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Jun 30, 2009
How To

How to Use Dynamic Pricing: Lessons Learned from the San Francisco Giants

SUMMARY: Ideally, prices should be set to maximize revenue. But if demand for a product changes, prices also have to change to reach total efficiency. How do you know if youíre setting the best price possible?

See how a major league baseball team tested dynamic pricing for tickets to home games. They sold 20% more tickets in sections where the dynamic pricing was offered -- but the program hasnít been a grand slam. Find out how they managed challenges, set prices and sold more tickets.
On Aug. 7, 2007, Barry Bonds of the San Francisco Giants needed one more homerun to break baseballís all-time career record. Tickets to this potentially momentous game at the Giantsí home field, AT&T Park, were surprisingly cheap.

It was a weeknight game against the Washington Nationals, a relatively weak opponent. According to the teamís three-tier pricing structure, those factors added up to a low, face-value ticket price.

"I think a ticket in the upper deck was $10," says Russ Stanley, Managing VP, Ticket Services, San Francisco Giants. "And yet, it was the hottest ticket weíve ever had in this park. It was bigger than Opening Day."

That day, Bonds hit the record-setting homer, and $10 tickets were selling for about $50 in the secondary market. The situation drove home to Stanley something he had heard at an industry conference earlier that year: A vendor told him he was leaving money on the table by setting prices many months before games were played.

This season, to more accurately measure demand for a given gameís tickets, Stanley and his team tested a dynamic pricing algorithm that adjusts prices up to the start of a game. They limited the test to 2,000 of the stadiumís 41,500 seats. After 17 games this year, they saw the following results:
o 20% more tickets were sold in the dynamically-priced sections, compared to last year
o 1.7% more tickets were sold overall, compared to last year

We spoke with Stanley to find out how this pricing system differs from the Giantsí traditional system, how his team manages it, and how it is helping to fill more seats.

-> Pricing systems compared: Tiered system vs. dynamic

The Traditional, Three-Tier System

The majority of the Giantsí tickets fall into three pricing tiers:
o Tier 1: Highest priced, such as Opening Day and late-season games against rivals such as the Oakland As and LA Dodgers
o Tier 2: Weekend games against popular teams, and games determined to be "bigger than most"
o Tier 3: Lowest priced, such as weekday games against poor teams

The prices are set in September for the following seasonís games, which start in April. Stanleyís team tries to predict demand for each game from a number of factors, such as the potential draw of specific matchups. But once the season starts, they canít make adjustments to reflect current conditions.

"If the team is on a five-day winning streak, our ticket is worth more than it would have been than if the team was on a five-day losing streak," says Stanley. "But yet, set the price eight months earlier and we have to live with it."

Dynamic Pricing System

The tested system was based on a pricing algorithm the team created with the help of a statistical analyst. Game prices regularly adjust as several factors in the algorithm fluctuate.

These factors include:
o Starting pitchers
o Weather
o Team performance
o Time of day, day of week of the game
o Special events or promotions
o Previous gamesí sales
o Ticket prices in the secondary market (ticket resellers)

Under this system, the prices change regularly. Some tickets for the tested seats have sold for a few dollars more than they normally would have, capturing higher-than-previously-estimated demand for that dayís game.

Other tickets sell for less than the previous price, ensuring that as many profitable tickets are sold as possible despite dampened demand.

The ticket prices do not keep pace with the secondary market of ticket resellers, Stanley says. Resellers have, at times, lifted prices of the upper deckís normally low-priced seating to $100.

-> Insights into dynamic pricing management

Below are three insights Stanley and his team gained during the trial.

Insight #1. The algorithm is not always right

The algorithm is designed to estimate the best possible price to maximize ticket revenue. To do that, it has to perfectly estimate demand for each game -- which is impossible. There are an infinite number of factors that influence demand.

The best the algorithm can do is estimate -- and it is a work in progress. Stanley and his team have been adding factors and tweaking their weight to "teach" the algorithm how to set the best prices.

"Iíve been doing this for 20 years, and the other person I work with has been with me for 12 or 13 years now. There are certain things that we know will make a price go up or down or stay the same, and the computer doesnít know that yet."

Two examples:

- During the three-day Memorial Day weekend, the team had to manually set prices higher for the game on Monday, because they knew demand would be higher since many people had the day off. The algorithm wasnít aware of the holiday.

- Group sales have a tendency to skew the algorithmís demand estimate. A youth organization could purchase 150 tickets to an otherwise low-demand game, causing the computer to overestimate the general publicís interest and raise prices.

Insight #2. Be cautious

Three ways the team is showing caution with its pricing strategy:

- Do not automate

The system does not automatically update prices, yet. The team is more comfortable with a manual update based on the algorithmís analysis, especially while the algorithm is still learning how to best gauge demand.

- Keep loyal customers happy

The team tested the two seating areas that are usually among the last to sell. More importantly, no season ticket holders are in this area, which avoids rubbing the Giantsí biggest fans the wrong way.

"I didnít want season ticket holders paying $12 and having them sit next to someone who came in for $8," Stanley says.

- Test a sample

The team tested a small sample, less than 5% of its seating capacity, to avoid pricing chaos.

"I wanted to just kind of dip our toe in the water. I didnít want to do the whole ball park. I wasnít sure that the world was ready for that."

Insight #3. Prepare for a lack of certainty

The team is continuing with the test for the remainder of the season. Stanley is confident that dynamic pricing is helping the ballpark make more money, but he cannot be certain. Thatís because ticket prices are just one factor in the teamís revenue stream.

Ticket sales in the tested sections are up 20% -- but revenue is about flat, compared to last year, he says. Overall the parkís ticket sales are up slightly, but the team is also a little better, he says.

However, bringing additional fans into the ballpark, even at a lower ticket price, is likely boosting concession sales, Stanley says. Also, lower ticket prices are likely persuading some people to experience Giants baseball who otherwise would not have.

"I absolutely feel that weíre getting some people in this park who would have not come were they not able to find a bargain," Stanley says. "And Iíll tell you, this park is a heck of a lot more fun when itís full. It adds to the excitement."

Useful links related to this article:

Creative Samples from the San Francisco Giantsí dynamic pricing test
http://www.marketingsherpa.com/cs/Giants/index.html


Special Report: How to Use Predictive Modeling to Pick Your Best Prospects and Boost ROI Up to 172%
https://www.marketingsherpa.com/barrier.html?ident=3059


AT&T Park
http://mlbnetwork.mlb.com/sf/ballpark/index.jsp


The San Francisco Giants
http://sanfrancisco.giants.mlb.com/index.jsp?c_id=sf



See Also:

Comments about this How To

Jul 01, 2009 - Eric Hyman of JWT says:
Funny dynamic pricing anecdote. During dotcom boom, our co, now part of JWT, had a client named Opensite Technology, that made auction software for business. Our analysis: businesses don't want to auction things the same way as consumer online, but they do want to engage in some sort of situational price negotiation. The example we looked to: "Dynamic Pricing" which reflects the changing impact of various factors on supply and demand and in turn, pricing. We combined this with a buzzword of the moment (ecommerce) and created a new concept, "Dynamic Commerce," and we made them the poster child with a great highly cost efficient ad campaign. This small co that never sold more than $10M of their own stuff suddenly upped their IPO target by 300% but before they got their, a bidding war by major tech co's drove their value up culminating in purchase by Siebel for nearly $750M. Cha ching! I hear people from those days at Siebel/Oracle still scratch their heads over that one. Sure it was a sign of the times. But I still say, now THAT'S the power of Dynamic Pricing!



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