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EEG Based Study of Pictorial Advertisement Impact on Customers’ Market Preferences

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Recent Innovations in Mechanical Engineering

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Neuromarketing is a way to detect elicited brain activities while customer is engaged toward various products and brands. This paper presents a study regarding customers’ engagement with various products and brands available in the market using electroencephalography (EEG). A total of 10 test subjects were presented with a collage of still pictures from the TV commercials, and their brain activity was recorded. Power spectral density (PSD) was obtained from the acquired signals using fast Fourier transform (FFT) technique, and absolute power was obtained. The results showed that test subjects felt change in elicitation in the theta wave and a pattern can be seen in theta band power. This study implies that the variation in theta band power when compared with the Delighted–Terrible (D-T) scale rating changes which signify the same outcome. Hence, the present work would help in effective evaluation of the change in market demands of various products and brands with the help of pictorial advertisement.

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References

  1. Morin C (2011) Neuromarketing: the new science of consumer behavior. Society 48(2):131–135. https://doi.org/10.1007/s12115-010-9408-1

  2. Vecchiato G et al (2011) On the use of EEG or MEG brain imaging tools in neuromarketing research. Comput Intell Neurosci May 2014. https://doi.org/10.1155/2011/643489

  3. Madan CR (2010) Neuromarketing: the next step in market research? Eureka 1(1):34–42. https://doi.org/10.29173/eureka7786

  4. Berns GS, Moore SE (2012) A neural predictor of cultural popularity. J Consum Psychol 22(1):154–160. https://doi.org/10.1016/j.jcps.2011.05.001

  5. Telpaz A, Webb R, Levy DJ (2015) Using EEG to predict consumers’ future choices. J Mark Res 52(4):511–529. https://doi.org/10.1509/jmr.13.0564

  6. Murugappan M, Murugappan S, Balaganapathy B, Gerard C (2014) Wireless EEG signals based Neuromarketing system using Fast Fourier Transform (FFT). In: Proceedings 2014 IEEE 10th international colloquium on signal processing and its applications CSPA, pp 25–30. https://doi.org/10.1109/CSPA.2014.6805714

  7. Kumar S, Yadava M, Roy PP (2019) Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction. Inf Fusion 52(Nov 2018):41–52. https://doi.org/10.1016/j.inffus.2018.11.001

  8. Yadava M, Kumar P, Saini R, Roy PP, Dogr DP (2017) Analysis of EEG signals and its application to neuromarketing. Multimed Tools Appl 76(18):19087–19111

    Google Scholar 

  9. Khushaba RN, Greenacre L, Kodagoda S, Louviere J, Burke S, Dissanayake G (2012) Choice modeling and the brain: a study on the electroencephalogram (EEG) of preferences. Expert Syst Appl 39(16):12378–12388. https://doi.org/10.1016/j.eswa.2012.04.084

  10. Baldo D, Parikh H, Piu Y, Müller K-M (2015) Brain waves predict success of new fashion products: a practical application for the footwear retailing industry. J Creat Value 1(1):61–71. https://doi.org/10.1177/2394964315569625

  11. Yilmaz B, Korkmaz S, Arslan DB, Güngör E, Asyali MH (2014) Like/dislike analysis using EEG: determination of most discriminative channels and frequencies. Comput Methods Programs Biomed 113(2):705–713. https://doi.org/10.1016/j.cmpb.2013.11.010

  12. Andrews FM, Withey SB (2012) Social indicators of well-being: Americans’ perceptions of life quality. Springer Science & Business Media

    Google Scholar 

  13. Westbrook RA (1980) A rating scale for measuring product/service satisfaction. J Mark 44(4):68. https://doi.org/10.2307/1251232

  14. Moldovan CP (2018) Am happy scale: reliability and validity of a single-item measure of happiness. Diss Abstr Int Sect B Sci Eng 79(1-B(E))

    Google Scholar 

  15. Kaur H Gross national happiness index: a nation’s pursuit of happiness

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India

    Anshika Kesari, Rajesh Singla & Prerna Singh

Authors
  1. Anshika Kesari
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  2. Rajesh Singla
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  3. Prerna Singh
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Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, India

    Meghanshu Vashista

  2. Department of Polymer and Process Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttar Pradesh, India

    Gaurav Manik

  3. Department of Instrumentation and Control Engineering, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India

    Om Prakash Verma

  4. Jaipur Engineering College and Research Centre, Jaipur, India

    Bhuvnesh Bhardwaj

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Kesari, A., Singla, R., Singh, P. (2022). EEG Based Study of Pictorial Advertisement Impact on Customers’ Market Preferences. In: Vashista, M., Manik, G., Verma, O.P., Bhardwaj, B. (eds) Recent Innovations in Mechanical Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-9236-9_7

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  • DOI: https://doi.org/10.1007/978-981-16-9236-9_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9235-2

  • Online ISBN: 978-981-16-9236-9

  • eBook Packages: EngineeringEngineering (R0)

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