In this project, I conducted a detailed analysis of customer churn using Microsoft Excel, with the goal of uncovering the primary factors driving customer attrition in a telecommunications company. Customer churn is a critical metric for service-based businesses, and understanding its underlying causes can significantly improve customer retention and revenue stability.
The main objective of this analysis was to identify key patterns and attributes associated with customer churn. By exploring demographic, behavioral, and service-related variables, the goal was to provide actionable insights into which customer segments were most likely to leave—and why.
To perform the analysis, I leveraged a variety of Excel functions and techniques, including:
Pivot Tables to group, filter, and summarize customer data across multiple dimensions such as age group, data usage, and contract type.
IF Statements and Conditional Formatting to flag high-risk customers and visually distinguish important trends.
Data Visualization through bar charts and comparison plots to present churn rates by demographic and behavioral factors.
Several notable insights emerged from the analysis:
Competitor Advantage Was the Primary Churn Driver: The most frequently cited reason for churn was customers leaving for competitors with superior products or service offerings.
Seniors Churned at a Disproportionately High Rate: Among all age segments, senior customers had the highest churn rate—over 38%—compared to just 22% among younger customers under 30.
Moderate Data Plan Users (5–10 GB) Were Most Likely to Leave: Customers with moderate data usage plans were found to churn more frequently than both low and high data users, indicating dissatisfaction within this pricing tier.
International Plan Customers Were at Greater Risk: Customers with international plans also showed a slightly higher churn rate, possibly due to cost or plan complexity.
Geographic Differences Were Present: Certain states exhibited significantly higher churn rates, with California and Indiana being among the most affected.
These insights can guide targeted retention strategies. For instance:
Tailored Plans for Seniors: Developing simplified, value-based plans for older customers could help reduce churn in this segment.
Competitive Benchmarking: Addressing service gaps identified through competitor analysis may help retain customers lost due to better offers elsewhere.
Re-evaluation of Data Plans: Adjusting the pricing or benefits associated with 5–10 GB data plans may curb dissatisfaction and reduce attrition.
This project demonstrates the power of Excel as a tool for uncovering deep business insights through accessible, yet robust, analytical methods. By identifying which customer segments are most vulnerable to churn and why, companies can take proactive steps to retain their customer base and improve long-term profitability.