Goodhart's Law states that when a measure becomes a target, it ceases to be a good measure. In modern marketing, elevating diagnostic metrics to definitive success criteria distorts objectives and creates harmful feedback loops. Focusing exclusively on immediate returns and efficiency can produce strong short-term metrics while undermining long-term growth. Hyper-targeting loyal customers can generate high ROAS but cause audience fatigue, neglect acquisition, reduce cross-selling, and stifle innovation. Over time, these effects plateau customer acquisition and erode market share, leaving revenue growth stalled despite impressive metric performance.
In this 'outcomes era' of advertising, good marketers are continually optimizing media buys and driving improved performance. This yields great results when done properly, but all too often, obsession with immediate returns and efficiency leads to stagnating growth. It brings to mind Goodhart's Law: "When a measure becomes a target, it ceases to be a good measure." This law, originally applied to monetary policy, is relevant in the world of modern marketing, where the pressure for measurable results often transforms useful diagnostic tools into a detriment to true success.
What does this actually mean? When we elevate a metric from its intended role (as an indicator) to the ultimate arbiter of success, we obscure the true goal we're trying to measure. The metric becomes divorced from its original purpose, creating a feedback loop that harms the very thing you're trying to improve. Consider this scenario: a direct-to-consumer skincare brand achieved a 6:1 return on ad spend (ROAS) on its Facebook campaigns by aggressively targeting only its most loyal customers with its best-performing products. The metric looked fantastic on paper, but they had unknowingly created several problems.
Firstly, the campaign led to audience fatigue, over-saturating their most engaged customers while ignoring potential new ones. It also resulted in product tunnel vision, whereby the brand stopped promoting its full product range, so missed cross-sell opportunities. Similarly, innovation stagnation meant that new product launches struggled because the algorithm only promoted proven winners. Lastly, it also led to market share erosion, with competitors capturing new customers that the brand had ignored.
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