Journal of Intelligent Marketing Management

Journal of Intelligent Marketing Management

A Framework for Realizing Digital Marketing Excellence Using the Natural Language Processing Technology: From Principles to Performance Evaluation

Document Type : The scientific research paper

Author
maryam nooraei abadeh
Assistant Professor of Computer Department, Abadan Branch, Islamic Azad University, Abadan, Iran
JABM.3.2.15564.358878790862
Abstract
Abstract: The increasing importance and proliferation of data has provided a unique opportunity and a new lens to study human communication in many business and marketing applications. Developments in the field of digital marketing have changed dramatically due to the use of artificial intelligence and natural language processing. The use of natural language processing (NLP) in digital marketing significantly enhances various aspects of marketing strategies by automating and optimizing communication and content creation, among other things. This article examines the intersection of natural language processing and digital marketing and shows how NLP technologies can increase the effectiveness of marketing strategies. In this paper, a framework and research roadmap for promising applications of NLP in digital marketing is presented to help interested researchers explore opportunities related to NLP in marketing. This framework addresses various applications of NLP in marketing, including sentiment analysis, personality profiling, and chatbots based on the precise determination of inputs and outputs. Also, two categories of key criteria are provided to evaluate performance and strategies and make changes and improve performance by understanding customers' reactions and making quick optimal decisions. Finally, challenges and potential future developments in this field are discussed, and in a vision for the future of NLP in digital marketing, approaches based on pre-trained linguistic models, and transfer learning for new tasks such as automatic text generation and multimodal representation learning is covered.
Keywords
Digital marketing
natural language processing
text analysis
digital age

Subjects

Allen, J. F. (2003). Natural language processing. Encyclopedia of Computer Science, John Wiley and Sons Ltd.: 1218–1222.
Allil, K. (2024). "Integrating AI-driven marketing analytics techniques into the classroom: pedagogical strategies for enhancing student engagement and future business success." Journal of Marketing Analytics: 1-27.
Bahirat, A. (2022). Contextual Recommendations Using NLP in Digital Marketing. Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 4, Springer.
Bird, S., E. Klein and E. Loper (2009). Natural language processing with Python: analyzing text with the natural language toolkit, " O'Reilly Media, Inc.".
Camilleri, S. (2021). Deriving Business Value From Online Data Sources Using Natural Language Processing Techniques. Natural Language Processing for Global and Local Business, IGI Global: 17-39.
Castillo, M. J. and H. Taherdoost (2023). "The Impact of AI Technologies on E-Business." Encyclopedia 3(1): 107-121.
Chaitanya, K., G. C. Saha, H. Saha, S. Acharya and M. Singla (2023). "The Impact of Artificial Intelligence and Machine Learning in Digital Marketing Strategies." European Economic Letters (EEL) 13(3): 982-992.
Dash, G., C. Sharma and S. Sharma (2023). "Sustainable marketing and the role of social media: an experimental study using natural language processing (NLP)." Sustainability 15(6): 5443.
Dr. Arun, K. and S. Dr. Sangheethaa (2024). "Impact Of Emotional Factor Segmentation Generated By AI On Digital Marketing Platform." Educational Administration: Theory and Practice 30(4): 2396-2402.
Eisenstein, J. (2019). Introduction to natural language processing, MIT press.
Fang, X. and T. Wang (2022). "Using Natural Language Processing to Identify Effective Influencers." International Journal of Market Research 64(5): 611-629.
Gjorgjevska, E. and G. Mirceva (2021). Content engineering for state-of-the-art SEO digital strategies by using NLP and ML. 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), IEEE.
Han, Y., J. Hou and Y. Sun (2023). Research and Application of GPT-Based Large Language Models in Business and Economics: A Systematic Literature Review in Progress. 2023 IEEE International Conference on Computing (ICOCO), IEEE.
Hartmann, J., A. Bergner and C. Hildebrand (2023). "MindMiner: Uncovering linguistic markers of mind perception as a new lens to understand consumer–smart object relationships." Journal of Consumer Psychology 33(4): 645-667.
Hartmann, J. and O. Netzer (2023). Natural language processing in marketing. Artificial Intelligence in Marketing, Emerald Publishing Limited. 20: 191-215.
Jain, A., C. Jain, R. G. Kargal and S. Suresh (2022). Advancements and Innovation in Digital Marketing and SEO. IoT and AI Technologies for Sustainable Living, CRC Press: 279-315.
Jiang, Y., P. C.-I. Pang, D. Wong and H. Y. Kan (2023). "Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review." Applied Sciences 13(22): 12346.
Kaperonis, S. (2024). How Artificial Intelligence (AI) is Transforming the User Experience in Digital Marketing. The Use of Artificial Intelligence in Digital Marketing: Competitive Strategies and Tactics, IGI Global: 117-141.
Khurana, D., A. Koli, K. Khatter and S. Singh (2023). "Natural language processing: state of the art, current trends and challenges." Multimedia Tools and Applications 82(3): 3713-3744.
Kushwaha, A. K. and A. K. Kar (2020). Language Model-Driven Chatbot for Business to Address Marketing and Selection of Products. Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation, Cham, Springer International Publishing.
Liddy, E. D. (2001). "Natural language processing."
Min, B., H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz and D. Roth (2023). "Recent advances in natural language processing via large pre-trained language models: A survey." ACM Computing Surveys 56(2): 1-40.
Mufadhol, M., A. Wibowo and J. T. Santoso (2020). "Digital marketing techniques for business intelligence systems use automated chatbot machine learning." PalArch's Journal of Archaeology of Egypt/Egyptology 17(7): 6895-6906.
Nadkarni, P. M., L. Ohno-Machado and W. W. Chapman (2011). "Natural language processing: an introduction." Journal of the American Medical Informatics Association 18(5): 544-551.
Paik, W., S. Yilmazel, E. Brown, M. Poulin, S. Dubon and C. Amice (2001). Applying natural language processing (nlp) based metadata extraction to automatically acquire user preferences. Proceedings of the 1st international conference on Knowledge capture.
Qin, C., A. Zhang, Z. Zhang, J. Chen, M. Yasunaga and D. Yang (2023). "Is ChatGPT a general-purpose natural language processing task solver?" arXiv preprint arXiv:2302.06476.
Rabbi, S. N. (2024). "Ai in Digital marketing."
Reisenbichler, M., T. Reutterer, D. A. Schweidel and D. Dan (2022). "Frontiers: Supporting content marketing with natural language generation." Marketing Science 41(3): 441-452.
Shankar, V. and S. Parsana (2022). "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing." Journal of the Academy of Marketing Science 50(6): 1324-1350.
Thilagavathy, N. and E. P. Kumar (2021). "Artificial intelligence on digital marketing-An overview." Nveo-Natural Volatiles & Essential Oils Journal| NVEO: 9895-9908.
Wu, L., Y. Chen, K. Shen, X. Guo, H. Gao, S. Li, J. Pei and B. Long (2023). "Graph neural networks for natural language processing: A survey." Foundations and Trends® in Machine Learning 16(2): 119-328.
Zhu, C., Y. Xu, X. Ren, B. Y. Lin, M. Jiang and W. Yu (2023). Knowledge-augmented methods for natural language processing. Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining.
 
 

  • Receive Date 09 March 2024
  • Revise Date 20 April 2024
  • Accept Date 21 April 2024