Measurement & Attribution Glossary

How credit for a conversion is assigned across ad touchpoints.

Incrementality test that turns spend on/off by region to isolate impact.

Measures the lift ads caused vs what would have happened anyway.

Statistical model measuring channel contribution using historical data.

Conversion from a user who saw but didn't click an ad.

Mobile device ID used for cross-app tracking, restricted since iOS 14.5.

How long after a click or view Meta can still credit a conversion.

Meta's randomised holdout test that measures true incremental conversions.

Google's ML model that distributes credit based on observed conversion paths.

Randomised users kept ad-free so you can measure true lift.

Apple's device ID; restricted by ATT since iOS 14.5.

Apple's App Tracking Transparency; broke cross-app deterministic tracking.

Conversions that would NOT have happened without the ad exposure.

YouTube conversion credited when a user watches ≥10s of a skippable ad then converts within 3 days.

Assigns 100% of conversion credit to the first touchpoint in the customer journey.

Apple's opt-in prompt that broke deterministic Meta attribution and forced CAPI + modelled conversions.

Assigns 100% of credit to the final ad click before conversion; GA4's legacy default.

Third-party SDK (AppsFlyer, Adjust, Branch) that attributes app installs across ad networks.

Model that distributes conversion credit across every touchpoint in the customer path.

'How did you hear about us?' checkout question; the cheapest zero-party attribution signal.

Drop in trackable conversions caused by ATT, ITP, cookie blocking, and consent refusals.

Apple's privacy-safe attribution framework for iOS app install ads; replaces IDFA-based tracking.

Attributing phone leads back to the ad or keyword that produced them via dynamic phone-number swap.

Privacy-safe environment where advertisers and platforms match audiences without sharing raw PII.

Bidding on downstream events (purchase, LTV, SQL) instead of top-of-funnel clicks or leads.

Attribution model that gives 100% of conversion credit to the first ad or channel a user encountered.

Turning ads on/off by region to isolate true incremental lift vs organic baseline.

Unattributed direct traffic driven by social shares, messaging apps, and word-of-mouth.

Holdout study proving whether ad spend actually caused conversions vs stealing organic credit.

Regression model that attributes revenue across channels using aggregated, cookieless data.

Estimated conversions Google or Meta report when direct measurement is blocked by consent or ITP.

Uploading closed-won or in-store sales back to Google/Meta to train bidding on true revenue.