Is 72 good?
On reading data in the business.
Two customers asked me some version of “what does good look like?” in the last two weeks. Different companies, same question.
If you’re building anything data-shaped, you get this constantly. What am I looking at? What does it mean? Is this number good or bad – and how would I even know?
The first instinct is to reach for a benchmark report. Makes sense. Those reports exist for a reason. But looking at industry benchmarks for a lot of data types is a waste of time.
The definition of good can be very flexible. It changes depending on things like your ACV, your product type, your market segment, whether your economic buyer is also your end user, whether you sell through partners or direct.
You have to build the benchmark yourself, from inside your own customer base.
The real question we ask of our data
When a head of CS asks of a health score “is 72 good?”, they’re actually asking: what am I going to do on the back of this? How do I react? That’s what’s underneath the question.
There are two sides to every data point. One is which direction it points you. The other is its inverse.
If you’re defining good, you’re also defining bad.
Once I have a definition of good, I spend most of my time looking at and looking for the inverse. It’s Charlie Munger’s “invert, always invert.” Understanding what makes a bad number happen is equally as important as understanding what makes a good number happen. That’s the half many of us skip.
How it looks in practice
Take account health. You’ve got an account in the bottom quartile of its customer segment, underperforming against the measures you’ve decided signal healthy or unhealthy. You’ve also got a peak performer in the same segment. The question worth asking: what’s the delta? Not “what’s the score” – what’s actually different between these two accounts?
The delta between the poor performer and the peak performer turns into a task list. Automated or human, doesn’t matter. That list is aimed at changing behavior on those accounts. That is the work.
That’s what all data intel points to.
We’re a data company and we’re still working on this
We built Accoil on a relative number, not an absolute one. That means “Good” can change.
Customers tell us they trust the signals Accoil gives them. But then it’s a matter of learning what to do with those signals.
That’s the gap pretty much all data hands us. Got a signal… what does it mean and then what?
With any data set we use to improve our business, new tools can help us all lean in:
here’s the signal
here’s what it means
here’s what you can do about it
here’s what happened after you did the thing
AI will help with a lot of this. But it still needs us to define what good looks like. That can come from our discernment. If we’re talking customer data, it can and should come from the customers themselves. Your best accounts are writing the definition every day – with how they use your product, when they pay, how they engage with support, and so on.
Draw the line
For any of this to work, you have to commit. Draw a line somewhere and say: above this is good, below this is not.
Then the job is doing the day-to-day work of moving more things above the line and understanding what’s holding the rest below it.
We’re all swimming in data. To make sense of yours, define what good looks like, define what not-good looks like, and spend your time closing that gap.
Peter