Rabbit Hole #2

AI, Data, and Analytics: Arts Nonprofits vs. Media & Entertainment

Data analytics are becoming increasingly prominent in the arts and entertainment world. While the most prominent examples are seen in the usage of algorithms by companies such as Netflix and Spotify, there are growing instances of these technologies in other segments of both artistic management and the creation of art. This post looks at how arts nonprofit organizations are using technologies for data analysis in their everyday operations, and compares the uses of these technologies with those of other leisure time activities, including the larger media & entertainment industry and sports management.

Identifying Key Terms

The main technologies for data analytics that I will be looking at are centered around artificial intelligence (AI), predictive analytics, and the use of big data. AI involves the use of machines to mimic the actions and decision-making processes of humans. AI is helpful in that it is able to identify patterns that emerge from data. The end goal of predictive analytics is to predict future outcomes; these outcomes are predicted using past data, machine learning, and algorithms. Big data is tied to both predictive analytics and AI, and generally refers to big amounts of complex data that can be analyzed for producing insights. Most of the examples found through my research involve a combination of these three processes for the purposes of assisting operations.

Arts Nonprofit Organizations

Arts nonprofits can use (and have used) data analytics and AI for assistance in internal operations. Museums specifically have been encouraged to utilize data analytics as a way of creating cases for support to existing and potential funders, and as a result, maintain relevancy for future museumgoers. AI and machine learning have both been utilized by nonprofits in general as a way of encouraging fundraising. GiveCentral suggests creating an algorithm that looks at data from funders as a way of being more in touch with donors. Similarly, AI can be used to quickly identify both individuals who are more likely to donate, and recently lapsed donors. In addition to fundraising, these technologies can also be used for general administrative tasks. Machine learning can be used for tracking data, rather than having a human employee do it. The use of chatbots for daily interactions with customers can also be helpful in making sure that employees can direct their attention elsewhere. When it comes to marketing, AI and machine learning is described as being able to help an organization develop better marketing strategies by analyzing data from different regions. While “traditional arts centers” may not be as advanced as digital platforms in their usage of data and data analytics, institutions such as the Mondavi Center for the Performing Arts in Davis, CA have used data for  creating socially-distanced seating models, predictive pricing, and profitability.

Aside from instances where AI has been used in both creative processes and in the restoration of art, these technologies have also been utilized for audience-facing experiences. This technology, while in some cases adds to the audience member’s experience, also helps management in the long-run by providing the institution with more interpretable data. It is worth noting that the visual arts world seems to have more pertinent examples of usage compared to the performing arts. One use of AI/machine learning has been deployed to predict the amount of time that an audience member will spend looking at a specific exhibit; the data helps the museum understand which exhibits attract the attention of audiences the most. Maintaining and understanding the interests of audiences is part of the ongoing work of a museum; the National Gallery in London, UK, utilized big data and machine learning for understanding the experiences and visitors as a way of “forecasting future engagement.” Similar institutions such as the British Museum have interpreted big data for the purposes of better understanding visitors and designing exhibits and programs that are more aligned with the interests of their patrons. The British Museum collected data from “audio guides and interactive exhibits” with the consent of visitors. In a slightly less clandestine way of collecting visitor feedback, many Swiss museums have used an app where visitors are able to share their experiences with museum directors directly while they are walking through the museum, as opposed to a completing a post-experience survey.

Entertainment & Sports Industries

There are significant uses of data analytics for the purposes of understanding and retaining audiences in the nonprofit arts, and within the larger media & entertainment industry, there seems to be an even larger emphasis placed on this. AI, predictive analytics, and big data are all utilized for the purposes of attracting customers, understanding what audiences like and dislike, and personalizing content for individual customers. Broadway uses a predictive analytic, big data utilizing software called PatronLink360, which is designed to assist in audience attraction and subscriber retention. In the film industry, executives have utilized big data as a way of better understanding what audiences do and don’t like in order to develop both creative and marketing strategies, and predict what audiences might be interested in. AI and predictive analytics can be useful in the process of personalizing content suggestions. One of the most prominent examples is through streaming sites such as Netflix; data on previous viewings are analyzed through AI and machine learning to give the customer suggestions on what to watch next. The common idea is that personalization can serve as an effective way of retaining customers, and as a result, creating a sense of loyalty. However, there are most likely other factors that play into whether a customer chooses to maintain a subscription beyond the content that is made available (such as pricing).

Aside from using these technologies for improving the performances and nutrition of athletes, and management of personnel, the sports segment of the entertainment industry uses AI and big data in ways that are similar to the arts, especially as it relates to the overall fan experience. The sports industry in general places a large emphasis on the relationship with fans. By looking at social media, in addition to attendance records, teams can use big data to understand fan sentiments and create marketing strategies as a result. Teams and stadiums have used data to analyze merchandise sales, and to see which events are more likely to sell higher numbers of tickets. With regard to ticketing, some teams, such as the soccer team Columbus Crew, have used facial recognition in place of utilizing physical tickets for entry. Additional examples of current uses of AI include facilitating stadium entry, and, similarly to some arts organizations, chatbots for customer inquiries. An example that is used in sports that has not been seen in other examples is the usage of data to analyze day-of conditions (including the weather) as a way of better preparing staff and management for the event. Aside from the facial recognition-ticketing, it seems like it may be difficult to utilize AI when attending a sports event in person; however, AI can also be used for advertising and adding to the viewing experience for people who may be watching at home.

Takeaways and Points for Consideration

One of the main similarities that I saw across these leisure industries was that the customer is generally at the forefront of consideration. These respective industries all have the goal of attempting to better understand their customers/patrons and effectively market to them, while also making sure that their audiences have a beneficial experience. A similarity between the nonprofit arts and the sports industry that I found both interesting and unexpected was the utilization of chatbots. However, it makes sense considering that both of these industries place a large emphasis on ticketing and making sure that the customers’ needs are met. A key difference is how nonprofit arts organizations have an added aspect of fundraising and developing/maintaining relationships with donors in their operations. Overall, there were more similarities between these industries than what I had initially expected. However, it seems that there are still differences in the scales at which these technologies are implemented.

Some of the implications or things to consider focus on the idea of big data, and data utilization in general. In order to derive results on audience insights, for example, an organization has to obtain a significant amount of data that is actually worth paying attention to and making decisions off of. Additionally, successfully implementing these measures requires a fair amount of both time and training. Arts nonprofits—and businesses across other industries—should consider what their organizational capacities are prior to delving in to large-scale changes that involve these technologies. At the same time, it is very possible to develop and utilize chatbots as an ‘introduction’ to incorporate AI and data. Lastly, when it comes to the collection of data, one aspect that actors across all industries should be aware of is increasing concerns over data privacy and recent laws regulations that require companies to protect personal data, such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). A recommendation is for businesses to consider these public policies and any new developments surrounding data compliance.  


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