Metrics are less useful than you’ve been told. Even the best are just ratios that tell you whether you’re making progress toward a well-defined goal.
But not why, how, or what to do if you aren’t. As last week’s KJR pointed out, not only aren’t metrics explanatory on their own, in most cases a metrics change won’t have a single root cause. If, for example, you’re losing marketshare, you might have:
- Missed a complete marketplace shift.
- Lousy advertising.
- No advertising, lousy or otherwise.
- Poor quality products.
- Deplorably ugly products.
- Products that lack key features competitors have.
- Hapless distributors.
- Hapful distributors who like your competitors better.
- A customer disservice hotline.
To list just a few possible causes, none of which are mutually exclusive.
Which is to say, root cause analysis is a multivariate affair, which is why analytics is, or at least should be, the new metrics.
But while multivariatism is an important complicating factor when business decision-makers have to decide what to do when success isn’t happening the way it should, it isn’t the only complicating factor.
Far more difficult to understand in any quantitative fashion is the nasty habit many business effects have of causing themselves.
Many cause-and-effect relationships are, that is, loops.
These feedback loops come in more than one flavor. There are vicious and virtual cycles, and there are positive and negative feedback loops.
In business, the cycles you want are the virtuous ones. They’re where success breeds more success. Apple under Steve Jobs was, for example, a successful fanbody fosterer. (Don’t like “fanbody”? Send me a better gender-neutral alternative).
The more fanbodies Apple has the cooler its products are, making it more likely the next electronics consumer will become another Apple fanbody.
These loops work in reverse, too: Start to lose marketshare and a vicious cycle often ensues. Corporate IT pays close attention to this effect: When choosing corporate technology standards, products that are failing in the marketplace are undesirable no matter how strong they might be technically. Why? Because products that are losing share are less likely to get new features and other forms of support than competing products.
So IT doesn’t buy them, and so the companies that sell them have less money to invest in making them competitive and attractive, and so IT doesn’t buy them.
A frequently misunderstood nicety: virtuous and vicious cycles are both positive feedback loops. In both cases an effect causes more of itself.
Negative feedback loops aren’t like that. Negative feedback as the term is properly used is corrective. With negative feedback loops, an effect makes itself less likely than it was before.
Take business culture. It’s self-reinforcing. When someone strays from accepted behavioral norms, their co-workers disapprove in ways that are clear and punitive.
Want an example? Of course you do. In many companies, employees are known to complain about management. Not necessarily any particular manager, but about management.
An employee who, in conversation, makes complimentary statements about management is likely to be ostracized, no matter how justified the statements might be.
Symmetry requires negative feedback loops to have unfortunate as well as fortunate outcomes, just as positive feedback loops do. Here’s a well-known one: Analysis paralysis. It’s what happens when corrective feedback overwhelms all other decision criteria.
Where does all this go?
The idea behind “if you can’t measure you can’t manage” is well-intentioned. Underneath it is an important idea — that you should prefer to base your decisions on data and logic, rather than your mood and digestive condition.
The point here is that those who lead large organizations need to kick it up a notch. Measurement isn’t the point, and it isn’t the be-all and end-all of decision-making. It’s just a part of something much bigger and more important: Leaders and managers need to understand how their organizations work. That includes understanding the simple cause-and-effect relationships metrics tend to be associated with, and the multivariate causal relationships multivariate analytics can help you understand.
And, you should add to that at least a qualitative understanding of the various feedback loops that drive success or failure in your line of work.
A quantitative understanding would be better. It’s just not often possible.
Qualitative might be inferior to quantitative, but it’s much better than ignoring something important, just because you can’t put a number to it.
As Einstein … by all accounts a bright guy … put it, “Not everything that can be counted counts, and not everything that counts can be counted.