Emotionally Resonant Branding: The Role of AI in Synthesising Dynamic Brand Images for Artists in the Music Industry ()
1. Introduction
Over the past two years, artificial intelligence has been rapidly reshaped the field of multi-media in content creation [1]. In 2023, a prestigious art gallery named Museum of Modern Art (MoMA) in New York City acquired its first AI-generated artwork highlighting how artificial intelligence-generated content (AIGC) is gaining recognition and becoming more prominent globally [2]. This innovative technique of content generation advanced the Professional Generated Content (PGC) and User Generated Content (UGC) [3]-[5]. Nevertheless, integrating a popular state-of-the-art generative AI model known as DALL-E an acronym for “Dali” and “Eve”, in reference to the surrealist artist Salvador Dali and the character Eve from the Pixar movie WALL-E [6] into the well-known AI chatbot named ChatGPT (Chat Generative Pre-trained Transformer), a language model developed by OpenAI to create art through texts by expanding its access for a wider user group plays a progressively important role in digital and AI-powered creations [7]. AI is constantly getting better and better, by offering fresh tools for creative processes, analysing existing works, and even producing entirely new art forms unlocking the possibilities we never thought we’d see [8]-[15]. Generative AI is removing the clear lines between human and machine creation as it drives to witness a major transformation and opens our eyes to new artistic possibilities in creative industry [16].
1.1. History of AI and Generative Content
In recent years, artificial intelligence-generated content (AIGC) has become a topic of interest not only in computer Science but also in general society due to the emergence of content generation products developed by giant tech companies [17], such as ChatGPT and DALL-E [18]. As AI started making significant progress since 2015, the release of ChatGPT in November 2022 can be considered as a turning point in the generative tools’ evolution [19]. Such Generative AI (GAI) algorithms plus models are progressively developing, and these are increasingly moving into domains that traditionally controlled by professionals [20]. Tasks like reading texts, speech recognition, identifying images and many other tasks are being handled by AI systems with higher efficiency compared to human performance [21]. AIGC is presented in many shapes and forms which makes the list of possible applications almost endless [19]. For instance, AI is capable of writing poems [22] or political manifestos [23] and academic papers [24] which are often indistinguishable from those written by humans. Prolific advancements in the generative technology have led to numerous discourses on how this technology is likely to shaped in future with influencing several fields including life, learning, and working, relationships and interactions [25]-[28].
1.2. Commercial Music Industry and AI
The commercial music industry stands as a major player within the creative industry, often identified as swarming with prominent artists. Thus the availability of tools in music distribution and advertising facilitated with technological advancements results in an argument that the market is oversaturated [29]-[32]. Consequently, with access to numerous free or low-cost tools for reaching their target audiences, artists must differentiate themselves from others by constructing a strong brand to cultivate a stronger presence through effective branding [32]-[35]. A stand-out brand can enhance the value and marketability of an artist. A successful artist brand takes place when their unique characteristics are authentically transformed into a brand that resonates with their audience [36]. Beyond the artist’s music, their attitudes and values influence the audience and strengthen the loyal fandom [37]. Consumers often perceive well-known brands as superior and higher in quality compared to less popular brands. It enables consumers to easily distinguish between product characteristics and understand the factors influencing purchasing decisions, such as product quality and brand image [38]-[40].
1.3. Personal Branding
Personal Branding is a deliberate process aimed at constructing and maintaining a distinct identity and image for an individual [41] and also serves as a mechanism for influencing how others perceive and regard oneself [42]. Burak and Gültekin [43] mentioned that the personal branding concept centres on creating consistent and authentic narratives to differentiate individuals from others, such as enhancing visibility, bolstering reputation, and cultivating a favourable image. Evey person develops their own level of personal branding aiming to facilitate understanding and leave a lasting impression on others [44]. Furthermore, [44] explained that, personal branding process involves crafting an appealing image to attract attention and make oneself memorable. Brand image is what consumers recall about a brand from previous experiences with its products or services, as well as from advertising or word-of-mouth within their social circles [45]. Therefore, personal branding is not only pivotal in business but also valuable for achieving social acceptance [46]-[48].
1.4. Emotional Branding
Emotional branding serves as a mechanism to establish a “personal dialogue” with consumers [49]. Emotional branding is a term used within marketing that aims to create deep, meaningful connections between consumers and brands appealing directly to their emotional states, needs, and aspirations, resulting in strong emotional responses and attachments [50]. Shaukat and Farid [51] indicated that emotional branding involves encouraging a strong relationship between consumers and products or brands by eliciting emotional responses and this emotional bonding cultivates a long-lasting relationship between the brand and the consumer. It happens when a brand’s message truly speaks to consumer feelings. According to Keller ([52], p. 6), “in many ways, brand-building can be thought of in terms of painting a picture of a brand in consumers’ minds and hearts.” Understanding people’s emotional needs and desires is crucial now more than ever. To win them over, it is essential to understand what triggers their emotional response. This emotional connection be the decisive factor in influencing a consumer’s final decision and how much they’re willing to spend [53]. For a successful emotional branding, it requires a strategic approach that involves deeply understanding target audience, including brand affinity, demographics, and the specific emotions targeted such as anger, sadness, or joy that influence decision-making. This will ultimately aiming to achieve the brand’s objectives and creating a powerful impact on consumers’ minds that can lead to significant purchases or donations [50] [54].
