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The new realities of sport management
This is an excerpt from Contemporary Sport Management 8th Edition With HKPropel Access by Paul M. Pedersen.
By Gashew Abeza, Liz Wanless, Yoseph Z. Mamo
Managing the sport experience is changing with the emergence of new realities. Do you want to predict which types of digital material will gain traction with fans? Companies can automatically locate social media posts with high traction to predict which types of future material will trend well with sport consumers. Do you want customers to be able to pick a seat location without setting foot in the stadium? With a pair of glasses or an app, sport fans can immerse themselves in a VR stadium to pick their seat. The aforementioned examples represent the development of AI, VR, and AR capabilities. The transformational arrival of these new realities delivers opportunity for sport managers if they choose to harness these resources. This chapter serves as a starting place to understand what these capabilities are, the opportunities they provide for sport business, and challenges associated with their use.
Artificial Intelligence (AI)
Conceptualizing AI can be confusing. The term artificial intelligence may conjure stereotyped images, such as an army of humanized robots that will eventually take over the world, destroying the entire human race. Instead, the development of AI has become a powerful and advantageous tool for sport business. Sport organizations can assess many types of data to derive insights. Customer posts in social media outlets, transcripts of customer interactions with the organization, and the vast number of articles and blogs that may mention the brand name—to name a few—can all provide valuable information. With the growth in the amount of data (volume), the different types of data (variety), and the speed at which data are received and consequently can be assessed for business insight (velocity), sport organizations feel the pressure to incorporate rapid analysis of such data to gain value. It is difficult to imagine how one person could comb through even a day’s worth of social media posts or find patterns in millions of rows of data. Training computers to mimic aspects of human intelligence can aid these and many other processes, such as generating written content (Deshpande & Kumar, 2018). The reality, though, is that humans are and become intelligent in many ways. So, how do computers mimic this intelligence for sport business gain?
Evolution of AI
The term AI and its meaning have evolved over time. The concept has progressed from the development of a conceptual test to assess whether machines exhibited human intelligence (the Turing test) to the development of algorithms trained to find patterns in text and images. A concept called machine learning represents the current state of the art. Algorithms (systematic processes designed for calculations) can be trained to find patterns in data and self-adjust to those patterns with more data (Deshpande & Kumar, 2018). This process is the essence of machine learning.
The utility of AI to evaluate large volumes, varieties, and velocities of data for value is relatable to the view of an ocean. If you want to understand what lies beneath its surface, you need more tools than simply looking at the surface or even knowing how to swim. If someone told you the ocean had colorful fish and you wanted to see them, swimming would be an attractive idea, but perhaps not with sharks or other potentially harmful creatures. You would need a means of seeing below the surface and an understanding of previous patterns of shark behavior. While sport managers can see certain aspects in data and make decisions about the business, using machine learning to assess data helps recognize patterns that an individual cannot see on their own or cannot handle within their capacity. Natural language processing (computer capability to find patterns in the human language) and computer vision (computer capability to gain deep insight from images and videos) are two notable fields within AI.
The broad effects of AI on sport are numerous and recognizable; they are becoming a part of the everyday consumer experience. However, individuals may not immediately identify these innovations as AI. One of the most popular examples of AI harnessed for sport business is the chatbot. When heading to a favorite team website, a chat dialog box might open and ask what questions customers have about buying tickets. It is not a real person; it is an algorithmically trained bot that can detect patterns in language to simulate dialog with website visitors. This chatbot is initially prepared to respond to certain questions in certain ways, and over time it learns the responses to typical questions that fit best with customers. This bot can interact with unlimited numbers of people at one time, track patterns in the conversations, and make recommendations for how sport managers should respond. Machine-learning algorithms can also learn to automate language translation of social media outlets for international mega events, to evaluate the tone of social media posts (sentiment analysis), to recognize and value the presence of sponsor logos in televised sporting events, and to recognize the presence of suspicious behavior from security cameras. The effects are numerous and broad. The overall theme of these effective events is that AI helps sport managers create efficiencies in automating certain tasks, reduce human error when making decisions, and interpret large volumes of data.
