Enhancing Customer Experience through Kafka Data Steams for Driven Machine Learning for Complaint Management
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© 2023 by IJCTT Journal
Volume-71 Issue-3
Year of Publication : 2023
Authors : Kiran Peddireddy, Dishant Banga
DOI :  10.14445/22312803/IJCTT-V71I3P102

How to Cite?

Kiran Peddireddy, Dishant Banga, "Enhancing Customer Experience through Kafka Data Steams for Driven Machine Learning for Complaint Management," International Journal of Computer Trends and Technology, vol. 71, no. 3, pp. 7-13, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I3P102

Abstract
Customer complaints are a crucial aspect of any business, and prompt and effective resolution of complaints is essential for enhancing customer experience and maintaining customer loyalty. Machine learning (ML) models can be used to predict customer complaints and provide personalized responses, but they require large amounts of data for training and real-time data for prediction. This paper discusses how Kafka data streams can be used for ML training and real-time prediction in customer complaint management, providing a scalable and reliable platform for handling large amounts of data in real time. We discuss the Kafka architecture, its integration with other tools such as Apache Spark and KSQL, and how it can be used for data ingestion, feature extraction, model training, and real-time prediction. We also discuss how ML models such as natural language processing (NLP) models, classification models, and clustering models can be trained using Kafka data streams and how predicted results can be used to identify the root cause of complaints, provide personalized responses, and generate insights for management. Using Kafka data streams for ML training and real-time prediction in customer complaint management offers a powerful and efficient solution for enhancing customer experience and maintaining customer loyalty.

Keywords
Artificial Intelligence(AI), Natural Language Processing(NLP), Machine Learning(ML).

Reference

[1] Apache Kafka Documentation. [Online]. Available: https://kafka.apache.org/documentation/
[2] Kiran Peddireddy, Enterprise Data Integration and Streaming Using Kafka, ActiveMQ, and AWS Kinesis, 2023. [Publisher link]
[3] X. Zhu, and Y. Zhang, “Real-Time Stream Processing with Apache Kafka,” Springer, 2020.
[4] V.V.Narendra Kumar, and T.Satish Kumar, “Smarter Artificial Intelligence with Deep Learning,” SSRG International Journal of Computer Science and Engineering, vol. 5, no. 6, pp. 10-16, 2018. [CrossRef] [Google Scholar] [Publisher link]
[5] S. Sabesan, and Y. Kwon, Predictive Analytics with Kafka, Packt Publishing, 2019.
[6] N. Narkhede, J. Rao, and S. Neha, Kafka: A Distributed Streaming Platform, Confluent, 2017.
[7] Nathan Marz, and James Warren, Big Data: Principles and Best Practices of Scalable Real-Time Data Systems, Manning Publications, 2015. [Google Scholar] [Publisher link]
[8] Uthkarsha Sagar, “A Broad Survey of Natural Language Processing,” SSRG International Journal of Computer Science and Engineering, vol. 6, no. 12, pp. 15-18, 2019. [CrossRef] [Publisher link]
[9] K. Grolinger et al., “Challenges for the Adoption of Big Data Technologies: A Systematic Review of Literature,” Journal of Big Data, vol. 5, no. 1, 2018.
[10] W. Ahmad, and J. Seong, “Machine Learning for Customer Complaint Management: A Review,” Journal of Big Data, vol. 7, no. 1, 2020.
[11] Kiran Peddireddy, Transforming Product Lifecycle Management with AI and Machine Learning, 2023. [Publisher link]
[12] R. Akkad, and C. Cheng, “A Big Data Framework for Customer Complaint Management,” Information Systems Frontiers, vol. 22, no. 2, pp. 435-448, 2020.
[13] T. Yu et al., “A Real-Time Customer Complaint Management System Based on Big Data Analytics,” Journal of Computational Science, 31, 15-24, 2019.
[14] A. Pande, and P. Singh, “A Review of Big Data Technologies for Customer Complaint Management,” International Journal of Computer Science and Information Technology Research, vol. 6, no. 3, pp. 73-80, 2018.
[15] C. Spathis, and M. Christofi, “A Systematic Review of Big Data Analytics Applications in the Healthcare Industry,” Journal of Health Informatics in Developing Countries, vol. 12, no. 2, pp. 1-11, 2018.
[16] J. Rees, and M. Roberts, “Making the Most of Big Data in Retail: A Case Study in Customer Complaint Management,” Journal of Retailing and Consumer Services, vol. 41, pp. 270-278, 2018.
[17] X. Wang, L. Chen, and P. Li, An AI-powered Customer Complaint Handling System Based on NLP and Sentiment Analysis, 2018.
[18] Pradeep Kumar Dhoopati, “Enhancing Enterprise Application Integration through Artificial Intelligence and Machine Learning,” International Journal of Computer Trends and Technology, vol. 71, no. 2, pp. 54-60, 2023. [CrossRef] [Publisher link]
[19] Wen Bin et al., “Text Sentiment Classification Research Based on Semantic Comprehension,” Computer Science, vol. 37, no. 6, pp. 261-264. 2010. [Google Scholar] [Publisher link]
[20] Md. Sirajul Huque, and V. Kiran Kumar, “A Study on Sentiment Analysis of Movie Reviews using ML Algorithms,” International Journal of Computer Trends and Technology, vol. 70, no. 9, pp. 33-37, 2022. [CrossRef] [Publisher link]
[21] M. Sakthivadivu, and P. Suresh Babu, “Analytical and Empirical Survival Study on Natural Image Compression and Classification using Machine Learning Techniques,” International Journal of Computer Trends and Technology, vol. 70, no. 8, pp. 21-29, 2022. [CrossRef] [Publisher link]
[22] Xiancheng Xiahou, and Yoshio Harada, “K-Medoids Clustering Techniques in Predicting Customers Churn: A Case Study in the ECommerce Industry,” International Journal of Computer Trends and Technology, vol. 70, no. 4, pp. 22-28, 2022. [CrossRef] [Publisher link]
[23] Arnav Ghosh, “Analytics and Project Management in Investment Banks,” International Journal of Computer Trends and Technology, vol. 68, no. 12, pp. 44-46, 2020. [CrossRef] [Publisher link]
[24] K. Patel, and J. K. Singh, AI-Powered Customer Service: A Case Study of a Major Telecommunications Company, 2019.
[25] A. K. Jain, Real-world Applications of AI in Customer Service, 2020.
[26] S. Datta, Kafka for Data Scientists, Packt Publishing, 2018.
[27] Jay Kreps, Neha Narkhede, and Jun Rao, “Kafka: A Distributed Messaging System for Log Processing,” Proceedings of the NetDB Workshop, 2011.