This repository contains a comprehensive and live dashboard for analyzing customer churn at a telecom company. The analysis includes data cleaning, visualization, and insights to identify key patterns in service usage across different regions and times.
- Missing Values: Identified and handled missing values to ensure data completeness.
- Outliers: Detected and treated outliers to maintain data integrity.
- Normalization: Standardized data to bring all variables to a common scale.
- Categorical Encoding: Converted categorical variables into numerical values for analysis.
- Exploratory Data Analysis (EDA): Conducted EDA to understand the distribution and relationships within the data.
- Feature Engineering: Created new features to enhance the predictive power of the model.
- Visualization: Used various charts and graphs to visualize data patterns and trends.
- Total Calls: Pie chart representing the percentage distribution of calls made by customers who have churned versus those who haven't.
- Total Minutes: Pie chart showing the distribution of minutes used by churned and retained customers.
- Total Charge: Pie chart depicting the revenue impact through charges from both segments.
- Churn Over Time: Line graph presenting how customer churn percentage fluctuates over time.
- Count of International Plan by State: Bar graph displaying the adoption rate of international plans across states.
- Voice Mail Plan Subscription: Comparison bar graph indicating voicemail plan subscription rates among customers.
A dedicated area providing strategic recommendations based on data analysis aimed at reducing customer turnover rates. Key insights include:
- Focus on high churn states like AK with targeted retention campaigns.
- Improve services and offers for daytime usage to meet customer expectations.
- Enhance customer service to address issues proactively and reduce churn.
- Develop solutions based on feedback to address specific account lengths and reduce churn rates.
The dashboard serves as an analytical tool for telecom companies looking to reduce their customer attrition rate through targeted strategies informed by data-driven insights.