In modern organisations, metrics have become a kind of organisational language. Engagement scores, NPS, conversion rates, brand awareness, retention, consideration… numbers increasingly shape how success is defined, and decisions are made. 

But metrics have a strange tendency to change behaviour in unexpected ways. 

This is the idea behind Goodhart’s Law, a principle coined by economist Charles Goodhart in the 1970s: 

“When a measure becomes a target, it ceases to be a good measure.” 

At first glance, the idea seems counterintuitive. Surely measuring something improves it? In many cases, it does. The problem emerges when people begin optimising for the metric itself rather than the broader outcome the metric was originally intended to represent. 

Once this happens, the measure stops reflecting reality and instead starts reshaping it. 

I remember working at Woolworths years ago, checkout staff were heavily assessed on “scan rates” – essentially how many items could be scanned per minute. On paper, it made sense. Faster scanning implied greater efficiency and shorter wait times. 

But over time, the metric began shaping behaviour in negative ways. The staff most obsessed with scan rates were often the roughest packers, prioritising speed above all else. Items were crammed into bags, groceries were damaged, and customer interactions became transactional. Meanwhile, I was usually slower because I took more care packing bags properly and interacting with customers, yet I frequently received positive customer feedback. 

The metric captured speed, but it failed to capture the broader experience that customers actually valued. Once the scan rate became the target, it stopped functioning as a reliable measure of good service. 

The same dynamic appears across all industries. 

In research, metrics are often treated as proxies for understanding. Response rates suggest engagement. Brand awareness implies market presence. Social mentions signal relevance. Net Promoter Scores stand in for loyalty. 

But once these measures become embedded as performance targets, behaviour adapts around them. 

A brand chasing engagement metrics may prioritise emotionally provocative content because outrage travels faster than nuance. A research team incentivised around survey completion rates may unintentionally optimise for speed over depth or representativeness. The metric still exists, but what it actually represents begins to shift. 

In this sense, Goodhart’s Law is less about bad metrics and more about the limits of simplification. 

Most organisational goals are complex. Customer trust, cultural relevance, innovation, brand strength and employee wellbeing are difficult to reduce to a single number. The risk is that organisations begin prioritising the metric itself over the thing the metric was originally designed to measure. 

This is particularly relevant in an era increasingly dominated by data visibility. Digital platforms provide organisations with unprecedented volumes of measurable information. Social media provides an obvious example. High visibility metrics (likes, views, shares, followers) create strong behavioural incentives because they are public, immediate and comparable. Over time, creators and brands begin producing content designed to satisfy the algorithm rather than audiences themselves. 

The result is a kind of cultural optimisation loop where engagement becomes both the measure and the objective. Ironically, this can undermine the very thing brands are often trying to build: long-term distinctiveness and trust. 

None of this means metrics is useless, rather the opposite. Measurement remains essential. The challenge is ensuring metrics remain connected to the broader human realities they are intended to represent. Goodhart’s Law simply reminds us that metrics should function as indicators, not substitutes for judgment. 

Strong organisations understand the difference between signals and goals. They combine quantitative measures with qualitative understanding. They recognise that numbers can illuminate behaviour, but rarely explain it fully. 

For market researchers, this distinction matters deeply. Research is ultimately an attempt to understand human behaviour in all its complexity and contradiction. The challenge is ensuring our tools continue reflecting reality rather than quietly reshaping it. 

Because the moment a metric becomes the objective itself, we may stop measuring the thing we actually care about.