Data Analytics Meets Performing Arts

Like most stories about data analytics, let’s begin with some statistics and facts.

According to a survey conducted by Nielsen Scarborough in spring 2016, over 47 million Americans had attended a live theatre event within the past month and around 73.5 million had visited a performing art event in 2013. In similar research, Nielsen Scarborough found that for-profit organizations such as The Broadway theatre in New York City generated approximately 1.7 billion U.S. dollars in revenue, with more than 13 million audiences in the 2017 and 2018 seasons. On the other hand, non-profit theatres have attracted over 10 million viewers annually in the U.S.

Potentials

As one of the most popular pastimes for many people in the United States, Performing Arts have tremendous potentials and opportunities in using data science to reach its full potential. Organizations can use the data that has been gathered over the years to dive deeper into understanding Performing Arts through the lens of data science. Whether you are in a non-profit or a for-profit organization, understanding the numbers behind the performances has an array of benefits for you.

Digital Spaces is Ahead of the Curve

Firms in the digital art space such as Netflix and Spotify are well known for using data science to perfect their business strategies. With the data that they collect from their users, they have everything they need to determine programming, define customer behaviors, create niche shows that attract specific customer tastes, and so much more. Because of their successful data-driven nature, they have built an ecosystem that, according to a McKinsey article “Building an Effective Analytics Organization” by Gloria Macias-Lizaso, are “enabled by deep functional expertise, strategic partnerships, and a clear center of gravity for organizing analytics talent.” Simply put, these firms have created an organizational structure that embraces data and have developed a culture that depends on data to make important decisions. They have an integrated system to collect data from their users and users benefit from data science. In a way, users are not just consuming art, they are also shaping it.

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Traditional Arts Centers are Lagging Behind

Traditional arts centers are far behind on unlocking the full potential of their data. This is partly because Data scientists are one of the most sought-after contributors in recent years, it is natural that most people in this talent pool are incentivized to join Silicon Valley giants that are leading the data industry and have the resources that allow them to shine and grow. Another big reason is that the performing arts industry is still very much a traditional business where decisions are made based on intuitions and experiences. Rarely, if any, do performing arts centers rely on data and have the desire to adapt to the modern world and apply change management to change their organizational structure to building a data-driven culture.

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Mondavi is Doing its Part

A forward-thinking arts organization, the Mondavi Center has pushed itself to be data-driven in making strategic and tactical decisions, implementing sophisticated dynamic pricing, revenue management, and patron loyalty initiatives, all of which have established the Center as a leader in the field. Unlike many traditional arts organizations, Mondavi prides itself in having an organizational structure that includes a data analytics function. In the article, “Competing on Analytics,” Thomas H. Davenport and Jeanne G. Harris stated that companies that successfully compete on analytics have analytical capabilities that are hard to duplicate, unique, adaptable to many situations, better than the competition, and renewable.

Mondavi Center is a great example of an organization that integrates analytics to its core business strategy and business making to outperform its peers in the environment.

Analytics Applications at Mondavi

Mondavi is committed to maintaining state-of-the-art, world-class performance facilities and providing the highest quality experience for both artists and audiences.

To do this, they strive to match their performance offerings to the taste of their customers and cope with current COVID restrictions. Success relies on understanding audience preference and figuring out consumer behavior. Similar to Netflix and Spotify, performance arts centers need to introduce the right performance and products to the right customer at the right time, especially during the time of COVID and having reopening in mind. As part of the UC Davis MSBA program, my team partners with UC Davis’ own Mondavi Center to conduct analysis and provide business insights.

This year is especially tough on the arts industry as they are going through a tectonic shift driven by demographic changes and a mass transformation in consumer behavior due to COVID-19. Our team has been tasked with deliverables to help them with seat allocation, product offerings, dynamic pricing, event profitability, and identity analysis. Our ongoing efforts provide a platform for Mondavi to apply data analytics to come up with data-driven solutions to counter the negative impacts caused by the Pandemic.

  • Seat Allocation Model: Using historical sales data, our team has optimized Mondavi’s performance half scaling map with a physical social distancing seating chart, which serves as a basis to create price zones that are most effective for both customers and Mondavi. With that information, we extended our output to include a dynamic model that recommends the number of seats to be allocated in every price zone for every genre. Users can adjust total capacity from 1–1800 seats, based on the higher of the predicted sales or the total capacity, to generate the number of seats that should be allocated in each zone. This enables the programming team to plan for ticket sales and predict sales for the production.
  • Event Profitability Analysis: This visualization project involves conducting constrained optimization on historical sales data to recommend the top profitable 35–40 productions to be featured in the 21–22 season. The outcome is used to predict the profitability of the productions in the 21–22 season under a smaller show capacity.
  • Predictive Pricing Analysis: This project aims to formulate pricing strategies to satisfy customers with different consumption preferences under social distancing. To draw insights from revisions of past pricing scenarios and thus provide more accurate predictions, our team ran the time-series analysis to figure out the time-of-days or week of days that attracted customers most, and then implemented linear regression to build the predictive model on sales based on genre. Teams at Mondavi started using our models immediately to plan for shows for the upcoming season, and found them insightful and actionable, especially in terms of decision-making in the Marketing and Programming teams, where much work overlaps and depends on the other team. Our model helped guide their work as it can pinpoint historic shows that are still likely to be profitable under COVID-19 constraints.
  • Product Offerings: Originally, Mondavis provided series packages that contained 20% discounts for customers who purchased a few shows under the same genre. However, it was canceled due to difficulty to provide a bundled series during the pandemic. To serve audiences with more flexibility, suit current audiences’ shifted consumption preferences, and re-engaged lapsed customers, our team helped Mondavi to design new package types for online users, onsite users, and hybrid users.
  • Identify Survey Analysis: This project aims to understand customer preferences and sentiments toward Mondavi. We will be working with text form data including extracting important information and interpreting based on domain knowledge and our understanding of Mondavi. The results will be used to build marketing strategies to target customers more effectively.

What Others Can Learn From Mondavi

Data science should not be a black box that is exclusive to Silicon Valley giants. As I’ve mentioned at the beginning of my blog, non-profit and for-profit performing arts venues alike are extremely important to the American culture and have a lot of potentials to perfect their operations and strategies. Like Mondavi, other arts venues need to start unlocking stories behind the numbers and use them to their advantage. Others can apply the same analytical approach by integrating data into their core business functions. Team Data should be involved in crucial team meetings and new initiatives to ensure the maximize both customer and business benefits.

Impacts and Risks

Although data is great, there are potential drawbacks associated with applying an analytics approach in traditional arts. Machine learning models are great at optimizing problems, but they don’t always have companies’ mission statements in mind in the calculations. There is the possibility that with increasing quantification of programming and a drift towards popularity over worth based on revenue considerations, Mondavi and others who apply the same analytical approach in determining their business decision could become more consumerist. As a result, they would fail to illuminate, educate, and connect with the highest quality experience for both artists and audiences.

However, the benefits far outweigh the impacts and risks. With the help of data analytics, traditional arts will reach a new height in the modern world.

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