Artificial Intelligence for Customer Complaint Management

  IJCTT-book-cover
 
         
 
© 2023 by IJCTT Journal
Volume-71 Issue-3
Year of Publication : 2023
Authors : Dishant Banga, Kiran Peddireddy
DOI :  10.14445/22312803/IJCTT-V71I3P101

How to Cite?

Dishant Banga, Kiran Peddireddy, "Artificial Intelligence for Customer Complaint Management," International Journal of Computer Trends and Technology, vol. 71, no. 3, pp. 1-6, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I3P101

Abstract
The primary goal of the proposed system in this paper is to improve the efficiency of customer complaint handling by automating the process, which analyzes complaints to identify patterns and provides quick and accurate responses by utilizing artificial intelligence techniques for improving products or services. The proposed system utilizes the natural language processing model, trained on a dataset of customer complaints, to understand and classify the complaints. Also, based on the classification, the system will provide appropriate responses to the complaints. The system will also analyze the complaints to identify common issues and patterns and make suggestions for improving products or services to improve customer experience. The proposed system is also evaluated on a dataset of customer complaints, and the results will be compared to traditional complaint-handling methods. The evaluation metrics include measuring the accuracy in classifying complaints, the time taken to respond, and the satisfaction level of the customers with the responses provided. In conclusion, the proposed AI-powered customer complaint handling system aims to improve the efficiency and effectiveness of complaint handling by automating the process and providing quick and accurate responses by analyzing complaints to identify patterns and make suggestions for improving products or services, which will lead to increased customer satisfaction.

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

Reference

[1] Xu, Y., Huang, J., and Li, P., “A Customer Complaint Handling System Based on NLP, Deep Learning, and Sentiment Analysis,” 2020.
[2] Zhou, Y., Chen, L., and Li, P., “An AI-powered Customer Complaint Handling System Based on NLP,” Machine Learning and Deep Learning, 2019.
[3] Li, W., Li, Y., and Wang, Y., “An AI-Powered Customer Complaint Handling System Based on Deep Learning,” IEEE Access, vol. 8, pp. 183371-183380, 2020.
[4] Sun, J., Li, Y., and Chen, X., “Sentiment Analysis of Customer Complaints and Its Application in Customer Service,” Journal of Business Research, vol. 84, pp. 54-63, 2018.
[5] Zhang, Y., Li, Y., and Chen, X., “A Machine Learning-Based Approach for Handling Customer Complaints,” IEEE Transactions on Engineering, 2019.
[6] Fabiyi Aderanti Alifat et al., “Web-based Campus Complaint Management System (WCCMS),” International Journal of Computer Trends and Technology, vol. 70, no. 10, pp. 28-36, 2022. [CrossRef] [Google Scholar] [Publisher link]
[7] Wang, X., Chen, L., and Li, P., An AI-powered Customer Complaint Handling System Based on NLP and Sentiment Analysis, 2018.
[8] Kiran Peddireddy, Transforming Product Lifecycle Management with AI and Machine Learning, 2023.
[9] Rob Malcolm. [Online]. Available: https://mobileecosystemforum.com/wp-content/uploads/2016/06/Messaging_Report.pdf
[10] S.S. Hui, and K.K. Wong, Customer Complaint Management: An Empirical Study, 2003.
[11] R. Tiwari, and J. B. Sharma, Customer Complaint Behaviour in Service Encounters, 2015.
[12] R. K. Verma, The Future of AI in Customer Service: Opportunities and Challenges, 2020.
[13] A. K. Jain, and M. Z. Raza, AI and Chatbots in Customer Service: Trends and Implications, 2021.
[14] K. Patel, and J. K. Singh, AI-Powered Customer Service: A Case Study of a Major Telecommunications Company, 2019.
[15] A. K. Jain, Real-world Applications of AI in Customer Service, 2020.
[16] Kiran Peddireddy, Enterprise Data Integration and Streaming Using Kafka, ActiveMQ, and AWS Kinesis, 2023.
[17] Kelvin K. Omieno, and Samson Kitheka, “Exploring Artificial Intelligence Integration in Supply Chain Management: A Review,” International Journal of Computer Trends and Technology, vol. 70, no. 8, pp. 1-7, 2022. [CrossRef] [Publisher link]
[18] Anish Rege, “The Impact of Artificial Intelligence on the SupplyChain in the Era of Data Analytics,” International Journal of Computer Trends and Technology, vol. 71, no. 1, pp. 28-39, 2023. [CrossRef] [Publisher link]
[19] 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]
[20] Yohanes Suhari, Kristophorus Hadiono, and Arief Jananto, “Customer Relation Management Features on Mobile Web and the Implementation for Universities in Central Java, Indonesia,” International Journal of Computer Trends and Technology, vol. 69, no. 1, pp. 1-5, 2021. [CrossRef] [Google Scholar] [Publisher link]
[21] 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]
[22] 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]
[23] Xiancheng Xiahou, and Yoshio Harada, “K-Medoids Clustering Techniques in Predicting Customers Churn: A Case Study in the E-Commerce Industry,” International Journal of Computer Trends and Technology, vol. 70, no. 4, pp. 22-28, 2022. [CrossRef] [Publisher link]
[24] 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]
[25] Neha A. Kandalkar, and Avinash Wadhe, “Extracting Large Data using Big Data Mining,” International Journal of Engineering Trends and Technology (IJCTT), vol. 9, no. 11, pp. 576-582, 2014. [Publisher link]
[26] Wen Bin et al., “Text Sentiment Classification Research Based on Semantic Comprehension,” Computer Science, vol. 37, no. 6, pp. 261-264. 2010. [Publisher link]