Nöropazarlama Stratejilerinde Yapay Zekâ Tabanlı Nörometrik ve Biyometrik Veri Analitiği

Öz

Pazarlama disiplini, dijital çağın hız kazandırdığı dönüşüm sürecinde tüketici karar verme mekanizmalarını daha derinlikli biçimde anlamlandırmak amacıyla nörobilim temelli yaklaşımlara yönelmektedir. Anket, odak grup ve görüşme gibi öz-bildirime dayalı yöntemler tüketici tercihlerini açıklamada önemli katkılar sunsa dahi bireylerin bilinçdışı bilişsel, duygusal ve fizyolojik tepkilerini doğrudan ölçme konusunda sınırlı kalabilmektedir. Bu bağlamda nöropazarlama, tüketicilerin pazarlama uyaranlarına verdikleri tepkileri nörometrik ve biyometrik ölçüm teknikleriyle inceleyen disiplinler arası bir araştırma alanı olarak öne çıkmaktadır. Bu çalışma, yapay zekâ tabanlı nörometrik ve biyometrik veri analitiğinin nöropazarlama stratejilerindeki rolünü literatür temelli kavramsal bir değerlendirme çerçevesinde ele almaktadır. Çalışmada herhangi bir saha uygulaması, deneysel analiz veya özgün veri seti sunulmamakta; mevcut literatür üzerinden yapay zekâ destekli nöropazarlama yaklaşımlarının reklam optimizasyonu, ambalaj tasarımı, marka algısı ve dijital deneyim süreçlerindeki katkıları ele alınmaktadır. Bu çalışma, gelecekteki nöropazarlama araştırmaları için yapay zekâ temelli analitik yaklaşımlara yönelik kavramsal bir çerçeve sunmayı hedeflemektedir.

Anahtar Kelimeler

• Pazarlama
• Nöropazarlama
• Yapay Zekâ
• Nörometrik Veri
• Biyometrik Veri
• Makine Öğrenimi
• Derin Öğrenme

Artifical Intelligence-Based Neurometric and Biometric Data Analytics In Neuromarketing Strategies

Öz

In the transformation process accelerated by the digital age, the discipline of marketing is turning to neuroscience-based approaches in order to gain a deeper understanding of consumer decision-making mechanisms. Whilst self-report methods such as surveys, focus groups and interviews make significant contributions to explaining consumer preferences, they remain limited in their ability to directly measure individuals’ unconscious cognitive, emotional and physiological responses. In this context, neuromarketing emerges as an interdisciplinary research field that examines consumers’ responses to marketing stimuli using neurometric and biometric measurement techniques. This study examines the role of artificial intelligence-based neurometric and biometric data analytics in neuromarketing strategies within the framework of a literature-based conceptual evaluation. The study does not present any field applications, experimental analyses or original datasets; instead, it examines the contributions of AI-supported neuromarketing approaches to advertising optimisation, packaging design, brand perception and digital experience processes based on the existing literature. This study aims to provide a conceptual framework for AI-based analytical approaches for future neuromarketing research.

Anahtar Kelimeler

• Marketing
• Neuromarketing
• Artificial Intelligence
• Neurometric Data
• Biometric Data
• Machine Learning
• Deep Learning

Kaynakça

1. Alsharif, A. H., Wang, J., Isa, S. M., Salleh, N. Z. M., Dawas, H. A., & Alsharif, M. H. (2025). The synergy of neuromarketing and artificial intelligence: A comprehensive literature review in the last decade. Future Business Journal, 11(1), 170. https://doi.org/10.26441/RC21.1-2022-A1
2. Analuiza, J. C. C., & Chimbaria, V. M. P. (2023). Systematic review: emotional brands from perspective based on advertising/Revisión sistemática: marcas emocionales desde una perspectiva de la publicidad. Cuadernos de Administración, 39(76), 1-14. https://doi.org/10.25100/cdea.v39i76.12740
3. Anupama, T., & Rosita, S. (2024). Neuromarketing insights enhanced by artificial intelligence. ComFin Research, 12(2), 24-28. https://doi.org/10.34293/commerce.v12i2.7300
4. Arıcan Kaygusuz, N. (2023). Nöropazarlama ve Yapay Zekâ İlişkisinin Tüketici Davranışları Üzerindeki Etkisine Yönelik Kavramsal Bir Model Önerisi. Journal of Academic Social Science Studies, 16(95). http://dx.doi.org/10.29228/JASSS.67916
5. Beyari, H., Hashem, T. N., & Alrusaini, O. (2024). Neuromarketing: Understanding the effect of emotion and memory on consumer behavior by mediating the role of artificial intelligence and customers’ digital experience. Journal of Project Management, 9(4), 323-336. doi: 10.5267/j.jpm.2024.9.001
6. Bucea-Manea-Țoniş, R., Martins, O. M., Orzan, M. C., Goldbach, D., & Popa, M. (2026). AI-enhanced neuromarketing and social media communication: Evidence from PLS-SEM analysis in an academic context. International Journal of Engineering Business Management, 18, 18479790261420680. https://doi.org/10.1177/18479790261420680
7. Darázs, T. (2024). The Role of Artificial Intelligence In Neuromarketing: A Comparative Analysis of AI-Driven and Traditional Methods. Marketing Identity, 70. https://doi.org/10.34135/mmidentity-2024-07
8. De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91-105. https://doi.org/10.1016/j.intmar.2020.04.007

