Enhancing Customer Engagement in Fashion: Strategies for Optimizing Chatbot Performance

Authors

  • Dikshendra Daulat Sarpate, Manju D Pawar, Lekshmy P L, Shanthaamani V, Swati V Sakhare, Megha Kadam, Sarita Avinash Patil

Keywords:

Chatbots, Fashion Industry, Long Short-Term Memory, Zebra Optimization Algorithm

Abstract

In recent years, chatbots have become integral in the fashion industry, enhancing customer-brand interaction, facilitating communication, and contributing to e-commerce growth through personalized shopping experiences. This research aims to reveal strategic approaches in luxury fashion, translating insights for small and medium-sized enterprises. It investigates the impact of chatbot integration on customer sentiment, aiming to provide practical recommendations for improving customer interactions. Emphasizing the importance of intelligent chatbot design, the research recognizes their crucial role in enhancing customer experience through valuable insights. The primary objective is to optimize chatbot performance, ensuring precise and effective responses to user queries and, consequently, adding significant value to the business. The proposed approach incorporates optimally configured Long Short-Term Memory (LSTM) networks, Seq2Seq Architecture, Attention Mechanism, Bag of Words (BOW) model, and Beam Search decoding. The effectiveness of the optimally configured LSTM, in combination with attention mechanisms, extends to both longer and shorter sentences. Through rigorous testing, the proposed model achieves an exceptional accuracy rate of 99%, surpassing other state-of-the-art techniques. This outcome underscores the efficacy of the implemented strategies in elevating chatbot performance.

Downloads

Download data is not yet available.

References

Aslam, U., "Understanding the usability of retail fashion brand chatbots: Evidence from customer expectations and experiences". Journal of Retailing and Consumer Services, 74, pp.103377, (2023). https://doi.org/10.1016/j.jretconser.2023.103377

Jiang, L., Sun, W., and Ren, L., "Bridging the Gap: Anxiety’s Role in Shaping Consumption Patterns of Chinese University Students in the O2O E-Commerce Landscape”. Journal of the Knowledge Economy, pp.1-22, (2023). https://doi.org/10.1007/s13132-023-01642-w

Kumar, A., Bapat, G., Kumar, A., Hota, S. L., Abishek, G. D., and Vaz, S., "Unlocking Brand Excellence: Harnessing AI Tools for Enhanced Customer Engagement and Innovation". Engineering Proceedings, 59(1), pp.204, (2024). https://doi.org/10.3390/engproc2023059204

Li, W., Liu, K., Belitski, M., Ghobadian, A., and O'Regan N., "e-Leadership through strategic alignment: An empirical study of small-and medium-sized enterprises in the digital age”. Journal of Information Technology, 31, pp.185-206, (2016). https://doi.org/10.1057/jit.2016.10

Almansor, E. H., and F. K., Hussain. "Survey on intelligent chatbots: State-of-the-art and future research directions". In Complex, Intelligent, and Software Intensive Systems: Proceedings of the 13th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2019), pp. 534-543. Springer International Publishing, (2020). https://doi.org/10.1007/978-3-030-22354-0_47

Kaushal, V., and Yadav, R., "Learning successful implementation of Chatbots in businesses from B2B customer experience perspective". Concurrency and Computation: Practice and Experience, 35(1), pp. e7450, (2023). https://doi.org/10.1002/cpe.7450

Tahayori, B., Chini‐Foroush, N., and Akhlaghi H., "Advanced natural language processing technique to predict patient disposition based on emergency triage notes". Emergency Medicine Australasia, 33(3), pp.480-484, (2021). https://doi.org/10.1111/1742-6723.13656

Sarker, I. H., "Machine learning: Algorithms, real-world applications and research directions". SN computer science, 2(3), pp.160, (2021). https://doi.org/10.1007/s42979-021-00592-x

Muzaffar, S., and Afshari, A., "Short-term load forecasts using LSTM networks". Energy Procedia, 158, pp.2922-2927, (2019). https://doi.org/10.1016/j.egypro.2019.01.952

Choudhary, P., and Chauhan, S., "An intelligent chatbot design and implementation model using long short-term memory with recurrent neural networks and attention mechanism". Decision Analytics Journal, 9, pp.100359, (2023). https://doi.org/10.1016/j.dajour.2023.100359

Tax, N., Verenich, I., La Rosa, M., and Dumas, M., "Predictive business process monitoring with LSTM neural networks". In Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings 29, pp. 477-492. Springer International Publishing, (2017). https://doi.org/10.1007/978-3-319-59536-8_30

