Relevance of Artificial Intelligence in Marketing: A Narrative Review and Future Directives
Keywords:
Artificial Intelligence, Content Analysis, Machine Learning, Marketing, Technological DisruptorsAbstract
Technological innovations, for instance, Internet of Things, big data analytics, blockchain & Artificial Intelligence led to a paradigm shift in the ways the firms function. Amongst the total technological innovations, Artificial Intelligence is the contemporary innovation & acquires a huge marketing transformation competence. Theorists across the globe are striving to decipher the gold-standard Artificial Intelligence results for their marketing functions. Specifically, Artificial Intelligence agents led by machine learning formulas are significantly altering the marketing space, creating huge inquisitiveness from the empiricists. In the current paper, qualitative content review is done to analyze the secondary data sources and the findings of the content analysis resulted in an in-depth apprehension of Artificial Intelligence significance in the discipline of marketing. Deriving upon the evidence from the text analysis, the current paper provides a narrative review of the significance of Artificial Intelligence in the context of conventional marketing mix - product, price, place & promotion management. This article further uncovers the Artificial Intelligence driven marketing industry trends which include - interactive & media-enhanced, individualization & targeting, real-time optimization & computerization, and lastly consumer-journey attention. We then conclude with the future research directions of Artificial Intelligence & machine learning in the marketing context.
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References
Pennachin, C., and Goertzel, B., Contemporary approaches to artificial general intelligence. In Artificial general intelligence, 2007. p. 1-30. [Online]. Available: https://doi.org/10.1007/978-3-540-68677-4_1
W R Ashby, C E Shannon, and J McCarthy., Automata Studies. Princeton University Press, 1956.
Mitchell, T. M., Does machine learning really work?. AI magazine, 1997. 18(3): p. 11-11. [Online]. Available: https://doi.org/10.1609/aimag.v18i3.1303
Goodfellow, I., Y. Bengio, and A. Courville., “Deep learning”. MIT press, 2016.
Huang, M. -H., and Rust, R. T., Artificial Intelligence in Service. Journal of Service Research, 2018. 21(2): p. 155–172.
Huang, M. -H. and R.T. Rust., Engaged to a Robot: The Role of AI in Service. Journal of Service Research, 2020. [Online]. Available: https://doi.org/10.1177/1094670520902266
Davenport, T., Guha, A., Grewal, D., and Bressgott, T., How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 2020. 48 (1): p. 24–42. [Online]. Available: https://doi.org/10.1007/s11747-019-00696-0
Gacanin, H., and Wagner, M., Artificial intelligence paradigm for customer experience management in next-generation networks: Challenges and perspectives. IEEE Network, 2019. 33 (2): p. 188–194. [Online]. Available: https://doi.org/ https://doi.org/10.1109/MNET.2019.1800015
Nguyen, Q. N., and Sidorova, A., Understanding user interactions with a chatbot: A self-determination theory approach, 2018.
Maxwell, A. L., Jeffrey, S. A., and Lévesque, M., Business angel early stage decision Making. Journal of Business Venturing, 2011. 26 (2): p. 212–225. [Online]. Available: https://doi.org/10.1016/j.jbusvent.2009.09.002
Chatterjee, S., Ghosh, S. K., Chaudhuri, R., and Nguyen, B., Are CRM systems ready for AI integration? A conceptual framework of organizational readiness for effective AI-CRM integration. The Bottom Line, 2019. 32: p. 144–157. [Online]. Available: https://doi.org/10.1108/BL-02-2019-0069
Seranmadevi, R., and Kumar, A., Experiencing the AI emergence in Indian retail–Early adopters approach. Management Science Letters, 2019. 9 (1): p. 33–42.
Sujata, M., Khor, K.S., Ramayah, T., and Teoh, A.P., The role of social media on recycling behaviour. Sustainable Production and Consumption, 2019. 20: p. 365–374. [Online]. Available: https://doi.org/10.1016/j.spc.2019.08.005
Sha Nazim, S., and Rajeswari, M., Creating a Brand Value and Consumer Satisfaction in E-Commerce Business Using Artificial Intelligence with the Help of Vosag Technology. International Journal of Innovative Technology and Exploring Engineering, 2019. 8 (8): p.1510–1515.
