How can Target predict which of its customers are pregnant? How can a bank predict the likelihood you will default on their loan, or crash your car within the next five years, and price accordingly? And if your firm only has the budget to reach a few customers during a marketing campaign, who should it target to maximize profit? The answer to all these questions is… by building a scoring model, and targeting your customers accordingly. In this module, you will learn how to build a customer score, which in marketing usually combines two predictions in one : what is the likelihood that a customer will buy something, and if he does, how much will he buy for?
Target is storing data about its customers.
use your store loyalty card when making a purchase, or login to the website.
They can track, store, and retrieve information about you.
They know who you are, where you live, what you buy, and how often you buy it.
that could work pretty well to identify who might be pregnant.
Well the first one is gender.
all male customers in the database.
although we can't be absolutely sure about that.
are not saying much, but when combined together tell an interesting story.
If a woman begins to buy more comfortable and slightly larger clothes than usual.
Suddenly decides to switch soap brand and buys only hypoallergenic products.
Doesn't buy tobacco products anymore, and changes her eating habits.
about whether she's pregnant or not.
But understanding that intuitively is not enough.
and quantify the impact on the likelihood you are trying to asses.
Whether it's the chance of being pregnant, or the risk of switching to a competitor.
Suddenly buying a different brand of soap may be a very bad predictor.
And it doesn't predict anything.
Maybe buying prenatal vitamins does.
You need a calibration data and you need a statistical model to analyze it.
That's what we'll talk about in the next video.