A data-driven project focused on analyzing customer churn in the telecom industry and uncovering insights that can help reduce churn, improve customer experience, and support strategic decision-making.
Customer churn is one of the most critical challenges in the telecom industry, directly impacting revenue and long-term growth.
Telecommunications ChurnQuest explores patterns and factors that contribute to customer churn using real customer data. The project analyzes customer behavior and builds a predictive model to identify customers at risk of churning, uncover the key drivers of churn, and provide actionable insights for retention strategies. The project includes
- Data cleaning
- Exploratory data analysis
- Data analysis & feature engineering
- Predictive modeling (Logistic Regression)
- Business-driven insights
- The creation of a churn analysis dashboard.
The goal is to help telecom companies identify at-risk customers early and take proactive steps to increase customer retention
- Identify customers likely to churn
- Understand the key factors driving churn
- Quantify potential revenue loss
- Enable proactive customer retention strategies
The dataset includes customer-level telecom data such as:
- Account information (account length, area code)
- Service usage (day, evening, night, international minutes)
- Billing information (charges across usage types)
- Customer behavior (customer service calls, voicemail usage)
- Target variable: Churn (1 = Yes, 0 = No)
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Cleaned the dataset and handled missing values
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Encoded categorical variables
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Created meaningful features:
- Total charges
- Total minutes
- Service call groups
- Customer risk segmentation
Key patterns identified:
- Customers with frequent service calls show significantly higher churn
- High-usage and high-paying customers are more likely to churn
- Customers with international plans have higher churn rates
- Customers with voicemail plans tend to be more retained
A Logistic Regression model was built to predict churn probability.
- Accuracy: 83%
- ROC-AUC: 0.79
- Recall (Churn): Improved from 21% → 42% → 87% (via threshold tuning)
Instead of using the default 0.5 threshold:
- Lower thresholds significantly improved churn detection
- Optimal range: 0.2 – 0.3
This increased the model’s ability to identify at-risk customers, enabling earlier intervention.
- Customer service interactions are the strongest predictor of churn
- High-value customers are at higher risk of leaving
- International plan users churn more frequently
- Customer dissatisfaction drives churn more than usage alone
Provides a high-level summary of churn and its causes:
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Churn rate and retention rate
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Revenue at risk
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Churn distribution
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Key drivers:
- Customer service calls
- Usage patterns
- Pricing and plans
Focuses on actionable insights:
- Identification of high-risk customers
- High-value vs high-risk segmentation
- Customer-level risk analysis
- Prioritization for retention efforts
- Prioritize customers with frequent service complaints
- Focus retention efforts on high-value, high-risk customers
- Improve customer support experience to reduce dissatisfaction
- Re-evaluate pricing and value of international plans
- Use predictive scores to trigger proactive retention campaigns
- Python (Pandas, NumPy, Scikit-learn)
- Power BI (Data visualization & dashboarding)
- Jupyter Notebook
- DAX (for calculated metrics and KPIs)
This project demonstrates how data analytics can:
- Reduce customer churn through early detection
- Protect revenue by targeting high-risk customers
- Enable data-driven decision-making
- Bridge the gap between data science and business strategy
This project goes beyond predictive modeling by translating data into actionable business insights and strategic recommendations, making it highly relevant for real-world telecom operations.
