This study explores customer churn in the telecom industry, revealing that older customers with lower credit scores are more prone to churn. We suggest a nuanced approach, considering psychographic segmentation and context-aware interventions for effective retention. Tailored strategies, rather than demographic generalizations, are crucial. Collaboration is invited to integrate predictive insights for a customer-centric retention framework, enhancing engagement and loyalty.
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Steps:
Data Exploration:
I analyze the "Churn-Modelling-Dataset" variables and conduct exploratory data analysis.
Correlation Analysis:
I explore linear relationships between variables, addressing challenges like outliers.
Logistic Regression Models:
I develop Full, Simple, and Interaction models, assessing assumptions and evaluating with AUC.
Results:
This bar plot shows the imbalance between churned and non-churned customers in the dataset, with a much higher number of non-churned customers. This imbalance needs to be addressed during modeling.
Gender: In the dataset, gender serves as a categorical variable distinguishing between male and female customers. The analysis indicates a slightly higher churn rate for females compared to males, suggesting a potential correlation between gender and customer churn.
Countries: The geographical variable includes customer locations in France, Germany, and Spain. Churn rates vary across these countries, with higher rates observed in France and Spain compared to Germany. This implies that geographic location plays a role in influencing customer churn behavior.
The age distribution in the project dataset is relatively normal, with a peak observed between 30-40 years old. This implies that a substantial portion of the customers falls within the middle-aged demographic. Understanding the age distribution is crucial for tailoring retention strategies and predicting churn, as different age groups may exhibit distinct patterns of behavior and preferences. Analyzing the distribution allows for targeted interventions and personalized services based on the specific needs and characteristics associated with different age brackets.
The Area Under Curve (AUC) values indicate the predictive performance of different models in distinguishing between customers who churn and those who don't. The Full Model excels with the highest AUC (0.7578), signifying superior discriminatory power. The Simple Model (AUC 0.7256) and Interaction Model (AUC 0.7258) also demonstrate reasonable capabilities in distinguishing churn, albeit slightly lower than the Full Model. Overall, these AUC values highlight the models' effectiveness in predicting customer churn, with the Full Model leading in discriminatory performance.
Conclusion:
In summary, the logistic regression analysis identified key predictors influencing telecom customer churn, including Age, CreditScore, Gender, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary, and Germany. The Full Model demonstrated exceptional predictive power, providing valuable insights for crafting targeted retention strategies. Prioritizing older customers, those with low credit scores, and higher account balances could effectively reduce churn. Despite some variables lacking statistical significance individually, the holistic approach acknowledges real-world complexities, empowering the telecom company to enhance customer satisfaction and implement impactful retention strategies.
If you would like to check the full project click here!
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