A Chatbot Evolution: Digital Marketing as Case Study

A Chatbot Evolution: Digital Marketing as Case Study

  IJETT-book-cover           
  
© 2025 by IJETT Journal
Volume-73 Issue-6
Year of Publication : 2025
Author : Sara AHSAIN, Yasyn EL YUSUFI and M’hamed AIT KBIR
DOI : 10.14445/22315381/IJETT-V73I6P142

How to Cite?
Sara AHSAIN, Yasyn EL YUSUFI and M’hamed AIT KBIR, "A Chatbot Evolution: Digital Marketing as Case Study," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.508-519, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P142

Abstract
This paper explores the research field of chatbots, covering historical evolution, types, technological advancements, and how it has impacted digital marketing. It also introduces a new pioneering architecture designed to enhance chatbot findings. It considers the construction of a conversational companion and the underlying infrastructure to ensure a robust user experience. This architecture aims to set the stage for future research where boundaries of the existent task-oriented chatbots are pushed. The paper also pays special attention to many challenges and limitations in this domain, such as dialectal Arabic, focusing on the complexities of Maghrebi dialects. It also examines security concerns, data privacy, and the ongoing pursuit of innovation in chatbot development.

Keywords
Chatbot, Digital marketing, User experience, Natural language processing, Machine Learning.

