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TRENDING:

GS-101 Installing the KNIME Analytics Platform
GS-102 Adding Scientific Strategy to KNIME
GS-111 Understanding the Market Simulation Nodes
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ED-211 SPRINGSTEEN ON BROADWAY

Posted by Scientific Strategy | Nov 5, 2020 | Education | 0 |




SPRINGSTEEN ON BROADWAY


INSTRUCTORS – CASE SIMULATION

Bruce Springsteen performed his Springsteen on Broadway show from October 2017
to December 2018 in an intimate, 960-seat, theater on Broadway. An online
“Verified Fan” system discouraged scalpers and kept tickets off the secondary
market. This Case Simulation provides both a case study of the concert and a
software simulated model of the market for tickets. Students are asked to
analyze the market and optimize prices in the model.

Students will learn:

 1. How to generate a demand curve,
 2. How to set the price of goods having capacity limitations,
 3. How to manage good-better-best prices, and
 4. How to optimize revenue from a portfolio of perishable goods.

Prerequisites:

 * Introductory statistics (including regression and correlation)
 * Introductory economics (including consumer surplus)



This Educational Case Simulation is for instructors. Case Simulations are
Business & Economic Case Studies plus Market Simulations used by students to
crack the case. Get Started by downloading KNIME and installing the Market
Simulation plugin.


DOWNLOADS

Case Study PDF
Market Simulation
Instructor Zip File


ABOUT CASE SIMULATIONS

Case Simulations are a combination of Case Studies and Market Simulations.


CASE SIMULATION = CASE STUDY + MARKET SIMULATION

Case Simulations extend business and economics case studies by reproducing key
market dynamics in a software simulation. Students can actively explore this
simulated environment to analyze the problems presented in the case. Data
analytic tools are provided alongside the simulation to answer specific
questions. Student solutions can be tested in software before recommendations
are made.


MARKET SIMULATION DETAILS

The software simulation runs an Agent-Based Model (ABM) built upon Mainstream
Economics. Consumer Agents make Rational Decisions based upon their Willingness
To Pay (WTP) and Consumer Surplus for goods and services. Vendor Agents can
follow the rules of Game Theory to maximize profitability.

The simulation runs within an open-source Data Analytics platform called KNIME.
This platform, along with the Market Simulation extension, must first be
installed by students before the Market Simulation workflow can be explored and
analyzed. See Getting Started for details.


TEACHING CASE SIMULATIONS

Case Simulations are flexible. They can supplement lectures, textbooks,
assignments, and class discussions according to the needs of the instructor and
the learning styles of the students.

Below is a suggested way the material could be taught:

 1. Students read the case study and review the discussion questions
 2. Instructor leads a case study class discussion
 3. Instructor presents the market simulation to the whole class
 4. Instructor leads brief analysis to answer introductory questions
 5. Students form small groups to develop hypotheses (optional)
 6. Students themselves run the simulation and the data analysis
 7. Students attempt simple extensions to the model
 8. Students identify phenomenon and build models from scratch
 9. Instructor leads class review and provides feedback




CASE STUDY

Growin’ Up was one of Bruce Springsteen’s first notable songs and the opening
number of his Broadway show. He played 15 songs on an empty stage and closed
with his famous title-track Born to Run.

“It’s going to feel like a garage workshop”, said Springsteen, “I’m going to
play my songs and tell my stories.”


BACKGROUND

15-year-old Bruce Springsteen started playing guitar in 1964 after he saw the
Beatles’ appear on the Ed Sullivan Show.

Greetings from Asbury Park, N.J. came 8 years later and included the song
Growin’ Up. It was Springsteen’s debut studio album cut with his E Street Band
and released through Columbia Records in 1972. Around this time Springsteen also
acquired his nickname “The Boss”. Springsteen’s future wife, Patti Scialfa, was
to join the E Street Band later in 1984.

