In this project, I will build dashboards that will Analyze telecommunication companies' data To determine the reasons for churning out from it!
The Customer Churn table contains information on all 7,043 customers from a Telecommunications company in California in Q2 2022 Each record represents one customer, and contains details about their demographics, location, tenure, subscription services, status for the quarter (joined, stayed, or churned), and more!
- question under analysis
- data acquisition
- data cleaning
- dashboards
- recommendations
- POWER BI
- EXCEL
In this Power BI project, I conducted an in-depth analysis of customer churn for a telecom company. The analysis provides insights into customer behavior, demographic patterns, and factors that contribute to customer churn. These insights are vital for businesses to implement targeted strategies for customer retention, reducing churn, and improving customer satisfaction.
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Customer Demographics Analysis:
- Gender and Marital Status: The gender split is almost even, and slightly more single customers churn compared to married customers. This insight helps the business understand whether marketing strategies or services should be tailored differently for single customers.
- Dependents: Customers without dependents make up a larger portion of the customer base, and they tend to have month-to-month contracts. The business can offer family-friendly plans or incentives for customers with dependents to enhance retention in this segment.
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Age Group Impact:
- Highest Churn Risk Age Groups: The analysis showed that customers between the ages of 35-50 and 50-60 are the largest segments. However, churn is higher among younger customers (18-25), with significant differences in average monthly charges. Businesses could create customized offers or loyalty programs targeting specific age groups to reduce churn.
- GB Downloading by Age: Younger customers download more data on average. Offering them data-heavy plans could help keep them from switching to competitors.
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Average Monthly Charges:
- By Number of Dependents: Customers with dependents have higher average monthly charges, but they also have a higher likelihood of staying with the company. Offering these customers better loyalty perks could reduce churn.
- By Age: Customers aged 18-25 pay more but churn at higher rates. Adjusting pricing strategies or improving customer service for this group might help reduce churn rates.
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Contract Type Influence:
- Contract Duration: A significant portion of churned customers had month-to-month contracts, while those with longer contracts (one or two years) are less likely to churn. This insight helps the company focus on promoting long-term contracts with better incentives, reducing the likelihood of churn.
- Dependents and Contracts: Customers with dependents are more likely to commit to longer contracts. The business can consider targeted retention efforts toward these customers with value-added services or promotions.
- Targeted Marketing Campaigns: By understanding the demographics of customers most likely to churn, businesses can tailor marketing campaigns to specific segments, such as young, single customers or those without dependents.
- Improved Customer Retention: Offering discounts or improved services to customers with high monthly charges and dependents can increase customer loyalty and reduce churn.
- Contract Strategy Optimization: Insights on contract types can guide businesses in offering better incentives for long-term contracts, especially to high-risk groups like younger customers and those on month-to-month plans.
- Revenue Growth: By addressing high-churn age groups and providing attractive plans based on usage patterns (e.g., data-heavy plans for younger customers), businesses can reduce churn and boost revenue.