From a consumer behavior perspective, the purple pricing system contains a significant benefit. The system starts with a high price and prices decrease until the section is sold out. Customers are protected as the price eventually paid is the LOWEST price at which tickets are sold.
So how does purple pricing compare to other dynamic pricing efforts? Perhaps the biggest difference is that the price structure is largely dictated by the school rather than the market. In NU’s program the school sets the initial price and from there the prices can only come down. This means that NU can potentially be leaving revenues on the table. The second and more subtle factor is related to how the system impacts consumer’s decision making process. The system pushes consumers to buy quickly at higher prices in order to avoid being left out. The system compensates by providing the safety net of all consumers paying the lowest price. The system works for Northwestern if the fear of being left out leads consumers to pay a bit more than they would like. If enough consumers feel this pressure then the low price guarantee is irrelevant. In this way, purple pricing transfers risk to consumers. Of course, while this transfer of “pricing” risk might have negative implications for customer relationship management, the Northwestern program seems much more targeted to extracting revenues from Michigan and Ohio State fans.
The dynamic pricing trend keeps accelerating with the recent announcement that the Toronto Maple Leafs will be pursuing a form of dynamic pricing. The Leafs’ policy seems to involve more of quality based pricing than any true dynamics in that the plan involves charging different prices for different tiers of games (based on opponent and game time). Some might quibble that this should be termed variable rather than dynamic pricing, but ultimately this is a form of price discrimination that can adversely affect how fans think about teams.
In our first two entries on dynamic pricing, we have discussed some basic principles of dynamic pricing (revenue management). In this entry, we begin to consider the specific challenges of implementing these practices in sports contexts. Industries that employ dynamic pricing will often make impassioned defenses of the practice. Typically, the industry defense emphasizes that dynamic pricing is “fair” because market forces are dictating prices. What could be more “fair”?
Obviously, many consumers don’t view dynamic pricing as a positive development. Dynamic pricing is something that at best makes consumers nervous, and at worst angry. This is not a surprising reaction, since dynamic pricing is at heart a system of price discrimination and inventory rationing. If we think about the connection that exists between a consumer and a firm as a relationship then these practices are obviously not going to improve the “relationship.” In the previous paragraph, we placed fair in quotes for a specific reason. Fairness is subjective and teams do not get to decide how fans feel.
If consumers accept dynamic pricing in travel industries, why should these techniques be a problem in sports? Let’s consider a couple of key differences between air travel and sporting events. First, in the airline industry, the consumers who typically pay the highest rates do not pay for their own tickets. In the airline industry, the business customer often chooses the travel options while the firm pays the price of the travel. And as a bonus, the employee collects loyalty points. A second, key difference is that air travel is purchased in order to achieve some other goal, whether it is a business meeting or a visit to a resort. In other words, air travel is a product that is purchased so that the buyer can do something else. In contrast, dynamic pricing of tickets affects the focal experience rather than an intermediary transaction cost.
A third issue is that the relationship between a fan and a team is fundamentally different than the relationship between an airline and a traveler. There is a reason why we call consumers of sports “fans” rather than “customers.” In what other categories, do consumers proudly wear the brands they consume on their clothing? For example, Manish’s wardrobe seems to consist of mainly t-shirts emblazoned with team logos and cargo shorts. In the last week I have seen Cubs, Maple Leafs and Redskins t-shirts. I raise the issue of clothing because it highlights that consumers want to have a strong relationship that includes being publically associated with the team. In marketing, we might refer to this as a desire for a “communal” relationship. In contrast, a reliance on strict demand based pricing will tend to reduce the fan-team relationship to a series of cold economic exchanges.
Mike Lewis & Manish Tripathi. Emory University 2013
As we noted in our first dynamic pricing entry, the basic tools for implementing revenue management originated in the airline industry. In today’s installment, the goal is to provide some insights into how revenue management works in the airline industry and to begin to consider how these tools apply (or don’t apply) to ticket pricing.
