Journals / Middle Black Sea Journal of Communication Studies / 2026 / Cilt: 11

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

Nöropazarlama Stratejilerinde Yapay Zekâ Tabanlı Nörometrik ve Biyometrik Veri Analitiği

Elif Asan(ONDOKUZ MAYIS ÜNİVERSİTESİ)0009-0003-1047-9510
Pages
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Publication Date
2026-06-11
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study aims to provide a conceptual framework for AI-based analytical approaches for future neuromarketing research.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact:This study aims to provide a conceptual framework for AI-based analytical approaches for future neuromarketing research.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Pazarlama · Nöropazarlama · Yapay Zekâ

Abstract

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.

Özet

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.

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Cite This Article

Asan, E. (2026). Artifical Intelligence-Based Neurometric and Biometric Data Analytics In Neuromarketing Strategies. Middle Black Sea Journal of Communication Studies, Cilt: 11. https://doi.org/10.56202/mbsjcs.1953948

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