Dekimpe, M., Retailing and retailing research in the age of big data analytics. International Journal of Research in Marketing, 2020. 37: p.3–14. [Online]. Available: https://doi.org/10.1016/j.ijresmar.2019.09.001
Antons, D., and Breidbach, C. F., Big data, big insights? Advancing service innovation and design with machine learning. Journal of Service Research, 2018. 21 (1): p. 17–39. [Online]. Available: https://doi.org/10.1177/1094670517738373
Dzyabura, D., and Hauser, J. R., Recommending products when consumers learn their preferences weights. Marketing Science, 2019. 38 (3): p. 365–541. [Online]. Available: https://doi.org/10.1287/mksc.2018.1144
Guo, J., Zhang, W., Fan, W., and Li, W., Combining geographical and social influences with deep learning for personalized point-of interest recommendation. Journal of Management Information Systems, 2018. 35 (4): p. 1121–1153. [Online]. Available: https://doi.org/10.1080/07421222.2018.1523564
Misra, K., Schwartz, E. M., and Abernethy, J., Dynamic online pricing with incomplete information using multiarmed bandit experiments. Marketing Science, 2019. 38 (2): p. 226–252. [Online]. Available: https://doi.org/10.1287/mksc.2018.1129
Bauer, J., and Jannach, D., Optimal pricing in e-commerce based on sparse and noisy data. Decision Support Systems, 2018. 106: p. 53–63. [Online]. Available: https://doi.org/10.1016/j.dss.2017.12.002
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., and Mar- tins, A., Brave new world: Service robots in the frontline. Journal of Service Management, 2018. 29 (5): p. 907–931. [Online]. Available:https://www.emerald.com/insight/content/doi/10.1108/josm-04-2018-0119/full/pdf
Verma, S., Online customer engagement through blogs in India. Journal of Internet Commerce, 2014. 13 (3–4): p. 282–301. [Online]. Available: https://doi.org/10.1080/15332861.2014.961347
Tripathi, S., and Verma, S., Social media, an emerging platform for relationship building: A study of engagement with nongovernment organizations in India. International Journal of Nonprofit and Voluntary Sector Marketing, 2018. 23 (1): e1589. [Online]. Available: https://doi.org/10.1002/nvsm.1589
Verma, S., and Yadav, N., Past, present, and future of electronic word of mouth (EWOM). Journal of Interactive Marketing, 2020. 53: p. 111–128. [Online]. Available: https://doi.org/10.1016/j.intmar.2020.07.001
Chung, T. S., Rust, R. T.., and Wedel, M., My Mobile Music: An Adaptive Personalization System for Digital Audio Players. Marketing Science, 2009. 28(1): p. 52–68. [Online]. Available: https://doi.org/10.1287/mksc.1080.0371
Rust, R. T., and Huang, M. -H., The Service Revolution and the Transformation of Marketing Science. Marketing Science, 2014. 33(2): p. 206–221. [Online]. Available: https://doi.org/10.1287/mksc.2013.0836
Chung, T. S., Wedel, M., and Rust, R. T., Adaptive Personalization Using Social Networks. Journal of the Academy of Marketing Science, 2016. 44(1): p.66–87. [Online]. Available: https://doi.org/10.1007/s11747-015-0441-x
Cambria, E., Affective computing and sentiment analysis. IEEE Intelligent Systems, 2016. 31 (2): p.102–107. https://doi.org/10.1007/978-3-319-55394-8_1
Tripathy, A., Agrawal, A., and Rath, S.K., Classification of sentiment reviews using n-gram machine learning approach. Expert systems with application, 2016. 57: p.117-126. [Online]. Available: https://doi.org/10.1016/j.eswa.2016.03.028
Zhang, H., Cao, X., Ho, J. K., and Chow, T. W., Object-level video advertising: an optimization framework. IEEE Transactions on Industrial Informatics, 2016. 13 (2): p.520–531. [Online]. Available: https://doi.org/10.1109/TII.2016.2605629
Poria, S., Cambria, E., Gelbukh, A., Bisio, F., and Hussain, A., Sentiment data flow analysis by means of dynamic linguistic patterns. In Proceedings of IEEE computational intelligence magazine, 2015. [Online]. Available: https://doi.org/10.1109/MCI.2015.2471215
Wunderlich, N. V., Heinonen, K., Ostrom, A. L., Patricio, L., Sousa, R., Voss, C., and Lemmink, J., “Futurizing” smart service: Implications for service researchers and managers. Journal of Services Marketing, 2015. 29 (6/7): p. 442–447. [Online]. Available: https://doi.org/10.1108/JSM-01-2015-0040
Giatsoglou, M., Vozalis, M. G., Diamantaras, K., Vakali, A., Sarigiannidis, G., and Chatzisavvas, K. C., Sentiment analysis leveraging emotions and word embeddings. Expert Systems with Applications, 2017. 69: p.214–224. [Online]. Available: https://doi.org/10.1016/j.eswa.2016.10.043
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