Leading retailers are finding machine learning innovations tailor-made for fast fashion, providing data-driven insights to make sure they always have the top-selling clothing colors and styles on hand to maximize revenue and delight demanding shoppers. When retailers can connect fashion trends to actual sales and inventory levels using predictive analytics, even the most dedicated fashionistas won’t know what hit them. This concept is an example of the new technologies presented to retailers and other industries at the 2017 SAPPHIRE NOW and ASUG Annual Conference held in Orlando, Florida.
Insights Faster Than Social
Imagine a life-size screen that collects in-the-moment data from shoppers while delivering essential information to store managers. With SAP Leonardo, this could soon become a reality.
“It’s very important to see what 16-year-old girls, who typically love to shop on a weekly basis, are wearing so you can understand trends before they peak and as they wane,” said Klaus Schimmer, Director of Business Development, SAP Leonardo Machine Learning. “Using machine learning that scans shoppers who opt in as they walk by, retailers can immediately conduct intelligent advertising, recommending other available items personalized to someone’s tastes by color, style, gender, age, or even emotion based on their facial expression.”
At the same time, store managers can discover what’s hot, as analytics collect and analyze real-time information from social feeds, including posts and pictures on fashion blogs, Instagram, and Facebook. Combined with data from all the retailer’s stores, these analytics can help decision makers stay one step ahead of lightning-fast fashion trends. The impact is tremendous on design, production planning, inventory control, and dynamic pricing to ensure that the right items end up in the right locations to reach the right consumers.
“There may be differences between what’s selling in London versus Paris, but with real-time data, retailers can quickly see that cool color is already finished in Paris but still trending high in London,” said Schimmer. “Instead of reducing the price of clothing in that color in the Paris stores, they can ship it quickly to London to take advantage of customer demand there. They can also calibrate markdowns depending on sales volumes in Paris. Maybe it’s more profitable to only reduce prices by 10% before demand reaches a certain tipping point.”
A Conversational Business Tool
Even if data is collected and analyzed in real time, it can be difficult for businesses to view it and use it for decision making. SAP CoPilot, the digital assistant for the enterprise, solves this problem, accessing critical data and quickly turning it into actionable insights. Forget static, backward-looking dashboards: Using SAP CoPilot, retailers can have real-time conversations about the best course of action for their businesses. Schimmer demonstrated this at the event by putting both SAP Leonardo Machine Learning and SAP CoPilot through their paces, asking a series of questions in conversational language to instantly compare predicted demand with inventory. SAP CoPilot then offered a suggestion to order more inventory, which was automatically routed to procurement. Easy-to-read, colorful graphical displays popped up on the large screen with each spoken answer. With the help of SAP CoPilot and the power of SAP Leonardo Machine Learning, Schimmer demonstrated how issues that used to take retailers days to rectify can now be solved in minutes.
Machine learning has the power to drive not just one part of a business process, but the entire organization for intelligent decisions leading to faster innovation. Shoppers can expect a whole new level of personalized shopping. For more, visit www.sap.com/products/leonardo.html.