The Most Quoted Line in Management Isn't Goodhart's.

What they were looking at

In the early 1970s the Bank of England changed how it managed the money supply, and came to rely on statistical relationships between money and the wider economy. Charles Goodhart, then a monetary adviser at the Bank, described how that went in a paper for a Reserve Bank of Australia conference in July 1975. It opens:

“The subsequent experience of trying to operate this revised system has, however, been troublesome and at times unhappy.” Goodhart, “Problems of Monetary Management: The U.K. Experience,” 1984 reprint

Twenty-two years later the anthropologist Marilyn Strathern was writing about something else entirely: the boom in auditing and ratings in British universities, in a paper adapted from a 1997 lecture at Girton College, Cambridge.

What they found

Goodhart’s observation, as his paper is usually quoted:

“Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” Goodhart (1975), as reprinted in A. S. Courakis (ed.), Inflation, Depression, and Economic Policy in the West (1981), p. 116

The relationship the Bank relied on held while nobody leaned on it. Once policy was steered by it, behavior adjusted and the relationship broke.

Strathern’s sentence is the one everyone quotes:

“When a measure becomes a target, it ceases to be a good measure.” Strathern (1997), p. 308

She credits the “Goodhart’s law” label to Keith Hoskin (“Hoskin describes this as ‘Goodhart’s law’”), and gives her own example from university degrees, where an upper-second-class result (a “2.1”) had become the expected grade:

“The more a 2.1 examination performance becomes an expectation, the poorer it becomes as a discriminator of individual performances.” p. 308

Her broader point was that audit “does more than monitor—it has a life of its own that jeopardizes the life it audits.”

What it means

Both versions say the same thing from different ends. A number is useful because it moves with something you care about. Put pressure on the number, and people (and algorithms) find ways to move it that don’t move the thing.

That doesn’t mean you should stop measuring. It means the measure you reward will be gamed first, so it had better be as close to value as you can get, and you should keep watching the measures nobody is rewarded on.

Where it shows up in a revenue team

• Cost per lead was a good measure of marketing efficiency until it became the target. Then the cheapest leads won.
• Approval rate, churn rate, meetings booked: each is a decent measure right up until a team is paid on it.
• Algorithms are the purest case. Tell an ad platform what counts as a conversion and it will hit that number with no regard for anything else.
• Anchor the target to value, and keep reporting what each cohort returns over time, which is the hardest number to game.