In the world of data, businesses are constantly worried about meeting targets. This is not new, and not a uniquely corporate phenomenon. The pirates that worked off the coast of Somalia gave bonuses, including for being the first on the ship, for example. In a corporate job, a target might be finding new targets, rather than armed maritime assault, but the idea is that we want to incentivize higher performance.
A metric is a way of measuring something. In a hospital, blood pressure and pulse rate are useful metrics. Businesses look at measures such as employee and customer churn, units produced, and the like. The idea is, like a sudden drop in blood pressure, a sudden dropoff in quantity ordered might indicate that the firm is having trouble of some sort. As such, monitoring metrics is a key task of data analysts.
In short, the diffrence is Goodhart’s Law. My personal favorite example, introduced by Tim Harford’s book “Messy”, is how the APGAR measure went from a metric for newborn babies to the target that drove (at least in part) the rise of C-sections in the United States. Most importantly, this never accuses doctors of actively pursuing the goal. However, simply moving a procedure towards a default selection, rather than an opt-out, can shape outcomes (just as any behavioral economist about nudges).
This has nothing to do with Amazon, in particular. However, in hearing multiple times about monitoring metrics, I wanted to take a minute and say out loud that a target is not a metric. Now if you’ll excuse me, I have to contemplate factor analysis.