9. Deckker, D., & Sumanasekara, S. (2025). AI and neuromarketing- Understanding consumer decision making with artificial intelligence: Systematic review. Indonesian Journal of Business Analytics, 5(2), 1929-1946. https://doi.org/10.55927/ijba.v5i2.13990
10. Fici, A., Bilucaglia, M., Casiraghi, C., Rossi, C., Chiarelli, S., Columbano, M., ... & Russo, V. (2024). From E-commerce to the metaverse: A neuroscientific analysis of digital consumer behavior. Behavioral sciences, 14(7), 596. https://doi.org/10.3390/bs14070596
11. Ghazvini, S. N., Nazari, E., Salleh, N. Z. M., & Baharun, R. (2024). The Role and Application of Artificial Intelligence in Neuromarketing Research Based on Electroencephalography. International Journal of Academic Research in Business and Social Sciences, 14(12), 1556-1567. http://dx.doi.org/10.6007/IJARBSS/v14-i12/24111
12. Goncalves, M., & Dangelo, D. (2025). Regulating the Mind: Neuromarketing, Neural Data and Stakeholder Trust Under California’s CCPA. Administrative Sciences, 15(10), 386. https://doi.org/10.3390/admsci15100386
13. Juárez-Varón, D., Tur-Viñes, V., Rabasa-Dolado, A., & Polotskaya, K. (2020). An adaptive machine learning methodology applied to neuromarketing analysis: prediction of consumer behaviour regarding the key elements of the packaging design of an educational toy. Social Sciences, 9(9), 162. https://doi.org/10.3390/socsci9090162
14. Khondakar, M. F. K., Sarowar, M. H., Chowdhury, M. H., Majumder, S., Hossain, M. A., Dewan, M. A. A., & Hossain, Q. D. (2024). A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques. Brain Informatics, 11(1), 17. https://doi.org/10.1186/s40708-024-00229-8
15. Lee, N., Broderick, A. J., & Chamberlain, L. (2007). What is ‘neuromarketing’? A discussion and agenda for future research. International journal of psychophysiology, 63(2), 199-204. doi: 10.1016/j.ijpsycho.2006.03.007.
16. Marques dos Santos, J. P., & Marques dos Santos, J. D. (2024). Explainable artificial intelligence (xAI) in neuromarketing/consumer neuroscience: an fMRI study on brand perception. Frontiers in human neuroscience, 18, 1305164. https://doi.org/10.3389/fnhum.2024.1305164
17. Morin, C. (2011). Neuromarketing: the new science of consumer behavior. Society, 48(2), 131-135. DOI 10.1007/s12115-010-9408-1
18. Rawnaque, F. S., Rahman, K. M., Anwar, S. F., Vaidyanathan, R., Chau, T., Sarker, F., & Mamun, K. A. A. (2020). Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Informatics, 7(1), 10. https://doi.org/10.1186/s40708-020-00109-x
19. Song, G., Gazi, M. A. I., Waaje, A., Roshid, M. M., Karim, R., Rahaman, M. A., ... & Senathirajah, A. R. B. S. (2025). The neuromarketing: bridging neuroscience and marketing for enhanced consumer engagement. IEEE Access, 13, 40331-40353. DOI:10.1109/ACCESS.2025.3545742
20. Šola, H. M., Qureshi, F. H., & Khawaja, S. (2024). AI Eye-Tracking Technology: A new era in managing cognitive loads for online learners. Education sciences, 14(9), 933. https://doi.org/10.3390/educsci14090933
21. Šola, H. M., Qureshi, F. H., & Khawaja, S. (2025). AI and Eye Tracking Reveal Design Elements’ Impact on E-Magazine Reader Engagement. Education sciences, 15(2), 203. https://doi.org/10.3390/educsci15020203
22. Ural, T. (2008). Pazarlamada yeni yaklaşım: Nöropazarlama üzerine kuramsal bir değerlendirme. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 17(2), 421-432.
23. Ustaahmetoğlu, E. (2015). Nöropazarlama üzerine bir değerlendirme. Business & Management Studies: An International Journal, 3(2), 154-168. https://doi.org/10.15295/bmij.v3i2.109
24. Wolpe, P. R. (2019). Neuromarketing and AI—powerful together, but needing scrutiny. AJOB neuroscience, 10(2), 69-70. https://doi.org/10.1080/21507740.2019.1618414
25. Yadav, T. (2024). AI in neuromarketing: Understanding consumer emotions and behavior through machine learning. International Journal For Multidisciplinary Research, 6(5), 28154. https://doi.org/10.36948/ijfmr.2024.v06i05.28154