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Definition:

. attribution window is the defined timeframe in which a user interacts: a click or an impression that can be credited for driving a conversion, such as an app install or in-app event.

What is an Attribution Window?

In mobile marketing, users rarely install an app the moment they see an ad. They might click, get distracted, come back later, and install hours or even days after first exposure. An attribution window accounts for that gap.

It sets the boundaries for what counts. If a user installs your app within the defined window after clicking or viewing your ad, that install gets attributed to the campaign. If they install outside of it, it's recorded as organic.

Getting this window right is one of the more important, and often overlooked, decisions in campaign setup. Too narrow and you undercount your campaign's impact. Too wide and you start crediting campaigns for installs they had nothing to do with.

Why Attribution Windows Matter

Attribution windows are fundamental to understanding how your campaigns are actually performing. Without them, conversion attribution becomes unreliable because you can't accurately connect ad spend to outcomes, and your optimization decisions suffer as a result.

A well-configured attribution window helps you:

  • Credit installs and conversions to the correct source
  • Eliminate false organic users, or installs that were actually driven by paid campaigns
  • Compare performance fairly across different ad networks and channels
  • Make smarter budget decisions based on accurate data

The attribution window is also how ad networks get credited for installs. If a user installs your app after clicking an ad from a specific network within the predefined window, that network receives credit. This makes window configuration a meaningful factor in how your network performance data reads.

Click-Through vs. View-Through Attribution Windows

There are two main types of attribution windows, and they typically run on different timeframes:

Click-Through Attribution Window 

Measures the time between a user clicking an ad and completing a conversion. This is the most common and widely accepted form of attribution. A 7-day click window is standard across most platforms.

View-Through Attribution Window 

Measures the time between a user viewing an ad (without clicking) and completing a conversion.  The user saw the ad but didn't engage directly, so the signal is weaker and view-through windows are typically much shorter, often set to 24 hours or less.

Understanding the difference matters because the two window types can produce very different attribution outcomes, especially when running campaigns across multiple channels simultaneously.

有点困惑对不对，我给您举一个例子： 

A mobile game developer runs a paid campaign across several ad networks, setting a 7-day click attribution window.

A user sees the ad on a Tuesday, doesn't install immediately, but comes back on Friday and downloads the game. Because Friday falls within the 7-day window, that install is attributed to the campaign and the network that served the ad.

If the same user had installed on the following Wednesday, or eight days after clicking, the install would be recorded as organic, and the network would receive no credit.

This example also highlights why window length is a strategic decision. A longer window captures more conversions but risks over-crediting campaigns. A shorter window is more conservative but may underreport true campaign impact.

Attribution Windows at Tenjin

By default, Tenjin's attribution windows are set as follows:

Window Type Default Setting
Click-through 7 days
View-through 1 hour

Both windows are fully configurable. You can adjust them independently for specific channels or campaigns, giving you the flexibility to match your attribution setup to how different networks and ad formats actually perform. You can read more about how to set up the attribution window 这里.

The most widely used attribution method today is last-click attribution — meaning the last ad a user clicked before converting receives full credit. Tenjin supports this model as standard, while giving you the tools to dig deeper into your data when you need a more complete picture.

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