How advertisers plan to score big this Super Bowl


by Duncan MacRae| 31 January 2014

With companies already having forked out about $4m for a 30-second TV advertisement slot during this weekend’s Super Bowl - and many millions more to produce their ads - how can technology predict a worthwhile ROI? Duncan MacRae spoke with Jon Gibs, VP of analytics at full service digital agency Huge, to find out.

How can companies utilise data analytics to help make advertising decisions?

The first questions you need to answer is where to advertise, to who and when. The 'who', normally a tough question, has a pretty easy answer if you are advertising on the Super Bowl - it's everyone.

The 'where' can be a difficult decision - do you pay exorbitant in-game TV ad fees and risk a lot in one place? Do you spread it out on digital? Do you counter-programme on TV? Do you go heavy in social? Do you do everything? Certainly, if you're going heavy in TV, you are going to need significant digital support both in social and digital video, just for the pure viral impact alone. The 'when' - when do I run the ad during the show, when do I tweet? When do I post my ad video online and start promoting it to build hype?

The good news is that there are independent analytic systems for all of these questions. The problem is that they are independent. They don't talk to each other very well. For social media we can learn a lot simply by using a combination of our web analytics, platform analytics (i.e. twitter analytics) and a social listening service like Brand Watch or Crimson Hexagon. Combining the trends we see in those, as well as tagging the content (it is a "photo" of a "blue car" on a "sunny day"), we are able to understand what receives the best response (i.e. new followers or retweets) when and to whom.

For TV there is obviously a well established system of Nielsen data and their analytical services. The benefit of using their systems that they are literally the currency of that industry. They are the numbers that everyone has agreed on to be true. We don't have that typically in digital. Additionally, TV and digital people don't speak the same language, thought that's getting a little better. It does mean, however, that there is a certain level of lack of interoperability simply because people can't understand each other. In TV, you can look to data sets such as set top box data to better understand what people are watching when with a different level of precision.

So using these tool sets, as well as other forms of online advertising tracking, we can get a good sense of what consumers are accessing and when and can start to put together a variety of different modelling methods that allow us to understand when and how you should typically pace these types of activities. This type of modelling isn't perfect, but it does give you a directional sense of how these various pieces work together.

The key, however, is not just relying on the off the shelf solutions. You need to be able to do the actual modelling and collect historical trends that will let you plan for the future. The off the shelf systems won't do that for you.

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