Springsteen’s career as a singer, songwriter, and musician spanned five decades.
He has sold more than 135 million records and is one of the world’s best-selling
music artists. Springsteen’s 2016 autobiography, Born to Run, was named after
his iconic 1975 album and song.


BROADWAY CONCERT

Springsteen on Broadway kicked off October 3, 2017 at the Walter Kerr Theatre in
New York City.

The 68-year-old Springsteen performed his 15 songs over two hours every
performance. Most of the show he played solo with his acoustic guitars,
harmonica, and piano. Scialfa sang backing vocals on two of the songs.

Between songs, Springsteen would reminisce and recall stories from his book,
Born to Run. As Variety magazine described it, the concert was a kind of live
autobiography.

According to Springsteen himself:

“I wanted to do some shows that were as personal and as intimate as possible. I
chose Broadway for this project because it has the beautiful old theaters which
seemed like the right setting for what I have in mind. In fact, with one or two
exceptions, the 960 seats of the Walter Kerr Theatre is probably the smallest
venue I’ve played in the last 40 years. My show is just me, the guitar, the
piano and the words and music. Some of the show is spoken, some of it is sung.
It loosely follows the arc of my life and my work. All of it together is in
pursuit of my constant goal to provide an entertaining evening and to
communicate something of value.”

Springsteen performed five shows a week: Tuesday through Saturday. “I’ve never
worked five days a week until right now”, he quipped.

The concert was an immediate hit. Springsteen’s first five concerts were all
sell-out performances that grossed $2.33 million (about $466,000 per night).
Only two Broadway shows did better: Hamilton and Hello, Dolly!

Playlist from the Springsteen on Broadway performance.




SCALPERS

Tickets went on sale for between $75 and $850 face value. But with such a small
venue hosting such a big star, the concert was always going to cause a stampede.
All scheduled tickets, as well as tickets for the first extension, were sold out
almost immediately.

The online “Verified Fan” system employed by Ticketmaster helped to discourage
scalpers and ticket-buying bots. The system was designed to check the purchase
history of buyers and confirm their social media activity. Verified and
pre-registered fans were then contacted randomly and invited to purchase up to
two tickets. According to Ticketmaster, 90% of the tickets sold through Verified
Fan were kept off secondary market resale sites like StubHub, TicketIQ, and
Seat-Geek.

“The beautiful thing about Springsteen is that if we had not done Verified Fan,
100 per cent of the tickets would have gone to secondary market sites,” said
David Marcus, Ticketmaster’s Executive Vice President and Head of Music. “There
would have been no chance for consumers to get tickets, because the bots would
have overwhelmed (the system)”.

But fans quickly noticed the $75 face-value tickets selling for $1,400. And
premium tickets selling for as much as $17,000 per pair on StubHub.

Springsteen had seen this before.

In the lead-up to Springsteen’s 2009 concerts at Giants Stadium in New Jersey,
secondary market ticket sellers had already started advertising steeply
marked-up tickets before they had even gone on sale. This led Congress to
propose the BOSS Act [Better Oversight of Secondary Sales and Accountability in
Concert Ticketing] to help eliminate fraudulent behavior.

Then tickets for Springsteen’s 2016 The River Tour that had sold out within
minutes remained available for sale on secondary markets at inflated prices.
This helped spur Congress to pass the BOTS Act [Better Online Ticket Sales Act
2016].

The secondary market for tickets is estimated to be about US$15 billion per
year. “Ticketing is still a rigged system,” said New York Attorney General Eric
Schneiderman.

But many economists believe that scalpers can benefit both the buyer and the
musician. Buyers benefit by having tickets available when they want them. And
musicians benefit by selling tickets far in advance of concerts and having
scalpers take on inventory risk. Economists argue that musicians can discourage
scalping more effectively by (a) increasing the price of tickets, and (b)
increasing the number of concerts.


CONCERT EXTENSIONS

The Springsteen on Broadway concert was originally scheduled to run from October
2017 for four months. But the show was extended twice to both satisfy demand and
placate fans. It ultimately ran for 14 months, and closed December 15, 2018.

