TVadSync team up with Centro

We recently announced the expansion of our offering across the US, and our team-up with Centro. Their Basis platform provides us with best-in-market attributes when it comes to real-time retargeting, and helps further cement our position as the leader in this area.

Check out more details here.

Attribution Modelling: Why you’re doing it wrong


Once upon a time, I loved my Nokia 3210. I had never owned anything like it. Being able to text people? Instant connectivity on the move? Snake?! I got so used to (and dependent on) my precious little best friend that I felt that nothing could improve on it. It was everything I’d ever need from a phone. Fast forward a few years, and I’ve an iPhone in my hand. Enough said.

The same sort of thinking can often lead even smart marketers into a world of myopia, where what’s ‘worked’ before will always be enough, or at least will do just fine for now. One of these areas is attribution modelling.

A big problem with traditional attribution models such as last touch, first touch, linear, position based, and time decay is that they rely on a predetermined, subjective weighting being placed on a touchpoint. Additionally, these models often only consider one consumer path at a time, without any consideration for the performance of key variables in any of the other paths. Mostly the only paths that are considered are those that led to a conversion, without considering those that didn’t lead to a conversion. This is where short-sightedness kicks in.

Consider an example. 100 users interact with 3 advertisements (A, B & C) in various ways, but only one user converts.  That user’s path to conversion looks like this: A > B > C > Conversion.  Let’s apply some traditional models.

First Touch: A (100%), B (0%), C (0%)

Last Touch: A (0%), B (0%), C (100%)

Linear: A (33.3%), B (33.3%), C (33.3%)

Time Decay: A (10%), B (30%), C (60%)

A quick look at these and C is the clear winner, with A performing quite well, right?  As an astute marketer your instinct may be to redirect budget from B, to C and A proportionately.  But, you’re not seeing the whole picture.  You’re not considering the paths that weren’t successful.

Now suppose I told you that in the other 99 user’s journeys A featured in 99 of them, C was in 50 of them, whilst B was only in 5.  Do you still think C and A performed best?  Would you still move budget from B?

A and C were involved in so many users’ journeys not leading to conversion. Their hit rate is really low compared to B, but without considering all paths, you wouldn’t know this.

Tip: when considering platforms and providers of attribution solutions, find out what their methodology looks like. Do they use an algorithm that can consider millions of these data points at a time, and apply a data driven performance index to each touchpoint? Or are they reliant on subjective weighting and blinkered analysis? In other words, are they an iPhone of attribution modelling or a poor old Nokia? If they’re the latter, you need an upgrade.

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Smart TV data sources – a few pointers for brands & agencies

The Rise of the Planet of the Smart TVs 

Smart TV

Smart TVs are everywhere. Or, at least, will be very soon. A recent study by eMarketer showed that showed that over 168 million people will use connected TVs in the US this coming year, with smart TV usage accounting for half of these – a figure almost 31% higher than in 2017, with the rapid growth set to continue.

ACR and the World of Tomorrow (and Today)

What does this mean for brand managers, marketers and agencies playing in the TV advertising arena? Well, one of the big opportunities the rocketing number of smart TVs presents is the sheer amount of granular insights that can be gathered on audiences’ TV viewing behavior. Smart TVs, due to the development of Automated Content Recognition (ACR for short), can now recognize what a TV is showing. In other words, there is no guessing anymore as to whether a particular piece of content was shown on a specific device.


Knowing what a TV has shown is one thing, but being able to link that device to a household is another. Through the use of IP address-mapping techniques, however, some vendors are able to do just this, as well as tie other mobile devices to the same household. All of this means cross-platform (TV and online) campaigns can begin to really work as a combination through better-defined audience targeting, and assessing TV’s impact on consumer behavior is now a much more data-driven process.

Sources of data

There are some things for marketers to keep in mind when considering providers however. Scale and privacy issues may crop up, for instance. Players in the market source their TV data in different ways, but the main two options stem from data collection via apps installed on users’ devices, and chips built into the smart TVs themselves. The difference is that the former relies on ‘listening’ to content being shown on nearby TVs in order to understand what is being shown, whereas the latter recognizes the content via the TV itself having an understanding of what it is showing. Both approaches can have merit, but there are issues of scale and privacy for the wary traveler (see: marketer) to keep abreast of.

Scale is the first point of call, with it being an obvious advantage to be able to know, on a definite basis, whether a TV has shown a household particular content. ACR tech built into the TVs means the TV will know what it has displayed automatically whenever it is turned on. However, apps that rely on audio recognition often require the user to have the app open to do this – an obvious problem if reaching millions of households means all of those people need to be using said apps precisely when the content you are looking to recognize is displaying on TV. Some operate quietly in the background, but privacy may become a big concern on this front.

TV manufacturers are now being set a high standard by the Federal Trade Commission when it comes to being very explicit about how they collect and use viewing data. This means that any data collected via built-in ACR chips is subject to very stringent opt-in rules. Apps that rely on listening (either while the app is open or running in the background) are not yet subject to quite the same set of rules (at least if they’re on Android) – but may yet be. Last year the CTD alerted the FTC to a technology called Silverpush, which used this audio recognition ability to track users TV viewing, and called out the the fact that TV viewers are not made aware of the apps that use this tech. Smart marketers may do well to remain aware that regulators are keeping a hawkish eye on this area and make sure their vendors adhere to best-in-class criteria when it comes to user privacy and opt-in processes. Should advertisers’ data sources suddenly come under this kind of scrutiny, it could disrupt campaigns or result in messy legal entanglements.

Where to from here?

ACR tech has the potential to provide brands with an incisive new method of evaluating TV performance and informing campaigns. The reach of this kind of tech is only set to grow, meaning the treasure trove of TV data available will likely increase substantially. But caveats exist, which means that finding providers who adhere strictly to the highest standards as they pertain to privacy is an absolute must.

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