This paper aims to answer the following research question.
RQ1: How can AI be successfully employed to translate live audience emotions into dynamic and evolving visual elements of brand images for a singer?
Objective of this study is:
To develop a model to assess and predicts the emotional reactions elicited by an artist’s music and to dynamically update an artist’s brand image associated with identified feelings while updating in real time for a positive audience perception.
2. Method
This research uses a quantitative approach to assess the success of synthesising a dynamic personalised brand image for artists based on emotional responses received for their music. There are two distinctive approaches to data gathering for the research: primary and secondary [55]. We gathered primary data through a survey. The participants selected by posting the link on social media platforms aiming on collecting data of preferences, perceptions, and behaviours related to music consumption, artist branding, and fan engagement. The sample size was 200, and 191 responses were received from the diverse pool of music lovers, artists, and producers as the participants. SPSS software is used for data analysis. To get better understand the emotional trends, we presented the data graphically in the form of bar and pie charts. The integration of emotional data into branding strategies represents a profound achievement in the field, paving the way for more effective and emotionally engaging artist brands.
The AI-driven singer brand generation system (see Figure 1, data flow diagram) illustrates the basic flow of data between entities: the Singer/Artist, the Audience, and the AI system. The process starts with collecting emotional cues from audience in real time in response to artist’s music. In addition, artists provide input in the form of brand preferences, detailing the desired style, colour, or name they wish to convey. The core system processed these two inputs to craft a brand image for artists. Further expanding the process (see Figure 2), the identified emotions mapped to the relevant colours, as [56] [57] emphasis (red: anger, blue: sadness, green: calmness, grey: depression, yellow: happiness, red: love, disgust: green-yellow). The mapped data used to generate a conceptualise brand imagery data (a text prompt) before transferred into the AI model to start synthesising process. The generated images will be refined through the initially analysed emotional feedback and finalised brand images will be sent to the artist.
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Figure 1. Level 0 Data-flow diagram for personal singer brand generation.
Figure 2. Level 1 Data-Flow diagram for personal singer brand generation.
3. Results
To formulate the answers to the research questions, the survey data present an extensive picture of a comprehensive understanding of the public’s awareness and perception of synthesizing a dynamic brand image for artists for personal branding purposes within the music industry. Considering general awareness, the survey indicates that a significant portion of sample population appears to lack how one can be used AI for personal branding processes. Precisely 40.8% of respondents (Figure 3) are not aware of the use of AI applications in the given context and followed by 31.9% who are somewhat familiar, 18.8% moderately familiar, 7.9% very familiar, and a small percentage of 0.5% who are extremely familiar highlights a substantial gap in understanding applications integrated with AI for personal branding. This underlines a significant opportunity for educational programs to improve knowledge and practical uses of applications of AI in personal branding. Conversely, the survey findings indicate a positive perception of dynamically adjusting a singer’s brand image in accordance with audience emotions with a significant 57.6% of respondents (Figure 4) agree that such adaptability can enhance audience perceptions of the artist. This is further supported by 8.9% of respondents who strongly agreeing to this notion suggesting such a dynamic approach to personal branding facilitated by AI resulted in a positive shift towards more adaptive and responsive branding strategies can make a positive impact in helping artists build a better brand image that can bring them closer to their fans. Furthermore, it is evident (Figure 5) that 52.4% respondents interested in experiencing a live event where the singer’s brand is dynamically made based on the emotional responses received for artist music, offers a positive view regarding the applications of AI in enhancing live experiences and shaping audience perception.
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Figure 3. Familiarity with AI in branding.
Figure 4. Perception of dynamic brand images.
Figure 5. Willingness to experience AI-Driven branding.
