Wednesday, June 05, 2013

Old and New School Marketing

What can policy wonks teach us about big data?

In a great essay in Foreign Affairs on "The Rise of Big Data" the authors describe the implications and meaning of it all.  Since that is a premium article, I'll summarize a few key points. 

The sacred cows of the analytic kingdom from research to finance have rested on three tenets.
  1. Quality of the data
  2. Representativeness of the sample
  3. Causation of the outcome
For someone who started out in market research, has taught statistics and has an MBA in Finance, these were the inviolable crown jewels - something debated as much as the findings themselves.   However, in the world of 'big data' none of that really matters any more.  In fact,  we now use the terms..
  1. Messy
  2. All
  3. Relationship
as the lingua franca of the realm.   The reason: damn near everything has been datafied - a term the authors use to describe process of reducing everything to a stream of data.  Correlations of events based on all possible data, even if some is messy, is better for a business than a well selected sample from which we try to prove a hypothesis.  Data is now an operational function.

From your butt's imprint on a car seat (think anti-theft) to the spread of flu based on search terms to serving eviction notices based on the risk of fire, data now serves the role of providing the basis of taking action rather than just recommendations.  

Marketing, like many other functions, has been datified. The path-to-purchase is riddled with opportunities to leverage intent signals from one touch point in the business rules for the next.  We should now be asking questions like: 
  • What do consumers do before they do something next?
  • What sequence of content consumption relates to making a decision?
  • Where and when is the best place to facilitate choice?
These are the kinds of business requirements that marketers should be stating.  As marketing technologists, it is our job to architect a solution that provides the means to find and implement the answers.

If we're thinking about a report or a 3" research binder as the output from the data team, then we're thinking old school. 

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