Predictive analytics: the next media disruption tool
Yeah, sure — Big Data. We get it, right?
We all know the digital age is producing huge amounts of data about consumers and their behaviour. And, sure, we know that anybody who’s in the marketing and advertising business — like local media companies — needs to get good at it. Right?
Not that we’ve quite learned how to do it yet. But surely we know — don’t we? — that we simply must master it to benefit both ourselves and our customers? And we’re working on it, right?
Well, I am. I hope you are, too.
Why? Because somebody is going to bring Big Data to Main Street. If it’s not us, Big Data will be the next big wave of disruption in our advertising and marketing business. It’s guaranteed to whittle down our local media ad revenues still further.
I’ve blogged about the huge opportunity and threat of Big Data for local media companies four times in the last 13 months. If you’re a regular reader, you may be thinking, “What, again!?”
If you’re not a regular reader, I strongly recommend you catch up on Big Data and its local media possibilities.
But I can’t stop there. I keep digging deeper to learn more about what Big Data can do and how we can master its potential for ourselves and our customers. And I keep learning.
For the last couple of months, I’ve been digging into predictive analytics (PA), a narrower niche in the vast expanse of Big Data. It’s the sharp cutting edge that is making Big Data even more powerful.
It started when a colleague recommended a book by Eric Siegel. He said it’s the easiest path to understanding what PA is and why it’s important. The title is Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.
Siegel is a data scientist who seems to be making a career of helping the rest of us understand what PA is. I’ve been plowing through his book and taking multiple side trips on the Web to learn more as I go.
Believe it or not, Siegel actually makes PA fun. In fact, before you read any further, you should stop right now and check out his rap video.
PA uses masses of data about customer attributes, behaviours, and life changes to predict which individuals are most likely to take a desired action or manifest a certain outcome.
For example: Who is most likely to buy a product or service? To list a home for sale? To buy a print or digital subscription? To develop a certain medical condition? To default on a loan? To commit a terrorist act?
In advertising and marketing, PA is rapidly emerging as the source of the next competitive edge. When likely buyers can be accurately identified, they can be marketed through highly targeted channels, cutting marketing costs and reducing wasted sales time.
And PA can also forecast which next step in marketing is likely to work best with any given potential buyer — e-mail, phone call, direct mail, or even banner ad A versus B.
This is marketing with a laser beam instead of a mass-media shotgun.
How do they do it?
Now that so much data exists about everyone, the best way to know who’s most likely to make a certain kind of purchase is to examine the deep data about those who have already made that purchase.
A data scientist builds a software programme that can sort through all the data on the past buyers — characteristics (e.g., age, income, education, family status, etc.), behaviours (Web searches, credit card purchases, clicks on ads, etc.), and life changes (marriages, divorces, child-bearing, college graduation, etc.).
The software can examine far more variables and combinations, far faster, than any human ever could.
The software boils the data down to those data points, or combinations of data points, that appear to be the best predictors of that particular type of purchase.
Then the scientist turns the model on the far, far larger number of individuals who have not made that purchase recently.
Posted on 7wData.be.