Over half of the respondents (52.4%, Figure 6) consider it very important for a singer’s brand image to reflect real-time emotional connections. The survey participants suggest that efficiently incorporating real-time emotional feedback with AI can create more unique and authentic brand images, providing a genuine representation of artist highlighting that those who successfully leverage real-time emotional connections can significantly elevate their brand perception and audience loyalty. However, 51.3% of respondents (Figure 7) consider AI-generated brand images to be moderately trustworthy, following 29.3% find them slightly trustworthy, and 5.8% do not trust them at all. This mixed perception emphasizes the complex relationship between AI-generated content and the perceived authenticity requires a greater transparency, understanding, and demonstration of AI’s capabilities and limitations in maintaining brand integrity. 71.2% respondents expressed concerns about AI in branding focusing on accuracy in its representation, copyright issues (55.5%), ethical use and consent (47.6%), and the risk of misrepresentation or false endorsements (44.5%, see Figure 8), which emphasize the need for ethical guidelines and legal frameworks to ensure the responsible use of AI, protecting artists’ rights and the validity of their brand.
Survey respondents express a strong optimism that dynamic AI-driven brand imaging can enhance the relationship between artists and their audience with 84.3% (Figure 9) anticipating an improvement. This suggests a belief in AI’s ability to personalize and enhance content, thereby strengthening connections. However, concerns regarding content standardization and loss of authenticity indicate the need for a balanced approach that respects creative integrity while leveraging AI’s capabilities. A majority of 51.3% of respondents (Figure 10) would be willing to share their emotional responses for the purpose of dynamically adjusting an artist’s brand image that reflects the expectation of enhanced artist-audience relations. However, 48.7% are reluctant to share their emotional data which reflects concerns about privacy, data security, and the ethical use of emotional data in branding, which should be adequately addressed.
Figure 6. Importance of emotional connection.
Figure 7. Trust in AI-Generated content.
Figure 8. Concerns about AI in branding.
Figure 9. Impact on Artist-Audience relationship.
Figure 10. Willingness to share emotional data.
4. Discussion
In recent years, the importance of AI across all sectors has grown substantially [58]. McCormack, Gifford, and Hutchings [59] emphasized the changes brought about by AI need critical examination and attributes like creativity plays a crucial role when it comes to art significantly impacting consumer behaviour [60]. This study was initiated by posing a core question: Can AI accurately synthesis a brand image based on the emotions that fans are experiencing and adjust a musician’s brand image to those vibes? This research sought to explore the potential of AI to change the personal branding landscape for musicians by adapting brand images based on the real-time emotional reactions of their audience which also align with the existing literature [61] highlights that “emotional branding (EB) forms significant lifelong relationships with consumers” [62] and emotions appropriately linked with a brand can foster long-term connections between the consumer and brand. The respondents valued the thought that their live emotional cues were used to create an authentic identity for their beloved artist. Unlike traditional branding methods that often failed to capture and respond to the emotional landscape of artists’ music, this method shows a significant advancement. The study found a significant gap in awareness and familiarity with AI applications in personal branding which can be attributed to the current lack of AI tools available in the market. This shows substantial opportunity for developing and introducing such tools, as well as implementing educational programs to enhance knowledge and practical applications. Synthesizing a brand image that points a connection to emotional impact of artist’s music offers a powerful platform to connect with audience on a deep emotional level.
Although people are still unfamiliar with AI in this context, there is a significant positive perception towards the use of AI in dynamically adapting a singer’s brand image as [63] indicated that building a strong brand relies on positive consumer attitudes and perceptions towards the brand. Although the possibilities of AI in music brand is intriguing, there were concerns occurred regarding its capabilities in synthesizing such an image for personal branding such as could AI truly capable of incorporating emotions to an image that reflect artist’s true essence, or will it just become another technological advancement? While respondents are enthusiastic about the new idea, they emphasize this should not overshadow the genuine character of their beloved artists. Respondents have different opinions about trust and authenticity with AI generated brand images. To address these concerns, it is critical to be transparent about AI’s role and its impact on the integrity of the brand image.
According to the study findings, two biggest considerations remaining are addressing privacy and data protection concerns when there are varying degrees of willingness to share their emotional cues for dynamically synthesis an artist’s brand image. This underlines the importance of prioritizing privacy and ethical issues that should inform the use of emotional data to build brand images to gain credibility and wider acceptance. Moreover, AI’s ability to automate repetitive tasks [64] as well as its capacity to train on large amount of data [65], which enables them to produce unique, engaging content tailored to audience preferences. The characteristics expected from AI-generated brand images including high-quality outcomes, uniqueness, versatility, and authenticity, which are essential in building a distinct brand identity that resonates emotionally with audiences. By balancing AI’s capabilities with creative integrity and ethical standards, the artists can maximize the benefits of AI to create more personalized, engaging, and authentic brand images.
