How to model customer churn with survival analysis?

How to model customer churn with survival analysis?

Treselle Systems, a data consulting service, analyzed customer churn data using logistic regression. This approach works for a binary classification of whether or not a customer has left, but survival analysis is more appropriate. The data can be found here. Our goal is to identify ways for the telecom company to reduce customer churn.

How can we use data science to better understand customer churn?

All code related to the article below can be found here. ‘Customer Churn’ is the loss of clients or customers. In order to avoid losing customers, a company needs to examine why its customers have left in the past. How can we use data science to better understand customer churn?

When to use churn prediction to improve retention rate?

If your PMF is not satisfactory, you should see your customers churning very soon. One of the powerful tools to improve Retention Rate (hence the PMF) is Churn Prediction. By using this technique, you can easily find out who is likely to churn in the given period.

How is the C-index used in churn prediction?

Because the C-index is high, the model will be able to properly rank the survival time of random units of each group, such that thigh ≤ tmedium ≤ tlow t h i g h ≤ t m e d i u m ≤ t l o w. Let’s randomly select individual unit in each group and compare their probability of remaining a customer for all times t.

What’s the chances of a customer not canceling service?

The survival curve is cumulative. Meaning, in the graph below, after 20 months, the chance of a customer not canceling service is just above 80%. The above should give us some basic intuition about the customers. As we would expect for telecom, churn is relatively low.

What does the last EXP in survival analysis mean?

This is interpreted as the scaling of hazard risk for each additional unit of the variable, 1.00 being neutral. For example, the last exp (coefficient), corresponding to PaymentMethod_Mailed check, means a customer that pays by mailing a check is 1.68 times as likely to cancel their service.

What makes survival analysis superior to other regression models?

Survival analysis was first developed by actuaries and medical professionals to predict survival rates. Survival analysis works well in situations where we can define: The component that makes survival analysis superior to other regression models is its ability to deal with censorship in data.

How to do a Survival Regression in Lifeline?

Lifeline offers a built in check_assumptions method for the CoxPHFitter object. After some data cleaning, including encoding categorical variables (k-1 dummies), we can fit a survival regression model to the data. In the above regression, the key output is exp (coef).