Subscriptions still active at month x are represented as `l(x)` and subscriptions that "die" (cancel/expire) in a given month are represented as `d(x)`.
This gives you a "life expectancy" and a "mortality rate" (so, churn) for any given number of months that a customer has been subscribed. So I can project how long someone will stay subscribed when they're brand new (at month 0) and how long they likely still have when they're at month 8 (longer than at month 0, funnily enough).
With those subscriptions where the month-specific churn will largely decrease the longer someone's subscribed (after passing the initial high-churn first months), this allows measuring/projecting churn on a much more granular level.
Subscriptions still active at month x are represented as `l(x)` and subscriptions that "die" (cancel/expire) in a given month are represented as `d(x)`.
This gives you a "life expectancy" and a "mortality rate" (so, churn) for any given number of months that a customer has been subscribed. So I can project how long someone will stay subscribed when they're brand new (at month 0) and how long they likely still have when they're at month 8 (longer than at month 0, funnily enough).
With those subscriptions where the month-specific churn will largely decrease the longer someone's subscribed (after passing the initial high-churn first months), this allows measuring/projecting churn on a much more granular level.