Database Analytics and Performance on Data From Telecom Customer Service

This work focuses on the analysis and performance optimization of a telecom company’s database using indexing and related technologies. The dataset consists of records from 7,000 customers across various cities in California, capturing diverse customer behaviors, preferences, and service usage patterns. The analysis aims to identify key trends in customer churn, acquisition, and retention by examining demographic information, service details, and contract changes up to June 22, 2022. Additionally, the study explores how database indexing techniques improve query performance, enabling efficient data retrieval and enhancing decision-making. The findings provide insights into optimizing database operations while helping the company develop strategies to reduce churn and improve customer satisfaction.