It’s the Data, Stupid!


As the 2016 American election season starts to heat up, and primary candidates fight for position in their party, it isn’t unusual to hear the oft-remarked James Carville quote “It’s the economy, stupid.”  It came during Carville’s strategizing for Bill Clinton’s successful 1992 campaign against George H. W. Bush.  It may not be the most eloquent quote, but it’s one of the most memorable.  In today’s programmatic ecosystem, vendors are lining up for agencies, brands and, well, other vendors.  They’re lobbying for position just like a political candidate.  Some add this, others add that, but far too many forget what could also be defined so aptly:  “It’s the Data, Stupid!”

We’re now up to 2000+ marketing technology vendors in the industry.  There are E-Mail platforms, Display Platforms, Marketing Automation Platforms – platforms for just about everything.  And while each facet of the landscape has a unique pitch, they all require targeted data to execute.  Content without an audience calls to mind another famous quip:  “If a tree falls in the woods and nobody is there to hear it, does it make a sound?”  With poor data, that’s the question many campaigns have to answer.

Programmatic currently solves the data challenge with complexity.  Where there is poor data, there is optimization.  Where there is no data, there is prediction.  Where there is great data, there is personalization.  This is a good effort.  However, with mixed results, and a lack of demonstrable success in programmatic, many brands are still steering clear of pouring budget into the space.  And many marketers are yet to understand it.  If the complexity, and blurred ROI comes from compensating for poor data, better data should surely solve everything.

So what constitutes better data?  Programmatic essentially has two categories of data.  First, segment data.  This is data typically associated with a demographic.  It’s largely B2C.  Next, is behavioral data.  This is data associated with an action, such as a search query or abandoned shopping cart.  It’s much better but too scarce on which to build a multi-billion dollar industry.  Individual brands can pay a premium for behavioral data and find success, but the industry at large must find another answer.

Quietly building steam is another category of data.  In fact, it’s so smoothly entered out vocabulary that we hardly realize it’s the first data source that’s actually native to programmatic – behavioral and segment data have existed since the beginning of digital marketing.  The new category is geo-data, or even geo-behavioral data, the amassed non-PII information generated through mobile, social, beacons and other local pings. Geo-behavioral data gives programmatic it’s best opportunity yet to solve its challenges.

Most readers will say “geo data is simple.”  “I can use geo.”  But that’s exactly the problem.  Early usage of geo-data has been just as it sounds – data about where someone is or was.  It might try to push a coupon if you’re in the mall, or some similar gimmick.  In a worst use case, it might utilize segment data to determine user demographics by where they are.  Because geo-data is native to programmatic, it needs another dimension to be activated.  It’s not just about where someone is.  It’s about what that place represents, when they are there, who else is there and how they are moving through that space which ultimately solves for ‘why they are there’.  In assessing geo-data, programmatic should realize two concepts:

  1. Each place has it’s own identity, which can change at different times of day
  2. A consumer’s geo-behavioral characteristics are defined not just by the place identity, but by other consumers there at the time. We are a product of who we hang out with.

If these facets are important to the simple, successful processing of geo-data, to augment the poor segment data and scarce behavioral data for programmatic, then why aren’t they more widely used?  Because analyzing this data contradicts a lot of the traditional AI/Machine Learning algorithms on which programmatic was built.  Traditional binary computing struggles to identify such human nuances as passage through time and space with a crowd.  So how can programmatic improve its process to activate this data and make profitable use of it?

Biological Intelligence is the simplest solution to enabling marketing tech to digest, make sense of and scale geo-behavioral data, the missing component in programmatic.  BI, considers human behavior and the power of association and affinity as a native computation.  Biological Intelligence has a major advantage at it’s core:  It understands that systems have intelligence and a collective behavior, even without a brain (no group has one brain).  Therefore, instead of trying too hard to predict one consumer’s behavior in isolation, BI can effectively crowd source behavior, and save a big computational headache.

With programmatic iterating into the future, the complexity and dubious results many marketers are experiencing can be traced back to one thing:  poor data.  However, trying to solve the data conundrum with the two standard data types will prove difficult – especially as programmatic scales to demand even more scarce data.  The only solution is to improve the use of geo-behavioral data, and in doing so leverage new ways of analyzing that data.   In an industry that uses “Intelligence” all too often, it’s an interesting contrast to see the word ‘stupid’ in a post about data.  But until we realize the potential of new data sources, our technology will be just that.  Because of it’s superior methodology for unlocking our richest data set yet, Biological Intelligence, and the easy interpretation of geo-data should get everyone’s vote as programmatic seeks new ways to drive and prove ROI for a simpler, more marketable future.

photo credit: Sunny Pubs in Google Earth via photopin (license)