Every survival analysis method I've talked about so far in this series has had one thing in common: we've only looked at one event in a customer lifetime (churn). In many cases, that's a perfectly fine way to go about things... we want our customers to stick with us, so churn is *the* event of interest. So why would we ever need to think about competing risks?

There's actually a critical assumption undergirding most survival analysis methods for right-censored data - that censored individuals have the same likelihood of experiencing the event of interest as individuals that never got censored. If this assumption ever gets violated, things like Kaplan-Meier estimators can become wildly inaccurate. (If you need a refresher on Kaplan-Meier curves and other concepts, take a look at my earlier post on basic survival analysis.)

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