Learn Introduction to Churn Prediction | Customer Health and Churn Prediction

Python for Customer Success Managers

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Churn refers to the loss of customers over a given period. When a customer stops using your product or service, they are said to have churned. For Customer Success Managers, understanding and predicting churn is crucial because it directly impacts revenue, growth, and team goals. A high churn rate can signal problems with customer satisfaction, product fit, or support effectiveness. Predicting churn early allows you to proactively engage at-risk customers, address their concerns, and improve retention, ultimately supporting business stability and growth.


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import pandas as pd

# Create a small hardcoded dataset of customer attributes and churn status
data = {
    "customer_id": [1, 2, 3, 4, 5],
    "usage_frequency": [5, 2, 8, 1, 3],  # times used per month
    "support_tickets": [0, 3, 1, 5, 2],  # tickets raised last month
    "churned": [0, 1, 0, 1, 1]           # 1 = churned, 0 = retained
}
customers = pd.DataFrame(data)

# Simple rule-based predictor: churn if usage < 3 or support_tickets > 2
def rule_based_churn_predictor(row):
    if row["usage_frequency"] < 3 or row["support_tickets"] > 2:
        return 1  # Predict churn
    else:
        return 0  # Predict retention

customers["predicted_churn"] = customers.apply(rule_based_churn_predictor, axis=1)
print(customers[["customer_id", "usage_frequency", "support_tickets", "churned", "predicted_churn"]])

The rule-based churn predictor above uses simple business logic: if a customer uses the product less than three times a month or has raised more than two support tickets in the last month, they are predicted to churn. This approach is easy to implement and understand, making it a good starting point for churn prediction. However, its simplicity is also a limitation. It cannot capture complex patterns in customer behavior, may miss important signals, and often produces many false positives or negatives. Real-world churn drivers are usually more nuanced, requiring more sophisticated models to improve prediction accuracy.


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# Evaluate the predictor's accuracy
correct_predictions = (customers["churned"] == customers["predicted_churn"]).sum()
total_customers = len(customers)
accuracy = correct_predictions / total_customers

print(f"Prediction accuracy: {accuracy:.2f}")

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Section 2. Chapter 3

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Section 2. Chapter 3

Introduction to Churn Prediction

1. What is customer churn?

2. Why is it important to predict churn early?

3. What is a limitation of rule-based churn prediction?