The Complete Guide to CTV Attribution Models

One of the most important shifts in modern advertising is that television can finally be measured like digital. Connected TV (CTV) has made that possible by turning what used to be broad, untrackable awareness campaigns into data-rich environments where every impression leaves a signal.

But to understand how your streaming ads are really performing, you need the right attribution model — a framework for connecting CTV ad exposure to real-world actions such as website visits, app installs, or purchases.

This guide breaks down how CTV attribution works, the main models used today, and how advertisers can use them to prove the impact of their campaigns.

Attribution is the process of determining whether an ad exposure influenced a specific outcome.

In traditional TV, attribution was nearly impossible. Advertisers could measure awareness lift through surveys or broad correlations, but they couldn’t tie a specific commercial to a website visit or sale.

CTV changed that. Because ads are delivered digitally, each impression carries data that can be connected to downstream behavior. This allows marketers to measure not just who saw their ads, but what those viewers did afterward.

Attribution helps answer key questions such as:

Did households exposed to our CTV ads visit our website or app?

How many conversions were driven by CTV versus other channels?

Which audiences or creatives delivered the highest return?

CTV attribution typically happens at the household level. When a CTV ad is served, the platform records the household’s IP address, device ID, or other anonymized identifiers.

If someone in that household later takes a measurable action—such as visiting the brand’s website, downloading an app, or making a purchase—attribution systems look for a match between the ad exposure and the conversion event.

This process is often powered by identity graphs or third-party measurement providers that connect devices within the same household.

For example:

A viewer sees a CTV ad for a meal delivery service on their Smart TV.

Later, they visit the brand’s website from their phone and sign up.

Attribution software matches the two events based on IP address and time frame, crediting the CTV ad with influencing that conversion.

There’s no single way to assign credit for a conversion. Different models weigh exposures differently depending on timing, sequence, and other factors.

Here are the main attribution models used in CTV advertising today.

Last-touch attribution gives full credit to the most recent ad exposure before a conversion.

If a viewer sees multiple ads across different channels but makes a purchase after seeing a CTV ad, the CTV placement receives 100% of the credit.

This model is simple and easy to implement, but it often overvalues the final touchpoint and undervalues earlier exposures that helped drive awareness.

Best for: Campaigns focused on immediate conversions or lower-funnel actions.

First-touch attribution assigns full credit to the very first ad exposure in the conversion path.

If a CTV ad introduced the viewer to the brand, but later digital or search ads closed the sale, the CTV ad still receives all the credit.

This model highlights CTV’s role in generating awareness but ignores other interactions that may have reinforced the purchase decision.

Best for: Measuring brand discovery or top-of-funnel impact.

Multi-touch attribution spreads credit across all ad exposures in the customer journey.

For example, if a viewer saw a CTV ad, then a YouTube ad, then clicked a paid search ad before converting, each channel would receive partial credit.

The challenge is that not all publishers or platforms share exposure data, which makes true multi-touch attribution difficult to execute at scale in CTV. Still, as identity graphs and cross-platform measurement improve, MTA is becoming more feasible.

Best for: Cross-channel campaigns that integrate CTV with display, social, and search.

Time-decay attribution gives more weight to exposures that happened closer to the conversion event.

If a household saw a CTV ad two weeks ago and a display ad yesterday, the display ad would receive more credit, but the CTV ad would still get some.

This approach recognizes that multiple touchpoints matter, but it prioritizes recent interactions.

Best for: Campaigns where timing plays a strong role in purchase intent, such as seasonal promotions.

Linear attribution distributes equal credit across every exposure in the path to conversion.

If a household saw five ads across three channels, each exposure receives 20% of the credit.

It’s straightforward and fair but assumes every ad had the same influence, which may not always be true.

Best for: Simplified measurement where consistency matters more than precision.

Every attribution model depends on an attribution window — the amount of time allowed between ad exposure and conversion for the action to be considered related.

In CTV, common attribution windows are:

1 day: Used for short-term or immediate response campaigns.

7 days: Standard for most CTV performance campaigns.

30 days: Used for larger purchases or longer consideration cycles.

Choosing the right window depends on the product, buying cycle, and how your audience typically engages.

Unlike digital display or social media, most CTV ads don’t generate clicks. Instead, advertisers rely on view-through attribution (VTA) — tracking conversions that happen after an ad is viewed, not clicked.

View-through attribution connects exposure on the TV screen to actions on another device within the attribution window.

Click-through attribution (CTA), by contrast, applies to interactive or shoppable CTV ads where viewers engage through a QR code, remote prompt, or on-screen CTA.

Most CTV attribution today is view-through, and that’s perfectly valid. It measures the influence of exposure, not just direct response.

While attribution in CTV has come a long way, it’s not yet perfect.

Some of the biggest challenges include:

Fragmentation: Each platform (Roku, Samsung, Amazon, etc.) operates in its own ecosystem with limited data sharing.

Cross-Device Tracking: Matching ad exposure on a TV to behavior on mobile or desktop requires strong identity resolution.

Privacy Regulation: Compliance with data protection laws means identifiers must remain anonymized and secure.

Overlapping Impressions: Multiple campaigns may target the same households, complicating credit assignment.

Despite these challenges, CTV still offers far more measurable insight than traditional television ever could.

Attribution isn’t just about proving value — it’s about learning what works and what doesn’t.

Advertisers who use attribution data effectively can:

Identify which audiences and creatives drive the most conversions.

Adjust targeting to focus on high-performing segments.

Optimize budget across platforms based on incremental contribution.

Quantify how CTV complements other channels in the marketing mix.

The key is consistency. Use the same attribution model across campaigns so results remain comparable over time.

The next evolution of CTV attribution will focus on unifying fragmented data and improving transparency.

Emerging technologies such as clean rooms, universal IDs, and cross-platform measurement frameworks are helping advertisers connect the dots between exposure and outcome without compromising privacy.

As these tools mature, CTV attribution will move beyond post-campaign reporting and become a real-time optimization engine — turning television into a fully accountable digital channel.

CTV attribution gives advertisers what traditional TV never could: proof of performance.

By understanding how different attribution models assign credit, marketers can more accurately measure the role of CTV in driving awareness, engagement, and sales.

Whether you rely on simple last-touch or sophisticated multi-touch models, attribution is what turns CTV from a branding medium into a measurable growth channel.