Digital platforms and artificial intelligence in sports marketing: boosting customer interaction and personalization

DOI:

Over the past decades, the sports industry has experienced sustained growth in both market size and revenue, alongside expanding media exposure and global influence. Professional sport increasingly operates as a complex business ecosystem connecting clubs, leagues, sponsors, media organizations, technology firms, and fans, where decisions are informed by strategic planning, analytics, and resource management. In such an environment, the demand for efficiency, process optimization, and value creation becomes critical - particularly in the context of intensifying competition, shifts in consumer behaviour, and rapid technological advancement.

Against this backdrop, artificial intelligence (AI), as one of the most dynamic contemporary technologies - supported by digital platforms - has assumed an increasingly prominent role across industries, including sport AI is leveraged to enhance operational efficiency and data-driven decision-making by enabling the rapid processing of large-scale information, pattern recognition, and the generation of forecasts that can be decisive for business performance. In the sporting context, AI is increasingly applied in areas such as operations management, event planning and logistics, marketing and communications, and the development of new models of fan engagement (Karimi et al., 2025).

Artificial intelligence is increasingly becoming a key intermediary in the way athletes communicate with their target audiences in marketing terms, as it enables a systematic understanding of audiences, the tailoring of messages, and the scaling of communication across digital platforms. The starting point of this transformation lies in the ability to extract insights into fans’ preferences and motivations from large volumes of audience-behaviour data - views, comments, shares, attention retention, purchasing patterns, and reactions to different content formats. In this way, supported by AI analytics, athletes move from intuitive “content posting” to datadri-ven communication in which themes, narratives, and visual codes that generate engagement and strengthen perceptions of authenticity are identified (Glebova, 2024).

A particularly notable impact of AI concerns the relationship between athletes and audiences. Digital platforms, social media, and personalized content have become dominant communication channels, while fan expectations continue to rise as audiences seek faster access to information, greater interactivity, more personalized experiences, and a stronger sense of closeness to a club or athlete (Du et al., 2023).

Ai enables precisely such approaches through content recommendation systems, automated communication (e.g., chatbots), audience segmentation, and predictive analytics that support a deeper understanding of fan behaviour.

The aim of this review paper is to examine the role and implementation of diverse digital platforms and AI tools that support athlete branding, as well as the marketing communications athletes develop through personalization and interactivity with their target publics.

Literature Review

In the digital era, it is evident that sport and sports content production are undergoing a highly visible transformation. In the past, athletes were often positioned in the shadow of clubs and sport federations, whereas today their individual affirmation through personal branding is increasingly common. These developments are enabled by the latest technological trends (Doyle et al., 2023) grounded in digital platforms and artificial intelligence (AI). As a result, the contemporary athlete is increasingly bypassing traditional intermediaries and focusing on self-directed branding and content production (Stegmann et al., 2023).

The implementation of AI tools in the sports industry directly affects management practices, athletes’ activities, and the broader commercialization of sport. Content is now produced in increasingly automated ways, while fans are algorithmically segmented and personalized, which further supports athlete autonomy. Due to the growing penetration of modern technologies (Dašić & Jeličić, 2016), content production costs have decreased and opportunities for personalized fan engagement have expanded across a wider range of channels.

At the same time, these developments raise critical questions regarding ethics and regulatory frameworks in AI-driven sport (Napoli, 2017). Specifically, campaign personalization via social media and the deployment of AI frequently rely on the use of personal data, thereby intensifying concerns related to privacy and data protection (Karimi et al., 2025). In this regard, the literature underscores the importance of consistent transparency in data collection and processing, as well as ensuring algorithmic fairness within advertising and promotional systems.

Athlete marketing supported by social media and AI has become an indispensable component of sports marketing, given that athletes are central to both the sports industry and its marketplace (Sotiriadou et al., 2024). Through the strategic development of an athlete’s brand, a distinctive identity is created, strengthening the athlete’s position in the sports market and increasing their market value and influence within sport and beyond (Arai et al., 2023).

