What surprises founders in their first causal read: Four reactions come up almost every time a founder reads their channels causally for the first time. None of them mean the tool is broken.
Read the full article below for detailed insights and actionable strategies.
The attribution problem
One sale. Four channels. 400% credit claimed.
Reported revenue: €400 · Actual revenue: €100 · Gap: €300
Four reactions come up almost every time, and none of them means the tool is broken. Knowing them in advance turns a confusing first read into a useful one.
The four
| Reaction | What is actually happening |
|---|---|
| "These numbers are much lower than my dashboards" | Platform reporting credits touches; this asks what caused the order |
| "It refused to score one of my channels" | That channel's spend is below what any method can resolve |
| "The ranges are enormous" | Your window did not contain enough variation to separate them |
| "Branded search barely counts?" | It captures demand other channels created |
Lower than the dashboards
Expected, and the size of the gap is itself informative. Ad platforms count conversions they touched under rules they set, and touches overlap, so the platform numbers sum higher than your actual revenue. Sum them for one window and divide by what your store took: above one is normal and quantifies the overlap. Method in the claim ratio.
A channel with no score
The honest behaviour. If a channel spends too little for its effect to be separated from ordinary week-to-week movement in your orders, any number produced for it is filled in rather than measured. Naming it as unmeasurable is the correct output, explained in the measurability floor.
The decision it points to is real: either fund that channel to a level where it can be read, or run it on stated judgement and say so.
Wide ranges
Usually a window problem rather than a data problem. If spend was flat across the period, there is nothing for the method to compare, and honest output is wide ranges everywhere. Pick a window with real variation in it, ideally eight to twelve weeks including a change you made deliberately.
Branded search
The one that provokes the strongest reaction. Branded search converts well because the person already typed your name, and something earlier made them type it. A causal read asks whether those orders would have arrived anyway, and often the answer is largely yes.
That does not mean stop bidding. There are defensive reasons that have nothing to do with incremental revenue. It means the reported figure is not the argument for the current spend level. More recurring patterns are in the lift surprises hiding in your own data.
What to do with a surprise
Not act on it that week. Check the window for anything unusual, check whether the surprising channel's range is narrow, and if the decision is large, run a proper test on that one channel before moving money. The order is in cut the channel you would holdout first.
The read itself is €99 once on a Google Analytics export, refundable if it does not move a budget decision, with a confidence interval, coverage and design label on every channel. The interactive demo shows the same output on a sample store with no signup.
Related answers
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Key Terms in This Article
Analytics
Analytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
Attribution
Attribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
Causality
Causality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
Confidence Interval
Confidence 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.
Conversion
Conversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
Dashboards
Dashboards are graphical user interfaces that provide at-a-glance views of key performance indicators (KPIs). They monitor campaign performance and visualize attribution insights.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
Incrementality
Incrementality measures the true causal impact of a marketing campaign. It quantifies the additional conversions or revenue directly from that activity.
Related Articles
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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