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Measurement

What is Attribution Window?

An attribution window is the period following an ad interaction during which a resulting conversion will be credited to that ad.

Windows come in two kinds. Click-through windows credit conversions after someone clicks. View-through windows credit conversions after someone merely saw the ad without clicking, which is a far weaker signal.

Meta's current default is 7-day click and 1-day view. Google Ads defaults vary by conversion action and can extend to 90 days. TikTok, LinkedIn, and others each set their own.

The critical consequence is that ROAS figures measured on different windows are not comparable numbers. Widening a window raises reported performance without changing anything real, which makes window changes a common source of phantom improvement.

Why Attribution Window matters

Window settings are one of the most common sources of confusion between marketing and finance. A campaign that 'improved 40%' after someone widened the attribution window didn't improve at all — and rebuilding trust after that's discovered is expensive.

Why view-through inflates

With 1-day view attribution, someone who scrolled past your ad without engaging and bought eight hours later gets credited to that ad. In high-frequency remarketing, this attributes a large share of conversions that would have happened regardless. It's the single largest contributor to platform ROAS overstatement.

Common mistakes

  • Comparing periods across a window change

    Any before-and-after comparison spanning a settings change is invalid. Document window changes in the same place you record campaign changes.

  • Using long windows in short-cycle categories

    A 28-day window on a product people buy within a day mostly captures coincidence rather than causation.

  • Summing across platforms with different windows

    Combining Meta 7-day-click with Google 30-day-click produces a number that means nothing.

  • Never testing without view-through

    Comparing click-only against click-plus-view gives a rough sense of how much of your reported performance rests on the weaker signal.

Applied, not theoretical

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