Aalipour, G., Kumar, P., Aditham, S., Nguyen, T., and Sood, A., "Applications of sequence to sequence models for technical support automation". In 2018 IEEE International Conference on Big Data (Big Data), pp. 4861-4869. IEEE, (2018). DOI: 10.1109/BigData.2018.8622395

Qader, W. A., Ameen, M. M., and Ahmed, B.I., "An overview of bag of words; importance, implementation, applications, and challenges". In 2019 international engineering conference (IEC), pp. 200-204, IEEE, (2019). DOI: 10.1109/IEC47844.2019.8950616

Vijayakumar, A. K., Cogswell, M., Selvaraju, R. R., Sun, Q., Lee, S., Crandall, D., and Batra, D., "Diverse beam search: Decoding diverse solutions from neural sequence models". arXiv preprint arXiv:1610.02424 (2016). https://doi.org/10.48550/arXiv.1610.02424

Rana, A., Khurana, V., Shrivastava, A., Gangodkar, D., Arora, D., and Dixit, A. K., "A ZEBRA Optimization Algorithm Search for Improving Localization in Wireless Sensor Network". In 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), pp. 817-824, IEEE, (2022). DOI: 10.1109/ICTACS56270.2022.9988278

Ortiz-Garces, I., Govea, J., Andrade, R. O., and Villegas-Ch, W., "Optimizing Chatbot Effectiveness through Advanced Syntactic Analysis: A Comprehensive Study in Natural Language Processing". Applied Sciences, 14(5), pp.1737, (2024). https://doi.org/10.3390/app14051737

Anki, P., Bustamam, A., Al-Ash, H. S., and Sarwinda, D., "High Accuracy Conversational AI Chatbot Using Deep Recurrent Neural Networks Based on BiLSTM Model". In 2020 3rd International Conference on Information and Communications Technology (ICOIACT), pp. 382-387, IEEE, (2020). DOI: 10.1109/ICOIACT50329.2020.9332074

Pandey, S., and Sharma, S., "A comparative study of retrieval-based and generative-based chatbots using Deep Learning and Machine Learning". Healthcare Analytics, 3, pp.100198, (2023). https://doi.org/10.1016/j.health.2023.100198

Dhyani, M., and Kumar, R., “An intelligent Chatbot using deep learning with Bidirectional RNN and attention model”. Materials today: proceedings, 34, pp.817-824, (2021). https://doi.org/10.1016/j.matpr.2020.05.450

Trojovská, E., Dehghani, M., and Trojovský, P., "Zebra optimization algorithm: A new bio-inspired optimization algorithm for solving optimization algorithm". IEEE Access, 10, pp.49445-49473, (2022). DOI: 10.1109/ACCESS.2022.3172789

Tran, A. D., Pallant, J. I., and Johnson, L.W., "Exploring the impact of chatbots on consumer sentiment and expectations in retail". Journal of Retailing and Consumer service, 63, pp.102718, (2021). https://doi.org/10.1016/j.jretconser.2021.102718

Assayed, S. K., Alkhatib, M., and Shaalan, K., "Artificial intelligence based chatbot for promoting equality in high school advising". In 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), pp. 1-4, IEEE, (2023). DOI: 10.1109/ICIEM59379.2023.10167112

Zhong, Q., Ding, L., Liu, J., Du, B., and Tao, D., "E2s2: Encoding-enhanced sequence-to-sequence pretraining for language understanding and generation". IEEE Transactions on Knowledge and Data Engineering, (2023). DOI: 10.1109/TKDE.2023.3341917

Luong, M.T., Pham, H., and Manning, C.D.,"Effective approaches to attention-based neural machine translation". arXiv preprint arXiv:1508.04025 (2015). https://doi.org/10.48550/arXiv.1508.04025

Zhao, R., and Mao, K., "Fuzzy bag-of-words model for document representation". IEEE transactions on fuzzy systems, 26(2), pp.794-804, (2017). DOI: 10.1109/TFUZZ.2017.2690222

Freitag, M., and Al-Onaizan, Y., "Beam search strategies for neural machine translation". arXiv preprint arXiv:1702.01806 (2017).https://doi.org/10.48550/arXiv.1702.01806

Downloads

Published

12.06.2024

How to Cite

Dikshendra Daulat Sarpate. (2024). Enhancing Customer Engagement in Fashion: Strategies for Optimizing Chatbot Performance. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 4759–4768. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7181

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.