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Introduction
Overview
This case study focuses on analyzing customer churn for Databel - a telecom company, leveraging Power BI to uncover actionable insights.
The goal is to identify trends and key factors contributing to churn, enabling data-driven strategies to enhance customer retention.
By employing a structured data analytics workflow, the study transforms raw data into comprehensive insights, visualizations, and recommendations.
What is Churn?
Churn refers to the rate at which customers stop doing business with a company over a given period. It is a critical metric for subscription-based businesses as it directly affects recurring revenue.
High churn rates can signal dissatisfaction, competitive pressure, or mismatches between customer expectations and service offerings.By analyzing churn, businesses can identify underlying issues, predict future risks, and implement targeted interventions to retain customers.
Churn Rate = Customers Lost / Total Number of Customers
Problem Statement
Customer churn is a significant challenge for businesses, impacting revenue and growth. Understanding why customers leave and identifying at-risk segments is critical for proactive retention efforts. For Databel, addressing churn effectively can lead to improved customer satisfaction, loyalty, and profitability.
The primary objectives of this analysis are:
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To identify patterns and trends associated with customer churn.
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To understand key drivers influencing customers' decisions to leave.
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To provide actionable insights for reducing churn and enhancing retention.
Data Analysis Process
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Perform a Data Check
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Asking business questions and exploring the data
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Discovering insights & visualizing them
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Building a dashboard/story to cohesively share information
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Walkthrough of results with stakeholders.
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Dataset Description
Data Source
Data Fields
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Databel, a fictitious Telecom provider
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One data table with 29 columns
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One row per customers
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Snapshot of the database at a specific moment in time
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Data Check
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Checking for duplicate or missing values
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Sense check with other resources
Overview
The first step in a Data Analysis process is a Data Check. Most of the Data in this project dataset is clean.
We begin by creating two measures - Number of Customers & Churn Rate.
Churn Label is changed from a "Yes/No" to 0/1. This is useful in calculating Churn Rate.
A Churn Rate around 27% is a key insight we will use in our analysis.
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Explore Data
Analyze & Visualize Data
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Asking the right questions
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Choosing the right visualization to convey a message
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Creating initial visualizations
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Perform more advanced analysis - dig deeper
Why do Customers churn?
The top three reasons are:
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Competitor made better offer
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Competitor had better devices
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Attitude of support person
What do Churn Categories tell us?
Churn Reasons are grouped together in Churn Categories
Almost half of all customers churning are related to the Competitor category! (~45%)
Is Databel competitive enough?
How does Churn Rate vary by State?
The churn rate of 63% in California seems to be the highest, something to explore into further.
Analyzing Demographics
Do Age Groups tell a story?
We create a new measure Demographics and split customers into 3 Age Groups.
The churn rate for Seniors is 10% above the average.
It might be a good idea to analyze the customer age in general with Age Bins.
The age bracket of 45 had the highest number of customers.
Are discounted household groups performing better?
Since Databel offers group contracts to households at discounted rates it's worth exploring if this has an impact on Churn Rate.
Monthly Charge is significantly lower of groups.
Which Contract category has higher churn?
Observing the difference between customers who have yearly vs monthly contracts.
Monthly contracts churn more than the customers who have yearly based contracts.
Are people not on an Unlimited Data Plan more likely to Churn?
Those on unlimited plans appear more likely to churn.
The churn rate for those on less than 5GB had a drastic difference in churn.
How about International usage and its relation to Churn?
Churn rate for customers who pay for an international plan but don't call internationally was high.
Proposing a cheaper plan and explaining the rationale will increase customer satisfaction and could stop churning.
Does churn depend on months with provider?
Churn rate does decrease with time that a customer has been with Databel.
Further exploring Payment methods.
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Dashboarding
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Combine results into one or more Dashboards
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Sense check with other resources
Overview
Age Groups
Payments & Contracts
Extra Charges
Insights
Conclusion
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Competitive offers and customer service experiences are primary drivers of churn.
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Nearly 50% of customers who left cited competitor-related reasons, such as better offers or superior devices. Customer service interactions significantly impact churn, with poor experiences leading to higher attrition.
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Senior customers exhibit a higher churn rate, approximately 10% above average, necessitating targeted retention strategies.
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Certain regions, such as California, show higher churn rates, indicating the need for localized customer engagement efforts.
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Monthly contract customers are more likely to churn compared to those on annual contracts, presenting an opportunity to promote long-term commitments through incentives.
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Churn rate does decrease with time that a customer has been with Databel.
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Insights emphasize the importance of enhancing competitive positioning, improving customer service interactions, and tailoring retention strategies to specific customer segments to reduce churn and foster loyalty.
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Future Steps
Competitive Positioning
Conduct market analysis to benchmark pricing, device offerings, and service plans against competitors. Introduce exclusive loyalty programs or personalized discounts to retain customers.
Localized Engagement Strategies
Launch region-specific initiatives in high-churn areas like California, including localized promotions, community engagement events, and dedicated regional support teams.
Senior Customer Retention
Develop tailored retention plans for senior customers, such as personalized assistance programs, exclusive discounts, and simplified support services.
Contract Optimization
Encourage customers on monthly plans to switch to annual contracts through incentives such as discounted rates, added benefits, or early renewal perks.
Loyalty Building
Implement long-term engagement programs that reward customer tenure, such as anniversary discounts, referral incentives, and enhanced support for long-standing customers.
Predictive Modeling
Sentiment Analysis
Industry Benchmarking
Implementation Roadmap