The AI referral baseline window is closing
A clean before reading for assistant referrals only exists while the channel is still small. Once it is material, the comparison period has already gone.
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By Joris van Huët, Founder & CEOUpdated 3 min read
Every before-and-after needs a before. For assistant referrals, the before period is being consumed right now, and it does not come back.
Why this one is genuinely time-limited
Most measurement problems can wait. This one has a specific structure: to know what a channel added, you need a period where it was absent or negligible, measured the same way.
That period is currently in your analytics history. Each month it recedes further from the current mix, and comparisons across it get weaker as everything else about the business changes too.
What a baseline actually is
Not a screenshot. A baseline is the raw export for a defined window, kept in a form you can re-read later with a different method than the one you would use today.
| Keep | Why |
|---|---|
| Raw session and order export, monthly | Re-readable with any method, later |
| The hostname list you used | So a later comparison uses like for like |
| A note of what else changed | Campaign launches and site changes confound the read |
The third row is the one people skip and regret. A baseline with no changelog is a number with no context, and a year later nobody remembers the site migration that sits in the middle of it.
The honest limit
Even a perfect baseline will not settle whether assistant traffic is incremental. Before-and-after across a period where everything else also moved is a weak design, and calling it strong does not make it so.
What the baseline buys is the option to run a real comparison later. Without it, the option is gone.
The cheap action
Export this month. Write down the hostname list and anything material that changed. That is the whole thing, and it takes about an hour.
If you want the read as well as the baseline, a causal read on that Google Analytics export shows what each channel caused next to what last-click gave it, with a data-health score for how far to trust each number rather than a confidence interval, for 99 euro once, refundable within 30 days, no questions asked.
Related answers
Frequently asked questions
Why is there a deadline on measuring AI referral impact?
Because a before-and-after needs a period where the channel was absent or negligible, measured the same way. That period is in your history now and recedes every month as the rest of the business changes around it.What should a baseline actually contain?
The raw session and order export for a defined window, the assistant hostname list you used, and a note of anything else material that changed. The changelog is the part people skip and later regret.Will a baseline prove AI traffic is incremental?
No. Before-and-after across a period where other things also moved is a weak design. What the baseline buys is the option to run a stronger comparison later, which is lost entirely without it.
Go deeper: Incrementality testing, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Keep reading
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.
- Attribution ReportAttribution Report shows which touchpoints or channels receive credit for a conversion. It identifies which campaigns drive desired actions.
- ClickClick is the action a user takes to interact with a digital advertisement, redirecting them to a website or landing page. Clicks are a fundamental metric for measuring ad engagement and a primary input for click-based attribution models.
- 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.
- 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.
- SessionA Session is a group of user interactions with your website within a given timeframe. It can include multiple page views, events, and transactions.