Netflix released its film version of Springsteen on Broadway on December 16,
2018 – the day after the concert closed.


DISCUSSION QUESTIONS

These questions can be prepared by students after reading the case and before an
instructor-led class discussion.

 1. Does it make sense that premium tickets are ten times more expensive than
    economy tickets in such a small theatre?
 2. Why do artists dislike their tickets being sold on the secondary market? Are
    artists economically rational?
 3. Is ticketing a “rigged system” as was described by New York’s Attorney
    General?
 4. Do systems like Verified Fan solve the problem of scalpers? Do you see any
    flaws with the system?
 5. Should musicians adopt dynamic pricing strategies like those employed by the
    airline industry?
 6. Fans were upset that Springsteen did not announce the first extension of
    tickets even earlier, accusing the musician of creating an artificial
    shortage when he always knew there would be more tickets. Is this a fair
    criticism?
 7. How much would you pay for tickets to an intimate show by your favorite
    performer?
 8. If you knew Netflix was going to release the film version the day after the
    concerts finished would you still pay for the privilege of going?


SIMULATION QUESTIONS

A Market Simulation has been prepared that mimics the market for concert tickets
in 2017. The simulation is an Agent-Based Model (ABM) comprising of many
concertgoing customer agents interested in Springsteen on Broadway tickets. Some
adjustments to the input parameters have been made to simplify the analysis.

The model covers a single week. Concerts are held on the following evenings:

 * Tuesday
 * Wednesday
 * Thursday
 * Friday
 * Saturday

Four seating sections are available at the Walter Kerr Theatre (in order of
price):

 * Luxury
 * Orchestra
 * Mezzanine
 * Balcony

Customer agents can choose between the four seating sections at each of the five
concerts. The customers’ Willingness To Pay (WTP) for each of these 20 choices
has already been determined. Customers can buy a maximum of one ticket each.

Using the Market Simulation and the data analytics tools provided, answer the
following questions. Some of this analysis can be performed in a spreadsheet but
most needs to be performed in the simulated environment. Your instructor may
demonstrate how to get started.

Create a spreadsheet to collect your answers.


SECTION 1: EXPLORING THE DATA

This section requires you to conduct basic statistical analysis of the data used
by the market simulation.

For this section, you may conduct your analysis in the spreadsheet you created
to collect your answers. But note this will not always be possible as you will
need to modify and run the simulation to generate results.

Question 1. Find the Input Product Array table in the Market Simulation. What is
the capacity of each section? What is the price of a ticket in each section? Do
ticket prices vary by day of the week? Fill in the following table.

Section Capacity Price Luxury Orchestra Mezzanine Balcony

 

Question 2. Find the Input WTP Matrix table in the Market Simulation. Verify the
number of customers included in the model? Verify the number of tickets (called
“products” in the simulation) customers can choose between?

How many Customers in the WTP Matrix? How many Products in the WTP Matrix?


Question  3. Look more closely at the Input WTP Matrix and note the zero (0)
values some of the customers have for some of the tickets. This indicates that
the customer could not go to the concert on that day regardless of the ticket
price. Those tickets are not within the customer’s “consideration set”.

For the first five customers, count the number of days each customer is
available to go to a concert.

Customer # Days Considering C00001 C00002 C00003 C00004 C00005

 

Question 4. Using the first row of the Input WTP Matrix, rank the top five
ticket preferences for the first customer (C0001) by their Willingness To Pay.
Remember, Willingness To Pay does not take into account price.

Preference of C0001 Ticket Day / Seat Section First Second Third Fourth Fifth

 

Question 5. Using the Input WTP Matrix, find the minimum, maximum, and average
Willingness To Pay customers have for the Thursday tickets listed in the table
below.

Hint: First exclude customers who are not considering going on Thursday with the
Row Filter node. The Statistics node can then be helpful.

