This project focuses on analyzing call-center data to extract insights regarding performance, efficiency, and customer satisfaction. By leveraging various data analysis techniques, we aim to provide recommendations for improving call-center operations.
- Analyze call-center performance metrics.
- Identify trends and patterns in customer interactions.
- Determine factors affecting customer satisfaction and call resolution.
- Suggest strategies for improving overall call-center efficiency.
The dataset used in this project contains information on:
- Call durations
- Call resolution status
- Agent performance
- Customer feedback
- Programming Language: Python
- Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
- Descriptive Analysis: Summary statistics of call-center performance metrics.
- Correlation Analysis: Correlations between agent performance and call resolution rates.
- Customer Satisfaction: Analyzing customer feedback to identify satisfaction trends.
- Predictive Modeling: Developing a model to predict call resolution based on initial call metrics.
- Average call duration was approximately X minutes.
- Agents with higher average resolution rates tended to handle fewer calls per hour.
- Customer satisfaction was closely linked with the call resolution time and agent behavior.
Through this analysis, we found several factors impacting both call-center performance and customer satisfaction. Recommendations have been made to improve key areas like agent training, call handling efficiency, and enhancing the feedback collection process.
- Implement real-time dashboards for call-center monitoring.
- Explore advanced machine learning techniques for predictive analysis.
- Perform sentiment analysis on customer feedback for deeper insights.
- Clone the repository.
- Install the required libraries:
pip install -r requirements.txt