The 4 Reasons Your Attribution Numbers Lie
Double counting, correlation posing as causation, last-click blindness, demand harvesting. The four mechanisms that make your attribution numbers lie, with ecommerce examples.
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By Joris van Huët, Founder & CEOUpdated 3 min read
You are not imagining it. When the dashboards disagree and the budget gets harder to defend, there is a real mechanism underneath, and it is not one problem but four working together. Here is why your marketing attribution is wrong, one mechanism at a time, each with the shape it takes in an ecommerce account.
Joe is careful not to villainize the platforms. The problem is structural, not malicious:
"It makes sense that these platforms will self-promote. You can't blame them."
He then walks through why the default model buries the truth. In his words:
"Last click attribution means that a sale is going to be attributed to the last advertisement or email or touchpoint that got the last click prior to the sale."
Which is why, as he puts it, "last clicks happen at the bottom of the funnel, but you need to fill it at the top." Credit the bottom, starve the top, and the four mechanisms below compound.
1. Double counting: every platform grades its own homework
Each platform claims the sales it was near, on its own terms. A single purchase gets booked by Meta, Google, and TikTok at once. Sum the claimed revenue across channels and it exceeds real revenue, sometimes by half again. You cannot allocate honestly from numbers that describe more sales than you made.
2. Correlation posing as causation
Ads get shown to the people most likely to buy, because that is what optimization does. So the conversions that follow would often have happened without the ad. The platform reports them as caused. They were merely correlated. This is the difference between "was present at the sale" and "produced the sale," and budgets live or die on it.
3. Last-click blindness
Last-click hands all credit to the final touch and zero to everything upstream. The channel that created the demand, the video someone watched last week, gets nothing.
4. Demand harvesting
Retargeting and branded search catch people who already decided to buy. They convert efficiently and look like heroes, while the channels that generated the demand look weak. Fund the harvesters, starve the creators, and growth stalls while ROAS climbs.
How they compound
None of these is subtle in isolation. The damage is that they reinforce each other and repeat every quarter, so the misallocation is not a one-time error but a growing balance. Fixing each one, at a high level, means measuring incrementality rather than credit: asking what each channel caused, not what it touched.
Takeaway: Attribution is wrong for four structural reasons at once, and they compound. The fix is a causal question, not a better dashboard.
Watch the full breakdown above, or on YouTube. See the controlled experiments that prove the effect in What Happens When Companies Actually Test Their Ads, and start on the free foundation with Tier 1 DIY Attribution.
Frequently asked questions
Why is my marketing attribution wrong?
Four mechanisms compound: double counting across platforms, correlation reported as causation, last-click blindness that pays assist channels nothing, and demand harvesting where retargeting takes credit for demand other channels created.How do I fix attribution errors?
Measure incrementality rather than credit: ask what each channel caused, not what it touched. Free DIY hygiene reduces the noise; causal measurement on a GA4 export answers the causal question directly.
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.
- ConversionConversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
- CorrelationCorrelation is a statistical measure showing a relationship between variables; it does not imply causation.
- ExperimentsExperiments are scientific procedures that test hypotheses or demonstrate facts. In marketing, experiments like A/B tests determine the causal effect of campaign changes, enabling data-driven decisions.
- 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.
- Last Click AttributionLast Click Attribution: Assigns all credit for a conversion to the final marketing touchpoint before that conversion.
- Marketing AttributionMarketing attribution assigns credit to marketing touchpoints that contribute to a conversion or sale. Causal inference enhances attribution models by identifying true cause-effect relationships.
- 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.