Attribution model

Every model is a position on a question that has no factual answer: when several things preceded a sale, which of them caused it. There is no measurement that settles it, so the industry settled on rules instead, and each rule embeds an opinion.

Last click gives everything to the final touch. It is simple, unarguable and wrong in a specific direction: it credits whatever happened to be closest to the purchase, which is usually branded search or remarketing, and gives nothing at all to the advertising that created the demand in the first place.

First click does the opposite and is equally lopsided. Linear splits evenly and pretends every touch mattered the same. Time decay weights recent touches more heavily. Position-based typically gives 40% to the first, 40% to the last and divides the rest, which is a compromise rather than a finding.

Data-driven, now the Google Ads default, infers weights by comparing paths that converted against paths that did not. That is a genuinely better method than any fixed rule, and it still cannot see anything outside the platform operating it.

Learning weights from observed behaviour is a real improvement on asserting them, and it avoids the obvious distortions of the fixed rules. It is also proprietary, unauditable, and confined to one platform view of the world.

A model trained on Google data attributes confidently within Google, because that is the only data it has. The Meta impression three days earlier is not undervalued by it; the model never knew about it. Every platform data-driven model has the same blind spot in the shape of every other platform.

It also needs volume. Low-conversion accounts either cannot use it or get a model working from thin evidence, and the platform does not usually flag which situation you are in.

None of that makes it the wrong choice. It makes it a better rule with the same fundamental limit, which is worth understanding before describing it to a client as the accurate one.

Platforms change their defaults, and they have done so at scale. Google moved Google Ads to data-driven attribution as the default and has retired older model options; Meta has changed default attribution settings and reporting more than once.

When that happens, every historical number moves. Campaigns that looked strong under last click look weaker, upper-funnel activity looks better, and the totals shift. Nothing in the account changed.

The client sees a step change in a chart and asks what happened. An agency that can point at the migration, with the date, has explained a measurement change. An agency that cannot is explaining its own performance, and the difference in that meeting is considerable.

The habit that prevents it is boring and cheap: record the model and the conversion window on the report every month, so a change is visible as a change in the footnote rather than a mystery in the line.

The specific model matters less than keeping it stable, because trends are only meaningful when measured the same way throughout. A mediocre model applied consistently produces usable comparisons; the best model swapped in halfway produces none.

Where a choice exists, data-driven is generally the better description of reality for accounts with enough volume, and last click remains defensible for short, single-channel purchase journeys where there genuinely was not much before the final touch.

If a model has to change, change it at a period boundary, restate the previous period under the new model if the platform allows it, and say so in the report. Anything else hands the client a discontinuity to interpret on their own.

And do not change models because a quarter looks bad. It works exactly once, and the cost when somebody notices is the credibility of every number you have ever reported.