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January 31, 2014updated 22 Sep 2016 1:14pm

How advertisers plan to score big this Super Bowl

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.

By Duncan Macrae

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.

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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.

What do companies need to consider when using analytics to help shape their Super Bowl advertising?

The real question for super bowls ads is, and has always been, is it worth the money? This doesn’t necessarily mean, will it have a positive ROI, but I does mean you need to have defined and measurable goals you want to meet. Given the cost, simply going into the Super Bowl to "make a splash" is short sighted. The first question is how many people do you want to reach and are you willing to pay a premium to reach slightly more people at different parts of the game. This data is available through either Nielsen or set top box data solutions. The next question is how important is it for a campaign to go viral. Clearly this has a lot to do with the spot that is used on TV. That said, using social tracking data we can figure out what types of ads are more likely to go viral at different times of the game. Earlier is better, but how much earlier and is it worth the cost?

When judging the success of the game, it’s important to not just look at the final TV reach of the campaign, or the positive chatter, or the article in Ad Age, but it is also key to understand the viewership of the unit on other platforms, such as YouTube. Finally, there are a lot of brand awareness tracking services that come into play. They can explain not just if an ad generated sales, but rather did people become more aware of the brand. It’s worth examining if that changes at different times during the game and if the type of brand you are advertising tends to work best in that type of environment.

 

 

 

 

Advertising during the Super Bowl is particularly expensive. To what extent can data analysis confidently predict a decent return on investment?

I actually don’t think that an ROI for a Super Bowl ad is really predictable. This is true for a few reasons. First, the expenditure is so high as a one-time expense, it would have to drive significant incremental sales immediately to be measured by traditional means. Second, much of the value of Super Bowls as isn’t in the actual broadcast of the spot, it’s in the conversation around the spot. For this, we look to social data – that that can be challenging to turn into ROI. Finally, so much of the ROI is based on the quality of the creative. The super bowl is probably the highest density of quality TV creative shown to the most people though out the year. Because of this it is very easy to be lost in the noise. Rigorous channel support in digital can help as can pre-testing the units, but these types of things can be extremely unpredictable. What is most important is looking across the spectrum of media outlets to find out where people watched and how they reacted.

What advice would you give to companies using data analytics to help shape their advertising strategy?

Build your own platforms. This can sound really daunting, but all it really means keep track of all of the campaigns you run, the elements that go into them, and what their impact is. This allows you to create a database that no one else can replicate and lets you learn past successes and failures. The really hard parts come in the storage of the information (it should be standard across all media – some of which can be large scale information) and the meta-data associated with the campaign Campaigns should be categorised, by goal, content, topic etc. This meta-data layer will allow you to ask basic questions like: what kind of campaigns should I run for back-to-school in the south east region under a specific type of economic condition. If you don’t create that meta-data layer, all you’ll know is that a campaign did well. You’ll never know why, or how to replicated it.

 

 

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