Decoding Sonic Logos: Exploring the physiological, psychological and musical impact on consumers
Poster No:
1928
Submission Type:
Abstract Submission
Authors:
Sneha Jain1, Yash Chakarvarty2, Arvind Sahay2
Institutions:
1Indian Institute of Science Education and Research (IISER) Pune, Delhi, Delhi, 2Management Development Institute (MDI) Gurgaon, Gurgaon, Haryana
First Author:
Co-Author(s):
Introduction:
Consumers are exposed to numerous marketing messages, but few make a lasting impression. In today's competitive environment, brands must seize opportunities and create experiences that resonate on a deeper level through multisensory marketing to tap into the brain's ability to encode and recall information. Auditory branding has been studied extensively (Krishna, 2012; Minsky & Fahey, 2017; Spence et al., 2014), but sonic logos-distinctive audio identities for brands-remain underexplored (Bonde, 2013; Gustafsson, 2015). Sonic logos are a powerful form of sonic branding, yet the elements that make them successful, particularly their ability to be memorable while conveying brand distinctiveness, are not well understood.
This study explores how brand names and sonic logos are perceived by consumers, focusing on psycholinguistic aspects and the role of sound features in shaping preferences. Using EEG and music information retrieval, we investigate the neural and behavioral responses to real and fictitious brands' sonic logos and names. We propose a model to help brands create a potent audio identity based on consumer preferences and neural activity.
This study explores how brand names and sonic logos are perceived by consumers, focusing on psycholinguistic aspects and the role of sound features in shaping preferences. Using EEG and music information retrieval, we investigate the neural and behavioral responses to real and fictitious brands' sonic logos and names. We propose a model to help brands create a potent audio identity based on consumer preferences and neural activity.
Methods:
We conducted an EEG experiment with 10 participants to assess the perceptions of sound symbolism in brand names and sonic logos. The experiment involved two tasks, each with 80 trials (40 real brands, 40 fictitious brands). The tasks were presented in a randomized order.
Sound Task: Participants were exposed to brand names paired with sound stimuli-real brands with sonic logos and fictitious brands with neutral sounds. After each stimulus, participants answered two questions: "Which shape best matches the sound?" (choosing between a curved "bouba-like" shape and an angular "kiki-like" shape) and "I like this sound" (rated on a 5-point Likert scale).
Visual Task: Participants viewed brand names (real and fictitious) without audio and answered questions about the brand's familiarity and shape association (bouba-like vs. kiki-like).
Sound Task: Participants were exposed to brand names paired with sound stimuli-real brands with sonic logos and fictitious brands with neutral sounds. After each stimulus, participants answered two questions: "Which shape best matches the sound?" (choosing between a curved "bouba-like" shape and an angular "kiki-like" shape) and "I like this sound" (rated on a 5-point Likert scale).
Visual Task: Participants viewed brand names (real and fictitious) without audio and answered questions about the brand's familiarity and shape association (bouba-like vs. kiki-like).
·EEG experimental paradigm: stimuli design and timing of trial representation for Sound and Visual Task
Results:
Behavioral Data: In the sound task, we measured preference ratings for sonic logos. Real brands with bouba-like sounds received higher preference ratings compared to fictitious brands. In the visual task, familiarity with real brand names was higher than for fictitious brands.
Neural Data: EEG analysis focused on the N200 and N400 components, which reflect semantic processing and cognitive effort. The N400 component showed a larger negative amplitude for fictitious brand sounds compared to real brand sounds, suggesting greater cognitive effort for novel stimuli. Similarly, the N200 component was more negative for kiki-like sounds compared to bouba-like sounds, indicating a preference for the latter. Similar patterns emerged for the visual task, with stronger neural responses for fictitious brands and kiki-like names.
Neural Data: EEG analysis focused on the N200 and N400 components, which reflect semantic processing and cognitive effort. The N400 component showed a larger negative amplitude for fictitious brand sounds compared to real brand sounds, suggesting greater cognitive effort for novel stimuli. Similarly, the N200 component was more negative for kiki-like sounds compared to bouba-like sounds, indicating a preference for the latter. Similar patterns emerged for the visual task, with stronger neural responses for fictitious brands and kiki-like names.
·Grand Average ERP N200 component at electrode FCz for real bouba-like, real kiki-like, fictitious bouba-like and fictitious-kiki like brand sounds
Conclusions:
Neural data revealed that fictitious brands required more cognitive effort to process, possibly due to unfamiliarity, while kiki-like sounds were less preferred, aligning with previous research on product preference (Telpaz et al., 2015).
Our findings emphasize the importance of sound symbolism in brand perception. The preference for bouba-like sounds was consistent across both auditory and visual tasks, suggesting that consumers favor brand names and logos associated with smoother, rounded sounds. EEG data also showed lower theta power linked to higher preference for bouba-like sounds, indicating easier processing and preference.
This research contributes to the growing field of neuromarketing, offering insights for brand development. Understanding neural mechanisms behind consumer preferences for sonic logos and sound symbolism can help brands create more effective marketing strategies. These findings have implications for creating effective sonic branding strategies and contribute to the broader understanding of how multisensory marketing can shape consumer perception and behavior.
Our findings emphasize the importance of sound symbolism in brand perception. The preference for bouba-like sounds was consistent across both auditory and visual tasks, suggesting that consumers favor brand names and logos associated with smoother, rounded sounds. EEG data also showed lower theta power linked to higher preference for bouba-like sounds, indicating easier processing and preference.
This research contributes to the growing field of neuromarketing, offering insights for brand development. Understanding neural mechanisms behind consumer preferences for sonic logos and sound symbolism can help brands create more effective marketing strategies. These findings have implications for creating effective sonic branding strategies and contribute to the broader understanding of how multisensory marketing can shape consumer perception and behavior.
Emotion, Motivation and Social Neuroscience:
Social Neuroscience Other 2
Higher Cognitive Functions:
Music
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
Novel Imaging Acquisition Methods:
EEG 1
Perception, Attention and Motor Behavior:
Perception: Auditory/ Vestibular
Keywords:
Electroencephaolography (EEG)
Perception
Other - sonic branding, sound symbolism, auditory, music, brand preference
1|2Indicates the priority used for review
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Provide references using APA citation style.
Gustafsson, C. (2015). Sonic branding: A consumer-oriented literature review. Journal of brand management, 22, 20-37.
Krishnan, V., Kellaris, J. J., & Aurand, T. W. (2012). Sonic logos: can sound influence willingness to pay?. Journal of Product & Brand Management, 21(4), 275-284.
Minsky, L., & Fahey, C. (2017). Audio Branding: Using sound to build your brand. Kogan Page Publishers.
Spence, C., Puccinelli, N. M., Grewal, D., & Roggeveen, A. L. (2014). Store atmospherics: A multisensory perspective. Psychology & Marketing, 31(7), 472-488.
Telpaz, A., Webb, R., & Levy, D. J. (2015). Using EEG to predict consumers’ future choices. Journal of Marketing Research, 52(4), 511-529.