You don’t have to be Nostradamus to predict that predictive analytics is going to become more and more important to digital marketers. Gartner sees an ongoing shift from analyzing historical descriptive data in aggregate to tell the story of “what happened,” to performing calculations on data sets to predict — with more or less confidence — how a particular individual will react to a particular message, offer, or recommendation. That insight can be extended, using different math, to find people similar to the people who are likely to respond . . . until, at some point, all of us are presented only with marketing communications to which we are likely to respond.
At which point we will be powerless to resist the marketer. Or so they hope.
The predictive analytics surge is being driven by two forces: (1) the higher velocity of data processing, driven by frameworks like Hadoop, and (2) the increased availability of data at a customer level. By “customer level,” I mean the emergence of individual profiles — which can be either personally identifiable (in the case of a known customer), or anonymous yet unique (in the case of a site visitor or individual encountered on the web).
Predictive analytics as a discipline is not new. It has decades of applications in the fields of business intelligence, for example, predicting how likely a person is to default on a loan, or credit card. Actuarial science in the insurance industry is an example of predictive analytics with its own university degrees. However, its use in digital marketing has been more recent and narrowly applied. This situation is about to change.
To take a simple example, I logged into LinkedIn recently to see who was trolling my profile and was confronted with the following example of predictive analytics in action:
Notice (1) how specific these recommendations are, and (2) how easy they are to act upon. (“Follow Ryan Holmes and get +1% more page views!”) More than best practices, these recommendations come with a predicted impact variable. Also notice the class of persons the recommendation applies to (“Marketing and Advertising professionals like you”), which implies at least some of the recommendations hold true for anyone in that group. What we are seeing here looks like the output of a regression model familiar to any MBA, with the independent variable — the one we’re trying to increase — being page views.
Who cares? My point is that LinkedIn has had this data all along, but is only now deciding to offer it up to drive action. There may be another motive, as my next visit to LinkedIn served up a different set of recommendations:
One of the recommendations, to follow an agency, has no impact associated with it, but presumably helps LinkedIn with its own engagement efforts.
A similar feature is being introduced by social analytics vendors, who are providing predicted responses to social marketing posts before they are posted, based on factors like format and message length. A typical automated feedback message might be: “Add a picture and shorten the text to 12 words to optimize engagement.”
Both LinkedIn and the social analytics vendors are performing predictive calculations — including regression, classification, decision tree, and clustering models, — and providing the output to users. The result is a short list of specific, data-driven actions that are likely (though not guaranteed) to improve your efforts.
I predict all digital marketers will have to say to this development is: Like button.