How View-Through Attribution Works
View-through attribution tracks conversions that follow an ad impression where the user did not click. The technical flow begins when an ad is rendered on a user's screen. The ad network or MMP records an impression event with the device identifier, timestamp, campaign details, and creative information. No user interaction beyond the ad being displayed is required to register this event.
When the user later installs the app, perhaps after searching for it in the app store or clicking an organic link, the attribution provider checks its impression database for a matching record within the view-through window. If a match is found and no click-through attribution takes priority, the install is attributed to the viewed ad. The key distinction is that the user never actively engaged with the ad, they simply saw it.
The definition of a "view" varies by ad format and network. For display ads, a view typically requires the ad to be at least 50% visible on screen for one second. For video ads, the threshold is often higher, two seconds of continuous play or 50% of the video watched. These viewability standards matter because they determine which impressions are eligible for view-through attribution credit.
When View-Through Attribution Adds Value
View-through attribution is most valuable for measuring the impact of awareness-focused campaigns where the goal is exposure rather than immediate clicks. Video campaigns on platforms like YouTube, connected TV ads, and high-impact display placements are designed to build brand recognition and consideration. Users who see these ads may not click immediately but may search for and install the app hours or days later.
Without view-through attribution, these upper-funnel campaigns appear to generate zero conversions in your attribution data, even though they are influencing user behavior. This creates a systematic bias toward lower-funnel, click-driven channels and undervalues the campaigns that fill the top of your acquisition funnel.
Consider a practical example: you run a 15-second video ad on a streaming platform. A user watches the full video but does not tap the companion banner. Two hours later, they search for your app in the App Store and install it. Click-through attribution would classify this as an organic install. View-through attribution correctly identifies the video ad as the catalyst, giving your video campaign the credit it deserves.
The challenge is calibrating view-through attribution so it captures genuine influence without over-claiming. This is where window length and viewability thresholds become critical configuration decisions.
Configuring View-Through Windows
View-through window configuration is one of the most impactful decisions in your attribution setup. The window must be short enough to maintain a credible causal link between the impression and the conversion, but long enough to capture the natural delay between seeing an ad and acting on it.
Industry standard view-through windows range from 1 to 24 hours, with 24 hours being the most common default. Some networks push for longer windows (48 hours or even 7 days), but growth teams should resist this pressure. The longer the window, the more organic installs get misattributed to paid campaigns, inflating your reported performance and distorting budget allocation.
Linkrunner allows granular view-through window configuration at the network level, so you can set tighter windows for networks that serve high impression volumes and slightly longer windows for premium video placements where the influence is stronger and more durable. The platform's reporting shows how attribution counts change across different window lengths, helping you find the sweet spot between capturing genuine view-through conversions and avoiding over-attribution.
For most mobile growth teams, a 1-hour view-through window for standard display ads and a 6-24 hour window for video ads provides a reasonable balance. Test different configurations and compare the results against incrementality data to validate that your view-through attributed conversions represent real campaign lift.
View-Through Attribution and Ad Network Incentives
Understanding the incentive structure around view-through attribution is critical for growth teams. Ad networks that serve large volumes of impressions, programmatic display networks, in particular, benefit enormously from generous view-through windows. A network serving millions of impressions per day will inevitably have some of those impressions match to users who organically install the app. With a 24-hour view-through window, these organic installs get attributed to the network, making its performance look better than it actually is.
This dynamic is sometimes called "view-through attribution gaming" and it is one of the most common ways that ad spend gets wasted. The network reports strong install numbers and healthy ROAS, but the installs would have happened regardless of the ad spend. The advertiser keeps investing in a channel that is not driving incremental growth.
To protect against this, compare your view-through attributed installs against your organic install baseline. If adding a new network with heavy view-through attribution causes your organic installs to drop by a similar amount, the network is likely claiming organic users rather than driving new ones. Incrementality testing is the definitive way to validate view-through claims, run holdout tests where a portion of your audience sees no ads and compare their install rate to the exposed group.
View-Through Attribution in the Privacy Era
Privacy changes have complicated view-through attribution on both platforms. On iOS, SKAN 4.0 introduced support for view-through attribution, but with significant constraints. View-through postbacks receive less data than click-through postbacks and are subject to stricter crowd anonymity requirements. In practice, this means many view-through conversions on iOS receive null or coarse conversion values, limiting their usefulness for optimization.
Without IDFA access, matching impressions to installs for view-through attribution outside of SKAN relies on probabilistic methods, which are less accurate for impressions than for clicks. The reason is simple: there is no user interaction to narrow the matching window. An impression could be served to any device on a network, and matching it to a subsequent install based on IP and device signals alone produces higher false-positive rates than click-based matching.
On Android, view-through attribution still works reliably using the GAID, but Privacy Sandbox will eventually change this. The Attribution Reporting API supports both click and view attribution with differential privacy protections, but the available data for view-through events is more limited than for clicks.
For growth teams, the practical takeaway is to treat view-through attribution as a directional signal rather than a precise measurement, especially on iOS. Use it to understand which awareness campaigns are correlated with installs, but validate the causal relationship through incrementality testing before making major budget commitments based on view-through data alone.