Section Min WTP Max WTP Average WTP Luxury Orchestra Mezzanine Balcony

 

Question 6. Using the results from the question above, calculate the Average
Consumer Surplus for Thursday tickets for each section and fill in the table
below. Based upon this calculation alone, which section do most customers prefer
to buy?

Hint: Consumer Surplus is the difference between WTP and Price.

Section Average WTP Price Consumer Surplus Luxury Orchestra Mezzanine Balcony

 

Question 7. Using the Input WTP Matrix, generate histograms for the Thursday
Balcony tickets and the Thursday Luxury tickets showing the range of the
customers’ Willingness To Pay. Describe, in words, how the two histograms
compare?

Hint: The Histogram Chart (JFreeChart) node can generate histograms. Or you can
generate the histograms in Excel.

Question  8. Suppose all but one of the tickets have already been sold-out for
the week’s concerts, and all customers in the simulation are still looking for a
ticket. The last remaining ticket is a Luxury ticket for the Saturday night
performance, and a scalper is selling it.

What is the maximum price the scalper could get from selling the ticket (to the
nearest dollar)? You may assume all customers are in contact with the scalper.


SECTION 2: RUNNING THE SIMULATION

This section requires you to run the original Market Simulation and analyze the
tickets sold in each section on each day.

Connect the Input Product Array and the Input WTP Matrix to a “Simulate Market”
node and run it using the green arrowed button in the toolbar. Find the “Output
Product Array” and answer the following questions.

Question 1. How many customers in the simulation bought tickets on each day in
each section? The Quantity column tracks number of customers. Fill in the
following table and plot the results in a 3D bar chart or in a heatmap.

Section Tue Wed Thu Fri Sat Luxury       Orchestra       Mezzanine       Balcony
     

 

Question 2. How much Revenue did each section generate each day? Fill in the
following table and plot the results in a 3D bar chart or in a heatmap.

Section Tue Wed Thu Fri Sat Luxury       Orchestra       Mezzanine       Balcony
     

 

Question 3. Sum the Quantity of tickets sold and Revenue generated each day
across all sections. Fill in the following table and plot the results in a line
chart. Which day(s) brings in the most Customers and generates the most Revenue?

Hint: The GroupBy node can be used to sum the Quantity and Revenue across all
sections.

Section Tue Wed Thu Fri Sat Quantity       Revenue      

 

Question 4. Suppose Madonna launches her Madonna on Broadway concert the same
week and the first half of Bruce Springsteen’s fans attend that concert instead.
What happens to the Quantity of tickets sold and Revenue generated by the
Springsteen on Broadway concert? Fill in the following table and plot the
results in a line chart.

Hint: The Row Filter node can filter out the top half of Springsteen’s fans.

Section Tue Wed Thu Fri Sat Quantity       Revenue      


SECTION 3: DEMAND CURVE

In this section, you need to change the price of the Wednesday Orchestra ticket
multiple times to generate a Demand Curve.

Question 1. Change the price of just the Wednesday Orchestra ticket in
increments of $50 according to the table below. Run the Simulate Market node at
each step and make a note of the Quantity sold and Revenue generated. You only
need to track these metrics for the Wednesday Orchestra section itself, although
you may also calculate the theatre totals if you wish.

Hint: Manually change the price of the ticket in the Input Product Array Table
Creator node. Run the Simulate Market node and note the results.

Wednesday Orchestra Quantity Revenue $400 $450 $500 $550 $600 $650

 

Question 2. Create a Demand Curve by plotting these values in a chart in your
Excel answers spreadsheet. At which price is revenue maximized?

Question 3. Drag in the Demand Curve node to your market simulation to calculate
these values much quicker. At what price is Revenue for the Wednesday Orchestra
section maximized? How much additional Revenue is generated by this ticket when
priced at its Revenue Maximizing Price? Based upon this result alone, would you
recommend raising or lowering the price of the Wednesday Orchestra ticket?

