So essentially it's taking the average churn for a given customer, and running a monte carlo simulation to see what the expected scenario is?
Turns out, low churn and high margin companies are worth a ton! Especially with low interest rates and a public equities market that shouldn't return more than 4% real over the next few decades. Not a lot of other places to put your capital.
Turns out, low churn and high margin companies are worth a ton! Especially with low interest rates and a public equities market that shouldn't return more than 4% real over the next few decades. Not a lot of other places to put your capital.
What margins qualify as "high?" I have a SaaS offering I'm working on, and I was going to charge as a multiple of my compute and network costs. Is 85.7% "high" for SaaS? Or is that meh?
The script is so straightforward. Literally just loop 100,000 times and each time through a loop of 50 years, select a random sample of customer revenue amounts to delete. Sum up the revenue from each run and then use numpy.histogram....
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Please share the script!
So essentially it's taking the average churn for a given customer, and running a monte carlo simulation to see what the expected scenario is?
Turns out, low churn and high margin companies are worth a ton! Especially with low interest rates and a public equities market that shouldn't return more than 4% real over the next few decades. Not a lot of other places to put your capital.
Turns out, low churn and high margin companies are worth a ton! Especially with low interest rates and a public equities market that shouldn't return more than 4% real over the next few decades. Not a lot of other places to put your capital.
What margins qualify as "high?" I have a SaaS offering I'm working on, and I was going to charge as a multiple of my compute and network costs. Is 85.7% "high" for SaaS? Or is that meh?
85.7% is very high, that'd be a great gross margin (revenues - variable costs, such as compute/network costs).
The script is so straightforward. Literally just loop 100,000 times and each time through a loop of 50 years, select a random sample of customer revenue amounts to delete. Sum up the revenue from each run and then use numpy.histogram....
Yes. Margins of even 70% are amazing over a long period of time.