Customer Churn Report

Introduction

Customer churn is a critical metric for businesses aiming to retain their customer base and maintain profitability. This project explores churn behavior using a secondary dataset, transforming raw data into actionable insights through visualizations and KPIs.

Problem Statement

The goal is to identify patterns and drivers of customer churn in a subscription-based business. By analyzing customer demographics, product usage, and satisfaction metrics, we aim to uncover key factors influencing churn and recommend strategies to improve retention.

Skills Demonstrated

• Excel functions like Index and Match, Vlookup, REPL()
• Powerquery
• Powerpivot
• Slicers
• Pae Navigation
• Data cleaning and preprocessing
• KPI development and dashboard design
• Insight generation and storytelling

Data Sourcing

• Source Type: Secondary
• Origin: Public dataset simulating customer behavior in a subscription service
• Fields Included: Customer ID, Age, Tenure, Product usage, Card type, Revenue, Satisfaction score, Complaints

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Data Transformation

• Cleaning: Removed duplicates, handled missing values
• Feature Engineering: Created churn flag, grouped customers by card/product type
• Aggregation: Calculated KPIs such as churn rate, average satisfaction, and revenue per segment
• Normalization: Standardized numerical fields for comparison

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Visualization

The Report consist of 2 pages;

Demographic Page

Deeper Insights page

KPI Analysis & Insights

1. Total Customers

• Value: 10,000
• Insight: Represents the full customer base under analysis.

2. Retained Customers

• Value: 7,962
• Retention Rate: 79.62%
• Insight: High retention suggests overall customer satisfaction, but churn still affects ~20% of the base.

3. Churned Customers

• Value: 2,038
• Churn Rate: 20.38%
• Insight: Indicates a need to investigate churn drivers such as dissatisfaction or product mismatch.

4. Average Satisfaction

• Score: 3.01 / 5
• Insight: Below-average satisfaction may correlate with churn. Improving service quality could reduce churn.

5. Average Age

• Value: 45 years
• Insight: Middle-aged customers dominate the base. Tailoring services to this demographic may improve retention.

6. Average Tenure

• Value: 5.01 years
• Insight: Long tenure suggests loyalty, but churn among long-term users could signal deeper issues.

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Visual Analysis

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🔹 Churn by Revenue

• Observation: Higher revenue segments show lower churn.
• Action: Premium customers are more loyal—consider upselling strategies.

🔹 Churn by Product

• Observation: Certain products have higher churn rates.
• Action: Reevaluate product value propositions and customer fit.

🔹 Churn by Card Type

• Observation: Card type influences churn behavior.
• Action: Analyze benefits and usage patterns per card type.

🔹 Complaints vs. Churn

• Observation: Customers with complaints have significantly higher churn.
• Action: Enhance complaint resolution processes to retain dissatisfied customers.

🔹 Satisfaction vs. Churn

• Observation: Lower satisfaction scores correlate with churn.
• Action: Prioritize customer experience improvements.

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Extended KPI Analysis & Insights

🔹 Churned Customers by Gender

• Observation: Female customers show a slightly higher churn rate than male customers.
• Insight: Gender-specific engagement strategies may help reduce churn.

🔹 Churn by Location

• Observation: Churn rates vary significantly by region. South East and South West show higher churn.
• Insight: Regional churn patterns suggest localized issues—consider region-specific retention campaigns.

🔹 Churn by Credit Score

• Observation: Customers with lower credit scores tend to churn more.
• Insight: Financial stress may contribute to churn. Offering flexible payment plans could help.

🔹 Churn by Age and Age Group.

• Observation: Younger customers (under 30) show higher churn. Also, age group 30–40 has the highest churn volume
• Insight: Younger demographics may be more price-sensitive or less loyal. Tailored onboarding and incentives could improve retention.This group may be balancing financial and lifestyle changes—targeted support could reduce churn.

Conclusion

This analysis reveals that churn is influenced by satisfaction, complaints, product type, revenue tier, demographics, and geography. A segmented approach is essential to address the diverse needs of the customer base.

Recommendations

• Launch targeted retention campaigns for high-churn segments
• Improve complaint handling and customer support
• Enhance product offerings for low-satisfaction groups
• Monitor KPIs regularly to track churn trends
• Develop gender-specific loyalty programs
• Launch regional retention initiatives in high-churn areas
• Provide financial wellness tools for low credit score customers
• Create youth-focused engagement campaigns
• Monitor churn by age group to adapt lifecycle marketing