DEMAND CURVE NODE DIALOG BOX INSTRUCTIONS:

 * Double-click to open the node’s dialog box
 * Select the Wednesday Orchestra ticket as the Focus Product
 * Select “Focus Product Only” as the Demand Curve Product Set
 * Check the option “Override Price-Cost Gap Width with Fixed Price Percentage”
 * Set “Gap Width Fixed Percentage of Price” to 2.0
 * In the Market Size tab, select “Set to total number of Customers in the WTP
   Matrix”

After you run the Demand Curve node you will find results in the Output Demand
Curve table. Look at just those result rows in which the Scenario column is set
to “Product” (recall tickets are known as “Products” in the Market Simulation).
Hint: use the Row Filter node to filter Scenario by Product.

Question 4. Continue using the Demand Curve node output you generated, but now
look at just those result rows in which the Scenario column is set to “Market”.
Pay special attention to the Quantity and Revenue values when filtering Scenario
by Market. Why are these values so much larger than the Quantity and Revenue
values when filtering Scenario by Product?

What is the Revenue Maximizing Price when filtering Scenario by Market? Would
you raise or lower the price of the Wednesday Orchestra ticket based upon this
result?

Complete the following sentence:

To maximize the Revenue generated by just the Wednesday Orchestra tickets, the
theatre should raise / lower the price of the ticket to $__________. But to
maximize the Total Revenue generated by the theatre, the price of Wednesday
Orchestra tickets should be raised / lowered to $__________.

Question 5. Repeat the exercise above to determine the price of each Wednesday
section that maximizes Total Theatre Revenue. Test each section independently
one-at-a-time. Fill in the following table.

Hint: Find the Output Price Sensitivity Array output from the Demand Curve node.
Filter by “Change Method” equals “After Max Market”.

Wednesday

Section

Original Section Price Revenue Maximizing Price Original Theatre Revenue
Maximized Theatre Revenue Additional Theatre Revenue Luxury       Orchestra    
  Mezzanine       Balcony      

Total Expected Additional Revenue

 

 

Question 6. As shown in the table above, sum the Additional Theatre Revenue to
calculate the Total Additional Revenue the theatre might expect by changing the
price of each Wednesday section to its Revenue-Maximizing Price.

Question 7. Simultaneously set the price of all Wednesday section tickets to
their Revenue Maximizing Prices calculated above. Re-run the Market Simulation.
What is the total additional Revenue that was generated by the theatre? Is this
the result you expected when adding individual results? Explain.


SECTION 4: MAXIMIZE WEEKEND REVENUE (ADVANCED)

Fans understand that weekend ticket prices can be more expensive than weekday
prices. In this section, you need to maximize total theatre revenue by changing
the price of the weekend tickets. Friday and Saturday tickets are “weekend
tickets”.

Question 1. Change the prices of all Friday and Saturday tickets at the same
time by the same percentage according to the table below (leave the price of
Tuesday, Wednesday, and Thursday tickets unchanged). Sum up the total Quantity
and total Revenue for the theatre across all sections and performance days
(exclude the ‘No Sale’ customers). What percentage price increase of weekend
tickets would maximize Total Theatre Revenue?

 

Weekend Ticket Price Increase

Total Theatre Quantity

Total Theatre Revenue

+0%

 

 

+10%

 

 

+20%

 

 

+30%

 

 

+40%

 

 

+50%

 

 

+60%

 

 

+70%

 

 

+80%

 

 

+90%

 

 

+100%

 

 


SECTION 5: MAXIMIZE TOTAL THEATRE REVENUE (EXPERT)

Assume Bruce Springsteen wants to maximize the total Revenue the theatre
generates over the week. Assume he is not concerned about the reaction of
disgruntled fans.

Question 1. Describe, in words, how you might go about changing the prices of
the tickets for each section on each day to maximize the total week’s revenue.
All 20 tickets could now have different prices.

Question 2. Try changing individual ticket prices by trial-and-error or any way
you wish to increase the week’s total revenue. Boasting rights will be awarded
to the student or team that can generate the most revenue.

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