How Target Leveraged Data Analytics to Predict Customer Pregnancies


Predictive analytics refers to the use of data for forecasting purposes. It can be used to predict trends or events, both in your industry and for your customers


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Market Research » How Target Leveraged Data Analytics to Predict Customer Pregnancies

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Companies are always looking for ways to appeal to their target customer. Marketers, data analysts, advertisers, and sales teams want to be the first to know when their target audience is in need of products or services that they have to offer.

Target was in the same boat. They wanted to be the go-to store for literally everything, rather than having customers shop around. So they turned to data analytics to help them get a headstart on advertising to certain customers.


Predictive Analytics at Target Stores

Throughout this article, we’re going to talk about how data analytics can be used for predicting customer milestones, a case study in how Target was able to predict pregnancy in customers, and how you can gather your own data.

Let’s get started.


What Are Predictive Analytics?


Predictive analytics refers to the use of data for forecasting purposes. It can be used to predict trends or events, both in your industry and for your customers.

A few ways that predictive analytics can be used include:

  • Forecasting annual revenue
  • Determining when you’ll need to hire
  • Predicting customer behavior

Predictive analytics use data, statistical modeling, and historical information to predict these trends and events.


Predictive Analytics and Behaivor

A Case Study: How Target Used Predictive Analytics to Know When Customers Were Pregnant


One ingenious example of a retailer using predictive analytics is the case study where Target used historical data in order to predict when its customers were pregnant.

We mentioned that Target’s goal was to become the go-to store for everything their target customers need. After all, they have a pharmacy, grocery department, home furnishings, clothes, toiletries, and so much more.

So the marketing team put together a plan. The first step was recognizing an opportunity. The team identified that major life events such as getting married, moving, or having a baby often disrupt customers’ habitual shopping patterns.

During these transitional periods, customers are more likely to try new products and establish new buying behaviors. Among all life-changing events, pregnancy, in particular, was singled out as it triggers a burst of increased spending that can extend over several years as the child grows.

However, birth dates are public knowledge. Any company can start marketing to new parents once the birth announcement has been made. Target knew they needed to start early—which is exactly where their plan to use predictive analytics came in.

Target’s statistician Andrew Pole put together a list of items that women often started buying as they entered their second trimester of pregnancy. This included items like:

  • Unscented lotion
  • Supplements (i.e., magnesium, calcium, zinc)
  • Prenatal vitamins
  • Maternity clothes
  • Soap
  • Cotton balls

All in all, through his research pinpointed 25 different products that indicated a person might be pregnant. He then put together a “pregnancy score” algorithm that helped the team pinpoint how likely someone is to be pregnant based on how many of these 25 products they’ve suddenly started to buy.

Here’s an example from the New York Times:

Take a fictional Target shopper named Jenny Ward, who is 23, lives in Atlanta and in March bought cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug. There’s, say, an 87 percent chance that she’s pregnant and that her delivery date is sometime in late August.

Now that they had the ability to predict the likelihood that their customers were expecting, the marketing team could get started with the next phase: putting baby products and other targeted ads in front of them.

However, this is where the company started to face some backlash and privacy concerns. Using this data, the marketing team sent out pamphlets filled with products like cribs and baby clothes to customers that were high enough on their pregnancy prediction scale.

One of those booklets made its way into the mailbox of a young girl that was still in high school. The New York Times article relayed the story:

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.”

Target already knew they couldn’t send mailers that were as point blank as, “Congrats on the new baby!” However, this experience taught the brand that they needed to be even more discreet with their direct mail approach.

In response to the controversy and to address privacy concerns, Target revised its marketing strategies. The team became subtler with its approach, mixing baby-related product ads with unrelated items so the targeted advertising was less apparent. This allowed Target to maintain its customer relationships while respecting their privacy.

How to Use Predictive Analytics in Your Business


Soon after Target’s campaign targeting expecting parents launched, their sales for Mom/Baby items exploded. And, according to the New York Times, “between 2002—when Pole was hired—and 2010, Target’s revenues grew from $44 billion to $67 billion.”

So how can you use predictive analytics to generate results on a similar scale? Here are a few ways to gather data that can be used to predict behavior and events happening within your target audience.


Track Customer Data


Tracking customer data, purchases, and other behavior can be a huge help in understanding their buying patterns and predicting future purchases. This is exactly how Target managed to put together their pregnancy prediction—through Guest IDs.

Target would assign every customer a Guest ID, and any kind of transaction a customer made—like making a purchase, filling out a survey, opening an email, or visiting the website—would be linked to their Guest ID. Through this data, Target’s analytics team was able to track customer behavior and put together their pregnancy prediction score.

Store customer data within your CRM so you can easily access and analyze it for trends or patterns. You can also create a loyalty program similar to Target’s Circle Rewards program. This makes it easy to track purchases—customers put in their phone number and the purchase is immediately linked to their account.


Analyze Third-Party Resources


Use third-party resources to find information about your industry or target audience. Sites like:

      • Statista
      • Forrester
      • Nielsen
      • Gartner
      • IPSOS

These sites share data and statistics found from online sources or their own surveys, studies, and research. You can access this data and use it to help inform your hypothesis.


Work With a Market Research Firm


Work with a market research company to build out a survey or study based on your unique needs, audience, and industry. Develop a survey that helps you discover the exact data and information you’re looking for.

You’ll then receive the results of your project in a nicely wrapped up report, helping you pull the predictive analytics you need for your marketing campaign.


Predictive Analytics Expecting Mom Shopping


In Summary


One of the most significant takeaways from this case study is the power of interpreting seemingly unrelated data points. For market researchers, it emphasizes the need for creativity and out-of-the-box thinking when analyzing data to draw meaningful conclusions.

If you want to create your own unique marketing approach in order to target customers more effectively, get in touch with one of our representatives. We can help you compile actionable data that can inform your marketing strategy and better reach your target audience.


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