"Churn rates are constant given age" is a model which is generally false-but-useful at SaaS companies, particularly for ages over 2~3 billing periods (months), while folks are basically on extended trialing before really deciding on adoption or not.
I agree, if you're going to the trouble of modelling then a stochastic matrix (markov chain) is a much better model. You can model it as a markov chain where each month there's a probability of moving to age + 1 or a probability of becoming 'ex-sub' (general population).
You also have a transition from 'general population' to 'month 1' to represent new sign-ups.
That has the advantage that you can still find the stationary distribution (steady-state) of the chain without simulation and it doesn't treat churn as being the same for each subscriber age. (It still has it time-homogeneous for a given age of course).
