3 Ways Retailers Can Use Analytics To Improve Forecast Accuracy
It doesn’t take a crystal ball to find out what’s beyond the horizon of retail’s changing landscape. With ERP analytics, retailers can strategically navigate the ebb and flow of product supply and demand.
Accurate retail forecasts are critically important to businesses because they determine a store’s inventory, labor expenses and management of seasonal shopping patterns. And that’s just the beginning.
ERP software compiles data from vital areas of a retailer’s supply chain to develop accurate forecasts. Here’s a breakdown of the key analytics that improve retail forecast accuracy.
- Customer data: Retailers must be able to capture customer information, such as consumer demographics, age group and purchase history. This is a key feature to look for in an ERP system, as an article on Forbes.com explains. “ERP systems built on a strong foundation of personas, or clear definition of customers and their roles, will overtake those built just on features alone,” the article says. The feature will be the tipping point for companies evaluating their ERP systems in the next 12 months, the article continues. An ERP system must be able to manage a variety of critical needs, such as attracting new shoppers while cultivating current customers.
Historical data: Retailers use historical trends to serve as a baseline in retail forecasts to project expected changes. For example, if the economy is doing better compared to last year, the retailer may increase their forecast by 10 percent. On the other hand, if the economy is faring worse than last year, the forecast could decrease by 10 percent.
Follow this advice shared in an article on the Retail Info Systems (RIS) News website: “Leverage historical demand data with user-anticipated differences in order to get better results with each forecast.” In addition, retailers should consider putting sales history data online to improve accessibility, which will help promote accuracy.
- Inventory hierarchy: Structuring products in a clear and understandable inventory hierarchy helps to create accurate retail forecasts. It’s easier to manipulate a forecast based on groups of items rather than individual items. For example, if a retailer carries 10,000 items, it would be unwieldy to make individual adjustments to each item. But if the products are structured in a clear hierarchy, retailers can make better assumptions for the groups of items to formulate accurate forecasts. The assumption could be something simple along these lines: more people are eating fast food, so clothing stores need more large T-shirts and fewer small T-shirts. While it appears subjective, retailers still use historical information as a baseline and a proper inventory hierarchy to forecast groups of items.
After understanding which analytics help to create accurate forecasts, retailers need to implement best practices to ensure they are getting the most from their analytics investment. Best practices include making sure data is “clean.” In other words, the information should be accurate and free of duplicate or missing entries.
Above all, retailers should strive to understand how technology can help improve their business.