A few years ago, Stage Stores decided to implement predictive analytics in its day-to-day operations, and the results of that decision were overwhelmingly positive.

Stage Stores is a $2 billion department store chain that boasts locations in 40 states. According to Forbes, the company began using predictive analytics as a way to compete with larger chains like Macy's, which has enough personnel to sort through mounds of data. Stage Stores wasn't able to keep up using that approach. With predictive analytics, however, the data gleaned from every single transaction in every store across the United States could be quickly and effectively synthesized and interpreted, increasing yearly sales and revenue.

These are some of the ways Stage Stores utilized predictive analytics to streamline its operations and increase sales: 

  • Markdown optimization: Predictive analytics was able to sort through past sales histories in a certain store location and tell merchants in that store when to lower the price of a specific item to ensure its sale. At first, Stage's CIO, Steve Hunter, told Forbes reporters that his merchants were reluctant to accept the advice given by the analytics results, because they wanted to hold out for higher prices for a longer amount of time. However, the computers suggested that more sales could be achieved if prices were lowered earlier, before demand reached an all-time low. Hunter implemented a six-month trial period for the analytics program, and he reported that 90 percent of the time the predictive analytics system worked better than the one devised by human merchants. When store managers saw these results, they happily accepted the new technology as part of their operation.
  • Personalization: Stage is currently working on an analytics system that will allow sales associates to deliver customized recommendations to in-store customers, based on their previous shopping and buying behavior.
  • Size optimization: As a clothing retailer, Stage Stores also use predictive analytics for ordering goods for certain store locations. The company's managers knew that the average clothing size differed depending on location, but they were unable to translate that knowledge into concrete buying decisions. Predictive analytics allowed them to see exactly how much of one size of clothing was selling each year, giving them the power to order the correct amount of clothing in the right sizes for each store.
  • Weather: Data also has the power to take a store location's weather into account, so managers can stock items that customers are going to be needing.

"We're going towards retail being a science instead of an art," Hunter said . "We'll always have to buy the right merchandise. But the way we do it, and the way we leverage technology, will continue to evolve."

If you're looking for a way to bring your company into the future, try upgrading your credit processing software to give your customers a smooth and personalized check-out experience.