📈 Survival Analysis: Predict When, Not Just If#
Classic models predict if a customer will leave. Survival Analysis predicts when. A huge difference for customer retention.
🔍 What Is Survival Analysis?#
Originally from medicine (time until a patient’s death), today it’s widely used in business:
- ⏱️ When will a customer cancel their subscription?
- 🔧 When will a machine fail?
- 🛒 When will a user purchase again?
🧠 Why Not Use Classic Regression?#
Because of censored data: observations where the event hasn’t happened yet at data collection time. Linear regression simply ignores them, introducing massive bias.
📊 Two Main Models#
Kaplan-Meier — simple, visual, ideal for exploration
from lifelines import KaplanMeierFitter
kmf = KaplanMeierFitter()
kmf.fit(df['Subscription Length'], event_observed=df['Churn'])
kmf.plot_survival_function()Cox Proportional Hazard — the industry standard, supports multiple predictor variables
💡 Concrete Results (Telco Dataset)#
Customers without complaints → 93.94% retention at 34 months (expected churn at 41 months) Customers with complaints → 61.68% retention (expected churn at 31 months)
📌 A customer who complains is 5.4 times more likely to churn.
💡 In Simple Terms#
Survival Analysis is like predicting how fast a leaky bucket empties. Not just whether it will empty, but when, depending on the size of the hole (the customer variables).
More information at the link 👇
