Steve Jobs was inspired by an article comparing the efficiency of animals and machines. In the animal kingdom, a condor was the most efficient. Humans were an unimpressive also-ran. But the winner was a person on a bicycle!
The computer is the most remarkable tool that we've ever come up with. It's the equivalent of a bicycle for our minds. ~ Steve Jobs
He was right.
But…
“With great power comes great responsibility.” ~ Stan Lee (as said by Uncle Ben to Spiderman)
What does Stan Lee have to do with this?
Computers and software today make analytics easy and plentiful. This leads to potential abuse.
Easy doesn't mean useful. Ride a bicycle in circles. You'll get you nowhere. Efficiently.
In CRM analytics, we see this frequently. Sales leaders are over-instrumented with KPIs. Many of these are time-honored metrics. They undoubtedly provide comfort for some.
But we've looked at how these are used and computed. The results are discouraging.
Some metrics are confusing or misunderstood. Try asking three people in your company to define one of your metrics. You will get inconsistent and often ill-defined responses.
Some of these metrics are just wrong. They send mixed signals. At best these are a distraction. At worst, they create a false sense of security, or alarm.
The most common offenders: pipeline coverage ratios, win rates, stage-to-stage conversion rates, and commit lists.
Pipeline coverage ratio (PCR) is very weakly correlated to sales. It treats mature and new deals the same. PCR ignores prospective deals not yet in your pipeline. Worse, managing to a set ratio kills its predictive power. You’ll get the coverage you want, but you’ll need more.
Win rates can be very powerful if properly computed. But they are almost always computed wrong—they give businesses a bad estimate of the wrong thing.
Stage-to-stage conversion rates are also usually computed wrong. And they're not as useful as they seem. Changes at one stage do not translate to overall changes in your win rate. Win rates by stage matter a lot. Stage-to-stage conversion, not so much.
Commit lists are very useful to allocate and align resources. But too often they are used as club—defeating their predictive value. You’ll get accurate Commit’s but with a few days’ advance notice.
Why do businesses use these flawed metrics and methods? It’s the way things have always been done. People keep asking for them. And most CRM analytics vendors provide them.
Good news! We can channel Steve Jobs and Stan Lee. You can have a bicycle for the mind and use it responsibly. That's our job—to provide the most useful and impactful analytics possible. Usually that means that less is more. Sometimes it is a little more nuanced but it is a lot more robust, accurate, and useful.
Our recommendations:
Pipeline coverage ratio should be replaced with a weighted forecast that includes both existing deals and prospective deals not yet in your pipeline.
Win rates should be computed properly (with the time to event method) and used to feed that weighted forecast above. Or just use your judgement calls on individual deals.
Conversion rates should be replaced with win rates by stage.
Commit lists should be used to align resources. Holding people accountable? Firing people for missing a commit will make the commits more solid. But it also will shorten the predictive lead time and resource allocation value of a commit list. It's your call. Think about it.
If you do these things, you'll be on a better path. One that can lead you to understand:
What is realistic, based on your data?
Which factors within your control have the biggest effect on desired outcomes?
What are those factors?
Fundamentals: win rate, deal generation rate, deal size
Most impactful changes in fundamentals and deal progress
Most worrisome deals
The choice is yours. You can focus on useful things. Or you can ride your bicycle in circles.