Remember when Target used analytics to determine which customers were in the early stages of pregnancy?

Get the Most Out of Your Data By Finding Hidden Correlations

Retailers have the capacity to collect data like never before. But what if they don't know what they have? Analyzing data to collect clear surface trends is one thing. Digging deeper to uncover hidden correlations is another. This is particularly difficult when you are dealing with multiple streams of data, which is true for many complex operations.

What are "hidden correlations"? These are the trends that observers may not immediately think to look for in a data set. While the relationship between a product sale price and its sales numbers may be fairly obvious, not everything is so simple.

For instance, did you ever hear the story about Target guessing that a customer was pregnant before some of her family members? It's true: In 2012, the New York Times reported that Target started using analytics to predict pregnancy among customers in 2002. The company did so by comparing the shopping histories of random guests to those who had signed up for the baby shower registry. Eventually, this analysis managed to pinpoint the purchases most commonly made by women in the first and second trimesters.

This didn't just make for a fun headline. By creating a reliable pregnancy prediction model, Target was actually able to better distribute its product promotions aimed at new mothers. One result, according to an article on CRMsearch, was that revenue in that particular area increased from $44 billion in 2002 to $67 billion in 2010.

Finding those hidden correlations can help your business stay ahead of the competition. Interactive Edge offers a number of creative ways to get the most out of your data. Our solutions help retailers analyze impactful data from multiple sources and put it to good use – improving sales, revenue and ultimately strengthening their businesses.

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