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My wife uses Apple Watch for health-tracking and sleep scores, while I have a Garmin watch. There's something Garmin users like to joke about: the elusive 100 sleep score. Apple will happily give you a perfect night's sleep from time to time. Getting a 100 from Garmin is apparently enough of an achievement that people post screenshots of it online.
They're both measuring sleep on a scale from 0 to 100, so how can a perfect score be fairly normal on one and almost mythical on the other?
The answer, of course, is that 100 has no meaning of its own. The two companies designed their scales differently. Apple can decide that a good enough night's sleep earns full marks. Garmin can keep the very top of its scale for a rare mix of sleep time, recovery and whatever else goes into its algorithm.
Neither approach is wrong. But imagine taking the Garmin score and setting a nightly target of 100 because that's where the scale ends. You haven't set an ambitious sleep goal. You've misunderstood the ruler.
I think we sometimes do exactly this with quality scores.
What does 100 actually mean?
CallCoach scores behaviours such as needs analysis, objection handling, closing and tone of voice from 0 to 100. Our clients decide most of what they want measured. We design the scoring rubrics so that the scores are roughly normally distributed.
That last part matters. A score of 100 doesn't mean "the rep did everything they were supposed to do". A 100% on a compliance checklist might mean that, because every required step was done. Here, 100 sits at the extreme end of a distribution. A score around 50 describes something ordinary. The closer you get to either end, the more unusual the example.
There are good reasons for this. If every competent example of objection handling scored 100, we couldn't tell good objection handling from truly exceptional objection handling. The top of the scale gives us room to show that difference.
It also means that getting close to 100 on several behaviours at once should be extremely rare. We've now analysed enough calls to see just how rare.
Half a million calls later
We looked at more than half a million calls over twelve months, across many companies and industries. A call's overall score is the average of the behaviours assessed on it, and the distribution is remarkably consistent.
The median call scores about 62. Nine out of ten calls sit between 40 and 79. Only one in ten gets above roughly 74. By 90, you're looking at about one call in 250.
And 100? We looked at every call over that year where at least three behaviours were scored. Not one got there.
The best call of the month never reaches the top of the scale
The highest scoring call in each of twelve months, against the middle call of the same month.
The best call in any given month generally landed in the mid 90s. There were a tiny number of 100s in the data. All of them came from calls where only one or two behaviours had been assessed. Once you ask somebody to be exceptional at several parts of a conversation at the same time, perfection disappears.
I don't think that's a problem with the scoring. It's what I'd expect from a scale designed this way. I'd be a little worried if lots of calls hit 100. It would mean we had squashed the best calls against the top of the scale and lost some of our ability to tell them apart.
But it does cause a problem when somebody looks at that scale and decides the quality target should be 90.
When 70 feels like failure
This is where I think our habit with percentages works against us. Show somebody a score of 62 out of 100 and it doesn't feel good. Seventy sounds passable, 80 sounds pretty good, and 90 sounds like where a strong team ought to be.
But we're adding meaning that isn't in the score.
In our data, a call scoring around 75 is already among the better calls being made. A score in the low 80s is exceptional, and 90 is an outlier. Calling a 75 "only 75%" makes as much sense as telling someone in the 75th percentile that they got only three quarters of the answer right. The numbers look alike, but they mean very different things.
It gets worse when you turn 90 into a target. A rep could improve a lot over several months while almost every call they make stays below the line. If your dashboard paints all of those calls red, you've built a target that is very good at hiding improvement.
You could fix that by making CallCoach more generous. Shift all the scores up until strong calls get 90 and exceptional calls often get 100. Everyone's dashboard would look nicer, but nobody's performance would improve. We would only have repainted the ruler. We'd still need to decide where good performance begins on the new ruler.
I'd rather keep the information in the scale and put the target somewhere sensible.
Where to draw the line
Around one call in four in our data scores 70 or better. That makes 70 a good place to start thinking about a target. A rep can hit it often enough to do it consistently, and it's high enough to tell stronger calls from ordinary ones.
I wouldn't make 70 your CallCoach target without checking, either. Different organisations measure different behaviours, sell different things and have different distributions. Start with your own calls. Ask where the middle sits, where the top quarter begins and what the best 10% look like. Then decide what level is the performance you want your team to reach consistently.
That changes how you read improvement too. A rep moving their typical score from 60 to 67 hasn't gone from a bad percentage to a slightly less bad one. They've moved a real distance through the spread of actual calls. That's the kind of change a quality score should make visible.
So there's nothing wrong with a scale that runs to 100. There's nothing wrong with a Garmin sleep score that's almost impossible to max out, either. Just don't mistake the end of the scale for the place you're meant to be.
CallCoach is how we score every call, on the ruler described above. Try it on your own calls and see where your middle call, your top quarter and your best 10% actually sit before you pick a target.
All CallCoach data in this post is aggregated, anonymised performance data from hundreds of thousands of calls across a variety of industries. We report the trends we spot; full statistical analysis is beyond the scope of a blog post.