17.3. Case Study 1: Customer Churn Prediction in Telecom

A major telecom company wants to predict which customers are likely to leave their services.

The first step in this case study is defining the problem that needs to be solved. A major telecom company is trying to foresee customer churn, which refers to the situation where customers stop using their services. Understanding this problem is crucial as it helps the business identify customers who are at risk of leaving and allows for interventions to retain them.

Imagine a subscription service like Netflix. If Netflix notices that certain users are watching less content or are frequently browsing but not watching, they might want to predict whether those users will cancel their subscription. By understanding who might churn, Netflix can create special incentives to keep them.

• Customer demographics • Call usage patterns • Billing and payment data • Customer service call history

The next part involves examining the dataset that contains various features which might help in predicting customer churn. This dataset includes customer demographics (like age and location), call usage patterns (how often and when the customers make calls), billing and payment data (which can indicate if someone is having issues paying their bill), and the history of customer service calls (to see if customers have unresolved issues).

Consider a car insurance company trying to predict which customers might not renew their policies. They might look at data like the customer's age, driving record, claims history, and payment habits. Each feature can offer clues on whether a customer might leave or stay.

• Logistic Regression • Random Forests • SMOTE (Synthetic Minority Oversampling Technique) • SHAP for model interpretability

For this churn prediction task, multiple techniques were employed. Logistic Regression is used to model the probability of a customer churning based on certain predictors. Random Forests, a more complex ensemble method, are also used to capture interactions between features better. SMOTE is applied here to address the challenge of imbalanced classes, where most customers do not churn; this technique creates synthetic examples of the minority class (customers who churn) to help the model learn effectively. Finally, SHAP (Shapley Additive Explanations) is used to make the model interpretable, which is essential for explaining predictions to business teams.

Think of predicting which plants in a garden are likely to wilt based on their water needs and soil quality. Logistic regression is like a simple rule telling you that if a plant is in dry soil, it might wilt. Random forests are like combining many gardeners’ opinions to get a better guess. SMOTE is like bringing in extra photos of wilted plants to help everyone learn from them.

• Highly imbalanced data (few customers churn) • Noise in call data • Need for interpretability for business teams

This case study faced several challenges. The first challenge was the highly imbalanced nature of the data; there are far fewer cases of customers who churn compared to those who stay, making it difficult for the model to learn. Noise in the call data means that there might be irrelevant or misleading information that can confuse the model. The need for interpretability is also crucial because business teams must understand why certain predictions are made, so they can act on them effectively.

Imagine a fire alarm that only goes off when there's smoke. If 99% of the time there’s no fire, the alarm could fail to alert the few times smoke does occur. If it also gets confused by cooking steam (noise), it might not be good at detecting actual fires. Similarly, team members need clear explanations about why alarms (predictions) go off.

Achieved 84% AUC score with Random Forest. Business team used SHAP outputs to target high-risk customers with retention offers.

The outcome of the project was promising. The Random Forest model achieved an 84% Area Under the Curve (AUC) score, indicating good predictive power. With the help of SHAP outputs, the business team was able to identify high-risk customers accurately and then implement targeted retention offers to keep them from leaving, effectively reducing churn.

Think of a weather forecasting system that predicts storms with 84% accuracy. If it identifies areas that are likely to have severe weather, emergency services can prepare early to help residents. Similarly, identifying high-risk customers allows businesses to take proactive measures to retain them before they decide to leave.

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