SaaS Leaky Bucket Number

Software-as-a-Service (SaaS) has a number of important metrics for the business model with one of the most important being customer renewal rates / customer churn. Josh James, the co-founder of Omniture, which was bought by Adobe for $1.8 billion a couple years ago, sent this tweet out last week:!/joshjames/status/154644835490463744

The idea that they’d sign up 1,200 customers per month and have 800 customers leave per month drives home the need to closely watch customer renewals. The ratio of new customers to lost customers is critical and now has a formal name:

Leaky Bucket Number = New Customers Per Month / Customers Lost Per Month

Note that it doesn’t take into account the average revenue per new customer vs the average revenue per customer that leaves as well as customer upgrades or downgrades. Generally, it’s a good metric to monitor because it’s easier for people to understand we signed 29 customers and lost 11 customers because it continues to keep the number of lost customers top-of-mind. And, it’s especially important for this number to be greater than one otherwise the startup is losing more customers than it’s adding.

What else? What do you think of the SaaS leaky bucket number?

One thought on “SaaS Leaky Bucket Number

  1. Hey David,

    Great post. I measure and think about churn all the time. Dharmesh’s statement saying that “a SaaS business can’t survive long-term with a 10% annual churn rate” seems overly prescriptive. It completely depends on the market dynamics of the market in question. There are a ton of businesses that have been successful long-term (or, as long as SaaS has been around) on 15%+ annual churn.

    – Is the market mature?
    – Does the product serve a permanent need or a project-based need?
    – Do you deal through channel partners?
    – (and so many more)

    I’m a little surprised that Dharmesh would throw 10% out there as a benchmark. There is no benchmark. Your ratio–new/lost–is much more interesting and is more operationally useful.

    Something I’ve also found useful is to separate out “transient customers”. There are sometimes customers in certain markets that regularly churn and then re-subscribe multiple times. At one of my companies these people actually made up a significant % of the total churn. Once we separated them out we were able to develop a clearer view of “real churn”, which was lower and more predictable than we had previously realized.

    Good post / great topic.


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