View-through window length: how one setting moves attributed revenue

Extending a view-through window can move revenue onto a campaign without any new clicks. This lab shows the mechanical effect on a synthetic event list.

Define the calculation before using it

Enter click-through attributed revenue that does not depend on the view window, view-through revenue observed at the short window, and additional view-through revenue that appears only when the long window is enabled. Compare the two campaign totals.

Work through the synthetic example

A synthetic campaign with 6,000 click-through units, 800 view-through units at one hour and 1,500 extra units at 24 hours reports 6,800 versus 8,300. The extra 1,500 is a window effect, not proof of incremental players.

Use the artifact and preserve its assumptions

Open the editable calculator to change the inputs and inspect the sensitivity view. The CSV records synthetic inputs and expected outputs; the JSON fixture keeps the equations available for reproduction. These calculations have been checked against the stated example. No measured campaign data is included.

The sensitivity rows vary only extraLong by 20% below and above the entered value. They are scenarios, not confidence limits or a forecast distribution. A row outside the model’s constraints is labelled rather than turned into a plausible-looking result.

Evidence and limits

AppsFlyer documents configurable attribution windows, including view-through windows where enabled. Exact defaults vary by product and app setting. See AppsFlyer attribution window, especially “Attribution window”.

Windows, view definitions and eligibility are product-specific. Read the current MMP/network setting before copying these hours into an account.

Background: Reading an MMP dashboard without fooling yourself and Incrementality tests you can afford: geo holdouts, ghost ads, and what breaks. These existing articles provide context; the present calculation does not verify every archived claim.

Draft prepared 19 September 2026. Synthetic teaching example; human editorial and specialist review pending. No actual campaign outcome or recommended industry default is claimed.