Churn Rate Dashboard: Customer Retention Insights

OBJECTIVE: Design a dashboard to help stakeholders analyze customer churn patterns by contract type, payment method, tenure, and pricing structure. The goal is to surface actionable insights that can inform retention strategies and pricing models.

BACKGROUND: The telecom company needed to understand why customers were leaving and how churn varied across customer segments. Key focus areas included:

1. Do certain contract types experience higher churn?

2. Does payment method influence churn behavior?

3. Are new users more likely to churn than long-term users?

4. How do pricing and billing trends relate to churn?

The dashboard was built to allow non-technical users to quickly explore these questions using interactive filters and segmented visualizations.

KEY QUESTIONS:

1. Which contract types have the highest churn rates?

2. Are customers on auto-pay (e.g., bank transfer or credit card) less likely to churn?

3. How does churn behavior change as tenure increases?

4. Is there a correlation between monthly charges and total lifetime charges among churned customers?

TECH STACK:

1. Microsoft Excel: Used only to preview and inspect the structure of the raw dataset

2. Tableau Desktop: Interactive dashboard development with calculated fields, binning parameters, and visual filtering

3. Churn Logic: Derived churn counts across different contract types, payment methods, and tenure groups

4. Data Transformation Techniques: Created tenure bins using parameters, applied conditional logic to count churn cases, and used scatter plots to examine relationships between lifetime and monthly charges

PROCESS:

1. Data Structuring in Tableau:
   ◇ Created custom bins using [Tenure (bin)] and a user-controlled [Tenure (bin) Parameter] to analyze churn distribution across time
   ◇ Built a scatter plot to explore correlations between monthly and total charges for churned users
   ◇ Aggregated metrics by daily, hourly, and monthly frequency

   ◇ Linked churn fields to visual filters to enable dynamic exploration by customer segment

2. Dashboard Design in Tableau:
   ◇ Organized the dashboard into three main sections:
   1. Churn by Payment Method (summary cards)
   2. Churn by Contract Type (stacked bar chart)
   3. Churn by Tenure (bar chart with binning)
   ◇ Included an interactive scatter plot to assess how billing amounts relate to churn likelihood
   ◇ Presented written insights and actionable recommendations next to visualizations for immediate stakeholder reference

KEY FINDINGS:

1. Churn Patterns:

   • Churn by Payment Method (summary cards)

   • Churn by Contract Type (stacked bar chart)

   • Churn by Tenure (bar chart with binning)

2. Tenure Trends:

   • New users (0–10 months) had the highest churn rates

   • Churn consistently decreased as tenure increased, indicating value in retaining customers long-term

3. Billing Insights:
   ◇ Technical Support was the most common issue, followed by Admin and Payment

   • Churn was more likely when monthly charges were high among newer customers

   • Long-term customers showed greater tolerance for price increases over time

RECOMMENDATIONS:

1. Offer discounted rates for new users to reduce early churn

2. Promote auto-pay options (bank transfer, credit card) to improve retention

3. Encourage long-term contracts through loyalty incentives or discounts

4. Consider pricing models that gradually increase over time for long-tenure customers

CHALLENGES & SOLUTIONS:

⚠️ Challenge 1: Identifying churn risk without time-series data
✅ Solution: Used tenure and billing information to model risk over customer lifecycle using custom bins and filters

⚠️ Challenge 2: Understanding correlation between pricing and churn
✅ Solution: Created a scatter plot comparing monthly and total charges to highlight trends in churned accounts

⚠️ Challenge 3: Making churn insights actionable for business teams
✅ Solution: Added a summary section with visual insights and strategic recommendations embedded directly in the dashboard

DATA SOURCES:

• Dataset: Telco-Customer-Churn.csv

• Source: Internal telecom customer churn records

DATA DICTIONARY:

• Contract: Customer’s contract term (Month-to-month, One year, or Two year)

• Total Charges: Total amount charged to the customer over their lifetime

• Churn: Indicates whether the customer discontinued service with the company (Yes = customer left, No = customer remained active)

VIEW THE LIVE DASHBOARD:

Want to explore all the visualizations interactively?

👉 Click here to view the full Tableau dashboard — built in Tableau Desktop to help telecom analysts and business managers explore churn by tenure, contract type, and pricing patterns.

BEHIND THE DASHBOARD:

Curious how the dashboard was built?

👉 View the Tableau workbook (.twb) and underlying code logic — includes all parameters, churn segmentation fields, and binning techniques used in the analysis.