By leveraging social media and AI for branding, athletes can shape public perceptions and trends, thereby becoming role models for many audiences. Moreover, strategic athlete marketing is increasingly intertwined with digital identity, commercial partnerships (Dašić et al., 2021), and broader social influence. In this context, examining these dynamics contributes to a more nuanced understanding of sports marketing (Taniyev & Gordon, 2022). The intersection of AI and athlete branding opens new possibilities for transforming how audiences consume sports content, and this potential is becoming increasingly pronounced as technology continues to evolve.

Digital Platforms as Support for Marketing Communications in Sport

In today’s digital environment, audiences demand more than traditional advertising; they seek personalized, immersive experiences that emotionally engage them and connect them with their favourite athletes, teams, and brands at a deeper level.

Digital platforms have expanded athletes’ capacity to act as direct communicators, enabling them to reach global audiences through a range of digital formats. In this way, athletes’ visibility is amplified most immediately, their results and achievements are continuously quantified and displayed, and their influence is publicly enacted. These personalized experiences are then translated across multiple platforms, strengthening emotional bond - most notably with fans, but also with potential sponsors - while fostering long-term loyalty. In this context, YouTube is commonly used to showcase interactive sporting moments through more in-depth video content; Instagram and TikTok compete through virality and visually driven formats that particularly resonate with younger audiences; and Twitch is primarily leveraged for more immediate, real-time communication between athletes and their audiences.

The widespread adoption of digital media has enabled athletes to share their experiences and communications not only with fans, but also with sports organisations, media outlets, and prospective sponsors, marking a new era of marketing communication

in sport. Social media has made it possible for audiences to monitor athletes’ activities continuously and for athletes to disseminate information directly to their target publics. In practice, certain sports platforms employ artificial intelligence to generate short video reports that athletes can download and share with their followers. These outputs may include match analysis, forecasts, and statistical insights that have been pre-processed and interpreted in advance. Such formats are particularly relevant for social media, which requires consistently dynamic content and sustained interaction with audiences.

Notably, athlete engagement metrics on social media have become highly important in sponsor communication and are often considered as influential as on-field performance (Brison & Geurin, 2021). This suggests that athletes who communicate more directly with their audiences - through regular social media posting and practices perceived as highly authentic - are typically valued more highly by sponsors. Empirical evidence (Nichols & Shapiro, 2023) further indicates that both audiences and sponsors tend to favour athletes who cultivate digital authenticity, as this perceived closeness facilitates stronger emotional attachment and a more genuine shared experience of athletic outcomes.

Social media platforms function as digitised environments in which athletes - ranging from amateurs to professionals - construct their public image daily. Across sporting communities, it is increasingly observable how athletes shape digital communication with diverse target publics, build recognition, and thereby unlock various forms of partnership and collaboration, a phenomenon often described as “micro-influencerism” (Du et al., 2023).

At the same time, there are potential adverse effects for athletes. Excessive engagement on social media may entail mental-health consequences, as continuous activity and visibility can blur boundaries between private life and constant surveillance (Qi et al., 2024).

The digital transformation of athlete communication with target publics, especially the shift from traditional channels to digital and online platforms has further accelerated the adoption of artificial intelligence. Real-time audience behaviour analysis, forecasting viewer interests, and personalising multimedia content enable sports media organisations to offer more relevant and interactive experiences. In this way, AI becomes a key factor in increasing viewership, improving advertising models, and securing stable revenue streams.

As AI becomes increasingly embedded in the sports industry, it is also optimising numerous dimensions of sports management. With respect to athlete support, beyond its role in analysing physical and physiological performance, AI tools enable athletes to monitor relationships with fans and other stakeholder groups and to manage their communications and interactions with audiences more effectively (Schut & Glebova, 2022).

Implementation of Artificial Intelligence in Sports Marketing

Through advances in machine learning and natural language processing (Trango-Tech, 2018), AI enables athletes to develop brand-driven experiences that are highly relevant to the fast-paced and dynamic world of sport. Historically, sports advertising relied predominantly on mass-market messages directed at broad audiences, and it was often nearly impossible for individual athletes to engage in one-to-one communication with their target publics. Today, however, the capacity to analyse fan data - ranging from viewing habits and social media interactions to purchasing behaviour and sentiment analysis - allows elite athletes who have established their personal brands to deliver targeted campaigns that resonate more directly and meaningfully with their audiences.

