Modelled conversions are Google’s estimates of what happened where it could not watch, and they arrive in your reports in the same column as the ones it actually counted. Nothing labels them. Nothing separates them. A figure you quote in a meeting is a blend of measurement and inference, in proportions you cannot see.
That is not a scandal. It is a reporting characteristic worth understanding before somebody asks you how confident you are in a number.
This is for you if you run a consent banner and read Google Ads reports.
What modelled conversions are estimating
They fill gaps created by consent refusals and unmatched clicks.
Somebody refuses tracking consent, clicks your advert, and converts. That conversion happened. Google cannot observe it, because the visitor declined the mechanism that would have connected the two.
So Google looks at the traffic it can observe, works out the rate at which comparable visits convert, and applies that rate to the traffic it cannot see. The output is an estimate of how many conversions probably occurred.
The logic is defensible. It is the same reasoning any survey uses when it generalises from a sample, and the sample here is genuinely large.
What differs from a survey is presentation. A survey reports a margin of error. This arrives as a whole number in a column labelled Conversions.
Why modelled conversions sit unlabelled
There is a design decision here that shapes everything about how you should read your reports.
Google puts them in the standard Conversions column. No asterisk, no separate line, no toggle to isolate them. From the interface you cannot determine what share of any figure was observed.
I understand the reasoning. Splitting them out would invite people to ignore the modelled half, which would systematically undercount and produce worse decisions than including them does.
The consequence is still that your reporting confidence is unmeasurable from the outside. When somebody asks how solid a number is, the honest answer involves a shrug you cannot quantify.
Which is a reason to describe your reports differently rather than to distrust them. “Google’s reported conversions, part observed and part modelled” is accurate. “Conversions” implies a precision that is not there.
The five day settling period
Modelled conversions can take up to five days to stabilise, and this causes more confusion than the modelling itself.
Recent data keeps changing after you look at it. A campaign that read poorly on Tuesday can read acceptably by Sunday, with no change made and no action taken.
The practical damage is that people react to the unsettled version. Somebody sees a weak Monday, pauses a campaign, and never learns that the figure would have corrected itself by Friday.
So the rule I work to is simple. The most recent five days are provisional. Do not judge them, do not report them as final, and do not act on them unless the movement is far too large to be a settling effect.
That single habit prevents a whole category of unnecessary intervention.
Who actually gets modelled conversions
Not everybody, and the requirement is stricter than it first appears.
Google needs 700 ad clicks over a seven day period, per country and domain grouping before modelling engages. That is per country, not across your account.
An advertiser spread across several markets can fail that threshold in each one while looking substantial overall. Below it, consent-denied conversions are lost and nothing fills the gap.
So the smaller advertiser carries the full cost of consent refusal without access to the mechanism designed to offset it. Worth knowing before you build an expectation around recovery that will not arrive.
There is no indicator in the interface telling you which side of the threshold you are on. You have to work it out from your own click volumes.
What cannot be checked
Google publishes no accuracy figures for this, and gives you no way to test it on your own account.
You cannot compare modelled against actual, because if you could observe the actual you would not have needed the model. That circularity is inherent rather than a gap somebody forgot to fill.
There is also no independent study of real-world modelling accuracy that I can find. What exists is vendor material and anecdote, mostly from companies selling consent or measurement products.
So the honest summary is that modelled conversions rest on sound reasoning, at large scale, with no published validation and no way for you to audit them. That is a reasonable thing to accept and an unreasonable thing to forget.
Why they are still worth having
The argument for them is about bidding rather than reporting, and it is the stronger case.
Smart bidding learns from the conversions it can see. If a meaningful share of your real conversions are invisible, the algorithm builds its model on a skewed sample, and it will systematically undervalue whichever audiences refuse consent most.
Modelled conversions feed that same optimisation. Even an imprecise estimate of the missing group is better input than pretending the group does not exist.
So I would rather have them than not, while reporting them honestly. Those two positions are compatible and they frequently get treated as opposites.
How modelled conversions change what you should argue about
There is a category of internal disagreement this makes unwinnable, and recognising it early saves everybody time.
Somebody in finance compares Google’s reported conversions against the sales system and finds a gap. Somebody in marketing points out that consent refusals mean the platform cannot see everything. Both are correct, and neither can prove their position from the data available, because the disputed portion is precisely the portion nobody can observe.
That argument runs indefinitely if you let it, and it produces nothing.
The way out is to stop trying to reconcile the two numbers exactly and agree what each is for instead. Your sales system is the record of what the business earned. The platform figure, modelled portion included, is the steering instrument for deciding where next month’s budget goes.
Write that down once, get both parties to agree to it, and the monthly disagreement stops. It is a governance fix rather than a measurement one, and it is more durable than any amount of investigation.
What to change this week
Three steps.
Work out whether you clear 700 clicks in seven days in each country you advertise in, so you know whether any of this applies to you at all.
Then stop treating the most recent five days as final in any report that leaves your desk, and say why in a footnote.
Then change how you describe the number. “Google-reported conversions, partly modelled” costs you nothing and prevents a conversation you do not want to have later.
Google Consent Mode covers the threshold and the banner configuration behind this, what conversion numbers actually measure is the counting underneath, server side tracking is the architecture that addresses a different part of the same loss, and why your reported cost per lead is wrong covers what all this does to your efficiency figures. The case studies show the numbers once the measurement is honest. To find out how much of your reporting is estimated, book a teardown.