Every retailer wants to have the right products available to customers at the right place and the right time. Making this happen, however, is much easier said than done.
To achieve maximum efficiency -- and sales -- retailers must painstakingly plan their inventory management, allocation, distribution and replenishment processes to the finest possible degree. Getting all of these steps to work in close synchronization with consumer demand is essential to retail success. Yet it is also a goal that is difficult to achieve.
Historically, retailers relied on insights gleaned from past experiences, analyst forecasts, customer feedback and even educated guesses to gain a big picture view of their inventory processes. Now, a growing number of retailers are turning to predictive analytics — specialized software that analyzes data and projects the likely outcome of future events — to plot their inventory needs far more accurately than at any time in the past.
Employing a wide array of technologies and approaches—including statistical modeling, data mining and various mathematical techniques, predictive analytics examines current and past data to predict what should happen at a specific time based on the supplied parameters. In a retail setting, predictive models exploit patterns found in historical and transactional data to spot both risks and opportunities. Models can be designed to capture relationships among multiple consumer behavior factors that enable the assessment of either the risk or potential benefit associated with a particular set of conditions, guiding decision-making for various retail events. “More retailers are beginning to see the inherent value in predictive analytics,” says Jon Stine, global director of retail sales for Intel. “It's a growing trend and one that's becoming increasingly necessary to remain competitive.”
The use of predictive analytics is only limited by the amount of data a retailer has on hand, enabling retailers to quickly anticipate consumer behaviors from different angles. For example, using data collected from past sales, a retailer could pinpoint customers who purchased computer printers during a promotion last year and then employ predictive analytics to forecast the promotion's likely effect on ink cartridge sales this year, or to determine how far printer sales would rise (or fall) if a similar promotion were to be held in the next month. The retailer could also experiment with different price points to see the impact each price would have on sales during the promotion.
Predictive analytics is also a powerful tool with which retailers can optimize pricing based on consumer demand. Before the arrival of predictive analytics, many retailers would simply slash prices at the end of a buying season for certain product lines in response to falling consumer demand. Yet predictive analytics has shown that in some cases a gradual reduction in price, beginning at the moment demand begins to sag, can actually lead to increased revenue. “Predictive analytics also has the potential to save money by reducing a tremendous amount of waste,” says Houman Salem, chief executive officer of Argyle Partners, a fashion apparel and retail management strategy consulting firm. Predictive analytics helps retailers unload fading inventory items at a price before they become candidates for charitable donation.
Predictive analytics can also be used to obtain insight about markets and market segments to detect trends, determine market directions and spot potential sales opportunities. For example, by factoring in data for the sizes of clothes sold in previous years, retailers can predict the need specific sizes at individual stores.
Understanding which shoppers are likely to want a particular product, and finding the best way of placing the item in front of them, is essential for success in today's highly competitive retail industry, says Vish Ganapathy, chief technology officer for the global consumer industry at IBM.
Retail predictive analytics' growing importance is made evident by the fact that it has spawned a rapidly expanding pool of sophisticated database, statistical, analytical and decision modeling solutions. While some of these tools are based on conventional statistical paradigms, many now feature complex and closely guarded algorithms developed by leading data scientists.
Time really does count for retailers, Salem says. “They really need to know how long to keep items on their shelves before discounting them or moving in something else,” he notes. “Predictive analytics, by revealing insights based on past trends and events, provides the best way to do this.”
Stine says that businesses getting started with predictive modeling should begin by answering three simple questions: “What's my competitive position? How do I need to improve it? If I knew certain things, would I be able to do better business?”
To learn more about how CDW’s solutions and services can provide retailers with a competitive edge, visit CDW.com/retail.