Friday, 25 of May of 2012
Analytics You Can Take to the Bank – Meta S. Brown on Predictive Analytics
Planned Parenthood is using Big Data analytics to find its supporters throughout the country – and we’re seeing the results play out in current events. My new post on Smart data Collective:
Date: March 13, 2012
New post on Smart Data Collective today, “Big Data Blasphemy: Why Sample?”
Date: March 6, 2012
In yesterday’s post, I mentioned an article from The New York Times which discussed customer research at Target. The folks at Target weren’t so happy about the journalist’s research. He offered them a draft for review, and they replied telling him the piece had a number of errors, but wouldn’t say what they were.
Let me take a stab at pointing out some concerns that come to mind as I read the article. I have no knowledge of what they do inside Target, so this comes purely from my own experience as an analyst.
The article says that Target tries to track every customer with an ID.
It’s not easy to do this well, but Target has a lot to work with. The usual means of tracking purchasing behavior is through a loyalty program. Target does not have a loyalty program, per se, but they offer a house credit card, which many people use. With their huge customer base, customers using that card represent a lot of information. However, customers may not always use the same card, and sometimes they may even pay in (gasp) cash.
The article says that Target used their baby shower registry to study the buying habits of pregnant women.
That made me think for a minute, as it slowly dawned on me that I was in that registry. My name was there, but was my husband’s? Maybe, maybe not. We probably paid for our Target purchases with his credit card much of the time. Another tracking problem.
For research purposes, you’d want to compare the behavior of pregnant women with demographically similar women who are not pregnant. No registry for those. Some educated guesswork needed to solve that problem.
The article gives a fictional example and a fictional probability that the fictional woman is pregnant and due in a certain month.
It’s just an example, and a little too perfect example, too. The idea is useful, but don’t get too wound up in the details of the example. Real life models are rarely, if ever, as perfect as that.
Feel free to add some thoughts of your own….
Date: February 17, 2012
There’s an article in The New York Times today, “How Companies Learn Your Secrets,” which offers a lot of insight into how practical predictive models are built, as well as how we need to put thought into how to integrate those models into daily business.
The predictive modeling theme is buried in with a lot of material about habit, neuroscience and how the author lost weight. If your interest is in analytics, you can just skim, maybe even skip all of that. Concentrate on the business process – Target wanted to build its shopper base, more shoppers, more often, buying more things. They knew that shoppers change habits during certain life changing events, and the biggest of these is the birth of a child. So they used their data to look for hints that a customer might be pregnant.
Read the article to learn more about the data exploration, interesting findings, and right and wrong ways to put model predictions into everyday use.
Date: February 16, 2012
In my new piece on Smart Data Collective, “It’s a bird! It’s a plane! No, It’s Just a Data Scientist.” I look at the popular new title, the expectations that go with it, and the performance limits of mere mortals.
Date: February 2, 2012
