What a real-time alert is actually detecting: Take a firing alert apart and you find one of four causes. Only one is a change in your marketing, and it is not the most common.
Read the full article below for detailed insights and actionable strategies.
Channel comparison
Platform-reported vs. causal contribution
Platform-reported numbers double-count assists; causal inference reveals reality
Take apart an attribution alert that fired and you usually find one of four causes, and only one of them is a change in your marketing. Knowing the four turns a fire drill into a two-minute triage.
The four
| Cause | Frequency | How to check |
|---|---|---|
| Sampling variation inside the interval | Most common | Is the move smaller than the interval width? |
| A collection change | Common | Did coverage move at the same time? |
| A definition change | Occasional | Did anyone alter grouping, window or timezone? |
| An actual change in your marketing | Least common | Is there a spend or creative change with the right timing? |
Check them in that order, because the first two account for most firings and take seconds to rule out.
Sampling variation
If the estimate moved less than the width of its own confidence interval, nothing has been detected. The number is a draw from a distribution and it landed somewhere else this week. This is not a subtle statistical point; it is the single most common reason an attribution alert fires.
A collection change
Coverage moving at the same time as an estimate is the signature. A consent banner change, a tag misfiring, an app update that strips referrers, a bot filter adjustment: all of these change what your analytics sees without anything changing in your marketing.
The estimate then shifts because the input shifted. Check coverage first, every time. The concept is explained in the unassigned traffic problem.
A definition change
Somebody regrouped channels, changed a lookback, or a platform quietly updated its default attribution window. Your number moves and your business did not. This is the one that produces the longest investigations, because there is no trace of it in the marketing data at all.
The defence is a written, dated record of your definitions, which is the first step in automating attribution reporting in one afternoon.
An actual change
Last on the list because it is least common, not because it is unimportant. If the first three are ruled out and the timing lines up with a spend or creative change, you have found something real, and the right response is to design a test rather than to act immediately.
Why this list is an argument against alerting
If three of four firings are artefacts, an alerting scheme is mostly a machine for generating investigations. A recurring review with the four checks built into its agenda catches the same real changes and produces far fewer false ones. The case is in a weekly budget review instead of alerts.
What our read gives you for the triage
Estimate, interval, coverage and design label per channel, so causes one and two are checkable directly from the output. From a Google Analytics export, €99 for a first read, refundable if it does not move a budget decision. No alerting layer and no automated budget action, as explained in why real-time attribution alerts mislead.
The interactive demo shows the fields you would triage against, with no signup.
The triage in one line
Smaller than the interval, coverage moved, definitions changed, or something real. In that order, every time.
Related answers
Get attribution insights in your inbox
One email per week. No spam. Unsubscribe anytime.
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.
Attribution Report
Attribution Report shows which touchpoints or channels receive credit for a conversion. It identifies which campaigns drive desired actions.
Attribution Window
Attribution Window is the defined period after a user interacts with a marketing touchpoint, during which a conversion can be credited to that ad. It sets the timeframe for assigning conversion credit.
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.
Google Analytics
Google Analytics is a web analytics service that tracks and reports website traffic.
Related Articles
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
Ready to see your real numbers?
Own the budget? Upload your GA4 export and see which channels drive incremental sales, with confidence intervals, in minutes. Have to defend it? Start with the live demo and take the read to your CFO.
Full refund if you don't see value.
Stay ahead of the attribution curve
Weekly insights on marketing attribution, incrementality testing, and data-driven growth. Written for the person who owns the budget and the person who has to defend it.
No spam. Unsubscribe anytime. We respect your data.