This project involves an in-depth analysis of customer churn within a telecom company, utilizing the Orange Telecom Churn Dataset from kaggle. The primary objective is to identify the key factors leading to customer churn and to develop a predictive model to accurately classify customers based on their likelihood to churn. The analysis and modeling efforts are supplemented with rich visualizations to provide actionable insights into customer behavior.
- Alteryx Designer Cloud: For data preparation, feature engineering, creating workflow.
- Alteryx AutoML Cloud: XGBoost-A machine learning algorithm used for the classification model in this project.
- Tableau Public: For creating dashboards and visualizations to analyze churn and present the model's findings.
The dataset used for this project includes 2666 rows (customers) and 20 columns (features) each. These attributes include customer demographics, account information, usage metrics, and a binary target variable indicating whether the customer has churned.
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Data Cleaning:
- Handled missing values and ensured consistency in data types across all attributes.
- Removed any obvious outliers that could skew the model's predictions.
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Feature Engineering:
- Derived Features: Created new features by normalizing usage metrics (e.g.,
DayChargePerMinute,EveChargePerMinute,NightChargePerMinute,IntChargePerMinute) to enhance the model's ability to predict churn.
- Derived Features: Created new features by normalizing usage metrics (e.g.,
- The dataset was split into two subsets using a 70-30 ratio using random sampling which can be seen through the workflow. Joins and unions as well as formulas and filters were used in order to achieve this:
- Training Set: Used for model training and cross-validation.
- Test Set: Reserved for final model evaluation and to assess its performance on unseen data.
Both the datasets can be found in Alteryx_folder.
- Overall Churn Rate: The dataset revealed that approximately 14.5% of customers had churned, indicating a relatively high churn rate.
- State-wise Churn Rate:
- Top States: Certain states exhibited significantly higher churn rates such as Texas, New Jersey, and California.
- Insights: States with higher churn rates often correlated with higher customer service call frequencies and larger customer bases, indicating potential service quality issues.
- Correlation: A strong positive correlation was observed between the number of customer service calls and the likelihood of churn. Customers who made more than 3 calls were significantly more likely to churn.
- Insight: This suggests that customers who are dissatisfied with service or facing recurring issues are more prone to churn, highlighting the importance of first-call resolution.
- Observation: Customers with an international plan had a higher churn rate compared to those without.
- Insight: This could indicate that the international plan may not be meeting customer expectations or that international callers are more price-sensitive and thus more likely to switch providers.
High Churn Rate Segments:
The highest churn rates (83.33% - 100%) are observed in:
- a) High Day Charge + High International Charge
- b) High Evening Charge + High Night Charge
- c) Medium Evening Charge + High Night Charge + High International Charge
Impact of Different Charge Types:
Day Charges: Strong influence on churn, with higher rates generally leading to higher churn
Evening Charges: Significant impact, especially when combined with high Night or International charges
Night Charges: Moderate impact, amplifies churn when combined with high charges in other categories
International Charges: Notable impact, especially in combination with high charges in other categories
Low Churn Rate Segments:
Low Day and Evening charges combined with Low or Medium Night and International charges show the lowest churn rates. Many segments with Low charges in multiple categories show 0% churn, indicating high retention
Charge Interactions:
High charges in any two categories seem to significantly increase churn risk. The combination of High Evening + High Night charges consistently shows high churn rates
All these Tableau workbooks are here and you can also check them on my Tableau Public
- Algorithm: Alteryx suggested XGBoost, a gradient boosting decision tree algorithm, was chosen for its robustness and high performance in tasks that are binary in nature.
- Training: The model was trained on the
trainingdataset which was created with Alteryx designer cloud.
The Alteryx AutoML provided a PPT which has the ROC curve, Confusion Matrix and the key features and its importance auto-generated by the model.
- ROC Curve: The ROC curve for the model indicated an area under the curve of 0.93, signifying a high true positive rate with minimal false positives.
- Confusion Matrix:
- True Positives: The model correctly identified 87% of the churned customers.
- False Positives: The model incorrectly labeled 5% of non-churned customers as churned, which is within an acceptable range for this type of predictive modeling.
- Feature Importance:
- Top Features: Total day charge, customer service calls, and international plan were among the top features influencing the model's predictions.
- Insights: These features highlight the importance of customer usage patterns and service satisfaction in predicting churn.
THE MODEL EVALUATION PART OF THE PROJECT WITH ITS ROC, CONFUSION MATRIX, AND FEATURE IMPORTANCE GRAPHS AS WELL AS INSIGHTS ARE GENERATED BY ALTERYX AUTOML.
Using the Alteryx AutoML evaluation and information gathered such as feature importance I did further analysis for the predicted data by the model in Tableau. Link to the predicted data
- Probability Distribution:
- The overlapping histogram with more data towards the edges indicates the probabilities predicted gave us definative results.
- High-Risk Segments: High-risk segments such as customer service calls and international plan from different states effects the median prediction probabilities of churn.
- Actual and predicted Churn: Analyzed actual and predicted churn which has less values in the middle indicating good modelling.
- Churn Analysis Dashboard:
- State-wise Churn Visualization: Interactive maps showing churn distribution across states, with filters to explore the impact of different features.
- Service Calls vs. Churn: A bar chart correlating the number of customer service calls with churn rates, providing clear visual evidence of the relationship.
- Charge Analysis: A heatmap showing the relationship between various charges (day, evening, night, international) and churn rates.
- International Plan Analysis: A comparative analysis of churn rates between customers with and without international plans with a pie chart.
For more interactive dashboard go to Link
- Prediction Dashboard:
- Probability Distribution: An overlapping histogram of churn prediction probabilities.
- Customer Segmentation: Clustered visualizations of customer segments based on their predicted churn risk, providing actionable insights for targeted retention strategies.
For more interactive dashboard goto Link
- High Churn in High Usage Customers: Customers with high day charges and frequent service calls are at a higher risk of churn, indicating dissatisfaction potentially due to billing or service issues.
- Regional Variations: Certain states with higher churn rates may require localized customer retention strategies.
- International Plan Sensitivity: Customers with international plans are more likely to churn, suggesting a need to reevaluate the pricing or quality of international services.
- Improve Customer Service: Focus on reducing the number of customer service calls by enhancing first-call resolution and proactively addressing common issues.
- Reassess International Plan Offerings: Consider revising international plans to make them more competitive and aligned with customer expectations.
- Targeted Retention Strategies: Use the model's predictions to identify high-risk customers and implement targeted retention campaigns, such as personalized offers or discounts for heavy day-time users.
The project successfully identified key drivers of customer churn and built a predictive model with strong performance metrics using Alteryx autoML. The insights derived from the data and the model can be leveraged to implement effective churn reduction strategies, potentially leading to significant cost savings and improved customer satisfaction created using Tableau Public.