Enhancing E-Commerce Platforms with AI-Driven Chatbots: Revolutionizing Customer Search and Engagement

Abstract

Thisresearchmixesdisciplinesregardingthecurrent analysisoftheapplicationofchatbotsine-commerceplatforms. The primary incidence of the chatbot is to provide consumers withaseamlessmeanstoarticulatetheirpreferencesandsearch usinganaturallanguage.Employingadvancednaturallanguage processing (NLP) and machine learning techniques, it is this chatbot that interprets user queries and retrieves relevant products from the online catalogue of the site. The discussion highlights the key aspects and advantages of the chatbot, proving its credentials in really enhancing e-commerce functionality and customer experience. The researchers believe thatthistechnologycanfurtherimproveonlineshoppers\'speed of searching for products and actively engage customers. Besides, customer satisfactionwith these sites and the potential forfurtherrevenuesaidtechnologycouldbringarediscussedin a broader context.

Introduction

Chatbots in e-commerce must meet key requirements like understanding natural language, maintaining smooth conversations, providing personalized product recommendations, and supporting scalable integration across various industries. Despite advancements, many chatbots still face challenges in contextual understanding, efficiency, scalability, and data utilization, leading to suboptimal user experiences and lower customer retention.

In other domains, such as college admissions, chatbots have proven valuable in enhancing communication and reducing administrative burdens. In mental health, chatbots like "Happy Soul" provide emotional support for adolescents, showcasing AI’s potential beyond commerce.

The fashion industry, especially accelerated by the COVID-19 pandemic, has seen rapid chatbot adoption for personalized shopping assistance and customer engagement. Technologies like deep learning and recommendation systems improve chatbot accuracy but limitations remain in personalization, response time, and multilingual support.

The project described developed a fashion e-commerce website using the MERN stack (MongoDB, Express, React, Node.js), integrating a Dialogflow-based chatbot trained on fashion datasets to offer real-time personalized clothing recommendations linked to inventory data. Features include secure payment processing, user authentication, and password management.

Challenges include maintaining recommendation accuracy, mitigating bias, ensuring data privacy, and ethical AI use to avoid harmful suggestions. Testing showed improvements in response accuracy, user engagement, and query resolution time.

Future directions involve enhancing multilingual support, augmented reality integration for virtual try-ons, and overall improvements in chatbot interaction fidelity, positioning AI-driven chatbots as essential for the evolving online fashion retail landscape.

Conclusion

In conclusion, a different approach to online fashion retail: implementconversationalsystems,settinganeasypathwayforusers tointeractwithproductsvianaturallanguageprocessing.Theadvent of these chatbots will enhance shopping experiences and improve customer engagement-intuitive and personalized environments for digital shopping. With the advancement of technology, these AI- enabled systems will become firms\' beating sticks in the ever- changingretailmarket.Hyper-personalizationwillgetanotherkick- up from chatbots in fashion, reaching the zenith through machine learning,predictiveanalysis,anddataanalysisforassessinguser

References

[1] Zhang, Y., Xu, K., Gao, L., Yang, M., & Wang, J. (2019). Fashion++:Minimal Edits for Outfit Improvement. In Proceedings of the IEEEInternational Conference on Computer Vision [2] Lin, J., Chen, C., & Fung, P. (2019). Fashion Chatbot: The Making ofFashion Recommendation in the Era of Chatbot. In Proceedings of theInternational Conference on Information and Knowledge Management(CIKM). [3] Lee, S., Kim, S., Lee, D., Kang, S., & Kim, S. (2019). FashionBot: AFashion Recommender Chatbot for Mobile E- commerce Applications. InProceedings of the International Conference on Human-ComputerInteraction(HCI). [4] Zhang, S., & Tao, D. (2020). Adaptive Style Support for FashionRecommendation.InProceedingsoftheEuropeanConferenceonComputerVision(ECCV). [5] Lin, Y., Lv, F., & Hu, W. (2017). Fashion Style in 128 Floats: JointRanking and Classification Using Weak Data for Feature Extraction. InProceedings of the IEEE International Conference on Computer Vision(ICCV). [6] Devlin, J., Chang, M., Lee, K., &Toutanova, K. (2018). BERT: Pre-trainingofDeepBidirectionalTransformersforLanguageUnderstanding.InProceedings of the International Conference on Human-ComputerInteraction(HCI). [7] Zhang, Y., & Chen, Y. (2020). Building Chatbots with GoogleDialogflow: Architecture and Applications. In Proceedings of theInternational Conference on Human-Computer Interaction (HCI). [8] Chandramohan, A., &Manogaran, G. (2020). Designing IntelligentChatbotswithDialogflowforCustomerSupportSystems.InProceedingsofthe InternationalConference Human-Computer Interaction. [9] Liu, C., & Wang, D. (2020). Recent Advances in Augmented Reality:From Entertainment to Practical Applications. In Proceedings of theInternational Conference on Human-Computer Interaction (HCI).

Copyright

Copyright © 2025 Pradnya Sabannavar, Jainam Sheth, Atharva Adam , Anvita Birje. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.