Hmm, I can see it tackling part of the problem, but I don't think it's quite the brand-advertising part of it. Foursquare can only differentiate long-term behavior of user A versus user B, and correlate that with ads, but it can't answer: how did a large advertising campaign change the long-term behavior of a large segment of society? The problem is that behavior in that case is highly correlated: the goal with brand advertising is to change general cultural perception of a product, which depends in large part on word-of-mouth and shared perceptions among social groups. You're hoping to somehow intervene into that so that in the 2010s, 7-Up will seem cool to young people (or at least, cooler than if you hadn't launched your big ad campaign).

So the A/B test you want can't be something as simple as: we show user A a 7-Up ad and don't show it to user B, and see if that changes 7-Up purchasing habits.

Rather, it's something like: in the year 2011, spend $m on brand ads trying to improve the image of 7-Up, so people think it's cool and not the red-headed step-child version of Sprite. Then, re-run that same year 2011 without spending the money, and see how much difference there was in total sales.

The problem, of course, is that it's really hard to rerun years. ;-) You can try it in different years, e.g. increase or decrease your ad budget in 2012 relative to 2011, and see if it has an effect. But then you have to find some way to control for the fact that 2012 is different than 2011 in a lot of other ways too, not to mention that it follows 2011 so isn't independent of it, which makes it hard to draw any firm conclusions about which level of ad spending had what effects.