# How to simply estimate lifetime of a customer in early days of a startup

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How do you estimate (simply) the lifetime of a customer in the early days of a SaaS startup?

E.g. in the first, say 3 months, you will have a number of customers who have already left and a number that are still customers - but you have no idea of how many of these will quit at month 4 or year 4 so how do you estimate the average lifetime to help determine Customer Lifetime Value (CLV)

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The simplest way to measure life time value is when the customer stops paying you. Typically saas companies charge for their software on a recurring monthly basis. If the average customer pays up until the sixth month you have a lifetime value of 6*\$monthly charge.

In the early days it's hard to get a statistically accurate LTV but you should have some baseline numbers you can work with. Keep in mind, I wouldn't bet the farm on those numbers and neither should you.

Alternatively, you can look at the industry you are in and try to find out what your competitor's average lifetime is and use that as a means to extrapolate your potential CLTV.

This is just something that gets better with time.

• I get ya but the problem is that its going to dramatically undervalue LTV in early days. I.e. on month 1 the highest LTV you can possibly have will be – Ryan 9 years ago
• It might be undervalued but at least it's truth. There is no room for doubt. IMHO it's better to project off of "real" undervalued numbers that I know to be true rather than ones that are incorrectly overvalued. – Theonetruepat 9 years ago

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I wanted something that would come up with a reasonable answer in the early days so couldn't use Patrick's suggestion.

From the wikipedia article on CLV you have this simple model

GC = Gross Contribution (gross revenue)
R = retention rate (1-churn)
D = discount rate

Simplifying further by removing discount rate (cost of capital) and substituting churn for retention (r=1-c) this gives :-

LT = Lifetime in whatever period you choose for c (churn)

Churn is the number of customers who cancel in any period or

An example - suppose we have been

• Running for 2 months
• Have had 20 people cancel
• Churn per month = (20/100)/2 = 0.1 or 10% (i.e. in any one month 10% of customers cancel)
• Average Life Time (LT) = (1-0.1)/0.1 = 9 months
Seems to me that this formula will

• Underestimate LT if your have a large enough subset of your customers who once hooked are unlikely to leave.
• Overestimate LT if you have large enough subset of your customers who are loyal until later on (e.g. imagine you were doing some app about tracking pregnancy. Even hooked customers are likely to cancel after 9 months!)