As a starting point, it is necessary to specify the revenue management goal. In the airline industry the most basic goal is to maximize the revenue collected from a given flight (the network structure of airlines can often motivate more complicated goals). In the case of sports, or performing arts, the corresponding goal is to maximize the revenue produced by a given event. More generally, we can think of the goal of revenue management is to maximize the value of a firm’s inventory. Often a distinction is made that revenue management is especially useful when inventory is perishable. The key point is that if an airliner takes off with an open seat or a stadium has empty seats at game time, that the firm has forever lost that unit of inventory.
The two key ingredients needed to implement revenue management are a system for segmenting customers and forecasts of segment level demand. In the airline industry, a very basic system of segmentation might be to group customers into a business and leisure segments. These segments are thought to differ in terms of traits such as price sensitivity and flexibility. Specifically, business travelers are thought to be willing to pay higher prices and to have more restrictive schedules. The second necessary input is segment level demand forecasts. There are many ways of forecasting demand ranging from complex statistical models to simple heuristics but the salient point is that the revenue manager needs to be able to accurately forecast how many customers from each segment will want to travel on a given flight or attend a specific game.
A core concept for implementing revenue management is the idea of “expected marginal revenue.” For example, let us assume that we are trying to manage the revenue produced by a flight with 100 seats. Also we have a business segment willing to pay a high price that tends to book close to the departure time, and a price sensitive leisure segment that books long in advance. The revenue manager’s job is to decide how many low priced seats to sell to the leisure segment or, differently stated, to decide how many seats should be reserved for the late arriving business segment. One way to do this is to reserve just enough seats such that the expected marginal revenue from saving a seat for a business customer is equal to the expected marginal revenue from allowing the seat to be purchased by a leisure customer.
Let us further assume that the business customer pays $1,000 per ticket while the leisure traveler pays $100. The revenue manager’s decision rule would be to reserve sufficient tickets for the business segment such that the expected marginal revenue produced by the last seat allocated to the business customers is the same as if the seat were sold to the leisure customers. Returning to our example, if we can sell unlimited $100 seats the revenue manager would reserve enough seats so that there is at least a 10% chance to sell a $1,000 business ticket.
In many ways we could make the preceding discussion sports specific simply by changing “flight” to “game” and by using a segmentation system that is more sports specific (casual versus hardcore fans?). But as you might expect, when we get to actual implementation it often becomes difficult to directly transfer travel industry yield management techniques to a sports context. In our next entry we will discuss several of these challenges.
Mike Lewis and Manish Tripathi, Emory University 2013.
The biggest dynamic pricing news this summer was the entry of Michigan football to the dynamic pricing world. We follow the Twitter buzz about various dynamic pricing techniques fairly closely and we are already seeing a significant negative reaction to Michigan’s program. One twitter report states that UMich-ND tickets are already selling for over $500 and another says that because of the dynamic pricing program he will no longer donate to Michigan football. These comments really highlight that dynamic pricing is a double edged sword.
Our view is that the REAL challenge in dynamic pricing is not in maximizing event revenue. The challenge is in balancing event level revenue generation with customer relationship management. This is an often neglected challenge that requires a different type of data and statistical analysis to address.
The shift towards dynamic pricing continues. This article describes Northwestern University’s approach to dynamic pricing. The big idea in NU’s system is that prices will start high and then be decreased until the season ticket price is hit or until tickets are sold. Winning buyers will then pay the lowest prices (i.e. if I buy at $300 per ticket and the section sells out at $150, then I pay $150).
Northwestern’s approach is interesting as prices drop over time and because the high price buyer has a bit of protection against over paying. We do have a couple of observations. First, these two consumer friendly features limit the value that NU can extract for the dynamic pricing. From a firm’s point of view, the most efficient form of price discrimination is where consumers all pay their reservation (maximum) prices. The NU system does provide a push to get consumers to buy quickly since the supply of tickets is limited. This is, of course, an important part of any dynamic pricing scheme in that it always helps if supply is limited.
We do, however, believe that something else is really driving this foray into dynamic pricing. The two games in the Purple Pricing plan are Ohio State and Michigan. Our guess is that this program is mainly designed to extract maximum revenue from these visiting fans rather than from the Wildcat faithful.
Mike Lewis & Manish Tripathi, Emory University, 2013.