You Can’t Analyze Churn by Analyzing Churn

One thing that amazes me is when I hear people talk about how they analyze churn in a cloud, software as a service (SaaS), or other recurring revenue business.

You hear things like:

  • “17% of our churn comes from emerging small business (ESB) segment, which is normal because small businesses are inherently unstable.”
  • “22% of our churn comes from companies in the $1B+ revenue range, indicating that we may have a problem meeting enterprise needs.”
  • “40% of the customers in the residential mortgage business churned, indicating there is something wrong our product for that vertical.”

There are three fallacies at work here.

The first is assumed causes.  If you that 17% of your churn comes from the ESB segment, you know one and only one thing:  that 17% of your churn comes from the ESB segment.  Asserting small business stability as the cause is pure speculation.  Maybe they did go out of business or get bought.  Or maybe they didn’t like your product.  Or maybe they did like your product, but decided it was overkill for their needs.  If you want to how much of your churn came from a given segment, ask a finance person.  If you want to know why a customer churned, ask them.  Companies with relatively small customer bases can do it via a phone.  Customers with big bases can use an online survey.  It’s not hard.  Use metrics to figure out where your churn comes from.  Use surveys to figure out why.

The second is not looking at propensities and the broader customer base. If I said that 22% of your annual recurring revenue (ARR) comes from $1B+ companies, then you shouldn’t be surprised that 22% of your churn comes from them as well.  If I said that 50% of your ARR comes from $1B+ companies (and they were your core target market), then you’d be thrilled that only 22% of your churn comes from them.  The point isn’t how much of your churn comes from a given segment:  it’s how much of your churn comes from a given segment relative to how much of your overall business comes from that segment.  Put differently, what is the propensity of someone to churn in one segment versus another.

And you can’t perform that analysis without getting a full data set — of both customers who did churn and customers who didn’t.  That’s why I say you can’t analyze churn by analyzing churn.  Too many people, when tasked with churn analysis:  say, “quick, get me a list of all the customers who churned in the past 6 months and we’ll look for patterns.”   At that instant you are doomed.  All you can do is decompose churn into buckets, but know nothing of propensities.

For example, if you noticed that in one country that a stunning 99% of churn came from customers with blue eyes, you might be prompted to launch an immediate inquiry into how your product UI somehow fails for blue-eyed customers.  Unless, of course, the country was Estonia where 99% of the population has blue eyes, and ergo 99% of your customers do.  Bucketing churn buys you nothing without knowing propensities.

The last is correlation vs. causation.  Knowing that a large percentage of customers in the residential mortgage segment churned (or even have higher propensity to churn) doesn’t tell you why they are churning.  Perhaps your product does lack functionality that is important in that segment.  Or perhaps it’s 2008, the real estate crisis is in full bloom, and those customers aren’t buying anything from anybody.  The root cause is the mortgage crisis, not your product.   Yes, there is a high correlation between customers in that vertical and their churn rate.  But the cause isn’t a poor product fit for that vertical, it’s that the vertical itself is imploding.

A better, and more fun, example comes from The Halo Effect, which tells the story that a famous statistician once showed a precise correlation between the increase in the number of Baptist preachers and the increase in arrests for public drunkenness during the 19th Century.  Do we assume that one caused the other?  No.  In fact, the underlying driver was the general increase in the population — with which both were correlated.

So, remember these two things before starting your next churn analysis

  • If you want to know why someone churned, ask them.
  • If you want to analyze churn, don’t just look at who churned — compare who churned to who didn’t

4 responses to “You Can’t Analyze Churn by Analyzing Churn

  1. Excellent post. As usual. As my father used to say, the fact that 80% of people die in a bed doesn’t imply that the bed is the most dangerous place on earth.

  2. Pingback: Churn:  Net-First or Sum-First? | Kellblog

  3. Great article, and anyway bed is really a dangerous place… As for correlations, this example I think is more suprising: The more fire-fighter is sent to a fire, the more damage will occur, there is a very strong correlation. You may conclude that it’s a wrong idea to send our more fire-fighters, if just look at the data and correlation. But this correlation is because of the underlying cause: original fire amount. I found this place the best for checking correlations: https://www.answerminer.com/calculators/correlation-test

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