The Future Of Data Is Sneaker Optimization

Two major data stories seem to have taken center stage over the past few weeks. The first is the transformation of everything imaginable -- and I mean everything -- into a source of actionable information. The second is that we’re finally starting to wrap our heads around syncing measurement across traditional and digital channels.

These are both obvious points. But taken together, they’re revolutionizing what cross-channel data and metrics are coming to mean.

Let’s start with the story of unleashing the “uncovered data.” Nike + is a good example here: a series of products that track users’ movements through a group of devices to help them manage their workout routine, and get a sense of their level of activity. It’s complete with a newly launched API for third-party developers, and can even be used to allow users to automatically check in to Foursquare locations via Nike gear.

I call this a story of uncovered data because it takes a part of life that we’ve lived with for a very long time -- moving around -- and turned it into a useful data stream.



There are many more examples of uncovered data. The business intelligence industry is one huge example. Foursquare is another (if smaller) one: it transforms your local bar or a pizza shop into shared information. And, of course, Facebook turns your pool of friends into a wealth of data (just ask any social marketer). Data has always lived all around us; the new information tools have made them easily accessible and understandable.

To be sure, not all of this untapped data is a part of the advertising landscape. I don’t see Nike selling media inventory anytime soon (although you never know). But that doesn’t mean that there’s no marketing value for Nike (or for other brands) in understanding how Nike consumers move around during the day. And when you throw in Foursquare check-ins, I’ll bet some marketer, somewhere, will be using this information to target consumers better.

Now let’s put aside the “uncovered data” story, and move to the second data tale: the one of cross-channel data analysis across traditional and digital. And there are plenty of recent stories about this second story. Group M and Nielsen have partnered to make TV and digital spending apples-to-apples. Viacom has come out with its “Surround Sound” measurement platform that runs across TV and digital. Yet another in a long string of studies has shown the positive impact that social media usage has on TV viewership -- not directly a story about metrics, of course, but one with huge implications about what you need to measure to understand cross-channel engagement.

And that’s just in the last few weeks. I could easily add in offline/online connections like QR codes and Shazam to this story -- and a lot more.

The basic point is that, as breathless as we get about the adoption of new media, interactions with old media aren’t going away. We’re still watching as much TV as ever. Out-of-home is on the rise (due largely to the strength of digital-out-of-home). Print publications might be hurting -- and in many cases, hurting quite a bit -- but they continue to command enormous amounts of the overall media pie.  To a very large extent, digital media isn’t supplanting traditional advertising; it’s growing alongside of it. The question for advertisers playing in the digital space isn’t how to shift brand dollars off old media and into new --  it’s how to make the old fit with the new. And that’s largely a cross-channel measurement question.

The bigger trend running across both these stories -- that of found data, and that of digital / traditional management -- is that we’re living in is a world in which managing data is often a matter of combining things we’re very familiar with (exercise, print ads, TV) with things that are much newer to us (the Internet, mobile devices, tablet computers). And getting the most of the old and new means understanding how to match the data across all of the touchpoints and data sources in the mix, so advertisers can optimize spend appropriately, and use the available data as a source of holistic planning. Today, that means trying to sync data across the Internet and TV; tomorrow, that will mean optimizing across digital, television, and your sneakers.

Of course, TV doesn’t organically link to the Internet, and your sneakers don’t organically link to either the Internet or TV. And as more aspects of daily life turn into information sources, old media channels continue to thrive, and entirely new ones come up, the new task that the media industry will face is understanding how to make all those connections work. Understanding TV- online measurement is critical here, but it’s also just the beginning. And there’s really no end in sight. Everything we interact with is a potential source of data, and so everything we connect with will need to be worked into the bigger picture.

In other words, we’re at the first leg of a very, very long race. As they might say in Niketown: On your mark.


1 comment about "The Future Of Data Is Sneaker Optimization".
Check to receive email when comments are posted.
  1. Ann Marie Lane from ThinkVine, April 6, 2012 at 9:45 a.m.

    As the author pointed out, today’s marketing landscape has fragmented tremendously due to the proliferation of emerging channels (digital, mobile, social, etc.), which has caused an explosion in data because each new channel has its own set of metrics. An agent-based (simulation) approach to marketing mix planning and optimization was developed, in part, to provide marketers with a fast, flexible, and data-agnostic way of measuring and forecasting the ROI across all of their marketing investments. By simulating how different types of consumers behave in an evolving and dynamic marketplace, agent-based modeling provides marketers with the information they need to understand and harness the power of today’s many marketing options and to create an integrated strategy that engages and influences their targeted consumers. And, all of this is achieved without the massive effort of collecting, processing, and storing large data sets, which is expensive and time consuming.

Next story loading loading..