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How Target Knows You're Pregnant: A Predictive Analysis Perspective

Posted by Jeff McKenna on Tue, Feb 21, 2012

Shopping CMBOn Sunday, The New York Times Magazine published a piece: How Companies Learn Your Secrets, by Charles Duhigg, author of the forthcoming The Power of Habit: Why We Do What We Do in Life and Business.  It’s an interesting article, especially for market researchers, and I recommend everyone take the time to read it.

Consumer "habits” are a big focus of the work we (market researchers) do as we seek to understand consumer behavior. From the perspective of the article, a large part of what we do is identify behavioral habits to help marketers find ways to insert their product or service into people's habit processes. 

In this blog, I want to focus on the insights the story shared about predictive analytics. Much of Duhigg's article looks at how Target conducts advanced analytics to identify data within their CRM system to predict whether a shopper is expecting a baby.  From a business process POV, and how we think about using predictive analytics, it’s important to point out a few relevant facts for market researchers:

  1. It wasn’t a “fishing expedition”: The analysis started with a clear marketing benefit as the outcome – Target wanted to begin promoting itself to expectant mothers before the baby is born. As the article points out, by marketing to these families before the baby becomes public knowledge, Target can get beat the flood of marketers that begin pitching a range of products and services once the birth is entered into public record.  It was the marketing team that came to the analyst with a high-value opportunity.  The analyst did not create the winning marketing idea (“Hey! Let’s market to expectant mothers before the baby is born!”).  Instead, the analyst looked under every stone and in every corner of the data to find the key to unlock the opportunity.

  2. The research didn’t stop with finding the key: The application of these insights required a lot more research to determine the best method of implementing the campaign.  For instance, Target ran several test campaigns to identify the best offers to send to the expectant mothers, and cycled through several messages to find just the right one in order to avoid revealing that Target was prying into the data.  Although the predictive analytics found the key, Target still relied on a comprehensive plan to make sure the findings were used in the best possible manner.

  3. Don’t let this story increase your expectations: The Target approach has had a big impact on how the company markets to a highly valuable segment of shoppers.  It's a great success story, but it's also something that happened ten years ago.  While I’m sure the Guest Market Analytics team achieves many victories along the way, they also spent a lot of time reaching “dead-ends,” unable to find that magic key.  And most of the time, the predictive solution yields valuable but incremental gains, these high-profile stories are few and far between.

The article shares many interesting ideas and insights; the story about the re-positioning of Febreze highlights another great research success. I'm looking forward to reading Duhigg's book, and if it covers more of these thought provoking business cases, I expect we will be seeing Charles Duhigg’s name popping up in other discussions on market research.

Did you read the article? What do you think?

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Topics: advanced analytics, consumer insights, marketing science, customer experience and loyalty, retail research