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    Connected TV

    Why CTV Attribution Needs a Shakeup

    Most brands are forced to rely on traditional attribution models to measure the impact of CTV, leading to reporting issues and insights that aren’t actionable. That needs to change.

    Why CTV Attribution Needs a Shakeup

    4 Min Read

    Simply put, TV and CTV attribution could use some work. Some legacy attribution models give CTV credit for performance-driven by other marketing channels. Other models don’t give CTV enough credit for the performance it drives. And some models estimate the credit TV might have earned—which in 2022, is pretty unacceptable. 

    That’s why MNTN developed a patent-pending attribution model to solve these problems and make sure Performance TV is only given credit for performance where it’s due.

    Here is a breakdown of why the three most common attribution models lack accuracy when measuring performance driven by CTV and why MNTN’s attribution model offers the accuracy marketers need to make informed decisions.

    Attribution Model and CTV Measurement Accuracy

    View-Through Attribution Takes Credit for Performance Driven by Other Marketing Channels

    Here’s how View-Through attribution works: A brand will serve a CTV ad, then any visit and conversion that happens within a set period of time after will be credited to that CTV ad. Since it’s commonly used to estimate performance for other digital video efforts, brands will often use View-Through attribution as a proxy for CTV’s performance. The problem? Brands are forced to over-credit CTV for every site visit that occurs within a set period—even if that visit was actually driven by another marketing channel, like search, social, or email. This creates muddled, duplicated reporting where CTV and other marketing channels can take credit for driving the same visit—limiting any marketer’s ability to use that data to effectively inform optimization decisions.

    In some circumstances marketers may attempt to de-duplicate View-Through attribution, but the process is incredibly manual and time-consuming—making it impossible to inform frequent optimizations. 

    Last Click Attribution Doesn’t Work For One Obvious Reason And Another Less Obvious Reason

    The way Last Click attribution works is simple—the last marketing channel that is clicked and drives a user to a site is credited for that visit. But therein lies the problem, as CTV devices aren’t clickable. In rare cases, a brand may retarget users exposed to CTV ads with display ads and attribute any click-through visits driven by those display ads back to CTV. The problem? This proxy use of Last-Click attribution misses out on common instances in which an individual watches a CTV ad, then decides to visit that brand’s website (without being driven there by another marketing channel) and take an action. Without a deterministic, cross-device understanding of that event, every one of those CTV-driven attribution events are lost. Again, this results in Connected TV losing credit for the performance it actually drove.

    Spike Analysis is Entirely Estimated

    Spike analysis is a legacy model used to estimate the impact of TV ads. The way it works is simple — after a TV ad runs, all site visits and conversions driven within a very short period of time (i.e. 15 minutes) will be credited to that TV ad. This isn’t accurate for a few reasons. First, all of the attribution is entirely estimated. Second, the attribution doesn’t take into account the impact of any other marketing channels. And third, the window used is often extremely small and doesn’t align with the view-to-action behavior of a good deal of consumers.

    MNTN’s Cross-Device Verified Visits Solves CTV’s Attribution Problem

    MNTN’s patent-pending attribution model was designed to solve the problems of legacy CTV measurement solutions and provide marketers with an accurate, deterministic way to view the unique performance-driven by their Performance TV campaigns.  We call this attribution model Cross-Device Verified Visits.

    Here’s how it works:

    1. MNTN leverages our unique, ID-agnostic understanding of 120MM+ connected households to determine when an individual views a Performance TV ad
    2. MNTN then tracks when a user organically visits a brand’s site 
    3. Finally, MNTN runs a diagnostic to ensure the user’s visit wasn’t driven by another marketing channel (i.e. paid search, social, emails, blog posts, etc).

    By using this methodology, MNTN’s attribution model ensures that Performance TV only takes credit for all of the visits it actually drove. In other words, MNTN provides brands with complete visit attribution that’s deduplicated with their other marketing efforts in real time — an industry first.

    By solving CTV’s attribution problem, MNTN offers brands the insights they need to make informed decisions across their marketing mix and get the most out of their investment in performance.

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