Challenges and Ethics Affiliated with AI
AI is not without its challenges for sport business implementation. Barriers to adoption include logistics such as data preparation and choosing a strategy and software that will scale (grow) as the organization or the volume of data grows. Notably, company culture can also be preventive of successful AI adoption. Communicating AI utility and process to nontechnical personnel in upper management can be a challenge for implementation. Change can make individuals nervous. Creating usable AI insights for entry-level personnel, such as the sales team, can also be a challenge.
In addition to changes and challenges brought on whenever aspects of AI are introduced to the sport industry, ethics are involved with using AI algorithms that people may not understand. Ethical considerations for AI include transparency, bias, privacy, and rights and licensing, especially as AI is utilized at an increasing rate to generate content (as is the case with what is termed generative AI, the most famous example being the large language model ChatGPT, which stands for Chat Generative Pre-trained Transformer). Algorithm application can often involve complex relationships among data. When those relationships are unintelligible to humans, decisions are made without knowledge of how the algorithm output was created. This “black box” process might be susceptible to bias when the algorithm produces discriminatory results according to embedded (but unknown) assumptions in the model. AI for hiring practices is under scrutiny; biases (e.g., race, gender) may be embedded in the algorithms that boast of being able to choose the best applicants for sport positions.
As more data are collected, privacy concerns for customers becomes an important issue. Sport organizations must establish trust with consumers not only for the types of data collected, but also how that information will be stored and protected. When applications such as ChatGPT are utilized to generate written content, images, and other creative materials, who owns the rights to the materials? When sport organizations stand to profit from generative AI, establishing the true author or creator is a difficult process. While many of the ethical issues associated with AI are continuing to evolve, sport organization leadership face pressure to develop governance strategies that increase transparency, protect privacy, and eliminate bias from decision-making processes.
The senior director of ticket sales and premium membership noticed a low engagement for the amount of effort expended on behalf of sales representatives—even with a ranked customer list. Sales representatives spent hours on the phone handling rejections with little opportunity to speak with truly engaged fans. Lack of time and energy contributed to a difficult work environment. A sales manager would hope to put their sales personnel in the best situations for success where personal communication would be most effective.
Enter Sara Martinez. Sara was not a human being; rather, “Sara Martinez” was the name of the chatbot the Kings employed to aid sales efforts. Conversica, a company that focuses on harnessing natural language processing for business advantage, created Sara for use by the Kings. Sara initiated automated conversations with leads. Based on customer responses, she would make recommendations to Kings sales representatives to follow up (via text, email, or phone) and would also provide information learned about the customer in the process. Sales initiatives changed from salespeople calling a limited number of campaigns to gauge interest, to Sara making that initial contact with all campaign leads to prequalify their interest. Sales team efforts were then shifted toward the interested fans.
The Sacramento Kings felt the effect of the results. In terms of ticket sales revenue, Sara added millions of dollars to the pipeline. Ranked campaigns of prioritized customers meant there were still customers not contacted. These customers, while not prioritized via the analytics process, were still missed opportunities. Sara added three times the number of qualified opportunities to the sales process. One salesperson could only make contact with one customer at a time, while Sara could orchestrate unlimited contacts and conversations. The Kings’ partnership with Conversica more than paid for itself in one year. The process is ongoing; qualified opportunities are continually added to the pipeline.
The sales culture at the Kings organization shifted in a positive direction. Sara took the burden of what used to be a time-consuming and emotionally taxing activity for sales representatives and freed their team to speak with qualified leads. The sales representatives spent more time talking with interested prospects rather than sifting through leads with the hope that they may eventually show interest. This shift not only changed the responsibilities of the sales team, it also changed the motivation. Consequently, recruiting new sales representatives to the Kings team was easier. College students found the prospect of joining this type of professional sport sales team attractive; they knew they would not have to spend long, arduous hours on the phone without gaining much traction.
The Sacramento Kings, among other sport business professionals, have innovated the sales process through automation—in this case, automated conversation. These teams harnessed and continue to harness computerized conversations to prequalify, target, and retain fans. To learn more about Conversica, the company’s capabilities, and relationship with the Sacramento Kings, see Conversica’s website (Conversica, 2023).
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