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There’s a paradox that does more damage to customer experience than bad data or tight budgets. It’s called Goodhart’s Law. British economist Charles Goodhart formulated it in 1975 in the context of monetary policy, but its reach extends far beyond central banking: once a measure becomes a target, it ceases to be a good measure.
In CX, this law plays out with remarkable precision and with consequences that can be fatal to a business, even when they’re completely invisible at first glance.
Scores go up. Customers walk out.
Imagine a company whose NPS climbs every quarter. Leadership is pleased. Bonuses are paid. Then the annual data arrives, and it turns out that churn has stayed the same or got worse.
How is that possible?
The answer has nothing to do with customers. It lies in a reward system that has quietly learned to optimise the metric rather than the experience. Contact centre employees whose bonuses depend on NPS will naturally start doing things that lift the score without necessarily improving the experience. They call customers before sending a survey. They ask for a high rating. They time the survey for a moment when the customer is in a good mood. Or they skip it altogether if the interaction went badly.
Ipsos has named this behaviour survey gaming the manipulation of surveys by employees or internal stakeholders to artificially inflate scores. Their analysis shows it can range from directly asking customers for a specific rating to tapping satisfaction terminals in stores with no controls over who presses the button or how many times (Ipsos, Gaming the Score: When Feedback Becomes Fiction, 2026).
The data is consistent. A 2024 study published in the International Journal of Market Research confirmed that the NPS calculation methodology itself introduces statistical noise that makes it difficult to detect genuine shifts in customer sentiment and that’s before anyone even tries to game the results (CMSWire, Why NPS Is Lying to You About Customer Experience, 2026).
Goodhart’s Law in action: from the NHS to Wells Fargo
To be clear that this isn’t a CX-specific problem, two classic examples are worth revisiting.
In the 1980s, British hospitals were given a target: no patient should wait more than 18 weeks for treatment. The target was met. Waiting times fell. But a closer look revealed that doctors were deliberately delaying formal referrals to avoid starting the clock. Complex cases were avoided because they dragged down the statistics. Simple cases multiplied at the expense of those that genuinely needed urgent attention. The metric was hit. The quality of care was not.
Wells Fargo is probably the most cited example of metric destruction in the corporate world. In the early 2000s, management began closely tracking the number of new accounts opened at each branch and tied that number to bonuses, career progression, and job security. The outcome was predictable: managers started opening accounts without customers’ knowledge. By 2017, the bank admitted to 3.5 million fraudulent accounts. Regulatory fines reached $185 million; the total settlement with the US Department of Justice and the SEC came to $3 billion. 5,300 employees were dismissed. None of them had dreamed up the fraud themselves — they were simply responding to a badly designed incentive system (Ethics Unwrapped, University of Texas, 2020).
The critical point in all these cases is the same: no one consciously set out to corrupt the metric. The system corrupted it by elevating the metric to a goal.
The problem with NPS, CSAT, and CES once bonuses are attached
The three most widely used CX metrics NPS, CSAT, and CES share one structural characteristic: they are proxy metrics. They don’t directly measure loyalty, satisfaction, or low friction. They measure what customers report about how loyal, satisfied, or unburdened they feel. That distinction matters enormously.
A proxy metric is inherently vulnerable. Once there is financial or career pressure tied to its outcome, people optimise the proxy not the reality it’s supposed to represent.
Fred Reichheld, the creator of NPS and a partner at Bain & Company, has named this dynamic the biggest systemic failure in NPS implementation: “Bad things happen when you just focus on it as a score. When scores are the objective, they no longer help people. It doesn’t inspire them to learn. All they want is a 10.” (Bain & Company, Refocusing NPS for Earned Growth, 2022). He went further still: programmes that link NPS to frontline employee bonuses typically collapse within one to two years.
In the Harvard Business Review article Net Promoter 3.0 (2021), Reichheld and his colleagues at Bain identified linking NPS scores to bonuses for frontline employees as one of the primary forms of misuse that has undermined the credibility of the entire system. In response, they proposed a supplementary metric Earned Growth Rate which measures the proportion of revenue coming from returning customers and those acquired through referrals. Unlike NPS, it’s a hard accounting metric that cannot easily be gamed through social engineering.
The situation with CSAT and CES is much the same. CSAT typically measured as an average rating on a 1–5 or 1–10 scale immediately after an interaction is highly sensitive to timing. A survey sent right after a problem is resolved generates a very different score from one sent a week later. Teams whose bonuses depend on CSAT know this, and they time their surveys accordingly.
CES, designed by the CEB (now Gartner) team in 2010 as a predictor of customer loyalty through low-effort interactions, is perhaps the most robust of the three but it isn’t immune either. Agents can learn to frame survey questions in ways that imply ease (“I hope we made that easy for you?”), or actively coach customers on how to respond.
Why this happens: psychology and systems
Goodhart’s Law is not the product of bad people. It’s the product of rational people responding to badly designed systems.
Psychologists refer to this as surrogation the state in which people begin to conflate a goal (better customer experience) with its proxy (a higher score). Research by Michael Harris and Bill Tayler in Harvard Business Review (Don’t Let Metrics Undermine Your Business, 2019) showed that surrogation kicks in almost automatically once metrics are tied to rewards, even among people who originally understood the goal correctly.
The outcome is well documented: the organisation optimises the number instead of the reality. The number goes up. The reality stagnates or deteriorates. Leadership is happy. Customers are not.
This state is also self-reinforcing. Once metrics are being gamed, they lose their diagnostic value. Managers no longer have reliable data to make decisions with. So they make decisions based on numbers that don’t reflect reality and the cycle continues.
What to do about it: from measurement to learning
The real question, then, is not “How do we protect our metrics from being gamed?” It’s “What are metrics actually for?”
Reichheld and Bain & Company have consistently argued that NPS and the same applies to CSAT and CES was designed as a learning tool, not a reward mechanism. When survey results feed into a team discussion about what’s frustrating customers and what’s working, the metric retains its value. When they feed directly into a bonus calculation, they lose it.
From a reward system design perspective, this leads to several practical conclusions.
Metrics tied to bonuses should reflect behaviour, not survey responses. Rather than using NPS as a KPI linked to incentive pay, it makes more sense to track retention rates, repeat purchase rates, or Earned Growth Rate in other words, what customers do, not what they say.
Every metric needs a shadow metric a counter-measure that reveals whether the problem has simply shifted elsewhere. If you’re driving down average handle time (AHT), you need to monitor repeat contact rates. If you’re optimising CSAT immediately post-interaction, you need to track NPS with a time delay. Without a shadow metric, Goodhart’s Law is virtually inevitable.
Survey data should be separated from managerial accountability for scores. An employee should be evaluated on whether customer feedback led to action not on what number the survey produced. That is a fundamental structural difference.
To close: metrics stop being useful the moment they become the objective. This isn’t an argument against measurement — it’s an argument for being far more deliberate about why and how we measure. Goodhart’s Law cannot be solved by adding more metrics. It can be mitigated by distinguishing between metrics for learning and metrics for accountability, and by making sure you never use one where the other belongs.
