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If you're measuring the LTV of your pipeline, then yes.

The challenge is measuring duration (retention length) -- especially early on when campaigns are typically run single threaded and have an outsized impact on LTV of individual cohorts. I.e. My company is 18 months old - what's my LTV?

One approach is factoring in the period-churn-rate (if measuring MRR then considering monthly churn). But again, modeling isn't always super defensible.

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Survival modeling is exactly what's needed for these situations. It allows you to (a) consider censored data (i.e., active customers who you know stay for at least X months) and, (b) use flexible survival distributions beyond the standard exponential distribution assumed in the typical monthly churn rate calculations.

Source: Run a data science company and we work on a lot of customer lifecycle modeling projects with companies much younger than yours.

I've done a bit of survival modeling, but my purpose was to understand retention across cohorts with certain attributes (typically, sign-up date, though occasionally campaign).

I'm interesting in how you've used this to model churn. Is there a blog post or resource you recommend to learn more about this?

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