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Exploring Artificial Intelligence Techniques’ Applicability in Social Media Marketing

Author

Listed:
  • Adrian MICU

    (Dunarea de Jos University of Galati)

  • Alexandru CAPATINA

    (Dunarea de Jos University of Galati)

  • Angela-Eliza MICU

    (Ovidius University of Constanta)

Abstract

The increasing interest in Artificial Intelligence (AI)’s impact on Social Media Marketing (SMM) creates new opportunities to be captured by software developers. Marketers become aware of AI powerful tools role in leveraging competitive advantage in social media campaigns. This study aims to test correlations between the experience in the field of SMM and the level of knowledge regarding the applicability of Machine Learning (ML) in SMM and the frequency of using of ML algorithms in SMM campaigns and to identify the perceptions of the potential users of an Artificial Intelligence (AI)-based software, which will embed deep learning algorithms and convolutional neural networks to recognize logos of brands or companies involved in social media content, regarding its proposed capabilities. The AI Media software capabilities were embedded into three clusters (audience analysis, image analysis and sentiment analysis), being assessed through a 3 points scale, revealing necessary vs. expected functionalities in the eyes of digital agencies’ representatives of freelancers. The results outline a high interest and trust of potential users of AI Media software on its value proposition.

Suggested Citation

  • Adrian MICU & Alexandru CAPATINA & Angela-Eliza MICU, 2018. "Exploring Artificial Intelligence Techniques’ Applicability in Social Media Marketing," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, vol. 1(1), pages 156-165, November.
    • Handle: RePEc:aes:jetimm:v:1:y:2018:i:1:p:156-165
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    References listed on IDEAS

    as
    1. de Vries, Lisette & Gensler, Sonja & Leeflang, Peter S.H., 2012. "Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 83-91.
    2. Singh, Sangeeta & Sonnenburg, Stephan, 2012. "Brand Performances in Social Media," Journal of Interactive Marketing, Elsevier, vol. 26(4), pages 189-197.
    3. Lee, In, 2018. "Social media analytics for enterprises: Typology, methods, and processes," Business Horizons, Elsevier, vol. 61(2), pages 199-210.
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    Cited by:

    1. Esther Calderon-Monge & Domingo Ribeiro-Soriano, 2024. "The role of digitalization in business and management: a systematic literature review," Review of Managerial Science, Springer, vol. 18(2), pages 449-491, February.
    2. Kanungo, Rama Prasad & Liu, Rui & Gupta, Suraksha, 2024. "Cognitive analytics enabled responsible artificial intelligence for business model innovation: A multilayer perceptron neural networks estimation," Journal of Business Research, Elsevier, vol. 182(C).

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    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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