Customer Churn Prediction Case Study
End-to-End Machine Learning Project with Business Impact
Project Overview
Customer churn is one of the biggest challenges for subscription-based businesses. For telecom companies in particular, losing a customer often costs significantly more than retaining one.
In this case study, I built an end-to-end machine learning solution to predict customer churn and translate the results into actionable retention strategies. The focus was not just on model performance, but on interpretability, decision-making, and real business impact.
Problem Statement
A telecommunications company was experiencing increasing customer churn and needed data-driven insights to support its retention efforts.
The business wanted answers to three key questions:
Which customers are likely to churn?
What factors are driving churn?
How can the retention team act on these insights to reduce customer loss?
Objective
The goal of this project was to:
Build a churn prediction model
Identify key churn drivers
Recommend practical, data-backed retention strategies
Design a solution that could realistically be used by business stakeholders
Dataset Description
The dataset contains 500 customer records with 19 features, covering customer demographics, billing, service usage, and support interactions.
Feature categories
Demographics
Age
Gender
Account and contract details
Tenure
Contract type
Payment method
Billing and usage
Monthly charges
Total charges
Internet service
Phone service
Customer experience
Support calls
Customer satisfaction score
Service add-ons
Streaming TV
Streaming movies
Online security
Tech support
Target variable
- Churn (0 = active, 1 = churned)
Approach and Methodology
I approached the project in structured phases to mirror a real-world data science workflow.
1. Data understanding and cleaning
Inspected data types and distributions
Checked for missing values and inconsistencies
Ensured the dataset was suitable for modeling
#Load dataset df = pd.read_csv("/kaggle/input/week-16-regression-3/customer_churn_prediction.csv") df.head()
#Data Overview
df.info()
df.describe()
#check missing values
df.isnull().sum()
2. Exploratory Data Analysis (EDA)
EDA was used to understand customer behavior and uncover churn patterns.
Key findings included:
EDA Summary (Key Insights):
Churn distribution is fairly balanced, with slightly more non-churn customers than churned ones. This means the dataset is suitable for classification without severe class imbalance.
Age shows a mild relationship with churn. Customers who churn tend to be slightly older on average, though the overlap is large, so age alone is not a strong predictor.
Monthly charges are higher for churned customers. Customers who left generally pay more per month, suggesting price sensitivity is a major factor influencing churn.
Tenure is clearly related to churn. Customers with shorter tenure are more likely to churn, while long-term customers tend to stay, indicating loyalty increases over time.
Overall, financial and engagement factors matter more than demographics. Monthly charges and tenure show stronger separation between churn and non-churn compared to age.
3. Feature engineering and preprocessing
Encoded categorical variables
Scaled numerical features
Split the data into training and test sets to evaluate generalization
categorical_cols = [ "Gender", "Contract_Type", "Internet_Service", "Payment_Method" ] le = LabelEncoder() for col in categorical_cols: df[col] = le.fit_transform(df[col])
#Define Features and Target
X = df.drop(columns=["Customer_ID", "Churn"])
y = df["Churn"]
#Train-Test split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, random_state=42, stratify=y
)
Modeling and Evaluation
I trained and evaluated two classification models:
Logistic Regression
Random Forest Classifier
Because churn prediction has asymmetric business costs, I evaluated models using accuracy, precision, recall, F1 score, and AUC.
Model performance summary
| Model | Accuracy | Recall | F1 Score | AUC |
| Logistic Regression | 0.576 | 0.544 | 0.539 | 0.586 |
| Random Forest | 0.528 | 0.491 | 0.487 | 0.578 |
Logistic Regression consistently outperformed Random Forest across all metrics.
Model Selection Rationale
Logistic Regression was selected for deployment for three main reasons:
Better overall performance and higher recall, which is critical for identifying at-risk customers
Strong interpretability, allowing business stakeholders to understand why customers churn
Better alignment with business needs, where missing a churner is more costly than a false alarm
To further improve recall, I recommended lowering the probability threshold from 0.5 to approximately 0.4.
Key Churn Drivers
Using feature importance analysis, the strongest churn drivers were identified as:
Monthly charges
Tenure
Total charges
Customer satisfaction score
Contract type
Support call frequency
Age
These results show that churn is driven primarily by pricing, customer experience, and relationship duration rather than static demographic attributes.
Business Recommendations
Based on the insights from the model and EDA, I proposed the following actions:
Offer targeted discounts or flexible pricing to high-billing customers
Strengthen onboarding and engagement during the first three to six months
Trigger proactive outreach when customer satisfaction scores drop
Improve support quality for customers with frequent service calls
Encourage long-term contracts through incentives
Personalize retention strategies by age group
These recommendations directly link model insights to measurable business actions.
Implementation Strategy
To ensure the solution remains effective in production, I recommended:
Retraining the model every three to six months
Monitoring recall, AUC, churn rate, and false negative rate
Measuring business impact through retention campaign success and customer lifetime value
Limitations and Future Improvements
While the model provides useful insights, its predictive power is moderate.
Future improvements could include:
Adding time-series and behavioral usage data
Incorporating complaint resolution history
Testing advanced models such as Gradient Boosting or XGBoost
Addressing potential class imbalance
Integrating near real-time customer activity
Results and Impact
Although this was an offline project, the expected business impact includes:
Earlier identification of at-risk customers
More targeted and cost-effective retention campaigns
Reduced customer churn and improved customer lifetime value
The project demonstrates how even moderately performing models can deliver meaningful value when combined with strong business understanding.
Key Skills Demonstrated
Business problem framing
Exploratory data analysis
Feature engineering and preprocessing
Classification modeling
Model evaluation and selection
Translating machine learning outputs into business strategy
Communicating insights to non-technical stakeholders
Final Reflection
This case study highlights an important principle of applied data science: models do not need to be perfect to be useful. What matters most is understanding the problem, interpreting results correctly, and turning insights into action.
This project showcases my ability to think beyond metrics and build solutions that support real business decisions.
image credit : Pinterest
