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.
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?
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.