Another Reason We Don't Apply the 80-20 Rule
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I’ve written about the 80-20 rule several times because it keeps coming up. I’d like to believe that each time I revisit it I understand it a little better.
In its simplest form the 80-20 rule says 80% of your outputs come from 20% of your inputs. You might find that 80% of your revenue comes from 20% of your customers, or 80% of your headaches come from 20% of your employees, or 80% of your sales come from 20% of your sales reps. The exact numbers 80 and 20 are not important, though they work surprisingly well as a rule of thumb.
The more general principle is that a large portion of your results come from a small portion of your inputs. Maybe it’s not 80-20 but something like 90-5, meaning 90% of your results coming from 5% of your inputs. Or 90-13, or 95-10, or 80-25, etc. Whatever the proportion, it’s usually the case that some inputs are far more important than others. The alternative, assuming that everything is equally important, is usually absurd.
The 80-20 rule sounds too good to be true. If 20% of inputs are so much more important than the others, why don’t we just concentrate on those? In an earlier post, I gave four reasons. These were:
- We don’t look for 80/20 payoffs. We don’t see 80/20 rules because we don’t think to look for them.
- We’re not clear about criteria for success. You can’t concentrate your efforts on the 20% with the biggest returns until you’re clear on how you measure returns.
- We’re unclear how inputs relate to outputs. It may be hard to predict what the most productive activities will be.
- We enjoy less productive activities more than more productive ones. We concentrate on what’s fun rather than what’s effective.
I’d like to add another reason to this list, and that is that we may find it hard to believe just how unevenly distributed the returns on our efforts are. We may have an idea of how things are ordered in importance, but we don’t appreciate just how much more important the most important things are. We mentally compress the range of returns on our efforts.
Making a list of options suggests the items on the list are roughly equally effective, say within an order of magnitude of each other. But it may be that the best option would be 100 times as effective as the next best option. (I’ve often seen that, for example, in optimizing software. Several ideas would reduce runtime by a few percent, while one option could reduce it by a couple orders of magnitude.) If the best option also takes the most effort, it may not seem worthwhile because we underestimate just how much we get in return for that effort.
Published at DZone with permission of John Cook, DZone MVB. See the original article here.
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