Knowing not just how often someone stops for Starbucks, but why, is the genesis of the thrust of Predictive Marketing.
Kim DavisSenior Editor, DMN, techwriter, editor, and
online publisher, writes that “… marketing has always been future-based… it typically takes place before a purchase happens… in today's terms, predictive marketing, based on
predictive analytics, amounts to much more than hoping and believing that a home-maker will buy dish soap in the very near future…
Predictive marketing is clearly a very big
deal right now… a perfect storm of technological and cultural factors… driving marketers to claim with ever greater confidence that they know what we're going to do next… and
why… “
To get clarity on what predictive marketing actually is, and the reasons everyone's talking about it, the author spoke with
some data mavens working at the cutting edge of future-directed marketing, says the report. Quoting from Davis, here’s some of the contributers:
“It's real (Predictive Modeling),
and it has been for a few years,” said John Young, Chief Analytics Officer at Epsilon. Epsilon runs analytics against hundreds of millions of demographic and anonymized
web-browsing records to help customers make optimal marketing decisions. Young says: ‘Predictive modeling is the single most impactful thing clients can do. Getting the right audience through
predictive modeling is mission critical.’”
“Jerry Jao of Retention Science puts predictive analytics at the core of knowing which customers are likely to
abandon a brand—and how to retain them. ‘It feels a lot more real today than a year or two ago, when everyone wanted to sound cool.’”
“Claudia
Perlich, Chief Scientist at Dstillery, gives the impression of someone who juggles billions of data points before breakfast, all in the interest of generating personalized programmatic
messaging. “A lot of what people call predictive marketing or modeling is actually no more than a back-to-the-future view of the consumer. Basing future messages on historic behavior is not a
new idea, she explained, but it's not enough. It's easy, for example, to buy data which identifies consumers as credit card “intenders.” The problem is that if it's historic
data—even just a month old—it's probably now a list of credit card owners… ‘”
“John Schiela of Phoenix Marketing International, would
agree with Perlich's distinction, says Davis.
Schiela offered the simple example of stopping at the same Starbucks for the same cup of coffee, three mornings a week. Someone might
confidently predict, based on six months of the same behavior, that Schiela will get the same coffee, same time and place, in the future. Predictive modeling? No, says Schiela: ‘That's really
more of a data mining exercise. You need to know what drives the behavior, and why it might stop. Maybe the Starbucks is on the way to work, but the office is moving next year. Correlation isn't
causation… ‘”
“So what distinguishes true predictive modeling from banal, backward-looking hypotheses? asks the report. As Perlich expresses it,
‘predictive modeling should tell her “not just what people will do, but how they will react to what I do…’”
Davis concludes by noting that
“…he consumer's new-found comfort with sharing real-time behavioral data, plus the technological capacity to respond to that data in huge quantities and at unprecedented speed… is
pushing predictive towards discerning the causality behind consumer behavior at an individual level. We're on the brink of a predictive revolution… “
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