🔗 View Dashboard on Tableau Public
This project presents an Exploratory Data Analysis (EDA) of customer churn patterns in the telecommunications industry using Tableau. The dashboard visually explores the key drivers behind customer churn, focusing on contract type, tenure, and monthly charges to uncover actionable insights for reducing churn and increasing customer retention.
Goal: Understand what factors most strongly influence churn and recommend data-driven strategies to reduce it.
- Are new or long-term customers more likely to churn?
- How does contract length affect churn rate?
- Is churn more common among customers with higher monthly charges?
- What pricing and contract strategies could reduce churn?
-
Tenure & Churn: Customers with 0–3 years of tenure have the highest churn rate. Retention strategies should focus on newer clients who are most at risk of leaving.
-
Contract Type & Churn: Month-to-month customers churn significantly more than those with one-year or two-year contracts.
Recommendation:
- Encourage customers to commit to longer-term contracts to stabilize retention. -
Monthly Charges & Churn: A large share of churn comes from customers with higher monthly charges.
Recommendation:
- Offer lower introductory pricing to attract new customers.
- Gradually increase rates over time for longer-tenured clients, who are less likely to churn and more likely to perceive value.
| Feature | Description |
|---|---|
| 📊 Dashboard Tool | Tableau |
| 🔍 Analysis Type | Exploratory Data Analysis (EDA) |
| 🧾 Metrics Used | Churn Rate, Tenure, Monthly Charges, Contract Type |
| 📈 Visuals Included | Tenure bins, churn by contract, churn by charges |
| 🎯 Focus Area | Customer Retention & Subscription Strategy |
- Tableau Desktop
- Data Binning & Calculated Fields
- Filters & Parameter Controls
- Business Insight Generation
- Subscription Modeling
- Customer Segmentation
- Include demographics (age, location, seniority) if available to deepen segmentation.
- Build a churn prediction model in Python using logistic regression or decision trees.
- Run A/B testing on new pricing or contract strategies to measure impact on churn.