In this context, it has become increasingly common for athletes to offer “authentic” engagement while using AI to optimise and automate content production (Breves et al., 2019). The primary advantages of generative AI tools lie in lower content production costs, whereas key limitations relate to insufficient analytics and the risk of overly automated, formulaic text, which can reduce the perceived human quality of athleteaudience relationships (Carlson, 2015).

AI algorithms process vast quantities of data about athletes and their teams - ran-ging from immediate in-game performance to attributes associated with physical endurance and readiness for competition. These data are subsequently translated into visual representations that facilitate audience understanding of the game. Notably, AI is also widely applied in predictive analytics: based on prior performances and an athlete’s current form, algorithms can generate forecasts regarding prospective match outcomes in which a favourite athlete may participate (Boyle, 2017).

At the same time, AI enables greater creativity and more efficient design of promotional activities in sports marketing. By leveraging AI-driven tools, athletes can structure diverse interactions with multiple stakeholder groups - rom fans to potential sponsors – connecting them in more distinctive and contextually tailored ways. This supports the development of experiential narratives with targeted audiences that go well beyond the impact of conventional advertising. Through AI-enabled communication, an intensified sense of proximity and belonging can be cultivated among fans, thereby generating higher levels of engagement and interaction with favourite athletes. Illustratively, fans may create personalised avatars through which they can compete in virtual games with legendary athlete-players. In this manner, the fan is no longer merely a passive spectator but becomes an active participant in a broader meta-narrative and experience (Leitte, 2022).

AI also plays a significant role in generating match-related or other sports content featuring particular athletes. Using machine learning techniques, athletes can select key moments they wish to share with their target audiences and communicate through the creation of customised highlight packages (Patel & Kumar, 2022). Nevertheless, caution is warranted when employing such AI tools (Popović Šević et al., 2025), given potential technical limitations that may affect the accuracy and reliability of outputs.

AI enables advanced audience segmentation and more precise definition of target groups. Rather than treating communication as homogeneous, algorithms can differentiate fans by interests (e.g., training, lifestyle, competitive content, fashion), location, language, level of engagement, and likelihood of conversion (e.g., purchasing tickets or products). This provides the basis for personalisation, namely directing different messages and offers to different segments. In this way, an athlete can simultaneously build emotional closeness through “behind-the-scenes” content, authority through educational training advice, and commercial value through targeted merchandising or sponsored campaigns - where personalisation is grounded in relevance rather than excessive frequency or aggressive targeting (Arai et al., 2023).

Beyond strategic targeting, AI also significantly affects the operational side of communication by accelerating and enhancing content production. Generative models are used to develop ideas, shape narratives, write captions and descriptions, adapt creative messages to different platforms and contexts, and localise content into multiple languages. This increases the consistency of an athlete’s brand identity across channels while reducing the costs and time required for content creation. In practice, it enables faster responses to trends and current sporting events, as well as longer-term planning of content series that strengthen the athlete’s recognisability and value positioning.

Within the broader AI ecosystem - including algorithms, machine learning, and cloud computing - data required for sports marketing are extracted, processed, and operationalised. Given the extent to which advanced technologies are reshaping contemporary realities, AI in sport stands out as a particularly transformative instrument among emerging technologies (Li & Huang, 2023).

AI-based tools - such as audience sentiment analysis systems, automated sports news generation, and intelligent recommender systems - contribute to higher engagement and improved user retention. These applications range from strictly structured, factual reporting to more complex analytical summaries.

User Interactivity and Personalization in Athlete Communication

The use of AI in sports marketing supports enhanced service personalization and increases user engagement (Rane, 2023). Through big data analytics and advanced algorithms, behavioural patterns can be identified across key target groups, including fans, broader audiences, potential sponsors, marketers, sports professionals, and other stakeholder categories. The development of sophisticated algorithms, the availability of large-scale data, and the growing demand for fast, personalized, and interactive content have collectively driven the expanding use of AI in the production and distribution of content that athletes deliver to their target publics (Linsner et al., 2020).