References
[1] Alan M. Turing, “Computing Machinery and Intelligence,” Mind, vol. 59, no. 236, pp. 433-460, 1950.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Joseph Weizenbaum, “ELIZA-A Computer Program for the Study of Natural Language Communication between Man and Machine,” Communications of the ACM, vol. 9, no. 1, pp. 36-45, 1966.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Kenneth Mark Colby et al., “Turing-Like Indistinguishability Tests for the Calidation of a Computer Simulation of Paranoid Processes,” Artificial Intelligence, vol. 3, pp. 199-221, 1972.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Robert K. Lindsay et al., “DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation,” Artificial Intelligence, vol. 61, no. 2, pp. 209-261, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[5] William van Melle, “MYCIN: A Knowledge-Based Consultation Program for Infectious Disease Diagnosis,” International Journal of Man-Machine Studies, vol. 10, no. 3, pp. 313-322, 1978.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Richard S. Wallace, The Anatomy of A.L.I.C.E., Parsing the Turing Test, Springer Netherlands, pp. 181-210, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Rollo Carpenter, and Jonathan Freeman, “Computing Machinery and the Individual: The Personal Turing Test, Computing, 2005.
[Google Scholar]
[8] Olga Chukhno et al., “A Chatbot as An Environment for Carrying Out the Group Decision Making Process,” Proceedings of the Selected Papers of the 9th International Conference Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems (ITTMM-2019), Moscow, Russian, pp. 15-25, 2019.
[Google Scholar] [Publisher Link]
[9] Thomas M. Brill, Laura Munoz, and Richard J. Miller, “Siri, Alexa, and Other Digital Assistants: A Study of Customer Satisfaction with Artificial Intelligence Applications,” Journal of Marketing Management, vol. 35, no. 15-16, pp. 1401-1436, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Luciano Floridi, and Massimo Chiriatti, “GPT-3: Its Nature, Scope, Limits, and Consequences,” Minds & Machines, vol. 30, no. 4, pp. 681-694, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Dhakshenya Ardhithy Dhinagaran et al., “Designing, Developing, Evaluating, and Implementing a Smartphone-Delivered, Rule-Based Conversational Agent (DISCOVER): Development of a Conceptual Framework,” JMIR Mhealth and Uhealth, vol. 10, no. 10, pp. 1-23, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Chisaki Miura, “Assisting Personalized Healthcare of Elderly People: Developing a Rule-Based Virtual Caregiver System Using Mobile Chatbot,” Sensors, vol. 22, no. 10, pp.1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Malik Sallam, “ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns,” Healthcare, vol. 11, no. 6, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Edyta Gołąb-Andrzejak, “AI-Powered Digital Transformation: Tools, Benefits and Challenges for Marketers-Case Study of LPP,” Procedia Computer Science, vol. 219, pp. 397-404, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Abdelrahman H. Hefny, Georgios A. Dafoulas, and Manal A. Ismail, “Intent Classification for a Management Conversational Assistant,” 2020 15th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt, pp. 1-6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Emanuela Guglielmi et al., “Sorry, I Don’t Understand: Improving Voice User Interface Testing,” Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Steven Wijaya, and Arya Wicaksana, “JACOB Voice Chatbot Application Using Wit.ai for Providing Information in UMN,” International Journal of Engineering and Advanced Technology, vol. 8, no. 6S3, pp. 105-109, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Akshay Kulkarni, Adarsha Shivananda, and Anoosh Kulkarni, Natural Language Processing Projects: Build Next-Generation NLP Applications Using AI Techniques, Apress Berkeley, CA, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Shuangyong Song et al., “TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce,” WWW '20: Companion Proceedings of the Web Conference 2020, Taipei, Taiwan, pp. 844-845, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Minghui Qiu et al., “Transfer Learning for Context-Aware Question Matching in Information-Seeking Conversations in E-Commerce,” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, pp. 208-213, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Zhenxin Fu et al., “Context-to-Session Matching: Utilizing Whole Session for Response Selection in Information-Seeking Dialogue Systems,” Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, CA USA, pp. 1605-1613, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Masoud AminiMotlagh, HadiShahriar Shahhoseini, and Nina Fatehi, “A Reliable Sentiment Analysis for Classification of Tweets in Social Networks,” Social Network Analysis and Mining, vol. 13, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Mouaad Errami et al., “Sentiment Analysis on Moroccan Dialect Based on ML and Social Media Content Detection,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 14, no. 3, pp. 415-425, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Victoria Oguntosin, and Ayobami Olomo, “Development of an E-Commerce Chatbot for a University Shopping Mall,” Applied Computational Intelligence and Soft Computing, vol. 2021, no. 1, pp. 1-14, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Customer Service Software to Grow Your Business, Tidio, 2023. [Online]. Available: https://www.tidio.com/
[26] The AI-Powered Customer Service Automation Platform, Ada, 2023. [Online]. Available: https://www.ada.cx
[27] WhatsApp Business API for Customer and Sales, Chatfuel, 2023. [Online]. Available: https://chatfuel.com
[28] SnatchBot, SnatchBot: Free Chatbot Solutions, Intelligent Bots Service and Artificial Intelligence, 2023. [Online]. Available: https://snatchbot.me/
[29] Chat Marketing Made Easy with Manychat, Manychat.com, 2023. [Online]. Available: https://manychat.com
[30] Jawad Iounousse, and Omar Temsamani, “Development of an Intelligent Virtual Assistant for Digitalization of Moroccan Agriculture,” ITM Web of Conference, vol. 69, pp. 1-3, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Cloud Computing Services, Microsoft Azure, 2024. [Online]. Available: https://azure.microsoft.com/en-us
[32] API Development for Everyone, Swagger, 2024. [Online]. Available: https://swagger.io/
[33] Hasan Beyari, and Tareq Hashem, “The Role of Artificial Intelligence in Personalizing Social Media Marketing Strategies for Enhanced Customer Experience,” Behavioral Sciences, vol. 15, no. 5, pp. 1-21, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Fredrik Filipsson, Case Study: Sephora’s Use of AI to Deliver Personalized Beauty Experiences, 2025. [Online]. Available: https://redresscompliance.com/case-study-sephoras-use-of-ai-to-deliver-personalized-beauty-experiences/ [35] BofA’s Erica Surpasses 2 Billion Interactions, Helping 42 Million Clients Since Launch, Bank of America, 2024. [Online]. Available: https://newsroom.bankofamerica.com/content/newsroom/press-releases/2024/04/bofa-s-erica-surpasses-2-billion-interactions--helping-42-millio.html
[36] Meet Dom: The Virtual Voice Ordering Assistant for Domino’s Pizza®, Domino’s Pizza 2014. [Online]. Available: https://ir.dominos.com/news-releases/news-release-details/meet-dom-virtual-voice-ordering-assistant-dominos-pizzar
[37] Yassine Saoudi, and Mohamed Mohsen Gammoudi, “Trends and Challenges of Arabic Chatbots: Literature Review,” Jordanian Journal of Computers and Information Technology, vol. 9, no. 3, pp. 261-286, 2023.
[Google Scholar] [Publisher Link]
[38] Abdulaziz M. Alayba, and Vasile Palade, “Leveraging Arabic Sentiment Classification Using an Enhanced CNN-LSTM Approach and Effective Arabic Text Preparation,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 9710-9722, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Nadrh Abdullah Alhassan et al., “A Novel Framework for Arabic Dialect Chatbot Using Machine Learning,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, pp. 1-11, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Meshrif Alruily, “ArRASA: Channel Optimization for Deep Learning-Based Arabic NLU Chatbot Framework,” Electronics, vol. 11, no. 22, pp. 1-16, 2022.
[CrossRef] [Google Scholar] [Publisher Link]