Turning Big Data Into Big Knowledge, Pt. 2 – Predictive Analysis

Surprised baby boy using a laptop computer

Last week, we discussed database marketing and SQL, the programming language used to communicate, manipulate, and navigate through databases. This week, we will look at some more examples of companies who turn their data into knowledgable action through the process of predictive analysis.

Take a glimpse at the baby in the picture above. He looks awfully shocked, right? Maybe it’s because Target knew his mother was pregnant with him before his grandparents did. How could it be possible for a retail store to predict which female customers are pregnant and how far along they are in their pregnancy? The answer lies in data and predictive analysis.

Predictive Analysis

Predictive analysis is a business practice that utilizes several techniques such as data mining, statistics and modeling to make future predictions based off based historical data. Typically, business analyze past purchase data for customers to predict what products certain consumer groups will buy, when they will buy them and which offers they will respond best to.

The Predictive Analysis Process

The Predictive Analysis Process

Target was interested in targeting soon-to-be mothers because they knew that new mothers spend a lot of money on new purchases they’ve never made before (diapers, bottles, baby clothes, etc.) and they knew that new parents typically shop at a few locations hoping to save time and eliminate the stress of having to run around to many stores for different purchases. For Target, which hopes to be a one-stop-shop in the mind of its customers, this consumer segment was a very attractive one.

In order to target (no pun intended) and attract soon-to-be mothers, Target enlisted the help of statistician and economist Andrew Pole. Pole was able to find patterns in transaction data using data mining and statistical techniques that accurately predicted when a woman was in her second trimester. For example, women seemed to purchase much higher quantities of unscented lotion during the start of their second trimester. Pole scaled this into a model that Target used to send coupons to the soon-to-be mothers with discounts on baby-related products. The following excerpt from “How Companies Learn Your Secrets” depicts one outcome that arose from Pole’s model and Target’s predictive analysis strategy:

About a year after Pole created his pregnancy-prediction model, a man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation.

“My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”

The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again.

On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”

As we can see above, predictive analysis is an extremely powerful tool. Here are some purchase behavior insights revealed through predictive analysis in a Procter & Gamble study during the launch of Febreeze:

  • When someone marries, he or she is more likely to start buying a new type of coffee.
  • When a couple moves into a new house, they are more likely to purchase a different type of cereal.
  • When a couple divorces, they are more likely to begin purchasing a new type of beer.

Here are some other examples of predictive analysis implementation.

  • Orbitz, the hotel booking company, analyzed that Mac users spend about 30% more on hotels than PC users. Orbitz is using this information to deliver more high-end, expensive hotel options to Mac users.
  • Google is able to predict (with an extremely high degree of accuracy) an upcoming movie’s box office success based off of search results leading up to the release of the film.
  • President Obama’s 2012 campaign utilized an entire team of data crunchers and predictive analysis modelers who monitored the polls and A/B tested nearly every aspect of the campaign, from email subject lines to celebrity endorsements to certain phrases and dialect in the President’s speeches. In case you weren’t aware, President Obama won the election that year.
The 2012 Obama Analytics Team

The 2012 Obama Analytics Team

Again, predictive analysis is a powerful, powerful technique that can give your company great insights  if utilized effectively. However, the implementation and action your company makes as a result of insights through predictive analysis need to be made with caution. As we can see with the Target example, the consequent actions of predictive analysis can not only be intrusive, they can be downright creepy.


One thought on “Turning Big Data Into Big Knowledge, Pt. 2 – Predictive Analysis

  1. Pingback: Winter Is Coming: The End of Digital Marketing…..class | DIGI MARK(man)ETING

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