The cost of cutting the wrong channel
A wrong cut costs twice: the revenue it removes, and the way that loss surfaces somewhere else entirely, where nobody connects it back to the decision.
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By Joris van Huët, Founder & CEOUpdated 3 min read
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A wrong cut costs twice: the revenue it removes, and the way that loss surfaces somewhere else, where nobody connects it back. The second is why wrong cuts persist.
Why the loss lands elsewhere
Cut an upstream channel that was creating demand, and the decline shows up in the channels that were harvesting it. Branded search softens. Retargeting pools shrink. Direct traffic thins.
Each of those looks like a problem with that channel, and each gets investigated as one. The cut that caused it is weeks in the past and appears in a different report.
The three ways a wrong cut happens
| Cause | Signature |
|---|---|
| Cutting on no evidence | The channel had no number, and no number read as no value |
| Cutting on an unresolved estimate | The interval spanned break-even and the midpoint was below it |
| Cutting on proximity credit | The channel created demand credited to something closer to the sale |
The first is the most common and the most avoidable. A channel too small for any method to resolve should be named as unmeasurable rather than scored, so that a decision about it is visibly a judgement call. The arithmetic is in the measurability floor.
Pricing a wrong cut
Take the channel's monthly spend and its plausible causal contribution from a read with a stated interval. The loss is not the spend saved, it is the revenue that spend was generating, and for a genuinely working channel that is a multiple.
Then add the investigation cost of the mystery decline in three other channels, which is real and rarely attributed to its source.
The cheap insurance
Stage the cut. Instead of removing a channel, reduce it by a third and re-read after a full window. If the causal estimate and your revenue both hold, reduce again. If they do not, restore it and you have lost a third of a month rather than a channel.
Staging costs almost nothing and it converts an irreversible decision into a reversible one, which is what you want when the evidence is an unresolved interval.
The check before any cut
Is the interval entirely on one side of break-even. Did coverage hold steady in this window.
The read that answers the first two is €99 once on a Google Analytics export, returning per channel what it caused next to what last-click gave it, a data-health score rather than a confidence interval, and a next step, refundable within 30 days, no questions asked.
The stronger evidence, when the stakes justify it
A geo holdout on the channel before cutting it. Weeks and some forgone revenue, and it is the only design that settles the question, which is why it belongs on the channel where being wrong is most expensive. The ordering argument is in cut the channel you would holdout first.
Related answers
Frequently asked questions
What happens when you cut the wrong marketing channel?
The revenue loss appears in the channels that were harvesting the demand it created: branded search softens, retargeting pools shrink, direct thins. Each looks like its own problem, and the cut that caused it is weeks earlier in a different report.How do I reduce the risk of a wrong cut?
Stage it. Reduce by a third and re-read after a full window rather than removing the channel. If the estimate and revenue hold, reduce again; if not, restore. That converts an irreversible decision into a reversible one.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Attribution ReportAttribution Report shows which touchpoints or channels receive credit for a conversion. It identifies which campaigns drive desired actions.
- Confidence IntervalConfidence Interval is a statistical range of values that likely contains the true value of a metric. In marketing analytics, it quantifies uncertainty around estimates, indicating the precision of an outcome or causal effect.
- Direct TrafficDirect Traffic refers to website visitors who arrive by typing the URL directly into their browser or through bookmarks. They do not come from search engines or referrals.
- Google AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.
- Holdout TestA holdout test is an experiment that keeps a random part of an audience from seeing a campaign and compares its results with those of the part that could see it. The held-out part is the control group, so the gap in outcomes estimates what the campaign added.
- IncrementalityIncrementality is the part of an outcome, such as orders or revenue, that a marketing activity caused: the extra results compared with what would have happened without it. It is a causal effect, not the number of conversions credited to a channel.
- RetargetingRetargeting is online advertising that targets users who have previously interacted with your website or content. Attribution analysis shows the causal role of retargeting in driving conversions and improving ad spend.