• Gabe Warshauer-Baker

Maybe you should be using your borrowing rate for the discount, which for a startup is usually pretty high. If you want to figure out if it’s worth paying $100 for a customer who will give$2/month, what really matters is how much it hurts to borrow $100. • http://blog.asmartbear.com Jason Cohen You could do that, except that’s not a real rate for many startups. Because: If the startup is growing and otherwise looking healthy, it can raise money at equivalently better terms than a bank loan. Even warrants will be better than a bank. If the startup isn’t looking good enough for investment, it’s unlikely a bank — in today’s economy — is willing to lend money at any interest rate. Generally for a startup money is worth VERY MUCH more today than it is in future, because the startup is likely to fail anyway, and because without the money today the risk of failure increases dramatically. It’s unclear how to account for that in the usual financial way. In any case, clearly this is a point of opinion, not fact, but it’s interesting to think about! • Richard Garand A discount rate can be very useful if you set it correctly, because it serves multiple functions. One is the time value of money.$100 that you receive today can be re-invested to grow your business, while \$100 you will receive in 3 years does little good for you now other than impressing investors. The appropriate discount rate reflects this more accurately than a simple sum of revenue in each month (leaving out the effects of inflation, which probably isn’t material to most SaaS companies).

It also accounts for uncertainty. If you think the revenue that’s further out has a lower probability, then increasing the discount rate will reflect that.

Coming up with the right discount rate isn’t easy but if you can find the right one it makes the calculations simple. In some cases it might help to have a multi-phase model, which is what you’ve done with a cancellation/discount rate of x% for the first 48 months and then 100% after that. If you notice that retention increases for customers who get past 2 years, you could do the calculation in two parts to reflect that.

• http://www.cazoomi.com/ Clint Wilson

Great read Jason and you should also provide the .xls files like David first did when I started putting our numbers @cazoomi into his presos back in ’09 (http://www.linkedin.com/in/dskok)

Makes it easy to see where numbers compare to how others do theirs.

~Clint
@cazoomi

• numerosiete

Really helpful post. I have a question regarding churn rate (c).

Are all of these methods assuming that c is constant in all periods? What about a subscription service where , for a given signup cohort, c may be higher in earlier periods and then decrease in later periods as users become more loyal?

When calculating N for such a business, how would you account for this variability in churn rate as cohorts of customers progress through their lifecycle?

• http://blog.asmartbear.com Jason Cohen

You’re exactly right to question that! I agree.

The ideal model would use “MRR still retained” at each month, and then perhaps also adding on a discount rate or monthly cap to account for future uncertainty and the lesser value of “money later.”

Specifically, say the amount of MRR retained in month n is Rn. For a truly constant c, this would be Rn = (1-c)^n. For example, with c=2%=0.02, it would be (approx) 98% in the first month, 96% in the second, etc.. You’d add all that up which in this idealized version is the same as above.

But in fact it’s often more like retention of 80-90% in the first month, then 70-85% by the second, and *then* maybe it stabilizes out for the customers who will in fact become long-term customers, and maybe decreases by 1-3%/mo.

In this article I propose a (relatively) simple formula that assumes there’s X% cancelled by the first M months (e.g. maybe 25% cancellations in the first 3 months), and then a relatively constant cancellation rate thereafter (e.g. 2%/mo after that). http://blog.asmartbear.com/cancellation-rate-in-saas-business-models.html

I think that model isn’t bad for many SaaS businesses, and keeps the formula relatively simple.

However, you’re right to want to measure true Rn over your cohorts, and match your LTV model as closely as you can to the actual data.

• Matthew Newton

Interesting stuff. I wonder how companies that sign up most of their customers on annual terms – thinking of some webhosts here, but also services like Amazon Prime – deal with this kind of metric.

You’re flying completely blind until your first few cohorts hit 12 months.

• http://blog.asmartbear.com Jason Cohen

You’re only blind if you refuse to look! :-)

For a SaaS company, because you own the servers, you ALWAYS can know what your customers are up to. A customer that hasn’t used the product in the past 30 days is probably not getting value and going to churn upon renewal, for example.

• Matthew Newton

hehe.

Depend if your SaaS is designed for regular usage I guess. I thought of that – tracking login data – but for the niche I’m sailing into (niche specific Web Design as a Service, a similar model to RestaurantEngine) most customers won’t login after month 1 unless some extraneous circumstance requires it such as a price change or someone on their team page quits.

My guess would be that it’s similar for your business, right? My supposition is that a lot of WPEngine users never login just because they’re happy with their website and it’s doing its thing.

• Matthew Newton

PS. I’ve just finished your entire series on SaaS metrics and they’ve been incredibly illuminating. Thanks.

• Donald Cameron

Hey Jason, good analysis.

Discounted cash flow’s always the answer. Until such time as there’s reasonable data that’s business specific, along with reliable market volatility information, discounting the years where there’s very little visibility, (ie 5 yrs and beyond) to 0 makes a lot of sense. At least you won’t go broke that way, and the results will certainly let you know if your pricing and operating model are competitive.
One can always run multiple scenarios to benchmark off of.

Another story altogether if the model’s being used for sale valuations or such like.

• Dennis

Great information guys
Web Designer in Bangalore

• Wagepoint

This is excellent, it was driving me crazy that there were so many articles out there that missed this point. I used the month cap as our business realtes well to that. Thanks so much! Excellent article. :-)

• Sam Levi

Pardon me for the silly question, but can you please explain how you got the formula for the capped LT formula? (N= (1-(1-C)^m)/C). Would be really appreciated!

• Sam Levi

• http://blog.asmartbear.com Jason Cohen

It’s not a silly question!

It’s the sum of a geometric series, which Wikipedia describes here: http://en.wikipedia.org/wiki/Geometric_series Applying that formula to the numbers in question gets you there.

• liordegani

My 2 cents:
As R[(1-c)^0+(1-c)^1+….(1-c)^n] is geometric series, you can look at the Geometric distribution.

If a user has a p=0.02 chance to cancel, and we’re counting the months until cancelation (geometric), Mean will be equal 1/p anyhow, regardless of infinitum. That actually makes a lot of sense for me to use N=1/c

Would love to hear your thoughts.

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