---
title: What is Survival analysis?
url: https://markin.ai/glossary/survival-analysis
category: Retention and churn
---

# Survival analysis

> Survival analysis models time until an event, handling customers who have not churned yet as censored rather than discarding them. It yields hazard over tenure, which shows when risk peaks instead of only whether a customer is risky.

## Why it matters for ARPU

Timing is half of a retention decision. Knowing that risk spikes in month two or at the first renewal tells the system when to act, not just on whom.

## Related terms

- [Churn prediction](https://markin.ai/glossary/churn-prediction), Churn prediction estimates the probability that a given customer will stop paying within a defined horizon, using behavioural, transactional and service signals.
- [Retention curve](https://markin.ai/glossary/retention-curve), A retention curve plots the share of a cohort still active against time since acquisition.
- [Time to churn](https://markin.ai/glossary/time-to-churn), Time to churn is the expected remaining tenure of a customer, derived from a survival model rather than a binary risk score.
- [Cohort analysis](https://markin.ai/glossary/cohort-analysis), Cohort analysis groups customers by a shared starting characteristic, usually acquisition month, and follows each group over time.

## Go deeper

- [Customer churn prediction guide](https://markin.ai/blog/customer-churn-prediction-guide), Hazard curves in practice.
- [Streaming](https://markin.ai/industries/streaming), Tenure-shaped risk.

Source: https://markin.ai/glossary/survival-analysis