Comprehensive analysis of customer churn patterns and recommendations to improve retention.
This analysis examines customer churn patterns in a telecommunications company's dataset, identifying key factors that influence customer decisions to leave the service.
The dataset includes 7,043 customers with 33 different attributes covering demographics, service subscriptions, billing information, and churn status.
Examining dataset structure, handling missing values, and converting data types
Creating informative charts to visualize patterns and relationships
Identifying key factors and patterns affecting customer churn
Providing actionable strategies to reduce churn rate
Understanding the telco customer dataset structure and characteristics.
| Feature | Description | Data Type |
|---|---|---|
| CustomerID | Unique identifier for each customer | String |
| Gender | Customer gender (Male/Female) | Categorical |
| Senior Citizen | Whether the customer is a senior citizen (1) or not (0) | Binary |
| Partner | Whether the customer has a partner (Yes/No) | Categorical |
| Dependents | Whether the customer has dependents (Yes/No) | Categorical |
| Tenure Months | Number of months the customer has stayed with the company | Numerical |
| Phone Service | Whether the customer has phone service (Yes/No) | Categorical |
| Multiple Lines | Whether the customer has multiple lines (Yes/No/No phone service) | Categorical |
| Internet Service | Customer's internet service provider (DSL/Fiber optic/No) | Categorical |
| Contract | Contract term (Month-to-month/One year/Two year) | Categorical |
| Monthly Charges | Amount charged to the customer monthly | Numerical |
| Churn Label | Whether the customer churned (Yes/No) | Binary |
| Metric | Tenure Months | Monthly Charges | Total Charges |
|---|---|---|---|
| Count | 7,043 | 7,043 | 7,043 |
| Mean | 32.37 | $64.76 | $2,283.30 |
| Std | 24.56 | $30.09 | $2,266.77 |
| Min | 0 | $18.25 | $18.85 |
| 25% | 9 | $35.50 | $401.45 |
| 50% | 29 | $70.35 | $1,397.47 |
| 75% | 55 | $89.85 | $3,794.74 |
| Max | 72 | $118.75 | $8,684.80 |
Explore the relationships between different variables and their impact on customer churn.
This scatter plot shows the relationship between monthly charges and customer tenure, colored by churn status. Note how customers with higher monthly charges and lower tenure are more likely to churn.
This bar chart shows the churn rate by contract type. Month-to-month contracts have a significantly higher churn rate (42.7%) compared to one-year (11.3%) and two-year contracts (2.8%).
This chart shows the churn rate by internet service type. Fiber optic internet service customers have a much higher churn rate (41.9%) compared to DSL (19.0%) and customers with no internet service (7.4%).
This visualization shows churn rates across different demographic segments. Customers without partners or dependents have significantly higher churn rates, while senior citizens show a slightly elevated churn rate.
This bar chart shows the top reasons customers cite for leaving the service. Competitor-related reasons and support issues are prominent factors in customer churn decisions.
This chart shows churn rates by different payment methods. Customers using electronic checks have a much higher churn rate (45.3%) compared to other payment methods.
Critical factors and patterns affecting customer churn in the telecommunications company.
Insight: Month-to-month contracts have significantly higher churn rates compared to one-year and two-year contracts.
Insight: New customers have the highest churn rate. Churn rate decreases significantly as tenure increases.
Insight: Customers using electronic checks have a much higher churn rate compared to other payment methods.
Insight: Fiber optic customers have a higher churn rate despite the premium service. Customers without online security show higher churn rates.
Insight: Customers without partners or dependents are more likely to churn. Higher monthly charges correlate with increased churn rates.
Insight: The top reasons for customer churn include attitude of support personnel (10.3%), competitor offerings with higher download speeds (10.1%), and more data from competitors (8.7%). Customer service quality and competitive offerings are critical factors driving churn.
Strategic approaches to reduce customer churn based on data analysis insights.
Incentivize month-to-month customers to switch to longer-term contracts through targeted promotions, discounts, or value-added services.
Develop a specialized onboarding program for new customers to improve their first-year experience. Implement a proactive check-in system at 3, 6, and 9 months.
Create compelling security and support bundles for fiber optic customers. Offer free trials of online security, tech support, and device protection to demonstrate value.
Encourage electronic check users to switch to automatic payment methods through incentives like one-time discounts or service upgrades.
Review pricing strategy for high monthly charge customers, especially those with fiber optic service. Implement a 'price match guarantee' for customers who receive competitive offers.
Invest in technical support training to improve customer service quality, addressing the attitude-related churn reasons identified in the analysis.
Develop a predictive churn model using the identified risk factors to proactively identify customers at high risk of churning.
Implement personalized retention strategies based on customer segments (e.g., high-value fiber customers, seniors, new customers).
Strategic timeline for implementing recommended changes to reduce customer churn.
Immediately address competitive offers with specialized retention team
Create one-time offers to transition electronic check users to automatic payments
Begin proactive outreach to customers at 3, 6, and 9-month milestones
Launch improved customer service training focusing on attitude and quality
Create targeted communications for high-risk segments
Offer 3-month free trials of online security and tech support to fiber customers
Launch comprehensive program to move month-to-month customers to longer contracts
Develop and roll out specialized packages for senior customers
Launch optimized service bundles for fiber optic customers
Start competitive benchmarking and price sensitivity analysis
Create first predictive model for identifying high-risk customers
Begin comprehensive tracking of customer satisfaction metrics
Enhance service quality especially for fiber optic customers
Launch automated interventions based on churn prediction
Optimize all initiatives based on performance data
Integrate all retention initiatives into unified loyalty program
Create ongoing monitoring of competitor offerings and pricing
Deploy AI-driven microsegmentation for personalized retention
Target: Reduce from 27% to 20% in year 1
Target: Improve from 55-60% to 70%
Month-to-month to longer-term contracts
Particularly for technical support
Specific focus area
Targeted improvement for senior segment
Customers saved from competitive offers
Ensure retention doesn't significantly impact revenue
Information about the analysis approach, methodology, and tools used.
This analysis followed a structured data science approach to understand customer churn patterns and develop actionable recommendations: