ViewersLogic claims that its panel-based, single-source data represents a revolution in measuring media effectiveness. On its website, it declares: “Reach, frequency, attention and emotional response are inaccurate proxies that are used to try to answer the real question – how did people change their behaviour after being exposed to your campaign? Stop measuring uncorrelated proxies and start measuring the actual uplift of your campaign.”

It gives examples of real outcomes, like product purchase, sign-up or website visits. Sales lift, and incremental app store visits and app downloads, can be linked back to TV ad exposure. As well as helping to demonstrate campaign ROI, the ViewersLogic technology shows how each cross-platform channel contributed to success.

The company offers a long list of how exposure measurement and/or advanced attribution can be used to boost media planning and campaign strategy. These span customer targeting, competitor customer targeting, inflight media mix optimisation, assessing competitor campaign performance, benchmarking against past campaigns, benchmarking against competitor campaigns, and more. All of this is underpinned by some smart technology for demonstrating when someone is in front of a television when an ad is played, and inventive use of GDPR to generate a waterfall of precious, free purchasing data.

ViewersLogic recruits its panellists online and they download the ViewersLogic app to their smartphones. They are incentivised to give access to their data: data collection is rewarded with points (the more data, the more points) and these are turned into Amazon vouchers. In return, the phone app is permitted to sync with a set-top box and track channel changes (by ‘speaking’ with the STB via the home Wi-Fi router, rather than directly). This syncing is also possible with some Smart TV models from LG and Samsung if viewers are using the Freeview service (UK free-to-air television) on them.

At the same time, the app senses the signal from all local Wi-Fi routers (in the home and from neighbours) to create a reception fingerprint that is unique for each room in a building. This Wi-Fi triangulation means the phone, carrying the app, can be located to a specific room. ViewersLogic looks at where the channel change takes place, and as 70-80% of the channel changes happen when the phone owner is in a specific room, this is used to determine that the television set is in this room. The company can therefore demonstrate ‘person present in front of television’. You can read more detail about the methodology at the bottom of this story – ultimately the system uses a probability threshold that is acceptable to advertisers.

Using BARB data, ViewersLogic then interrogates channel playout schedules to see which ads were shown at which times on the tuned-in channel, to make the connection between person present and the precise ads they were exposed to.

According to Ronny Golan, CEO and Co-Founder at ViewersLogic: “It takes two minutes for a panellist to sign up. The whole process is totally GDPR compliant because the app does nothing except collect your data and pay you money. There is a very clear value exchange there. The app tracks television behaviour, phone behaviour and location to show offline behaviour. We track activity right through to purchasing.

“Think of our app as a remote-control app that is not controlling anything. To see if our panellist is in front of the television, we look at Wi-Fi signal strength, using AI. You need to be carrying the phone, and most people do when watching TV, but if the phone is not present we will not consider it a view, as we cannot show someone is in front of the TV.”

ViewersLogic does not rely on ACR (automatic content recognition) for its UK operations, but Golan says this can be part of the solution and the company is talking to ACR vendors in other markets. “There are several technologies we can use – the architecture means we can change how we collect the data.”

ViewersLogic has found a way to access some of the most valuable purchase data available anywhere to show the downstream impact of the ad exposures. It gets free access to supermarket loyalty card data, like from Tesco Clubcard, by working GDPR laws to its advantage.

“GDPR is our greatest friend,” Golan declares. “When the law came out, everyone was afraid, but we were counting down the days until it arrived. Loyalty cards know everything you buy, but it is not their data, it is your data, and they must give it back to you in machine readable format on request. You [the consumer] can make a request through their website, so with the full consent of our users [panellists], we make a GDPR request on their behalf and ask for the data to be sent to ViewersLogic. The law says the data can go to a third-party – that is data portability’.”

Golan says loyalty card data is available for most of the ViewersLogic panel, and a subset of the panel can be assessed using both loyalty card data and NielsenIQ (Retail Measurement) data, which tracks sales in physical stores as well as ecommerce. He says his company is processing 2 billion data points every day. The solution keeps data live for a year so brands can interrogate the impact of older campaigns. Viewers can be split into exposed and non-exposed groups to show campaign effect.

The company claims it offers a better understanding of exposure-to-impact than alternative solutions and has various case studies to show what has been achieved. Proven brand uplift is one example, and another shows the value of a sponsorship when sponsorship was combined with regular TV ads (vs regular TV ads without sponsorship). ViewersLogic says new-to-TV brands can learn from what other brands have achieved in their sector, too.

The company, which has a UK HQ and R&D in Tel Aviv, claims it is disrupting ‘traditional’ attribution models that fuse data from different silos to find what it describes as “weak correlations between ad viewing and sales”. The innovation includes the option to extend the attribution window beyond the five-minutes considered typical on ‘traditional’ attribution models to one-week and beyond.

There are no patents on the ViewersLogic solution, with Golan declaring that “in software, patents don’t protect you, only good software engineering”. He points to barriers to replicating the ViewersLogic approach. “With data and AI, you have to develop technology to collect the data and then collect the data for a year before you can create a model to train your AI on, and you also have to build the panel,” he points out.

The ViewersLogic methodology – some more detail

To establish ‘person present in front of TV’, the mobile phone (containing the ViewersLogic panel app) constantly checks for Wi-Fi networks and every couple of minutes relays the results of this ‘survey’. ViewersLogic compares the fingerprint of the survey with the fingerprint of the rooms in the house (to determine which room the panellist is in).

What if the phone is on charge in the kitchen, where there is a television set playing, but the panellist is in their front room reading a book?

Golan admits this would cause a wrong reading, “but when you look at the entire data, these cases are so rare they do not affect the results of our analysis. We can know if the phone is being charged and the last time you touched it, and, for example, decide not to use the TV data if the phone is being charged and was not used for more than an hour. From our research, these cases are very rare.”

To cope with someone moving from watching television in one room to watching a different television in another room, each viewing instance would be treated as a separate process, with the viewing combined at the data analysis phase. Golan explains: “We connect to all the supported TVs and STBs in your house and constantly collect their data. We constantly get Wi-Fi survey results from the phone. We do nothing in real-time, but when we analyse the data, we only consider TV viewing if you are in the room with the TV.”

What about if someone is hanging around in the doorway, watching the television on their way to leaving the room – how does ViewersLogic understand whether you are ‘inside the border’ where you can still be watching TV, or outside it? Golan says: “The AI algorithm that looks at the in-house location gives us a probability that you are in the room – so if you are in the middle of the room, the probability that you are in the room will be very high, and when you are in the doorway it will be smaller.

“Clearly, we do not have an exact map of the house, so we decided that we consider you to be watching TV only if the probability is very high. We have performed a lot of testing to define this.”

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.

The platform leverages data from a representative panel of the UK population. Viewers are incentivised to download ViewerLogic’s panel-based app through a reward scheme and, once downloaded, the app passively gathers data on users’ activities across TV, mobile and tablets. The app also tracks geographical location and offline purchases. According to the company, this data enables brands to determine the uplift in their KPIs that can be attributed to their TV campaign.

The companies say their solution overcomes the weaknesses associated with the traditional attribution model which “fuses data from different silos to find weak correlations between ad viewing and sales.” ViewersLogic’s platform also allows brands to extend the attribution window to one week and beyond, revealing to brands how their advertising affects short-term sales as well as longer-term brand building.

The ViewersLogic platform will be offered to selected clients initially as part of 4Sales’ suite of advertising solutions. The partnership covers Channel 4’s full linear channel portfolio, as well as select UKTV and Discovery channels (of which 4Sales is the UK sales house.)

Ewan Douglas, Head of Sales (N&R) & Business Development, Channel 4, said: “We’re incredibly proud of what we have created with our partners at ViewersLogic, and we truly believe it will help transform how the market can attribute the success of a TV campaign.

“While the platform is exclusively available to Channel 4 advertisers, we hope in time we will be the first in a long line of adaptors of this technology which helps prove the continued power of TV advertising.”

Hassan Khan, VP Sales, ViewersLogic, commented: “Existing cross-media measurement approaches using dubious maths and probability are woefully inaccurate and waste significant portions of invested advertising budgets. While they used to be state of the art, today, they are unfit for purpose and in any other industry this situation would not be tolerated.”