Content personalization represents one of the most significant applications of AI within athlete branding processes, as it enables content to be tailored to specific audiences. These systems rely on audience behavioural data - such as clicks, dwell time, and prior interactions - to increase engagement and extend the time fans spend on athletes’ digital platforms (Lewis & Westlund, 2015).

Personalization is also applied within mobile applications of sports clubs in which a given athlete is featured. For example, an application may send push notifications exclusively for matches involving the club of a fan’s preferred athlete or display only information relevant to that athlete. Moreover, within match broadcasts, personalization can allow users to independently select the channels, statistics, or graphics they wish to view (Kunkel et al., 2019).

Using AI algorithms, tools can analyse user behaviour and preferences across the sports ecosystem with the aim of creating personalized experiences (Gao & Liu, 2023). From a technological standpoint, personalization may also be defined as the adaptation of sports-related products and services through information derived from user behaviour and from transactions previously conducted within sport-related activities (Montgomery & Smith, 2009).

At the same time, AI-driven digital platforms are increasingly central to athlete success and to the quality of athlete-audience interaction. AI provides substantial support for customer relationship management (CRM). Within sports marketing, AI helps athletes strengthen relationships with fans, sponsors, and other stakeholder

groups in the external environment (Pashaie et al., 2024). Whether independently or in collaboration with marketing specialists, athletes are continuously informed about relevant developments and the application of AI-enabled CRM tools. This is important for timely selection and implementation of the most effective CRM elements for managing relationships with target publics. The outcome is a more engaged and loyal audience base for a given athlete. Building on personalization, AI in sports marketing thus contributes to the strengthening of long-term relationships with users of sports-related services.

Conclusion

The adoption of AI tools is accelerating at an exceptional pace in sports marketing and across the sports industry more broadly. AI stimulates innovation in product and service development by enabling personalized fan-engagement platforms and immersive experiences, such as virtual reality applications. This paper has demonstrated the extent to which AI represents a powerful instrument for selecting and analysing user data in sport - whether for the personalization of user experiences or for enhancing interactions between a high-profile athlete and their target audiences. The findings further indicate that personalized user experiences constitute a critical foundation for designing effective interactions with an athlete’s key target groups. In addition, the paper provided an overview of market penetration in sport enabled by precise targeting through AI-based digital platforms. AI’s role in personalizing stakeholder experiences in sport emerges as another central strategy, as it facilitates deeper user engagement through predictive analytics and machine learning algorithms.

Through high-quality customer relationship management (CRM) in sport, organisations can build databases that subsequently support personalization and improve the effectiveness of marketing communication and interaction. Based on such personalization and audience interaction, athletes can more readily develop marketing and other forms of communication, as they have access to behavioural data that can be leveraged to further strengthen engagement and loyalty.

AI’s contribution is also reflected in the professionalisation of brand relationships and monetisation. By using predictive models and engagement indicators, it becomes possible to assess the alignment between an athlete’s identity and a sponsor’s values, as well as the potential effectiveness of campaigns across different audience segments. This allows athletes to make more informed partnership decisions, measure actual return on investment, and develop sustainable monetisation models grounded in long-term fan loyalty rather than relying solely on short-term reach.

However, this transformation simultaneously raises important ethical and governance issues. Personalisation and automation carry risks related to privacy infringement, non-transparent targeting, and the erosion of authenticity - especially when AI is used to simulate an athlete’s “personal voice” without clear oversight and accountability. Therefore, the responsible use of artificial intelligence in sports marketing must rest on clear data-protection standards, transparency in sponsored content, human supervision, and the strategic preservation of reputational capital. Within this framework, AI should be seen not as a substitute for the athlete, but as a socio-technical tool that, when properly governed, enhances the relevance of communication, deepens audience relationships, and increases the market value of the sports brand. To deliver enhanced personalization, appropriate audience interaction, and operational efficiency, it is essential for athletes to cultivate a culture of continuous adaptability and learning in their communications. Only in this way can a sustainable future for the sports industry be fostered.