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CTV needs better measurement to be ready for primetime

Ronny Golan, CEO & Co-founder, ViewersLogic

CTV ad spend in the UK is set to roughly double to £2.31bn by 2026, according to the IAB. This exponential growth is being driven by brands looking to tap into the inevitable shift of consumer viewing habits from linear to CTV, with 73% of the UK population now watching shows and films on streaming platforms. The promise is a medium that straddles the line between linear and digital, with the high quality, captive ad experience of the former and the measurement and targeting capabilities of the latter.

But this promise has not been fully delivered quite yet. Attempting to match ad exposure to consumer action reveals glaring holes in the data, while any attempt at cohesion across a CTV campaign — let alone the wider media mix — collides with the messy reality of a fragmented market. This should be CTV’s moment in the limelight, but shortcomings in measurement capabilities are holding it back from realising its full potential.

For CTV advertising to make the most of the consumer attention and ad spend coming its way, media owners need to recognise its current measurement shortcomings and fill the gaps with reliable, detailed, high-quality data on actual campaign effectiveness and the role CTV had in it.

IP and device graph matching: a match made in hell?

IP and device graph matching are currently used as the basis for many CTV measurement solutions. The assumption is, if the IP address of a household where an ad for an item was aired matches the IP address of a household where the item was purchased, it can be reasonable to infer that there was a relationship between the two.

However, IP and device graph matching suffer from several problems that make it almost impossible to really understand the effectiveness of a VOD campaign. On the TV side, the IP addresses identify the household and not the individual, so it is impossible to know who in the household was exposed to the TV ad. Furthermore, when the user leaves their house, they will be assigned a generic IP address from their mobile network which can be shared by multiple users, making it impossible to identify the user.

Brands are therefore ultimately not able to understand from IP matching whether the person who bought the product is the same person who saw the ad.

IP matching and device graph matching are also unable to detect attribution in cases where a brand sells through third-party websites such as Amazon. Finally, IP addresses are considered Personal Identifying Information and using them may create a GDPR liability for brands.

CTV has a fragmentation problem

But even if all the matching problems can be solved, CTV also has a fragmentation problem, as it’s another silo in the media mix. For example, a single consumer might have been exposed to an advertising campaign while watching TV, scrolling through social media, listening to a podcast, travelling on public transport, or while playing a game—isolating the effectiveness of the CTV exposure is close to impossible.

But the problem is even larger. A CTV advertising campaign will run across multiple VOD services, each with a different, siloed measurement system. For example, a user could see the same ad on Netflix, Samsung TV, ITVX, and YouTube. If IP matching is used, each of the services would claim 100% of the attribution. The brand will be unable to understand what worked and what didn’t.

Knowing the effectiveness of each component of the media mix is essential to campaign planning and in-flight optimisation but isolating the impact of CTV advertising in the current advertising environment is almost impossible.

It’s time to go back to the source

Much of the hype around CTV is focused on its likeness to the data-driven insights and scale of digital advertising, but to truly unlock its potential we need to use single-source data and measure it within the context of the entire campaign.

Thanks to several recent technological advancements, it’s now possible to build single-source panels that can track actual behaviours and actions in far more detail, right down to the ads they were exposed to, what sites they visit, apps they use, the online and offline purchases they make, and what physical stores they visit. With this approach, panellist consent is provided from the start and their participation rewarded so that data privacy is assured.

This model overcomes the inaccuracies of IP matching. By tracking individuals every step of the way, single-source data reveals the impact of campaigns on offline store visits, CPG sales, or online purchases through third-party websites, enabling accurate and deterministic measurement of CTV advertising for the first time.

The holistic view of consumer behaviour created by single-source data also solves the fragmentation issue, revealing how CTV advertising fits into the larger picture of the customer journey to purchase. By understanding the performance of the channel in context, marketers can use CTV advertising more effectively in their campaigns.

For too long, the CTV advertising ecosystem has been attempting to complete a puzzle with half the pieces missing. By breaking down silos and using single-source data, the industry can fill the gaps in their measurement capabilities beyond just reach and frequency to give advertisers a complete picture of campaign performance. Only then will CTV truly achieve its potential as the best of both the digital and linear worlds.

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