International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 0 | Views: 63

Informative Article | Engineering Science | India | Volume 9 Issue 9, September 2020 | Rating: 4.3 / 10


Improving Customer Service with Data-Driven Models: A Telecommunications Case Study

Arun Chandramouli [4]


Abstract: In the highly competitive telecommunications industry, providing exceptional customer service is crucial for customer retention and satisfaction. This case study explores how a leading telecommunications provider leveraged data-driven models to enhance its customer service operations, focusing on reducing call-in rates and improving chatbot performance. By implementing a K-Means Clustering Model to profile customers and optimize chatbot responses, the company achieved a 20% reduction in overall call-in rates and increased the percentage of customers getting self-serviced by the chatbot by 10%. Additionally, the company streamlined data management and reporting processes using SQL, enabling the identification of customer behaviours and the monitoring of key metrics such as chat deflection and call-in rate. This study demonstrates the potential of data-driven approaches to revolutionize customer service in the telecommunications sector.


Keywords: data-driven models, customer service, telecommunications, K-Means Clustering, customer profiling, chatbot optimization, SQL, data management, reporting automation, customer segmentation, behaviour analysis, personalization, self-service, call-in rates, customer satisfaction, loyalty, data analytics, machine learning, ethical implications, data privacy, business success


Edition: Volume 9 Issue 9, September 2020,


Pages: 1620 - 1627


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