How big of a concern is churn for CEO’s of SaaS seed A startups, and what other problems are they trying to solve?

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JASON LEMKIN

It’s a real concern — but the absolute metrics at the Series A don’t really tell much of a story.

It’s just too early at the Series A stage to really know what’s going on with churn, because of some conflicting forces that fade as you stage:

  • You’ll close customers that can’t really use your product at allThey’ll churn super fast and drag down your numbers.  Yes, this will happen later too, but less so.  They’ll think it can do something it simply can’t really do.  They will all churn fast.
  • True enterprise customers (big ones) churn slowly.  So slowly, you can’t see it for a few years.  Big enterprise business process change takes a full year to roll out, so the second year is almost always renewed, and often even the third, even if the ROI isn’t there.  It can take three years for customer issues in Large Deals to show up as churn.
  • Net negative churn (by revenue) takes a while to become material.  Upsells take months at least, usually, often longer.  And when you start, you’ll have so few customers — there won’t be a material number to upsell to.   It often takes 3 years for net negative churn to really impact your numbers.  At least, often 2 years.
  • The lack of a customer success team will drag down your numbers.  Until you put in a decent customer success team, customers will churn that shouldn’t.  Once you do, the absolute churn goes down fast, and net negative churn in dollar terms goes up.

So what I look for at this stage is — Do the customers love the product?  Did the ones that churn, churn for the “right” reasons?  Is net negative churn at least starting in some accounts?

If so, I don’t sweat the absolute numbers — at this stage.  I just suggest improving them materially, and setting quantitative goals here.

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Published on April 21, 2016
  • LouisCamassa

    Perfect timing on this article, I was just reviewing our churn rates! We decided to weight “new” users vs. long-term users (more than 1 month), as we know cancellation rate for new users is very different for existing users.

    Here’s the formula one of my guys put together:

    expected months = (1-r)^s × (s + 1/p)
    r = short-term cancellation rate (e.g. 0.15)
    p = long-term cancellation rate (e.g. 0.03)
    s = number of months in the “short-term” age group (e.g. 1) – you need to determine the account age at which cancellations level off

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