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.
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By Joris van Huët, Founder & CEOUpdated 2 min read
The four
| Reaction | What is actually happening |
|---|---|
| "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 |
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.
What to do with a surprise
Not act on it that week. Check the window for anything unusual, check the surprising channel's data-health score, 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 within 30 days, no questions asked, with a data-health score rather than a confidence interval, and a next step, on every channel.
Related answers
Frequently asked questions
Why are causal numbers lower than my ad dashboards?
Because platforms credit conversions they touched under rules they set, and touches overlap between platforms. A causal read asks whether the order would have happened anyway. Summing platform claims against your actual revenue quantifies the difference.Why are all my confidence ranges so wide?
Usually because spend was flat across the window, leaving nothing for the method to compare. Choose a window with genuine variation, ideally eight to twelve weeks including a change you made deliberately.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AnalyticsAnalytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- 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.
- ConversionConversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
- DashboardA dashboard is a visual display of key information required to achieve specific objectives. It consolidates data onto a single screen for quick review.
- DashboardsDashboards are graphical user interfaces that provide at-a-glance views of key performance indicators (KPIs). They monitor campaign performance and visualize attribution insights.
- Google AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.
- 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.