Welcome to my detailed churn analysis for an example telecom company, Databel. Using Power BI, I investigated various dimensions of churn, from reasons and categories to demographic and geographic influences. This project is a comprehensive deep dive into the different factors contributing to customer churn and provides insightful findings to mitigate the problem.
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Data Preprocessing: The dataset was preprocessed, including cleaning, formatting, and handling missing values.
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Time and Churn Calculation: Performed time-series analysis and calculated the churn rate over a specific period.
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Churn Reasons Investigation: Identified the main reasons behind customer churn, providing insights into possible problem areas.
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Churn Categories Analysis: Explored different categories of churn, presenting a segmented view of churn trends.
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Geographic Visualization: Leveraged map-based visualizations to understand geographical influences on churn rates.
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Demographic Analysis: Analyzed churn rate across different demographics, understanding how factors like age, account length, payment method, and contract type influence churn.
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Grouped Consumption and Unlimited Data Plan Analysis: Investigated how consumption patterns and data plans relate to churn rate.
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State-wise Churn Rate Analysis: Presented a detailed visualization of churn rate by state, revealing location-based patterns.
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Churn Rate by Account Length and Payment Method: Studied the churn rate in relation to the account length and payment methods, providing insights into customer loyalty and payment convenience.
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Churn Rate by Account Length and Contract Type: Explored how account length and contract type interplay in influencing churn rate.
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Churn Rate by Age Group: Investigated how age groups impact the churn rate, providing an insight into age-specific preferences and loyalty.
This project focuses on understanding and reducing customer churn for telecom companies. Customer churn is a significant issue in the telecom industry, causing loss of revenue and increased acquisition costs. By investigating churn from various dimensions - including demographic, geographic, and behavioral factors - we can gain a clearer understanding of why customers leave and what steps can be taken to retain them. This project offers insights and recommendations that can be applied to reduce customer churn and improve customer retention strategies.
Throughout this project, I have tried to weave a narrative around customer churn at Databel, beginning with an exploration of the raw data and culminating in a deep understanding of the key reasons behind churn. Through the lenses of geography, demographics, and consumption patterns, we follow the customer's journey, identify potential churn points, and propose data-informed strategies to improve customer retention. This project serves as a testament to the power of data analysis in transforming raw data into actionable business insights.
Feel free to explore the repository, use the findings, and get in touch if you have any questions, suggestions, or feedback.
