Target received a lot of hype in the news several months back when it was alleged that they used analytics to identify expectant mothers based on the consumer’s purchasing basket. Well, if you ask me, I think they were doing what any smart retailer should do, which is using data to harness knowledge and information. In the case of Target, if you are focused on mothers and babies, the most important thing for you to know is who’s expecting and when. Life events occur and there is a clear shift in consumer behavior as spending patterns shift in preparation for the new event. Proactively identifying this shift can create a competitive advantage for marketers giving them advanced placement in front of their targeted audience.
Identifying purchase triggers will only continue to advance as marketers become more and more sophisticated. Predicting an expectant mother is actually pretty easy if you have decent wallet share. Things like pre-natal vitamins, maternity clothes, and the book What to Expect when You’re Expecting are dead giveaways. But the predictive applications are seemingly endless. Just look at the baby example; marketers could predict the trimester along with whether it’s the first or second child. However, what about knowing if someone has a new pet or if they are getting ready to move? We could certainly predict things like ethnicity and sports affiliations, but what about knowing if a child is getting ready to start soccer camp?
The process marketers use to predict these patterns will only continue to get more sophisticated. First, as discussed, we can use potential purchase patterns to review what you are buying. However, what if you’re simply planning for something that hasn’t happened yet? What do most people do? Well, I tend to research things online. In this same manner, browsing behaviors, like the purchase basket, are a pretty good predictor of what to expect next. Additionally, social conversations may be relevant, so scraping Facebook feeds may also be predictive. These are all part of the digital exhaust system that is throwing out clues on what consumers are “in market” for every day. And predictive analytics is hot on the trail to integrating and leveraging the trace of data crumbs.
So as marketers, it’s really upon us to envision all of the possible applications of predictive analytics. Ask yourself, what do you really want to know about a consumer? What information, if you had it, would be the most valuable to you in getting your company’s products and services to the right audience, and more specifically, at the right time?