Predicting Customer Intent for Enhanced Support in the Telecommunications Industry Using Machine Learning
Keywords:
retention, engagement, BERT, significantAbstract
In the competitive telecommunications sector, accurately predicting customer intent is pivotal for improving service delivery, reducing operational costs, and enhancing customer satisfaction. This research introduces a machine learning-based framework that combines natural language processing (NLP) with behavioral analytics to identify customer intents such as billing inquiries, technical support, service upgrades, and potential churn. The framework processes structured and unstructured data from customer interactions, including call center transcripts, chatbot conversations, CRM data, and network usage logs. By leveraging state-of-the-art models like BERT and BiLSTM, the system achieves high precision in multi-label classification tasks. The proposed approach demonstrates that accurate intent prediction allows telecom operators to proactively resolve issues, personalize customer engagement, and implement timely retention strategies, thus providing a significant competitive advantage.
Downloads
References
Devriendt et al., "Why you should stop predicting customer churn and start using uplift models," Information Sciences, 2021.
Ullah et al., "A churn prediction model using random forest," IEEE Access, 2019.
Amin et al., "Customer churn prediction in the telecommunication sector using a rough set approach," Neurocomputing, 2017.
Vaswani et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems, 2017.
Howard and Ruder, “Universal Language Model Fine-tuning for Text Classification,” ACL, 2018.
Goyal, M.K. and Chaturvedi, R., 2022. The Role of NoSQL in Microservices Architecture: Enabling Scalability and Data Independence. European Journal of Advances in Engineering and Technology, 9(6), pp.87-95.
Downloads
Published
How to Cite
Issue
Section
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.
