TV analytics, TV attribution…two occasionally obscure expressions in marketing management jargon. You may not fully understand what that means, but it’s actually not so complicated. So, TV analytics — what is it good for? Here’s a short review of the hows and whys of the subject.
Why use TV analytics?
Before TV analytics came about, advertisers only had macro/audience data to help them make their decisions. First, they had the Nielsen ratings: calculated daily, they provide an estimate of how many television viewers are affected by a show or ad. They also had GRP (or Gross Rating Point), an indicator of the advertising pressure on the target; this indicates the average number of advertising contacts obtained out of 100 targeted individuals.
These indicators are helpful but aren’t sufficient on their own to provide a relevant analysis of an offline advertisement’s effectiveness. Why not? Simply put, these are static indicators based on old data, which measure coverage that is only an estimate. And herein lies the problem: compared with digital, where data reigns supreme, TV suffers from a cruel dearth of metrics for analysing performance. Or rather, suffered. Because thanks to TV analytics, it’s now possible to go beyond these basic audience estimates and obtain a real indication of performance. That’s TV viewer engagement, i.e. a TV ad’s ability to engage the target online.
Ok, a new KPI is always a good thing to have in your next PowerPoint presentation, but other than that, what is it really good for? In a sentence: refined targeting, media plan optimisation, and audience retargeting or expansion. But before we get into details, let’s take a little technical detour: how does TV analytics work?
Prof Brian Cox, please tell us how TV analytics works?
Let’s begin with the TV viewer. With the rise of smartphones, more than 80% of viewers today use another screen in front of the television*. At the same time, advertisers are providing content that encourages the viewer to continue their experience online. The consequence of these two phenomena converging is that when a TV ad airs, we can see a systematic increase in traffic on the advertiser’s website or application.
And this is the challenge with TV analytics: how can we precisely quantify and qualify these traffic spikes?
Here’s the recipe for TV analytics.
Start by placing trackers on the advertiser’s site in order to follow their traffic and the spikes that can be attributed to television. Then mix in some technology that can detect in real time the airing of each TV ad. Next, sprinkle with calculation some algorithms that are capable of determining what the traffic curve of the advertiser would have been without the TV ad (aka the “baseline”). Now let it cool for several minutes while you measure with precision the impact time of your TV ads. And voila, you have your TV incremental reach! Now hang on to all of that while we move on to the qualification aspect of this undertaking.
Take all of the traffic observed during your TV ad’s impact time. Run it through a user-centric analysis and identify the visitors according to precise criteria, such as their origin, visit time, and even behaviour on the site. Whisk it all together and what you’ll end up with are your freshly identified TV-exposed visitors. You can now track the conversions of TV visitors on your website or also retarget your TV visitors who weren’t immediately converted. But that’s a topic for another day!
By mixing both quantitative and qualitative analysis, you have all the ingredients you need to move on to the next step: optimising your media plan.
Obviously, just like in The Great British Bake Off, each TV analytics solution has its own secret recipe. At Admo.tv, our credo is transparency: no black box. As Nicolas Sailly, Marketing Director for ASSU 2000 , and an Admo.tv client for 3 years, says:
“With a TV analytics tool, it’s very important that the marketing teams feel at ease and fully understand the calculation methods behind it.”
Finally, it’s also essential to know the benefits of a TV analytics solution in order to motivate teams and use the tool to its full potential.
TV analytics, what is it good for?
KPIs imply analysis and therefore optimisation. The primary function of a TV analytics tool is consequently to give you the keys to optimising your media plan. Thanks to the harvested data and its growth, you can determine the best combinations of days, channels, dayparts, etc. The objective can then be to reduce the cost per visit, to boost conversions, or even to choose between channels with equivalent GRPs to find the one with the strongest Drive-To-Web impact. But saying TV analytics is a way to optimise media plans is an incomplete description. There are other opportunities, notably in terms of digital activation.
When a TV ad is aired, it naturally leads to a spike in searches on Google. Thanks (once again) to a technology that automatically detects TV ads, it’s possible to synchronise, in real-time, an advertiser’s AdWords ads with the airing of their television ads. If the technology permits (and at Admo.tv, it does!) it’s even possible to boost Adwords ads depending on the context or competitors’ TV ads. And then, when the TV viewers looking to take action head over to Google to look up information, they’ll immediately come across content related to the product they’ve just seen on television. In these scenarios, the conversion potential (CTR) skyrockets. To learn more about this subject, check out our e-book “Our 7 Best Practices for Engaging Your TV Audience Online”.
By boosting AdWords bids during certain TV airings, the advertiser maximises their chances of engaging these users.
And that’s not all! There is yet another way to capitalise on an audience coming from TV. TV-exposed visitors are naturally very engaged but they are generally not ready to convert immediately. Thanks to user-centric analysis, TV analytics is now capable of precisely identifying these users. In other words, TV analytics is able to dig through the data and create a new segment of users: the TV segment. With the right tech partners (like Criteo, to name just one), a TV analytics tool can then follow the path that TV-exposed visitors follow online and maximise their conversion by exposing them once again to the ad in question. Discover the inner-workings of this new generation of retargeting with our Adextend offer.
At the same time, it’s also possible to target a similar audience who hasn’t necessarily seen the TV ad. Since this audience has the same characteristics as the other, there is a high likelihood for them to convert when shown the content offered by the advertiser. A TV-exposed visitor can generate between 20 and 50 similar contacts: enough to create a TV-like audience pool!
TV analytics — what to expect?
Concretely, what can we observe with TV analytics? Without giving away any spoilers, we can follow traffic curves and understand globally how TV affects the advertiser’s site or app. There are several types: plateau curves, linear decrease curves, or M curves. Have a look at our glossary where we explain what these terms correspond to! Not all sites and applications react the same way to a TV campaign: it depends on many factors, such as the field, seasonality, and also the socio-demographic profile of the target audience. In addition to curve analysis, the advertiser has access, of course, to numerous data points.
One of the most scrutinized of these is the ADE (Average Direct Effect), which is the average traffic increase observed immediately after a TV ad airs. This indicator differs from one advertiser to the next, but there are general trends within each field. For example, we at Admo.tv observe a 60% average traffic increase after the airing of a tourism ad. For auto manufacturers, the average increase comes to 40%. Still not convinced? All fields combined, whenever a brand is mentioned in a broadcast, we observe up to a 40% increase in traffic. And this is only the direct impact. TV also has an indirect impact which is on average 3 times greater than the direct impact.
As mentioned above, progressive optimisations make it possible to increase the number of visits, lower the cost per visit, and improve conversions. These optimisations are made progressively. At first, the margin for improvement is very large. As the campaign progresses, the optimisations become more and more detailed. As an example, the three years of analyses that we conducted with Assu 2000 let us lower the CPV by 55% while increasing the visits by a GRP of 63%. These results were obtained in two stages, as you can see in our client case study. The results can also be seen in the advertiser’s notoriety. In this way, one of our clients saw themselves leaping in the rankings of the Brandindex study by YouGov (top 10 best improvements between 2016 and 2017), thanks to the optimisations that we recommended.
Ultimately, TV analytics has today become the indispensable ally of advertisers communicating via television. Having objective feedback on the performance of their media plans enables them to lean on a trusted third party in their relationship with media agencies. Digital activation and TV attribution scenarios also let you be sure that you’re not losing even the slightest bit of the audience your campaign engages.