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

Arun Chandramouli

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

How to Cite?: Arun Chandramouli, "Improving Customer Service with Data-Driven Models: A Telecommunications Case Study", Volume 9 Issue 9, September 2020, International Journal of Science and Research (IJSR), Pages: 1620-1627, https://www.ijsr.net/getabstract.php?paperid=SR24402121838, DOI: https://dx.doi.org/10.21275/SR24402121838

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