When asked how predictive analytics work, I like to tell people stories about how companies have used data to create predictions of people’s buying behavior, to increase sales. Predictive analytics help to predict many trends in the business world, but the concept is perhaps best understood by using purchasing habits in retail as an example.  
Women’s shoes can be found on the way to mens clothes, and bananas are at the front of the store while milk is in the back. Studies found that many people go to the store to buy milk; by placing it in the back, they must walk by all of the other goods in the store. Here, they are predicting that if people walk by the goods, they are more likely to buy the goods. In opposition, bananas are found to be an impulse buy, and are therefore at the front. One of the more famous examples is the Target ad that predicted a young woman’s pregnancy before her father even knew. The predictive analytic that catches people’s attention the most is the correlation between beer and diapers. Men who buy diapers for their kids, are also more likely to have beer in their carts. It is said that this particular study was done by a grocery store and found that men, shopping for diapers, between the ages of 30-40, that shopped between the hours of 5 and 7 pm on Fridays were the most likely to have beer in their carts. Thus, the grocery store moved their beer isle closer to the diaper isle. This may seem ridiculous at first, but low and behold, sales of both items did increase. Apparently, this caught on, and other stores began doing the same. It has been suggest online that Target also uses analytics to find items that are often purchased together. They use this information to place them next to other “impulse buy” items. If you go to buy something you need, you just might also find something you want. But this is just one of the many ways that retail stores can use data analytics to increase sales and reduce costs. Retail stores must purchase a great deal of goods, must distribute and store those goods. Analytics are great with discovering, and fixing transportation inefficiencies. They can save companies millions on distribution costs, alone. They can also be used for target marketing. The previously mentioned Target story is a great example. Analytics can also help retailers determine what products to sell, based off of consumer preferences. Finally, they can also be used in the same ways other companies use them for: ultimately to make better decisions. Businesses of all industries use data to provide valuable information that helps them retain employees, improve customer satisfaction, and reduce costs and overheads.  

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