In the developed prototype, primary goal was to explore the feasibility of dynamically synthesizing personal brand images for artists based on real-time emotion detection from listeners. Users can initiate the process by uploading the relevant music tracks (Figure 11) and start capturing listener’s emotions. Upon initiation, the live detected audience facial details continuously stream into the model as image frames (a frame will be detected after every two second gap) to detect a range of emotions. The identified emotions mapped into the corresponding colours ensuring that a wide range of emotional responses was captured throughout the playback of the music track. The mapped data is queued until the music playback stops or artist manually end the session, and the processed results will be sent to the artist (Figure 12) to review. In addition to the detected colours artist can prefer their own colour schemes, a brand name and send it to the generative model to initiate the synthesising process. Upon successful completion, the system will send the finalised brand images to the artist for review (Figure 13). The results (Figure 13) demonstrate that the mapping of emotional responses to colour schemes provides artists with a unique and personalized way to customize brand image. This is supported by the findings (see Figure 4), where 57.6% of respondents agree that dynamically adjusting a singer’s brand image based on audience emotions enhances perceptions. The finalised brand images (Figure 13) offered artists a definite way to visualize how their music emotionally resonates with their audience. This approach opens new possibilities for artists to shape their brand in a way that authentically reflects the emotional impact of artist’s music on their audience, supporting the idea that emotions can serve as a key component in shaping an artist’s brand image.
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Figure 11. User interface of live emotion detection and based on the results recommend colours.
Figure 12. User interface of finalised recommendations of colour for the recognized emotions.
Figure 13. User interface of generated brand image suggestions for the artist.
5. Limitations
Although this study is about assessing how artists and personal branding can be influenced by AI in creating a personal visual identity, this study faced several limitations that need to be addressed. First, the survey feedback focused only on a specific demographic, and this may not capture the more diverse audience perception of various music genres, impact of cultural background, and differences in interests and preferences across age groups. Second, there is a significant limitation that lies in the requirement on existing AI model at the time of study, particularly AI models that are based on the Generative Adversarial Networks (GANs) as well as emotion recognition algorithms. Nevertheless, the advanced algorithms are still in their development phase and may not perfectly interpret the aesthetic emotions of the listeners or the complex essence of the artist’s brand. Third, the research methodology is primarily based on surveys and a prototype AI which may not cover all the aspects of diverse cultural settings, music industry’s diversity, various genres, and individual artists’ characteristics. Lastly, this project involves analysing real-time emotion which initiates privacy, data protection and consent matters since it involves receiving audience emotional responses. Accurately interpreting a wide range of emotions in real-time and responding accordingly presents a substantial challenge because inaccurate detection can lead to unsuitable adjustments of an artist’s brand image, potentially damaging their professional standing.
6. Conclusion
In this study, evaluating AI’s role in the process of creating a dynamic brand image for the artists’ music based on the emotions received, has been a remarkable journey. Through this study, a novel contribution to our knowledge gained regarding how AI constructs an affectively rich brand image by recognizing and engaging with fans’ emotions and preferences in a manner that is not prevalent in earlier research [66]-[70]. From the research findings previously discussed, the following conclusions can be made. Incorporating necessary tools and knowledge to support the artist, designers, and branding teams is critical for enhancing the proper adoption AI in personal branding. This will help them in formulating effective strategies and increase trust in the application of generative AI technologies. To foster enhanced trustworthy relationship between artists and their audience, it is important to develop guidelines with appropriate ethical standards and privacy policies determining how audience emotional data should be collected and analysed. Moreover, well designed, easy to follow user interfaces of these AI solutions with step-by-step suggestions on how to use them, can increase usage of these AI applications. More specially, the study suggests that to capture the full spectrum of the audience’s emotional cues, the emotional recognition algorithms should be enhanced by using large datasets which represent diverse cultures, and ways the emotions are expressed.
This study demonstrates how AI can be applied to build brand images. Future studies should widen the range of GAN based models and algorithms to determine their applicability within this domain. However, the insufficiency of a diverse subject pool and potential ethical and privacy protection issues with emotional data usage for AI remain as limitations of this study while at the same time this study paves the way for responsible AI integration in brand image. It is important to have diversity in a dataset for the development of AI systems to grasp a wide range of cultures, preferences, and values. This diversity not only increases the brand images’ accessibility globally, but also expands the artist’s international recognition. Furthermore, future studies should include a more diverse sample of population to gain a better understanding of various cultures, ages, and music preferences among the respondents. Cooperating psychological contexts in brand imaging presents a novel approach and the use of such a framework for branding with the help AI to craft brand images that will touch the hearts and minds of people. Moreover, incorporating market research and trend analysis into the AI-driven brand image generating approach will help the researchers to capture the volatile market environment. This way brand strategies are not only psychologically appealing but also dynamic enough to adapt to the trends in the music industry.
In conclusion, AI has the potential of completely transforming branding strategies for artists in the music industry. Study highlights important and obvious problems such as the lack of consideration of ethical issues, the need for diversity of representative samples, and understanding the challenges in emotional engagement with audiences that require further research. Thus, by pursuing these studies, one could expand AI for synthesis brand images in a manner that would bring value to both the artists and fans.