Survival Analysis for User Churn: Concepts, Data Preparation, and Quantitative Modeling

Survival analysis, originally a medical statistical method, examines the relationship between specific events and time, and can be applied to user churn by treating the first purchase as birth and account cancellation or long inactivity as death.

In this example, the observation period is from Jan 1 2020 to Jun 30 2021. Users who made their first order are considered born; those who cancel or are inactive for a defined period are considered dead. The survival time is calculated accordingly, while users still active at the end are censored.

The analysis considers factors such as gender, age, membership status, purchase amount, and promotional intensity to assess their impact on survival time. Censored data are handled appropriately, which ordinary linear regression cannot address.

Data preparation requires a table with columns for observation start/end, birth time, death time, survival time (Y), event indicator (N), and covariates (X). Example images illustrate the raw and transformed data formats.

Descriptive analysis using the Kaplan‑Meier estimator shows three churn phases: low churn in the first 0‑3 months, rapid churn between 3‑12 months, and stable retention after 12 months. Targeted interventions during the 3‑12 month window can improve overall retention.

Quantitative analysis with Cox regression yields factor coefficients, e.g., membership (1.8), comments (2.1), maximum purchase interval (0.8), and maximum spend (1.3), indicating how each variable influences expected survival days.

Factor

Impact

Membership (yes)

1.8

Comment (yes)

2.1

Max purchase interval (days)

0.8

Max spend

1.3

In summary, survival analysis is versatile for any scenario with a defined event and time, offering insights for user churn, conversion, click‑through, and product decay analyses.