Essay
Why Marketing Metrics Drift From Reality
The measurements you rely on are probably telling you less than you think.
Marketing runs on metrics. Dashboards, reports, KPIs, scorecards. We measure everything we can measure and make decisions based on the numbers. But the numbers often lie, not through deliberate deception, but through drift. The metric that once meant something gradually loses its connection to the thing that matters.
The Mechanisms of Drift
Several mechanisms cause metrics to drift from reality:
Goodhart's Law. When a measure becomes a target, it ceases to be a good measure. People optimize the metric rather than the outcome the metric was meant to indicate. The metric improves while the underlying reality may not.
Proxy decay. Metrics are proxies for things we actually care about. Clicks proxy for interest. Leads proxy for potential customers. Over time, the relationship between proxy and reality weakens. The proxy keeps getting measured; the relationship gets assumed rather than verified.
Context change. A metric that meant something in one context loses meaning when context changes. A click on a desktop browser is different from a tap on a mobile phone. The same metric name covers increasingly different behaviors.
Gaming and manipulation. When incentives attach to metrics, people find ways to improve the metrics without improving outcomes. Not always consciously dishonest, but optimization pressure finds the gaps.
Examples of Drift
Email open rates. Once a decent proxy for engagement. Now distorted by image blocking, privacy features, and machine opens. The number still gets reported. The meaning has shifted dramatically.
Click-through rates. Higher CTR used to indicate more compelling ads. Now it can indicate broader match types, accidental clicks, or bot traffic. CTR as a proxy for quality has weakened.
Lead volume. More leads once meant more potential customers. As lead quality varies and definitions shift, lead volume can increase while actual opportunity decreases. More leads is usually the wrong goal for this reason.
Conversion rates. Optimizing conversion rates can improve the metric while degrading business outcomes. Filter harder at the top of funnel, conversion rate improves, total conversions might drop.
Why Drift Goes Unnoticed
Metric drift often goes unnoticed because:
Measurement continues. The dashboard keeps updating. The reports keep generating. Activity around the metric continues. This creates an illusion of connection to reality.
Comparison validates. If everyone uses the same drifted metrics, comparisons between companies or campaigns seem meaningful. The shared framework masks the shared drift.
Incentives preserve. When compensation, budgets, or status depend on metrics, people resist questioning them. Admitting the metric has drifted threatens the systems built around it.
Alternatives are hard. If the current metric is broken, what replaces it? Without clear alternatives, people stick with what they have.
The Attribution Problem
Attribution models are particularly susceptible to drift. They started as attempts to understand which marketing activities drive results. They have become:
- Tools for budget negotiation between channels
- Mechanisms for gaming to capture credit
- Generators of false precision about inherently uncertain relationships
The numbers look precise. The connection to actual causation has drifted substantially.
Signs of Drift
How to recognize when metrics have drifted:
Metrics improve but outcomes do not. If the dashboard looks great and business results are flat, the metrics may have decoupled from reality.
Metrics are easy to hit. If targets are consistently achieved, either you are excellent or the metrics have drifted to measure what is easy rather than what matters.
New tactics keep requiring new metrics. When each change requires redefining what gets measured, the underlying metrics may have lost meaning.
You can no longer explain the connection. If you cannot articulate why this metric predicts business outcomes, the connection may have weakened without your noticing.
Correcting for Drift
Anchor to business outcomes. Revenue, profit, customer acquisition cost. These drift less than intermediate metrics. Keep returning to them as reality checks.
Periodically validate proxies. Is the proxy still connected to what it is supposed to proxy? Check periodically rather than assuming.
Use multiple measures. Triangulate with different metrics. If they agree, greater confidence. If they diverge, investigate.
Question established metrics. Just because something has always been measured does not mean it should continue to be. Legacy metrics persist past their usefulness.
Resist over-targeting. The more precisely you target metrics, the more you incentivize gaming. Some imprecision preserves measurement validity.
The Operator Perspective
Systems scale judgment. When metrics drift, the judgment being scaled becomes flawed. Systems optimizing drifted metrics produce systematically wrong outcomes.
The best operators hold metrics loosely. They use metrics as inputs to decisions, not substitutes for judgment. They maintain skepticism about precision. They keep asking whether the numbers still mean what they used to mean.