Click-through attribution

Click-through attribution is an attribution method that focuses on measuring the effectiveness of advertising by attributing a conversion to the last ad that a person clicked before the conversion event occurred. That conversion could be an app install, a purchase, a sign-up, or any other desired user or customer action.

In the context of marketing attribution, click-through is often contrasted with view-through attribution, which attributes credit to an ad that was viewed but not clicked. View-through is important because some ads (video ads, ads on CTV, and so on) don’t get clicked on because they can’t get clicked on. View-through attribution is also important because some demographics (especially younger people) tend to click less than others.

But click-through is generally prioritized and highly valued because it’s a specific, trackable, and clear signal of user intent.

In simple terms, click-through connects user engagement (a click) to a business outcome (a conversion). If a user taps on a mobile ad and later installs an app, that install can be attributed to the clicked ad, assuming the install happens within a predefined attribution window … typically 7 to 30 days, but configurable by the advertiser.

This method assumes that a user who clicked an ad was influenced enough to take action, making it a direct and easily measurable form of attribution.

Marketers typically measure click-through attribution on mobile via mobile measurement platforms (MMPs) like Singular, Adjust, AppsFlyer, or Kochava. These platforms work in a variety of ways, including tracking links, deferred deep links, and matching with ad network data.

When someone clicks an ad, the MMP can also capture a unique identifier (such as IDFA on iOS or GAID on Android), and starts a tracking window.

If the person installs the app or takes another defined action during that window, the MMP matches the click to the conversion and attributes credit to the ad source. That, in turn, tells marketers which ads are working and which campaigns, channels, and ad networks are delivering return on investment.

Singular, for example, provides detailed dashboards and APIs that let marketers see which networks, campaigns, or creatives are driving performance via click-throughs, often down to the keyword or audience level, depending on integration depth.

The biggest benefit of click-through is clarity and intent.

A user who clicks an ad is clearly signaling interest and intent, so this form of attribution highly actionable. Last-click attribution provides marketers with relatively clean signals and makes optimization straightforward.

When used in a last-click model, click-through is easy to implement and understand: the last ad the user clicked before converting gets the credit. This simplicity is appealing for performance marketing teams looking for quick, iterative testing and scaling.

Despite its popularity, last-click attribution, which is the most common implementation of click-through, has some downsides.

First, it ignores the full customer journey. In reality, people engage with multiple ads and channels before converting. A viewable ad seen days earlier might have sparked awareness, but another ad from a totally different ad network might end up getting all the credit.

Second, last-click models can overvalue retargeting and bottom-funnel tactics while undervaluing upper-funnel strategies like brand awareness, influencer campaigns, or organic content. This skews budget allocation toward tactics that close rather than those that generate initial interest.

Finally, privacy changes, particularly on iOS due to Apple’s App Tracking Transparency (ATT) framework, make deterministic attribution harder. MMPs now rely more on probabilistic models, SKAdNetwork, and aggregate data, reducing the precision of click-through attribution in some environments.

Singular’s Unified Measurement takes into account clicks, views, engagement, and activity from multiple signals before assigning attribution credit.

Last-click measurement is still useful, but it’s best used in combination with other sources of data to build a more full picture. You can do that manually, or you can let Singular do it for you.