Enhancing Customer Experience through Kafka Data Steams for Driven Machine Learning for Complaint Management
|© 2023 by IJCTT Journal|
|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
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.
Artificial Intelligence(AI), Natural Language Processing(NLP), Machine Learning(ML).
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