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Predictive Analytics

For decades, retailers having been trying to gather their customer’s interest, habits, and behavior to better understand how they should be targeted.

Marketers are now using collected and sometimes purchased demographics to determine when your life is about to change. They do this because most people’s purchasing habits are grounded, and big changes for important life events, such as: moving, filing for bankruptcy, getting a new job, etc., are truly the only thing that affects how and why you buy. If retailers can find a way to predict when an event may happen, they can serve ads that they consider more relevant to you.

So how is this done? When we first wrote this post, we noted that one of the most successful retailers of predictive analytics is Target. Every shopper at Target is assigned a unique Guest ID that is tied to their credit card, name, or email address. Target then uses the demographic information gathered from this Guest ID to study shopping habits and provide marketing they think you want.

For example, Target's predictive analytics have found that women's shopping habits tend to change when they become pregnant, with increased purchases in unscented lotion, zinc and magnesium. Target can then use this information and send women ads specifically targeted towards them, based on how far along in the pregnancy Target’s algorithms think they are. Many of these algorithms are surprisingly accurate.

Marketers now have the extra advantage of remarketing and target audiences, as well. Both practices use predictive analytics that take into account web behavior (including cookies that sites load into your browser's temp files), to increase the number of views and impressions on ads. It means that if you hunted for backpacking trails, you may see ads for backpacking gear on the next site you land on.

With predictive analytics, marketers are working smarter to come up with new ways to reach more relevant audiences.


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