zarifshawon/customerchurnchronicals

In today's business landscape, nurturing customer relationships is crucial. This study explores churn complexities, unveiling causes and empowering effective responses. It develops predictive models favoring Random Forest over Logistic Regression, informing tailored retention strategies for enduring success.

CustomerChurnChronicals

In today's business landscape, nurturing customer relationships is crucial. This study explores churn complexities, unveiling causes and empowering effective responses. It develops predictive models favoring Random Forest over Logistic Regression, informing tailored retention strategies for enduring success.

About the project

In today's dynamic business environment, nurturing customer relationships is essential for lasting success. An ongoing challenge for organizations is customer churn—when customers discontinue using a company's offerings, impacting both revenue and customer loyalty. This study delves into the complexities of customer churn, seeking hidden patterns to inform strategic decision-making. The primary goal was to uncover the factors behind customer churn by examining the interplay between customer behavior, market trends, and business practices. Understanding these intricacies enables businesses to respond effectively and mitigate churn's adverse effects. Central to this research was the development of predictive models capable of anticipating churn. Leveraging historical customer data and contextual information, these models identify at-risk customers, allowing tailored retention strategies. I harnessed a comprehensive dataset from Kaggle, utilizing advanced data analysis and machine learning algorithms to uncover meaningful patterns. Results consistently favored the Random Forest Classifier over Logistic Regression in predicting churn, offering actionable insights. Key determinants of churn, including 'Total Spend,' 'Support Calls,' 'Payment Delay,' and 'Contract Length,' ‘Age’ were identified through feature importance analysis, guiding businesses in crafting effective retention strategies. In conclusion, this study highlights the power of predictive analytics in addressing customer churn. By understanding churn dynamics and proactively addressing its underlying factors, businesses can strengthen their competitive edge, foster loyalty, and pave the way for sustainable growth and enduring success.